Emerging Pollutants

Yawei Wang; Qiurui Zhang; Nanyang Yu; Yuan Wang; Si Wei; Mingliang Fang; Sinuo Tian; Yali Shi; Jianbo Shi; Guangbo Qü; Ying Zhu; Yumin Zhu; Chuhong Zhu; Min Qiao; Jianghuan Hua; Mei Liu; Guorui Liu; Jianguo Liu; Yanna Liu; Nannan Liu; Longfei Jiang; Shuqin Tang; Bixian Mai; Cheng Li; Pan Yang; Lihua Yang; Rongyan Yang; Lili Yang; Xiaoxi Yang; Ruiqiang Yang; Xinghua Qiu; Guangguo Ying; Yan Wang; Gan Zhang; Quan Zhang; Zhen Zhang; Ying Zhang; Qianqian Zhang; Rongjing Lu; Da Chen; Xin Chen; Hexia Chen; Jingwen Chen; Jiazhe Chen; Bingcheng Lin; Xiaojun Luo; Chunling Luo; Rong Ji; Biao Jin; Bingsheng Zhou; Minghui Zheng; Shizhen Zhao; Meirong Zhao; Fanrong Zhao; Lu Jiang; Lingyan Zhu; Linlin Yao; Jingzhi Yao; Yong He; Xunjie Mo; Chuanzi Gao; Yongyong Guo; Nan Sheng; Yunhan Cui; Chengqian Liang; Jian Han; Zhen Cheng; Yanhong Zeng; Wenhui Qiu; Yaqi Cai; Hongli Tan; Bingcai Pan; Jiayin Dai; Dongbin Wei; Chunyang Liao; Jincai Zhao; Guibin Jiang

doi:10.7536/PC241114

Progress in Chemistry >

2024 , Vol. 36 >Issue 11: 1607 - 1784

DOI: https://doi.org/10.7536/PC241114

Review

Emerging Pollutants

Yawei Wang ^,¹^,^* ,
Qiurui Zhang ¹ ,
Nanyang Yu ² ,
Yuan Wang ¹⁵ ,
Si Wei ^,²^,^* ,
Mingliang Fang ^,¹⁴^,^* ,
Sinuo Tian ¹³ ,
Yali Shi ¹ ,
Jianbo Shi ^,¹^,²⁸^,^* ,
Guangbo Qü ¹ ,
Ying Zhu ⁸ ,
Yumin Zhu ⁵ ,
Chuhong Zhu ⁴ ,
Min Qiao ¹ ,
Jianghuan Hua ⁶^,²⁵ ,
Mei Liu ¹ ,
Guorui Liu ^,¹^,¹⁹^,²³^,^* ,
Jianguo Liu ^,³^,^* ,
Yanna Liu ¹ ,
Nannan Liu ¹⁶ ,
Longfei Jiang ⁴ ,
Shuqin Tang ¹¹ ,
Bixian Mai ⁴ ,
Cheng Li ¹³ ,
Pan Yang ¹⁷ ,
Lihua Yang ⁶^,²⁴ ,
Rongyan Yang ⁵ ,
Lili Yang ¹^,²³ ,
Xiaoxi Yang ¹ ,
Ruiqiang Yang ^,¹^,¹⁹^,²³^,^* ,
Xinghua Qiu ³ ,
Guangguo Ying ^,⁹^,^* ,
Yan Wang ⁷ ,
Gan Zhang ^,⁴^,^* ,
Quan Zhang ^,⁷^,^* ,
Zhen Zhang ¹⁸ ,
Ying Zhang ⁵ ,
Qianqian Zhang ⁹ ,
Rongjing Lu ³ ,
Da Chen ^,¹¹^,^* ,
Xin Chen ⁵ ,
Hexia Chen ¹⁷ ,
Jingwen Chen ^,¹²^,^* ,
Jiazhe Chen ³ ,
Bingcheng Lin ¹⁹ ,
Xiaojun Luo ⁴ ,
Chunling Luo ⁴ ,
Rong Ji ² ,
Biao Jin ⁴ ,
Bingsheng Zhou ^,⁶^,²⁴^,^* ,
Minghui Zheng ¹^,¹⁹^,²³ ,
Shizhen Zhao ⁴ ,
Meirong Zhao ⁷ ,
Fanrong Zhao ²⁰ ,
Lu Jiang ¹ ,
Lingyan Zhu ^,⁵^,^* ,
Linlin Yao ¹ ,
Jingzhi Yao ²¹ ,
Yong He ¹ ,
Xunjie Mo ⁷ ,
Chuanzi Gao ¹⁰ ,
Yongyong Guo ⁶^,²⁴ ,
Nan Sheng ^,⁸^,^* ,
Yunhan Cui ¹² ,
Chengqian Liang ⁶^,²⁶ ,
Jian Han ⁶^,²⁴ ,
Zhen Cheng ³ ,
Yanhong Zeng ⁴ ,
Wenhui Qiu ^,¹⁰^,^* ,
Yaqi Cai ^,¹^,^* ,
Hongli Tan ²² ,
Bingcai Pan ^,²^,^* ,
Jiayin Dai ^,⁸^,^* ,
Dongbin Wei ¹ ,
Chunyang Liao ^,¹^,^* ,
Jincai Zhao ^,²⁷^,^* ,
Guibin Jiang ^,¹^,¹⁹^,²³^,^*

Expand

¹ State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
² School of Environment, Nanjing University, Nanjing 210023, China
³ State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environmental Science and Engineering, Peking University, Beijing 100871, China
⁴ State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
⁵ Institute of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
⁶ Institute of Aquatic Biology, Chinese Academy of Sciences, Wuhan 430072, China
⁷ College of Environment, Zhejiang University of Technology, Hangzhou 310032, China
⁸ School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
⁹ College of Environment, South China Normal University, Guangzhou 510006, China
¹⁰ Guangdong Key Laboratory of Soil and Groundwater Pollution Prevention, Control and Remediation, School of Environmental Science and Engineering,SUSTechin,Shenzhen 518055, China
¹¹ School of Environment and Climate, Jinan University, Guangzhou 510632, China
¹² Key Laboratory of Chemical Risk Prevention, Control and Pollution Prevention Technology, Dalian, Key Laboratory of Industrial Ecology and Environmental Engineering of the Ministry of Education, College of Environment, Dalian University of Science and Technology, Dalian 116024, China
¹³ Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
¹⁴ Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
¹⁵ College of Environmental Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
¹⁶ College of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China
¹⁷ School of Basic Medicine and Public Health, Jinan University, Guangzhou 510632, China
¹⁸ College of Environmental and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
¹⁹ School of Environment, Hangzhou Institute of Advanced Studies, National University of Science and Technology, Hangzhou 310024, China
²⁰ School of Science, China Agricultural University, Beijing 100193, China
²¹ Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
²² Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
²³ University of Chinese Academy of Sciences, Beijing 100190, China
²⁴ College of Modern Agricultural Science, University of Chinese Academy of Sciences, Beijing 100049, China
²⁵ College of Basic Medical Sciences, Hubei University of Traditional Chinese Medicine, Wuhan 430060, China
²⁶ College of Fisheries, Huazhong Agricultural University, Wuhan 430070, China
²⁷ Institute of Chemistry, Chinese Academy of Sciences,Beijing 100190, China
²⁸ Ministry of Education Key Laboratory of Groundwater Quality and Health, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China

* e-mail: ywwang@rcees.ac.cn(Yawei Wang);

weisi@nju.edu.cn(Si Wei);

mlfang@fudan.edu.cn(Mingliang Fang);

jbshi@rcees.ac.cn(Jianbo Shi);

grliu@rcees.ac.cn(Guorui Liu);

jgliu@pku.edu.cn(Jianguo Liu);

rqyang@rcees.ac.cn(Ruiqiang Yang);

guangguo.ying@m.scnu.edu.cn(Guangguo Ying);

zhanggan@gig.ac.cn(Gan Zhang);

quanzhang@zjut.edu.cn(Quan Zhang);

dachen@jnu.edu.cn(Da Chen);

jwchen@dlut.edu.cn(Jingwen Chen);

bszhou@ihb.ac.cn(Bingsheng Zhou);

zhuly@nankai.edu.cn(Lingyan Zhu);

shengnan@sjtu.edu.cn(Nan Sheng);

qiuwh@sustech.edu.cn(Wenhui Qiu);

caiyaqi@rcees.ac.cn(Yaqi Cai);

bcpan@nju.edu.cn(Bingcai Pan);

daijy65@sjtu.edu.cn(Jiayin Dai);

cyliao@rcees.ac.cn(Chunyang Liao);

jczhao@iccas.ac.cn(Jincai Zhao);

gbjiang@rcees.ac.cn(Guibin Jiang)

Received date: 2024-03-08

Revised date: 2024-08-31

Online published: 2024-11-01

Supported by

special fund of State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University)(23Y03ESPCP)

National Natural Science Foundation of China(22136001)

National Natural Science Foundation of China(22076204)

National Natural Science Foundation of China(U23A205)

National Natural Science Foundation of China(21936007)

National Natural Science Foundation of China(22376204)

National Natural Science Foundation of China(22320102005)

National Natural Science Foundation of China(22276198)

National Key Research and Development Program of China(2022YFC3902100)

National Key Research and Development Program of China(2022YFC3703200)

Second Tibetan Plateau Scientific Expedition and Research Program (STEP)(2019QZKK0605)

Consultative Review Project of Academic Divisions of the Chinese Academy of Sciences(2023-HX01-B-006)

Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0750400)

Program for the Top Young-aged Talents of Higher Learning Institutions of Hebei, China(BJK2022042)

Fold

Abstract

With the rapid development of current society and economy, as well as the accelerated process of industrialization and urbanization, the complexity and seriousness of environmental pollution issues are becoming increasingly apparent. Beyond traditional pollutants, the appearance of emerging pollutants on a global scale has brought new challenges to environment and public health. China’s “14th Five-Year Plan” and medium and long-term planning put forward “emerging pollutant control”, report of the 20th National Congress of the Communist Party of China also explicitly requested “carry out emerging pollutant control”. In 2022, General Office of the State Council issued “Action Plan for Emerging Pollutant Control”, followed by the Ministry of Ecology and Environment and various provinces, municipalities, and autonomous regions, which released corresponding implementation plans, China has transferred to a new phase of environmental protection that balances the control of both traditional and emerging pollutants. However, management of emerging pollutants is a long-term, dynamic and complex systematic project, which urgently needs to strengthen top-level design as well as scientific and technological support. Conducting systematic research on emerging pollutants not only provides effective scientific guidance for their control and improves the level of environmental quality management, but also assists our country in fulfilling international conventions, enhances the discourse power in global environmental governance, ensures our country environmental security, food security, international trade security, etc., and is of great significance for realizing sustainable development. This review aims to comprehensively explore various aspects of emerging pollutants, including their types and characteristics, production, use and emission, identification and detection, environmental occurrence, migration and transformation, ecotoxicological effects, human exposure, health risks, and management strategies. Furthermore, it looks forward to the future research direction, with a view to providing a scientific basis and decision-making support for control of emerging pollutants in China.

Contents

1 Concepts, types and characteristics of emerging pollutants

1.1 Definition and basic characteristics of emerging pollutants

1.2 Typical emerging pollutants

1.3 Scientific problems faced in the study of emerging pollutants

2 Production, use and emission of emerging pollutants

2.1 Production, use and emission of POPs

2.2 Production, use and release of antibiotics

2.3 Production, use and release of endocrine disruptors

3 Identification and characterization of emerging pollutants

3.1 Non-targeted analytical techniques for identification and characterization of emerging pollutants

3.2 Data analysis techniques for identification and characterization of emerging pollutants

3.3 Application of technologies for identification and characterization of emerging pollutants

3.4 Outlook

4 Environmental level and distribution characteristics

4.1 Regional distribution characteristics of emerging pollutants

4.2 Characteristics of emerging pollutants in environmental media

4.3 Bioconcentration and accumulation of emerging pollutants

5 Environmental transport and transformation of emerging pollutants, source and sink mechanisms

5.1 Multi-media process of emerging pollutants in the water environment and return tendency

5.2 Transport and transformation of emerging pollutants in soil-plant system

5.3 Atmospheric processes of emerging pollutants

5.4 Numerical modeling of regional environmental fate of emerging pollutants

6 Ecotoxicological effects of emerging pollutants

6.1 Ecotoxicology of perfluorinated and polyfluorinated alkyl compounds

6.2 Ecotoxicology of organophosphates

6.3 Integrated exposure assessment of novel nicotinic pesticides in honey crops

6.4 Ecotoxicology of PPCP-like contaminants

7 Human exposure and health risks of emerging pollutants

7.1 Human health risk-oriented screening of environmental contaminants

7.2 ADME processes and conformational relationships of emerging pollutants in humans

7.3 Environmental health risks of emerging pollutants

8 Management of emerging pollutants

8.1 Difficulties in the management of emerging pollutants

8.2 New pollutant management technologies

8.3 China's emerging pollutants environmental management policy

8.4 International experience in environmental management of emerging pollutants

8.5 Problems and suggestions of China's environmental management of emerging pollutants

9 Key scientific issues and prospects

9.1 Lack of emerging pollutants' bottom line

9.2 Environmental and ecotoxicological toxicological effects of low-dose prolonged exposure

9.3 Compound effects of emerging pollutants and histologic study of human exposure

9.4 Strategies for control and green development of high-risk chemicals

9.5 Construction of machine learning-based database for environmental samples and human exposure

9.6 Capacity building of scientific and technological support for emerging pollutants control actions in China

9.7 Coordinated development of ecological and environmental monitoring capability, fine support of emerging pollutant management, and construction of targeted new pollutant risk prevention and pollution prevention system

Key words： emerging pollutants; occurrence level; environmental behavior; ecological risks; control strategies

Cite this article

Yawei Wang , Qiurui Zhang , Nanyang Yu , Yuan Wang , Si Wei , Mingliang Fang , Sinuo Tian , Yali Shi , Jianbo Shi , Guangbo Qü , Ying Zhu , Yumin Zhu , Chuhong Zhu , Min Qiao , Jianghuan Hua , Mei Liu , Guorui Liu , Jianguo Liu , Yanna Liu , Nannan Liu , Longfei Jiang , Shuqin Tang , Bixian Mai , Cheng Li , Pan Yang , Lihua Yang , Rongyan Yang , Lili Yang , Xiaoxi Yang , Ruiqiang Yang , Xinghua Qiu , Guangguo Ying , Yan Wang , Gan Zhang , Quan Zhang , Zhen Zhang , Ying Zhang , Qianqian Zhang , Rongjing Lu , Da Chen , Xin Chen , Hexia Chen , Jingwen Chen , Jiazhe Chen , Bingcheng Lin , Xiaojun Luo , Chunling Luo , Rong Ji , Biao Jin , Bingsheng Zhou , Minghui Zheng , Shizhen Zhao , Meirong Zhao , Fanrong Zhao , Lu Jiang , Lingyan Zhu , Linlin Yao , Jingzhi Yao , Yong He , Xunjie Mo , Chuanzi Gao , Yongyong Guo , Nan Sheng , Yunhan Cui , Chengqian Liang , Jian Han , Zhen Cheng , Yanhong Zeng , Wenhui Qiu , Yaqi Cai , Hongli Tan , Bingcai Pan , Jiayin Dai , Dongbin Wei , Chunyang Liao , Jincai Zhao , Guibin Jiang . Emerging Pollutants[J]. Progress in Chemistry, 2024 , 36(11) : 1607 -1784 . DOI: 10.7536/PC241114

1 Concept, Types, and Characteristics of New Contaminants

1.1 Definition and Basic Characteristics of New Contaminants

In recent years, the health problems caused by global chemical environmental pollution have become increasingly serious and have become a focus of attention for governments, academic circles, and the public. As of September 2023, the American Chemical Society (Chemical Abstracts Service, CAS) has registered 204 million types of chemicals, and this number continues to increase at a rate of thousands per day^[1]. In order to control the environmental and health risks brought about by chemical production and use, various countries established specialized laws and regulations for chemicals in the 1970s. Subsequently, global actions were taken to regulate the production and use of related chemicals. In 2001, under the advocacy of the United Nations Environment Programme (UNEP), countries signed the Stockholm Convention on Persistent Organic Pollutants (hereinafter referred to as the "Convention"). Since its official implementation in May 2004, 186 signatory countries (or regions), including China, have conducted comprehensive risk assessments of chemicals related to persistent organic pollutants (POPs). However, the complexity of new chemical control issues varies among different countries and regions, and their development models and stages are also different. As a major chemical product country, most of the novel POPs that the Convention focuses on and new contaminants with potential POPs characteristics are produced and used in our country to a certain extent. Correspondingly, the environmental pollution and health problems caused by these new contaminants are more complex and severe. Moreover, due to differences in industrial structures, utilization modes, and emissions, some new contaminants may not attract foreign attention but are widely present in various environmental media in our country. Therefore, the governance of new contaminants is not only an environmental issue but also a public health, food safety, natural resource, and national security issue. It can provide scientific basis for the regulation of chemicals in our country and also offer important data support and technical guarantees for fulfilling international conventions, enhancing international influence, and improving discourse power.

Generally speaking, new pollutants usually include any artificial synthetic or naturally existing chemicals or microorganisms, whose environmental presence can cause significant known or potential toxic effects and health hazards. Internationally, the early literature on new pollutants can be traced back to the detailed list presented in the national monitoring project included in the Water Framework Directive (WFD) issued by the European Union in 2000^[2]. In November 2003, Jerald L. Schnoor first proposed the concept of "emerging chemical contaminants" in Environmental Science & Technology^[3]. Since then, research around the environmental behavior, ecological toxicological effects, exposure health risks, and other aspects of new pollutants has become a continuous focus of attention in the field of environmental science^{[4⇓⇓⇓⇓~9]}. In China, the new pollutants that have been concerned about have been designated as "new pollutants" in official government documents^[10](Table 1), involving several main types of pollutants: POPs controlled by international conventions, endocrine disruptors (EDCs), antibiotics, etc.

表1 《2023年重点管控新污染物清单》中列出的14种化学品^[10]

Table 1 Fourteen types of chemicals listed in 2023 List of Key Controlled New Pollutants^[10]

Numbering	New contaminant name	New Contaminants	CAS number
1	Perfluorooctane sulfonate (PFOS), its salts, and perfluorooctane sulfonyl fluoride (PFOS-related compounds)	Perfluorooctanesulfonic acid (PFOS) and its salts, and perfluorooctanesulfonyl fluoride (PFOSF) (PFOS group)	1763-23-1 307-35-7 2795-39-3 29457-72-5 29081-56-9 70225-14-8 56773-42-3 251099-16-8
2	Perfluorooctanoic acid (PFOA), its salts and related compounds	Perfluorooctanoic acid (PFOA) and its salts and related compounds (PFOA group)	―
3	Decabromodiphenyl ether	Decabromodiphenyl ether	1163-19-5
4	Short-chain chlorinated paraffins	Short-chain chlorinated paraffins(SCCPs)	85535-84-8 68920-70-7 71011-12-6 85536-22-7 85681-73-8 108171-26-2
5	Hexachlorobutadiene	Hexachlorobutadiene	87-68-3
6	Pentachlorophenol and its salts and esters	Pentachlorophenol and its salts and esters	87-86-5 131-52-2 27735-64-4 3772-94-9 1825-21-4
7	Trichlorfon propyl alcohol	Dicofol	115-32-2 10606-46-9
8	Perfluorohexane sulfonate (PFHxS) and its salts and related compounds	Perfluorohexanesulfonic acid (PFHxS) and its salts and related compounds (PFHxS group)	―
9	Dechlorane and its cis isomer and trans isomer	Dechlorane plus (both cis and trans isomers)	13560-89-9 135821-03-3 135821-74-8
10	Dichloromethane	Dichloromethane	75-09-2
11	Chloroform (trichloromethane)	Chloroform	67-66-3
12	Nonylphenol	Nonylphenols	25154-52-3 84852-15-3
13	Antibiotics	Antibiotics	―
14	Obsolete: hexabromocyclododecane, chloradan, endosulfan, hexachlorobenzene, dichlorodiphenyltrichloroethane (DDT), alpha-hexachlorocyclohexane, beta-hexachlorocyclohexane, lindane, endosulfan and its related isomers, polychlorinated biphenyls (PCBs)	Hexabromocyclododecane (HBCD)、Chlordane、Mirex、Hexachlorobenzene、Dichlorodiphenyltrichloroethane (DDT)、α-Hexachlorocyclohexane (α-HCH)、β-Hexachlorocyclohexane (β-HCH)、Lindane、Endosulfan (as a raw material and its isomers)、Polychlorinated biphenyls (PCBs)	―

Compared with conventional pollutants, new pollutants have the basic characteristics of more complex sources, more diverse existence forms, and more hidden hazards. Affected by factors such as China's unique industrial structure and economic development model, the ecological environment and health risks caused by the coexistence of multiple pollutants and the superposition of pollution effects are also more intricate and complex. This situation poses great challenges to the two important goals of environmental pollutant control in typical areas of China ("precise identification" and "personalized prevention and control").

1.2 Typical New Contaminants

1.2.1 Perfluorinated Compounds

Per- and polyfluoroalkyl substances (PFAS), also known as the collective term for a group of artificial synthetic organic compounds whose hydrogen atoms on the organic carbon skeleton are completely or partially replaced by fluorine atoms, have been produced and used for over 80 years^[11]. Due to the presence of the strongest bond energy C—F bond in their structure, PFAS exhibit excellent chemical stability and thermal stability. Moreover, the polar groups connected to the carbon-fluorine chain endow these compounds with high surface activity and hydrophilicity, hence PFAS are widely applied in various industrial and civilian fields^{[11, 12]}. According to the differences in terminal functional groups of PFAS, per- and polyfluoroalkyl substances mainly include perfluoro/ polyfluoroalkyl sulfonates (PFSAs), perfluoro/ polyfluoroalkyl carboxylic acids (PFCAs), and precursors that may degrade and convert into PFSAs and PFCAs, such as: fluorinated telomers (FTOHs), perfluoroalkyl sulfonamides (FASAs), perfluoro sulfonamide acids or alcohols (FASAAs and FASEs), etc.^[11]. Among them, PFSAs and PFCAs usually exist in an ionic form, called ionic PFAS, which possess strong water solubility and can migrate over long distances with water flow; their precursors typically exist in a neutral form, thus called neutral PFAS^[13]. These compounds tend to volatilize into the atmosphere more easily, and during their migration process, they can be photolyzed or biotic/abiotic transformed into more persistent ionic PFAS.

In the early 21st century, PFOS and PFOA with 8 carbon atoms have drawn extensive attention. Chinese researchers began related research reports since 2006^[14,15]. Research^[16]indicates that PFAS are ubiquitous global pollutants. PFAS can easily bind to specific proteins such as serum albumin and fatty acid binding proteins, accumulate in tissues and organs of organisms including blood, liver, and kidneys, and possess potential for further biomagnification along the food chain^[17]. Other studies^[18]have shown that PFAS exposure may cause reproductive and developmental toxicity, endocrine disruption effects, immunosuppression, and potential carcinogenicity. Considering the POPs characteristics (persistent, bioaccumulative, and toxic, PBT) demonstrated by typical medium- and long-chain PFAS such as PFOS, PFOA, and PFHxS, these products have been successively listed in the Convention to restrict their production and use^[19]. In addition, long-chain PFCAs are currently under review by the Convention, and once approved, they will be included in the POPs control directory^[20]. Moreover, the European Union proposed the elimination of all unnecessary applications of PFAS^[21], which would be the strictest control measures on PFAS. As a contracting party, China has also included three types of PFAS in the key controlled new pollutant list (Table 1). The implementation of these measures has promoted the development and production of PFAS alternatives. Common alternatives include short-chain PFAS such as PFBA and PFBS, perfluoro/ polyfluoroether substances (PFEAAs), and chlorinated perfluoroether sulfonates, among others, which introduce ether bonds or heteroatoms into the organic carbon skeleton^[22]. This substitution strategy is based on the assumption that alternatives have lower bioaccumulation or potential degradability, thus resulting in lower environmental risks. Besides alternatives, there are still large quantities of different types of so-called new PFAS that have been used for many years but have not received much attention in the global market. More than 12,000 substances belong to the PFAS category in the Chemicals Comprehensive List released by the United States Environmental Protection Agency (US EPA) in 2021^[23]. Additionally, certain new PFAS may also exist as impurities in related products.

In summary, the numerous types and diverse properties of PFAS present many challenges to research in fields such as environmental pollution and toxic effects. The current research trend shows a transition from traditional PFAS to novel PFAS, with a focus on the use of high-resolution mass spectrometry for suspected/non-targeted screening. By identifying new PFAS in the environment, studies are conducted on their environmental behavior, toxicity, and human exposure. At the same time, environmental scientists are dedicated to developing efficient and practical technologies for handling and disposing of PFAS to reduce the negative effects of their environmental persistence. Additionally, driven by strict regulation of PFAS and the huge market demand for related products, the development of environmentally friendly, fluorine-free green alternatives will become an inevitable trend. Before these alternatives are put into production, their economic applicability must be considered, along with improving their environmental friendliness assessment to avoid the emergence of "regrettable" substitutes.

1.2.2 Brominated Flame Retardants

Brominated flame retardants (BFRs) are a class of organic compounds with high flame-retardant efficiency, good stability, and insolubility in water, and they are usually added to various industrial products such as foams, resins, rubbers, adhesives, plastics, textiles, electrical and electronic equipment, etc., to reduce their flammability^[24]. So far, at least 75 kinds of BFRs have been commercially produced worldwide^[25]. The global production of flame retardants increased from 1.9 million tons in 2011-2014 to 2.3 million tons, among which BFRs accounted for about 2%^[26]. The BFRs market is expected to grow at a rate of 5.5% in terms of value and application^[26]. Asia consumes the largest amount of BFRs (44.2%), followed by the United States (21.2%), Japan (18%), and the European Union (18%)^[27]. The main five types of BFRs that have been or are currently widely used globally include tetrabromobisphenol A (TBBPA), polybrominated diphenyl ethers (PBDEs), hexabromocyclododecane (HBCD), polybrominated biphenyls (PBBs), and decabromodiphenyl ethane (DBDPE). Among them, TBBPA is the most produced BFRs on the market, with a global consumption of 2.1 million tons^[28]; PBDEs are the second highest produced BFRs worldwide, and this compound is usually produced in three different degrees of bromination, namely pentabromodiphenyl ether (penta-BDE), octabromodiphenyl ether (octa-BDE), and decabromodiphenyl ether (deca-BDE). Currently, novel brominated flame retardants (NBFRs), as substitutes for PBDEs, are being produced and used in large quantities^[29].

In recent years, some traditional BFRs have been banned or phased out due to their PBT properties^[30], such as HBCD, penta-BDE, octa-BDE, and Deca-BDE have successively been included in the POPs list controlled by the Convention^[31⇓~33]. In 2002, five BFRs were listed as priority pollutants for control by the US EPA^[34]. Currently, the BFRs on the market in China mainly include three major categories of "typical" BFRs (i.e., PBDEs, HBCD, and TBBPA) and four NBFRs including DBDPE. Among them, HBCD and Deca-BDE were included in the priority control list of China in January 2018^[35].

In the last few decades, BFRs have been widely detected in various environmental matrices (such as air, water, sediments) as well as aquatic organisms and humans all over the world (Table 2), including remote polar regions and the Qinghai-Tibet Plateau ^[36,37]. Experimental data indicate that soils, sediments, and dust are major sinks for BFRs in the environment (Table 2). With the development of China's economy, the production and consumption of BFR-containing commodities (such as electronic products, clothing, and furniture) have been continuously increasing. Additionally, e-waste dismantling sites in southern and southeastern China have "processed" a large amount of e-waste from developed countries ^[38,39], causing serious environmental pollution with PFRs locally.

表2 各类环境介质和生物样品中 BFRs 的浓度^[55]

Table 2 Concentrations of BFRs in environmental matrices and biota samples^[55]

BFRs	Soil(dw)/ (ng/g)	Sediment(dw)/ (ng/g)	Water/ [ng/(g/mL)]	Air/ (ng/cm³)	Dust(dw)/ (ng/g)	Fish(ww)/ (ng/g)	Bird(ww)/ (ng/g)
TBBPA	651.34±1.21E⁺⁰³ (n=14)	1.19E⁺⁰³±7.05E⁺⁰³ (n=39)	0.27±0.51 (n=20)	8.00E^-08 (n=1)	6.62E⁺⁰⁴±3.26E⁺⁰⁵ (n=34)	5.61±17.21 (n=12)	NA (n=0)
HBCD	1.65E⁺⁰³±5.43E⁺⁰³ (n=55)	104.21E⁺⁰²±334.23 (n=97)	0.13±0.33 (n=31)	5.37E^-04±3.94E^-03 (n=82)	1.25E⁺⁰⁴±4.32E⁺⁰⁴ (n=129)	14.92±59.34 (n=43)	3.24±7.66 (n=32)
DBDPE	2.38E⁺⁰³±7.21E⁺⁰³ (n=31)	83.41±44.92 (n=48)	700.31±200.21 (n=9)	4.40E^-06±1.87E^-05 (n=48)	1.55E⁺⁰⁴±5.56E⁺⁰⁴ (n=78)	0.74±1.41 (n=11)	0.41±0.39 (n=11)
BTBPE	341.32±7.22E⁺⁰³ (n=27)	230.12±259.53E⁺⁰² (n=45)	1.60E^-03±0.2 (n=6)	1.41E^-06±4.19E^-05 (n=39)	7.07E⁺⁰³±5.65E⁺⁰⁴ (n=42)	0.37±1.41 (n=7)	6.34±0.39 (n=6)
HBB	15.91±47.82 (n=22)	1.86±4.32 (n=27)	0.02E^-02±2.57E^-03 (n=12)	1.04E^-06±4.52E^-06 (n=42)	674.32±2.45E⁺⁰³ (n=32)	0.92±0.11 (n=11)	0.97±0.93 (n=6)
TBB	1.17±2.73 (n=15)	9.95E⁺⁰⁰±1.93E⁺⁰³ (n=14)	2.02E^-03±1.29E^-03 (n=11)	1.82E^-06±8.68E^-06 (n=30)	2.99E⁺⁰³±1.69E⁺⁰⁴ (n=42)	1.46 (n=1)	0.38±0.36 (n=6)
PBT	21.32±72.83 (n=9)	0.22±0.13 (n=15)	1.81E^-03±1.19E^-03 (n=3)	2.04E^-07±8.53E^-07 (n=23)	52.52E⁺⁰¹±141.12 (n=19)	72.71±35.23 (n=11)	5.98±13.01 (n=6)
TBPH	41.93±124.15 (n=17)	44.92±133.12 (n=11)	3.56E^-03±2.51E^-04 (n=9)	9.57E^-07±2.61E^-06 (n=24)	2.51E⁺⁰³±7.20E⁺⁰³ (n=50)	14.91 (n=1)	1.46±1.37 (n=6)
PBEB	1.71±1.77 (n=17)	3.34±13.01 (n=15)	6.51E^-04±2.85E^-04 (n=8)	1.71E^-07±7.87E^-07 (n=34)	18.21±47.71 (n=23)	2.63±3.45 (n=5)	1.23±2.54 (n=6)
TBECH	0.18±1.13 (n=2)	23.81±43.82 (n=5)	8.55E^-04±1.54E^-04 (n=3)	2.85E^-08±2.02E^-08 (n=7)	23.31±23.52 (n=8)	2.42±1.57 (n=2)	3.90 (n=1)

Note:The data in the table represent the average concentrations of BFRs in different substrates ± standard deviation; dw is dry matter, and ww is wet matter.

Due to their high hydrophobicity,BFRs tend to accumulate in organisms and humans^[40]. Wu et al.^[41] compared the occurrence characteristics of PBDEs and their alternative BFRs in wild animals, and found that the content of PBDEs in wild animals in the e-waste recycling areas of South China and East China was significantly higher than that in other parts of the world, indicating that these areas were severely contaminated by PBDEs.

BFRs have typical endocrine disruption effects. For example, some PDBE and its metabolites have cytotoxicity and genotoxicity, leading to oxidative stress imbalance^[42]; NBFRs have direct or indirect neurotoxicity and endocrine disruption effects^[43]. TBBPA can affect the endocrine system by interfering with thyroid hormone homeostasis^[44]. The human exposure and toxicological data of TBBPA are relatively scarce. Current studies on human and rat in vivo toxicokinetics show that the bioavailability and toxicity of this contaminant are low^[45,46]. Under high exposure conditions, studies have shown that BFRs have negative effects on reproduction and physiological development in aquatic organisms such as zebrafish^[47],frogs^[48]and terrestrial organisms such as earthworms^[49], which indicates that long-term high exposure to BFRs may pose potential risks to humans.

While the production of BFRs is restricted, the effective treatment of BFRs-containing waste by means of anaerobic degradation, ozonation, adsorption, and oxidation, as referenced in [50,51], can significantly reduce the environmental pollution and health risks caused by BFRs.

In fact, in the past 10 years, numerous scholars have extensively studied BFRs in various environmental matrices in our country^{[52⇓⇓⇓~56]}, these data have expanded understanding of the risk factors of existing BFRs in the environment and provided valuable information for regional environmental risk management planning. However, there are still certain limitations in existing research: 1) Most studies are based on reported polluted areas, while the pollution situation in unreported areas is unclear, especially the lack of research on BFRs in the western region of our country; 2) Due to the limitations of analytical technology, the pollution status of new BFRs is unclear, and relevant ecological assessment methods are still in their infancy, unable to comprehensively and systematically evaluate new BFRs, leading to an unclear understanding of the pollution characteristics and environmental risks of BFRs and their substitutes in different regions; 3) Existing acute toxicity experiments in organisms cannot fully explain the metabolism and transformation of BFRs in tissues, so the related issues of internal and external long-term exposure risks of organisms should be paid attention to.

1.2.3 Chlorinated Paraffins

Chlorinated paraffins (CPs) are mixtures of polychlorinated n-alkanes (C_n

H 2 n + 2 - m

Cl_m), which include tens of thousands of individual compounds ranging from C₆ to C₃₈. Based on the carbon chain length of the alkanes, CPs are generally divided into short-chain chlorinated paraffins (SCCPs, C_10–13), medium-chain chlorinated paraffins (MCCPs, C_14–17), and long-chain chlorinated paraffins (LCCPs, C_≥18)^[58]. Since 1930, CPs have been widely used as additives (flame retardants, softeners, water repellents, antifouling agents, etc.) in metalworking fluids, lubricants, coolants, or consumer products due to their low volatility, flame-retardant properties, and good electrical insulation. The global annual production exceeds 2 million tons^[59]. The quality balance analysis of SCCPs and MCCPs in China from production, use, to emission in 2019 is shown in Fig. 1.

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图1 2019 年中国 SCCPs 和 MCCPs 从生产、使用到排放的质量平衡分析^[60]

Fig. 1 Mass balance analysis of SCCPs and MCCPs from production, use to emission in China, 2019^[60]

Due to their large production and usage, CPs can be widely distributed in various ecosystems on the earth's surface through volatilization, leaching, and wear^[61,62]. The concentrations of CPs in many environmental media are often higher than those of other organic pollutants^[63,64], with a maximum increase of up to one order of magnitude^[65,66]. CPs have drawn extensive attention due to their characteristics such as environmental persistence, bioaccumulation, and ecological toxicity^[2]. Chinese scholars first reported on this topic in 2009^[67]. Since 1990, various international organizations have successively issued several documents involving human or environmental risks of CPs^[68⇓~70].

However, the toxicity and kinetics of CPs and their metabolites are still unclear, and the risk early warning and exposure assessment system remain to be improved. In addition, although various types of CPs have been detected in the environment, only SCCPs have been restricted in production and use worldwide as newly listed (in 2017) POPs under the Convention. However, the global output of this compound accounts for approximately 16.5% of the total CPs production, while MCCPs and LCCPs, as substitute products for SCCPs, account for a large share of the global CPs production, and their use will further increase with the implementation of global restrictions on SCCPs. Field observation results also show that the release amount of MCCPs in the environment is greater than that of SCCPs^[71]. Recent studies indicate that bioamplification of MCCPs and LCCPs exists in aquatic organisms, mammals, and humans^{[72⇓⇓⇓~76]}, which is highly consistent with the bioaccumulation model of POP chemicals^[77]. However, there are currently no international regulations addressing the increasing emissions of MCCPs and LCCPs, so further research is needed to elucidate their POP characteristics^[78]. Especially their persistence, bioaccumulation, and potential toxicity in marine environments have prompted researchers to conduct more extensive studies and assessments of CPs.

Among them, MCCPs were listed as substances of very high concern by the UK Environment Agency in 2019 and controlled by the Restriction of Hazardous Substances Directive (RoHS) of the European Union (EU) in 2020^[79]. In 2022, they were proposed for inclusion in the Convention and are currently under review by the POPs Review Committee^[80]. The distribution of S/M/LCCPs in industrial products in various regions of the world and in China is shown in Figure 2^[81].

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图2 世界各地区(A)与中国(B)CPs工业产品中S/M/LCCPs的分布情况^[81]

Fig. 2 Distribution of S/M/LCCPs in industrial products of CPs in various regions of world (A) and China (B)^[81]

China and India are currently the two major global producers of chlorinated paraffins (CPs), while some countries in Europe, North America, and the Middle East have smaller production scales^[82]. Although China is the most important producer of CPs in terms of output, its production mainly serves domestic consumption. Therefore, expanding investigations on CPs from different countries and production time ranges can help broaden international perspectives and provide necessary information for formulating effective regulations and policies. The composition of industrial CPs products varies worldwide, even showing significant differences between products from the same manufacturer (Fig. 2). Thus, to more effectively implement control restrictions on short-chain chlorinated paraffins (SCCPs), future CPs manufacturing specifications should control the chain length of normal paraffins used as raw materials and use it as a standard for CPs production, commercialization, and application. During the implementation of the Convention, besides evaluating the SCCP content in other CPs, comprehensive consideration and assessment of the environmental risks of SCCP substitutes, the SCCP release pathways during the entire life cycle of the CPs industry production (including related landfills), and the recycling of waste categories (PVC, rubber, oils/lubricants, construction and demolition waste) are required. Therefore, when fulfilling the Convention, it is necessary to address the overall usage and management issues of more than 1 million tons of CPs and a larger quantity of CPs-containing products.

Due to the complexity of CP mixtures and the large number of homologues, accurately quantifying the occurrence values of CPs in environmental media poses significant challenges. To date, complete separation or purification of individual CP isomers or homologues in industrial mixtures has not been achieved; only a limited number of discrete single homologues have been synthesized, mainly for use as analytical standards. Additionally, most existing literature reports are based on analysis using low-resolution mass spectrometry, which is currently the most commonly used detection method. Even the few international standards for short-chain chlorinated paraffins (SCCPs) are based on this technology. However, there are certain challenges regarding the accuracy and comparability of results when determining whether CP mixtures contain more than 1% SCCPs, which is important for judging whether they belong to persistent organic pollutants (POPs), implementing regulatory limits for consumer products or waste, and assessing the environmental risks caused by CPs. Currently, China has only two water quality standard methods (ISO 12010:2012 for water and ISO 18635:2016 for sediments, sewage sludge, and particulate matter) and one leather standard (ISO 18219:2015), while a textile standard is still under development (ISO/NP 22818). It is urgently necessary to supplement and improve the corresponding standard system.

1.2.4 Environmental Endocrine Disruptors

Environmental endocrine disruptors (endocrine disrupting chemicals, EDCs), also known as "environmental hormones" or "environmental oestrogens", are substances that exist in the environment and can interfere with various aspects of the endocrine system in humans or wildlife, causing abnormal effects^[83]. EDCs can disrupt the endocrine system within the body, affecting the synthesis, transport, and metabolism of hormones, leading to reproductive and developmental abnormalities, decreased reproductive capacity, organ dysfunction, and other adverse effects on offspring or populations, and may even lead to species extinction.

The types of EDCs in the environment are numerous and can be divided into two major categories based on their sources: natural hormone substances and synthetic endocrine disruptors. Natural hormone substances mainly include natural estrogens (such as estradiol, etc.), phytoestrogens (such as genistein, etc.), and mycoestrogens (such as zearalenone, etc.). Synthetic endocrine disruptors primarily include dioxin-like compounds (such as tetrachlorodibenzo-p-dioxin, etc.), pesticides (such as DDT, etc.), plasticizers (such as bisphenol A, etc.), and flame retardants (such as tetrabromobisphenol A, etc.). These substances can enter various environmental media such as water bodies, soil, air, etc., through human production and life activities and can accumulate in animals and even humans through food chain transmission, posing serious threats to organisms and human health. Studies have shown that some environmental endocrine disruptors can lead to massive deaths of wildlife such as seals and dolphins, reproductive organ developmental abnormalities in aquatic organisms such as fish and snails, decreased reproduction ability, and abnormal embryo development^[84]. Similarly, EDCs also pose potential health hazards to humans, such as affecting male and female reproductive health, being closely related to changes in menstrual cycles, endometriosis, uterine fibroids, polycystic ovary syndrome, abnormal development of reproductive organs, decrease in sperm quantity and quality, and infertility^[85]. In addition, EDCs are potentially associated with the occurrence of human metabolic diseases such as obesity, cardiovascular disease, and diabetes^[86]. Maternal exposure to EDCs may also increase the risk of language development delay in children^[87]. In short, the threat of environmental endocrine disruptors to the health of organisms and even humans cannot be ignored.

EDCs can exert endocrine-disrupting effects through multiple pathways. Nuclear receptor-mediated action is one of the most important mechanisms by which EDCs produce effects, as EDCs can act as ligands to bind various nuclear receptors, such as estrogen receptor (ER), androgen receptor (AR), thyroid hormone receptor (TR), retinoic acid receptor (RAR), etc., thereby interfering with processes such as hormone synthesis, secretion, and transport to exert endocrine-disrupting effects^[88]. In addition to nuclear receptor-mediated action, EDCs can also exert endocrine-disrupting effects by regulating membrane receptors or non-receptor pathways (such as interfering with the synthesis and metabolism of steroid hormones)^[89].

To address the challenges posed by EDCs to ecological security and human health, many countries around the world have conducted extensive work on endocrine disruptor screening and system construction, establishing technologies such as receptor binding activity tests based on in vitro cells, endocrine disruptor screening using living model organisms or rodent development models. Since 1999, Chinese researchers were the first to propose the concept of EDCs^[83,90]and have since carried out systematic research, achieving important progress in EDC detection methods, monitoring technologies, toxicity effects, and mechanisms. For example, Chinese researchers have established new methods for the separation and enrichment and detection of various endocrine disruptors, including estrogen and androgen, which are used for detecting EDCs in environmental water bodies such as river water, tap water, wastewater from sewage treatment plants, etc.^[91]. They have also developed a high-efficiency ion chromatography membrane chromatography method for rapid purification of serum vitellogenin (Vtg), as well as enzyme-linked immunosorbent assay and two-step chromatographic analysis methods for quantifying Vtg in plasma, all with advantages such as low detection limits and high efficiency^[91]. In terms of identification of new pollutants and their endocrine disruption effects, Chinese researchers have discovered the endocrine disruption effects of several new pollutants, including tributyltin^[92], perfluoroalkyl compounds^[93], tetrabromobisphenol A and its derivatives^[94], and synthetic phenolic antioxidants^[94]. The main types of action include activation of nuclear receptors, promotion of adipocyte differentiation, interference with steroid hormone production, impact on gonadal development in vivo, or causing hormonal imbalance in the body.

EDCs have become one of the new pollutants that are receiving much attention. Although various mature screening and detection systems have been established in different countries and regions around the world, which have certain reference significance for EDCs screening in China, with the rapid progress of China's economy and technology, it is of great scientific value to develop endocrine disruptor screening technologies suitable for the characteristics of China's environmental conditions and build efficient and rapid detection systems for the prevention and control of new pollutants in China.

1.2.5 Pharmaceuticals and Personal Care Products

Pharmaceuticals and personal care products (PPCPs), a concept first proposed by Christian G. Daughton in 1999^[95], has gradually been accepted by the scientific community. In fact, as a new type of contaminant, PPCPs are diverse and mainly include prescription and over-the-counter drugs used for treating and preventing diseases in humans and animals (such as antibiotics, hormones, non-steroidal anti-inflammatory drugs, beta-blockers, lipid regulators, analgesics, etc.)^[96], as well as daily life and personal care products (including soap, lotion, toothpaste, perfume, sunscreen, fragrance, etc.)^[97]. These substances can be discharged into various environmental media such as water bodies, atmosphere, and soil through different pathways^[98](Fig. 3), and may pose hazards to higher plants and animals and humans through bioaccumulation and food chain transmission, causing chronic, long-term, and cumulative negative effects on the ecological environment.

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图3 PPCPs的源和汇^[98]

Fig. 3 Sources and sinks of PPCPs^[98]

Unlike traditional pollutants, PPCPs are receiving increasing attention for the following reasons^[99]: 1) These pollutants are widely present at all levels of ecosystems; 2) The ecological and environmental risks of PPCPs are not yet fully understood (such as antibiotic resistance genes and endocrine disruptors). Although they do not persist in the environment like persistent organic pollutants, they can still have negative effects on the environment. These effects are chronic, long-term, and cumulative; 3) The lack of efficient detection technologies for trace PPCPs in the environment; 4) Insufficient effective removal methods for PPCPs. Moreover, the usage of these substances is steadily increasing. In 2010, the global consumption of antibacterial drugs was 63,151 tons, and it is expected to increase by 67% by 2030^[100]. Global urbanization and changes in people's lifestyles have further increased the use of personal care products. Additionally, the ongoing COVID-19 pandemic has changed the consumption patterns of PPCPs, posing new challenges to the ecological environment and human health^[98].

1.2.5.1 Pollution Characteristics and Levels of PPCPs

An increasing number of evidences suggest that the aquatic environment, as the main sink for PPCPs, is suffering from serious harm caused by these compounds^[101]. In recent years, more than a hundred kinds of PPCPs have been detected in groundwater, surface water, sediments and other aquatic environments worldwide^[102]. Researchers found more than 50 types of drugs in the detection of 139 streams in 30 states of the United States^[103]. A variety of these compounds with extremely high content were also found in sewage treatment plants in southern India. Among them, the mass concentrations of amphetamine, saccharin, cyclamate, and sucralose are as high as 4300, 303000, 3460 and 1460 ng/mL respectively^[104]. Some major rivers in China (such as Yangtze River, Pearl River, Liaohe River, Haihe River, etc.) have also successively detected various PPCPs with relatively high content in surface water^[105]. Some scholars reported that more than 60% of the water bodies in the Pearl River Basin detected estradiol, with the highest mass concentration up to 65 ng/L. At the same time, ibuprofen (IBU), cloprostenol, salicylic acid were measured in most domestic water bodies, with the highest mass concentrations being 2098, 248, and 1417 ng/L respectively^[106]. Moreover, pollutants such as sulfamethoxazole (SMX), sulfadimidine, trimethoprim, etc., with mass concentrations ranging from 7 to 360 ng/L were also found in the rivers of the Mekong River region^[107]. Kim et al.^[108] conducted a systematic survey on various water bodies in South Korea. The results showed that PPCPs residues were found in almost all water bodies, with the detection rate of antibiotics up to 80%. Murata et al.^[109] analyzed the pollution status of 12 antibiotics including sulfonamides, macrolides and trimethoprim in 37 rivers in Japan. They found that the mass concentrations of these pollutants range from 0 to 626 ng/L. In addition to rivers and lakes, antibiotics, antiparasitic drugs and sunscreens were also found in seawater and marine organisms^[110].

PPCPs are widely used in human disease treatment and animal husbandry, but their low metabolic levels in vivo result in large amounts of these substances being discharged with feces and urine. These substances enter the soil directly through methods such as direct irrigation with sewage, reuse or landfill of sludge, and application of livestock manure to farmland. In recent years, a large number of PPCPs or their metabolites have been detected in soils from different countries^[111]. Studies have shown that the mass fractions of tetracycline and chlortetracycline in manure are 4.0 mg/kg and 0.1 mg/kg, respectively. After fertilization with manure, the mass fraction of tetracycline in soil samples is 86.2–198.7 μg/kg, and the level of chlortetracycline is 4.6–7.3 μg/kg^[112]. Other studies have indicated that some PPCPs are persistent, and concentrations of analgesics/anti-inflammatory drugs, antiepileptic drugs, and preservatives in regenerated water-irrigated farmland are at ng/kg to μg/kg levels. For example, the mass fraction of carbamazepine reaches up to 549 μg/kg (dry weight), and the mass fraction of triclosan is 16.7 μg/kg (dry weight)^[113].

In addition to water environments and soil, some volatile PPCPs have also been widely detected in indoor dust and air. Scholars have detected siloxanes with mass fractions ranging from 21.5 to 21000 ng/g in indoor dust, and the generation of these substances is related to the number of electronic appliances and smokers indoors. Another study investigated the residues of two polycyclic musks, three nitro musks, and one HHCB metabolite (HHCB lactone) in indoor dust samples, among which HHCB was the compound with the highest detection rate^[114].Moreover, scholars investigating the contamination status of polycyclic musks at a cosmetics factory in Guangzhou found that apart from sewage and sludge, polycyclic musks were mainly present in the gas phase, accounting for 86.4%-97.7%^[115]. Research institutions surveyed the contamination status of parabens in indoor dust in the United States, China, Japan, and South Korea, finding that the maximum detection mass fraction of this compound was 110800 ng/g, with average levels as follows: South Korea (2320 ng/g) > Japan (2300 ng/g) > United States (1390 ng/g) > China (418 ng/g). The content of parabens in indoor dust in China is the lowest, which may be related to the lower per capita consumption of personal care products by Chinese people^[116].

It is noteworthy that PPCPs can enter the human body through different media. In recent years, such pollutants have been found in breast milk, blood, and urine. Synthetic musk was detected in breast milk samples from the Chengdu area of China at concentrations of 1.4–16.5 ng/g(lw), possibly due to the extensive use of personal care products such as hand sanitizers, moisturizers, shampoos, hair dyes, and hair sprays. Moreover, four synthetic musks (MX, MK, HHCB, and AHTN) were also found in breast milk samples from eastern Chinese cities, with average mass fractions of 4–63 ng/g(lw). Compared with detection results from the United States (2–917 ng/g(lw)), Denmark (38–422 ng/g(lw)), and Sweden (2–268 ng/g(lw)), the detection rate and concentration of synthetic musk in Chinese breast milk are the lowest. Based on this, it is estimated that Chinese infants ingest 277–7391 ng/d of synthetic musk from breast milk^[117].In addition, triclosan was detected in tap water and bottled water at mass concentrations of 14.5 ng/L and 9.7 ng/L, respectively. Furthermore, this compound may also be released from infant feeding bottles. Based on these findings, the estimated daily intake of triclosan for adults and infants is approximately 10 and 5 ng/d^[118].

1.2.5.2 Ecological and Health Risks of PPCPs

The continuous exposure to antibiotics can damage sensitive bacteria in aquatic systems, leading to the emergence of a large number of environmentally resistant strains, which seriously affect the balance of aquatic systems^[119].Researchers investigated the resistance status of various antibiotics (such as macrolides, sulfonamides, fluoroquinolones, and tetracyclines), and found that antibiotic resistance genes are widely present in hospital and livestock breeding wastewater, urban wastewater, surface water, and drinking water resources^[120].In recent years, scholars have detected tetracycline resistance genes in wastewater in Hong Kong, China and Shanghai, while other researchers have discovered multi-drug resistant Escherichia coli against sulfonamides, tetracyclines, and ampicillin in Beijing's surface water^[121].These resistant strains can disrupt the interdependent and mutually restraining relationships within microbial communities, leading to the disappearance of some beneficial bacteria and the massive reproduction of harmful bacteria, and spread to other animals and humans through contaminated drinking water, agricultural and aquaculture products^[122].

According to traditional views, antibiotics are considered a class of low-toxicity and safe drugs. However, numerous studies have shown that some antibiotics exhibit significant toxic effects on aquatic organisms. Reports indicate that the presence of 1 mg/L erythromycin (ETM) or tetracycline in water bodies can severely inhibit the growth of unicellular algae in freshwater and strongly stimulate the synthesis and release of abscisic acid in algae^[123]. Even some drugs show strong acute toxicity; for instance, quinocetone in water bodies has extremely high toxicity to daphnia. Since daphnia is a major food source for other aquatic animals in freshwater systems, even small amounts of quinocetone can completely destroy their reproductive capacity, which will lead to severe disruption of the nutrient levels in that area. It is worth noting that most antibiotics have relatively small acute toxicity to aquatic organisms, with the median effective concentration (EC₅₀) usually in the range of mg/L. Scholars studied the impact of nine antibiotic drugs on the growth and reproduction of daphnia through a 48-hour acute toxicity test and found that the EC₅₀ values were: quinocetone at 4.6 mg/L, tylosin at 40 mg/L, sulfadiazine at 13.7 mg/L, and oxytetracycline at 462 mg/L^[124].

In addition, as the main substances in PPCPs in aquatic environments, hormones can cause endocrine disorders in humans and animals, having a significant impact on reproduction and development, such as decreased fertility, masculinization of males, induction of vitellogenin production in males, and hermaphroditism. Compared with exogenous endocrine disruptors, their estrogenic potency is 10,000 to 100,000 times higher. It has been reported that many aquatic species, such as carp, trout, crucian carp, and softshell turtles, will experience sexual inhibition after long-term exposure to environmental levels of hormones^[125].At the same time, parabens, sunscreens, triclosan, and others are also considered to have endocrine-disrupting effects, and these chemicals have strong bioaccumulation and amplification capabilities^[121].

The PPCPs entering the soil not only alter the microbial community structure but also seriously affect important functions such as nutrient cycling in the soil^[126,127]. Additionally, after reclaimed water and animal manure are applied to the soil, they can cause severe contamination and ecological toxicity of PPCPs in the soil (Fig. 4). Researchers analyzing the field soils with high rates of livestock manure application found that 23 target PPCPs were detected, with their mass fractions reaching ng/g levels. Among the 41 sampling points, 12 had a medium combined ecological risk, and 17 had a high ecological risk^[128]. Some PPCPs in the soil can be absorbed by crops, leading to reduced crop yields, while some PPCPs accumulate in edible plants, posing a threat to human health, such as disrupting the endocrine system and inhibiting the growth of human embryonic cells^[129].

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图4 再生水浇灌的土壤具有一定的健康风险^[129]

Fig. 4 Reclaimed water-irrigated soil has some health risks^[129]

1.2.5.3 Analysis and Removal of PPCPs

Due to the characteristics of low content and complex matrix in the environment, the study of analytical methods for these compounds is particularly important. It was not until 1990 that scientists established an analytical method for pesticide residues and detected an unknown compound with a structure similar to that of phenoxyalkanoic acid herbicides (which was finally identified as a highly toxic chlorophenyl dicarboxylic acid) in German drinking water and groundwater, which began to attract widespread attention from people regarding the research on analytical methods and distribution characteristics of PPCPs in the environment^[130]. At present, the main analytical methods for PPCPs are liquid chromatography-mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS/MS). In addition, this method requires enrichment and purification by solid-phase extraction (SPE) and other methods. Instrumental analysis methods can accurately quantify pollutants, but they have shortcomings such as expensive instruments, high testing costs, long analysis time, large sample volume required for detection, and cumbersome pretreatment steps. In contrast, bioanalytical methods based on antibodies (or other biopolymers with recognition functions), such as enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), and time-resolved fluoroimmunoassay, have the advantages of simple pretreatment, fast analysis process, and high throughput, effectively compensating for the defects of instrumental analysis^[131].

The treatment processes of PPCPs are also the focus of research. As a complex system, this kind of substance exists in multiple migration and transformation modes. Part of them will enter soil organisms, water bodies, and atmosphere by absorption, runoff/migration, volatilization, etc., forming trans-medium pollution, and finally enter the human body through diet. Another part may be degraded, transformed or completely removed under the comprehensive action of environmental factors^[132]. The wastewater treatment plant (WWTP) is the main unit for removing or degrading pollutants in wastewater. However, traditional processes mainly treat inorganic substances such as N and P and major organic pollutants, with poor removal effect on PPCPs. Due to its environmental friendliness, no secondary pollution, high efficiency, low cost, and convenient operation management, biological treatment technology has been gradually used for PPCPs treatment in recent years. Among them, the activated sludge system can effectively remove PPCPs from wastewater, but its removal rate is affected by many factors, such as the physicochemical properties of compounds, the configuration and operating parameters of bioreactors (hydraulic retention time, sludge retention time, and pH)^[133]. Meanwhile, the emergence and development of advanced oxidation methods, aquatic organism methods, microbial methods, and membrane treatment technologies have contributed to the efficient removal of such pollutants. However, for the treatment of PPCPs, it is not enough to limit to end-of-pipe treatment. It is more important to control the source. Combining source control with end-of-pipe treatment will achieve the best removal effect.

1.2.6 Microplastics

1.2.6.1 Definition and Classification of Microplastics

Microplastics (microplastics), which refer to plastic particles, fragments, or fibers with a diameter smaller than 5 mm, were proposed by Thompson et al.^[135] in 2004. Microplastics with a diameter smaller than 1 μm are also called nanoplastics (or nanoplastics). Since microplastic pollution was first discovered by Carpenter et al.^[136] in the surface water of the Sargasso Sea in 1972, microplastics have been widely detected in ecosystems such as the atmosphere, soil, and hydrosphere, urban sewage treatment systems, as well as salt, drinking water, food, and biological (including human) samples. Their potential risks to the ecological environment and human health have attracted increasing attention^{[134, 135]}.

Microplastics in the environment are divided into primary and secondary sources^[134]. Primary microplastics refer to plastic particles or fibers manufactured as products, including plastic microbeads in personal care products and microfibers in textiles, which are released into the environment through product use and fabric washing; secondary microplastics are formed by the fragmentation and decomposition of larger plastic waste in the environment under physical, chemical, and biological effects, and are the main contributors of microplastics in the environment^[136]. Large-sized microplastics will form micrometer- and nanometer-scale microplastics after weathering through a series of physical, chemical, and biological degradation processes in the environment^[137].

Microplastics in the environment are varied in morphology and diverse in types. Based on shape, they are mainly divided into fibrous, fragmentary, granular, and foamy types; based on chemical composition, they can be categorized as polyethylene (PE), polyvinyl chloride (PVC), polypropylene (PP), polystyrene (PS), and polyamide (PA), etc. Among them, the monomer sources of microplastic polymers mainly include petrochemical-based and bio-based materials; according to whether they can be biodegraded, microplastics can be classified as biodegradable microplastics and non-biodegradable microplastics. Tire wear particles and silicone rubber particles are also important types of microplastics in the environment^[138⇓~140]. The common types of microplastics and their chemical structures^[141] are shown in Fig. 5.

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图5 基于聚合物单体来源和生物可降解性分类的常见微塑料类型^[141]

Fig. 5 Common types of microplastics classified based on polymer monomer source and biodegradability^[141]

1.2.6.2 Environmental Distribution and Abundance of Microplastics

There are 0.75 million to 1.99 million tons of plastic debris in the global ocean, of which about 13.5% is microplastics^[142]. Microplastics in coastal areas are mainly distributed in surface seawater, beaches, and nearshore sediments, influenced by human activities and natural conditions (such as wind, waves, and tides, etc.). Microplastics in the open ocean are mainly controlled by the spatial variation of deep-sea currents and surface currents, showing significant spatial heterogeneity^[142], with abundances of 0.0060–660,000 items/m³ in seawater and 0.0017–147.9 items/g in sediments^[143]. Microplastics can migrate horizontally and vertically in the ocean, affected by multiple complex processes such as thermohaline circulation, biological fouling, and gravitational settling. The horizontal distribution of microplastics entering the seafloor is regulated by deep-sea currents, remaining in low shear stress regions of submarine boundaries such as straits and trenches, coinciding with biological hotspots, thus causing higher ecological risks^[142].

Microplastics in freshwater (including lakes, reservoirs, and rivers) are mostly derived from terrestrial environments. It has been reported that the abundance of microplastics (> 250 μm) in 38 lakes and reservoirs worldwide ranges from 0.001 to 10 items/m³ (mean ± standard error = (1.82 ± 0.37) items/m³), and lakes and reservoirs with dense populations, long hydraulic retention times, and high sedimentation areas are more susceptible to microplastic pollution^[144]. River systems are the main transport pathways for microplastics from land to ocean. According to model predictions, the global annual river plastic flux into the sea is 57,000 to 265,000 tons^[145]. Microplastics discharged from domestic wastewater treatment plant effluents are also an important source of microplastics in natural water environments^[146]. The abundance ranges of microplastics in the effluent of global wastewater treatment plants and sludge are 0.01-297 items/L and 4.40 × 10³-2.40 × 10⁵ items/kg^[147].

Agricultural films are an important source of microplastic pollution in agricultural soil^[148]. The average abundance of microplastics in Chinese agricultural soils is (2462 ± 3767) items/kg, and its distribution is influenced by factors such as agricultural film recycling methods, irrigation methods, fertilization types, tilling frequency, altitude, meteorological factors, and soil properties^[149].

The abundance of microplastics in the atmosphere ranges from 0.01 items/m³ (Western Pacific) to 5,650 items/m³ (Beijing, China)^[150]. It is estimated that the global atmospheric emission flux of microplastics is approximately 324 Gg/a, and the main sources of microplastics in the atmosphere are oceanic transport and road sources (including tire and brake wear and poorly managed plastic waste)^[151]. Microplastics in the atmosphere are small in size and low in density, making them easy to transport over long distances in the atmosphere^[152].

1.2.6.3 Environmental Microplastic Characteristics and Effects

Microplastics are a class of environmental emerging pollutants with unique properties, distinct from other particles in the environment^[153]. Environmental microplastics have large density differences, strong persistence, a wide size range (spanning five orders of magnitude), various shapes (e.g., particles, fragments, fibers, etc.), complex chemical compositions (including various polymer materials and chemical additives such as plasticizers, flame retardants, and stabilizers), diverse surface groups, and can attach ecological crowns, etc.^[154]. Microplastics can enter the bodies of organisms through feeding and respiration, causing toxic effects on the gastrointestinal tract and other tissues and organs. They can also be transferred and accumulated through the food chain to higher-level consumers. Microplastics, especially nanoplastics, have high specific surface areas and strong adsorption affinity, making them prone to combine with other environmental pollutants to form composite pollution or "Trojan horse" effects. Nanoplastics can enter plant and animal cells, producing cytotoxicity^{[153, 155]}. The surface of microplastics can also attach microorganisms and antibiotic resistance genes, serving as carriers for the transmission of viruses, pathogenic microorganisms, and antibiotic resistance genes^[135]. Additives in microplastics will release secondary pollutants in the environment or within organisms; although the biotoxicity of large polymer plastic molecules is low, toxicity products may be produced after aging in the environment or degradation in biological systems. Oligomers formed by the self-aggregation of monomers released under the catalytic action of intestinal enzymes from polylactic acid plastics cause stronger toxic effects on organisms^[156].

Microplastics can directly influence carbon cycling in soil, freshwater, and marine environments (especially biodegradable microplastics), and they can also indirectly affect carbon, nitrogen, and phosphorus cycling by altering soil physicochemical properties (e.g., soil aggregate size, soil porosity, and water-holding capacity), microbial community composition, and enzyme activity^{[156⇓⇓~159]}.The impact of microplastics on soil nutrient cycling may negatively affect plant nutrient uptake, root growth, seed germination, and photosynthesis. However, further research is needed to investigate the ecological effects of microplastics in real environments and their potential human health risks.

1.2.7 Antibiotics and Antimicrobial Resistance Genes

Antibiotics in the natural environment are produced by microorganisms as secondary metabolites at concentrations far below the therapeutic dose. These low-concentration antibiotics can act as signaling molecules between microbial populations or within a population^[160].Since penicillin was first clinically applied to treat bacterial infections, humans have developed various antibiotics with different antibacterial spectra through screening and artificial synthesis within decades. The definition of antibiotics has also evolved from "chemical substances produced by microorganisms during metabolism that exhibit inhibitory or even lethal effects on the growth and activity of other microorganisms" to a broader one: "a class of secondary metabolites produced by microorganisms or higher animals and plants during their growth, which possess anti-pathogenic or other activities, as well as similar compounds synthesized or semi-synthesized by artificial chemical methods." This indicates that the scope of antibiotic use has expanded from clinical antibacterial treatment to animal husbandry. In 1950, the U.S. Food and Drug Administration first approved the use of antibiotics as feed additives, thus promoting their widespread application in animal husbandry. Antibiotics have played an important role in preventing and treating animal infectious diseases, promoting animal growth, and improving feed conversion^[161].

At present, there are already thousands of types of antibiotics, which can be mainly divided into the following categories: beta-lactams, macrolides, fluoroquinolones, tetracyclines, sulfonamides, aminoglycosides, polypeptide antibiotics, chloramphenicol, lincomycin, etc. Beta-lactams can be further subdivided into penicillins, cephalosporins, cephamycins, carbapenems, beta-lactamase inhibitors, and so on. The main macrolide antibiotics include azithromycin, ETM, tylosin, clarithromycin, etc. The main fluoroquinolone antibiotics include ciprofloxacin, enrofloxacin, levofloxacin, danofloxacin, pefloxacin, etc. The main tetracycline antibiotics include tetracycline, doxycycline, oxytetracycline, chlortetracycline, tigecycline, etc. The main sulfonamide antibiotics include sulfadiazine, sulfamethoxazole, sulfacetamide, etc. The main aminoglycoside antibiotics include streptomycin, kanamycin, gentamicin, etimicin, netilmicin, etc. The main polypeptide antibiotics include vancomycin, norvancomycin, teicoplanin, colistin B, bacitracin, etc. The main chloramphenicol antibiotics include chloramphenicol, thiamphenicol, florfenicol, etc. The main lincomycin antibiotics include lincomycin and clindamycin, etc.

Antibiotic resistance genes (ARGs), in a sense, are very ancient compared to the time when antibiotics were discovered by humans. Antibiotics, as signal molecules for antagonism among microorganisms, thus inherently exist in many microbial populations, and this intrinsic antibiotic resistance is referred to as "intrinsic resistance," which refers to the prototype of resistance genes, quasi-resistance genes, or resistance genes that are usually not expressed on the bacterial genome^[162]. Bacteria can acquire resistance by expressing potential resistance genes or through random mutations. Scientists extracted ancient DNA from the permafrost in the Arctic and found a relatively diverse range of antibiotic resistance genes, which showed resistance to beta-lactam antibiotics, tetracycline antibiotics, glycopeptide antibiotics, etc.^[163], proving that antibiotic resistance genes did not originate only after humans began using antibiotics.

Although some antibiotic resistance genes have existed in nature for a long time, the large-scale use and even abuse of antibiotics in medical and breeding fields, as well as the existence of complex pollution such as heavy metals and disinfection by-products, have greatly increased the direct and indirect selection pressures in the environment^[164,165], which has significantly accelerated the evolutionary process of antibiotic resistance, leading to the continuous development of antibiotic resistance. In 2006, Pruden et al.^[166]proposed that antibiotic resistance genes should be regarded as a new type of environmental pollutant. By 2021, the Comprehensive Antibiotic Research Database (CARD) had recorded more than 1600 known antibiotic resistance genes^[167]. High-throughput quantitative PCR has achieved specific amplification of 55 mobile genetic elements and 325 resistance genes^[168]. Unlike traditional chemical pollutants, antibiotic resistance genes possess inherent biological characteristics that enable them to transfer and spread between different bacteria, and even self-amplify, exhibiting unique environmental behaviors and pollution characteristics. In recent years, people have gradually realized that the persistence, enrichment, and diffusion of antibiotic resistance genes in the environment may pose greater risks than antibiotics themselves. It is estimated that currently about 700,000 people die each year from infections caused by superbugs worldwide. If antibiotic resistance is not effectively controlled, the number of deaths due to drug-resistant infections globally could reach 10 million per year by 2050, far exceeding the number of deaths caused by cancer^[169]. Therefore, while antibiotics bring health benefits to humans, they also introduce new risks to humans and the environment that cannot be ignored. The generation of antibiotic resistance and the dissemination and spread of resistance genes have become a global focus.

1.3 New Scientific Issues in the Study of Emerging Contaminants

The environmental risks of new pollutants are a common environmental issue faced by countries all over the world. So far, international organizations and institutions have reached broad consensus on strengthening control measures for new pollutants and promoting orderly global actions. However, new pollutants are a new thing and a new problem that inevitably arise with the process of industrialization. The rapid growth of various chemicals and their extensive use in production and life are the fundamental reasons why new pollutants continue to emerge and need continuous research and management. What we know about new pollutants is just the tip of the iceberg; many more new pollutants remain undiscovered and unattended, and there is still insufficient understanding and effective control of many new pollutants that have been discovered. In addition to multiple biological toxicities such as persistence, bioaccumulation, carcinogenicity, and teratogenicity, some new pollutants also have the potential for long-range migration, which can be transported across international borders via air, water, or migratory species and deposited in regions far from their emission sources, causing global environmental pollution problems^[170⇓~172]. To manage such new pollutants, global joint actions are required.

Compared with traditional pollutants, new pollutants show distinct characteristics such as the diversity and variability of sources and the uniqueness of challenges. New pollutants often coexist in wildlife and humans, forming complex mixed exposures. In addition, the precursors and transformation products of these pollutants may exhibit similar risk characteristics and require further consideration of their environmental processes, toxic effects, and interactions between analogs and related compounds. This poses a significant challenge to assessing their additive or synergistic effects as well as their hazards and interactions.

China is a major producer and user of chemicals, but systematic environmental risk assessments for chemical substances have not yet been carried out. A large number of chemicals have entered production and life before their harmful characteristics are understood. According to the United Nations Environment Programme's "Global Chemicals Outlook", in 2017, China's annual sales of chemicals were approximately 1.293 trillion euros, accounting for about 37% of global sales. Among them, the industries involved in the production and use of new pollutants cover a wide range, with long industrial chains, small individual numbers, low environmental concentrations, and scattered distribution. The types, regional distribution, industry sources, main environmental risks, and health risks of new pollutants in the environment are still unclear. The lack of scientific data on these chemicals hinders the formulation of effective control measures. In addition, new pollutants typically include recently synthesized compounds with superior product properties, making it difficult to find better alternatives and alternative technologies. Although regulatory efforts focus mainly on mitigating the chemical risks posed by new pollutants themselves, there is relatively little emphasis on assessing the safety of alternative chemicals used as substitutes. There is also a lack of systematic approaches in environmental management related to alternative chemicals, including analysis, environmental processes, toxicological health, etc.

As a country of rapid economic expansion in the late developing catch-up type, China is faced with a complex environmental challenge far exceeding that of developed countries under the dual drive of globalization external force and internal transformation of the country. Due to China's unique industrial structure, utilization mode and emission difference, some pollutants may be universally present in various environmental media in China but have evaded the scrutiny of foreign regulatory agencies. Various new pollutants have been found in human blood, breast milk and even embryos in our country, posing a significant threat to public health. While external input risks are rising, the internal risk chain linkage effect is enhanced. If risks are not prevented in time or control and governance are not effective, the environmental risks of new pollutants may form a "domino effect" and "butterfly effect", and then through transmission, superposition and evolution may have a major impact on the entire chemical control route and even the overall development of economy and society.

Besides, identifying and assessing priority new pollutants for control from an increasing number of chemicals is another major challenge in the study of new pollutants. Currently, whether it is the United States' Toxic Substances Control Act (TSCA), Canada's Prohibition of Certain Toxic Substances Regulations 2022, or the European Union's Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation, there are still significant demands for data and research assessment on new pollutants, and they cannot yet fully meet the basic goal of comprehensive control over new pollutants. At the international convention level, between 2017 and 2022, only 18 types of persistent organic pollutants (POPs) were added to the Convention. The explosive growth in chemical varieties has increased the complexity of environmental management, resulting in a severe mismatch between the quantity of new pollutants and the scope of control. China is one of the countries with the richest variety of chemical production, and its existing chemical inventory includes more than 40,000 varieties, with nearly 100 new chemical substances entering the market each year. Limited research information shows that according to the screening criteria of the Convention, more than 100 chemical substances on the aforementioned list simultaneously meet the two screening criteria of persistence and bioaccumulation, making them potential new pollutants requiring control. Although China's research contributions to the addition of new controlled substances in the Convention are increasing, China still needs to carry out a large amount of data investigation, environmental monitoring, environmental risk assessment, and control measures to accelerate the pace of managing new pollutant environmental risks.

In summary, new pollutants are closely related to production and life. They involve large-scale industrial industries with long industrial chains. Research needs to cover different fields, carry out relevant work such as identification detection, regional environmental process, environmental exposure and health, as well as research and development of optimization control and reduction technology, which increases the complexity of challenges. The new technologies and substitutes developed in the process of studying new pollutants also need to be comprehensively risk assessed to reduce possible new environmental problems and cost challenges. While standing on the basis of ecological environment protection and health risk prevention, balancing the demand for economic development is a major premise for carrying out corresponding research on new pollutants, and also an important foundation for formulating specific governance solutions. The study and governance of new pollutants is a long-term, dynamic and complex project, which requires all-round coordinated advancement.

2 New Contaminants: Production, Usage, and Emissions

2.1 Production, Use, and Emissions of POPs

POPs are currently the most concerned new pollutants and have been widely detected in the environment. So far, 35 POPs have been officially included in the Convention. Except for unintentionally produced POPs such as dioxins, these POPs are mostly chemicals intentionally produced and used by humans. According to their uses, they can be divided into industrial chemicals and agricultural chemicals. Among them, industrial chemicals refer to chemicals developed for use in chemical industry processing^[173], including chemicals used only in industrial production processes and chemicals used as important components of commercial products in consumer markets, such as plasticizers, flame retardants, stabilizers, etc. Agricultural chemicals refer to chemical agents used to control biological or viral hazards to crops, such as fungicides and insecticides^[174], which are generally called pesticides in China. According to their chemical composition, the main confirmed POPs are organic halides, which can be divided into organochlorine POPs, organobromine POPs, and organofluorine POPs; in addition, some compounds without halogens have been confirmed as POPs by the Convention, namely benzotriazole UV absorbers like UV-328.

The first batch of POPs confirmed by the Convention were mainly organic chlorine pesticides that had been produced and used in the 1950s to 1980s, such as DDT and hexachlorocyclohexane (HCHs). However, most of them have been eliminated and replaced in agricultural fields, ceased production and use, and their concentrations in global environmental media generally show a downward or stable trend^[175⇓~177]. The subsequent identified and controlled POPs by the Convention are mainly various industrial chemicals, including organochlorine industrial chemicals, brominated flame retardants, perfluorinated compounds, and UV-328. Additionally, some existing individual organochlorine pesticides, such as endosulfan, were added. The newly added POPs under the Convention still have a certain scale of production and use in the past ten years, constituting new pollutants widely detected in the domestic and international environments and mainly concerned at present. This article focuses on the production, use, and emissions of these POPs.

2.1.1 Organic Chlorine-based POPs

SCCPs and MCCPs are the largest global production and usage volumes of POPs known today^[178]. Fig. 6shows the changing trends in the annual production and use of commodity-level CPs worldwide and their application proportions in five major application industries from 1930 to 2020. By 2020, the cumulative production and use of CPs worldwide reached approximately 32.5 million tons and 33.2 million tons, respectively. Among them, the production volumes of SCCPs, MCCPs, and LCCPs accounted for 28%, 57%, and 15% of the total production, respectively. Historically, Western Europe and North America were the main production and use regions of CPs. However, after 2000, China's production and use of CPs increased significantly and became the main country for global CP production and use. SCCPs and MCCPs are mainly used in polyvinyl chloride products (40%-66%) and metalworking fluids (12%-34%), with smaller amounts used in rubber and other plastics (10%-14%), adhesives (5%-6%), paints (3%-10%), etc.; from 1930 to 2020, the cumulative emissions of CPs worldwide reached 5.2 million tons, among which SCCPs, MCCPs, and LCCPs accounted for approximately 30%, 40%, and 30%, respectively^[179].

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图6 1930—2020年全球商品级 CPs 年生产量、使用量和5个行业的应用比例(单位:百万t/a)^[179]

Fig. 6 Global annual production, use of CPs, and distribution of CP use among five major end-use applications in 1930-2020 (Mt/a)^[179]

The CP products in China are not classified by carbon chain length but divided into CP-42, CP-52 and CP-70 according to chlorine content, which makes the CPs produced and used in China often a mixture of SCCPs, MCCPs and LCCPs. Chen et al.^[179] estimated the production and use amounts of SCCPs and MCCPs in China in 2019 by means of investigation on the CP industry chain in China, detection of CP components in the products and quality balance method, respectively reaching 650,000 t and 687,000 t; they were mainly applied to polyvinyl chloride (79.0%) and rubber (13.4%), with a small amount applied to adhesives (5.5%), metalworking fluids (1.3%) and leather (0.8%); environmental emissions reached 8000 t, and soil was the main receiving medium (55.1%), followed by atmosphere (33.3%) and water (11.5%), as shown in Figure 7^[180].

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图7 2019年中国SCCPs和MCCPs从生产、使用到排放的质量平衡(单位:kt)^[180]

Fig. 7 Mass balance diagrams of SCCPs and MCCPs from production and use to emission in 2019 in China (kt)^[180]

In addition to SCCPs and MCCPs, the subsequent confirmation of additional organochlorine industrial chemicals listed as persistent organic pollutants (POPs) under the Convention also includes pentachlorobenzene (PeCBz), pentachlorophenol (PCP), hexachlorobutadiene (HCBD), polychlorinated naphthalenes (PCNs), methoxychlor, and dechlorane plus (DP). These chemicals are respectively used as intermediates in chemical or pesticide production, wood preservatives, bactericides, and flame retardants. However, their global production and usage volumes and ranges are relatively limited, and they have basically stopped or are gradually ceasing production and use, with relatively limited environmental pollution and impact. Endosulfan and chlorpyrifos are two more attention-worthy insecticide-type organochlorine POPs that were subsequently added to the Convention. Studies show that around 2010, the global average annual production of endosulfan was approximately 18,000–20,000 tons^[181]. From 1954 to 2000, the amount of endosulfan used for crops was about 338,000 tons, with an environmental emission volume of approximately 150,000 tons^[182]. China began using endosulfan in 1994, but its usage volume and range have remained relatively limited. From 2010 to 2016, China’s annual endosulfan production decreased from 6,000 tons to 700 tons^[181⇓~183], and production has now been completely halted. Chlorpyrifos is a chlorinated organophosphate insecticide that has been on the market since 1965, primarily used in crops such as grains, rice, corn, soybeans, cotton, etc.^[184]. The global usage before 2007 was approximately 10,000 tons annually, and it is estimated that the global production and usage have increased to about 50,000 tons annually, with an annual release of 25 tons into the air during the production process. China and India are currently the main producers of chlorpyrifos. In 2021, India's total chlorpyrifos production was 24,000 tons, of which 11,000 tons were used domestically, 12,000 tons were exported, and 1,000 tons were in inventory^[185]. China began producing and using chlorpyrifos in the 1990s, with an annual production of 35,000–53,000 tons between 2012 and 2017, and an air release volume of 2.92–4.42 tons during the production process^[184,185]. Between 2014 and 2020, China's average annual chlorpyrifos usage exceeded 2,000 tons^[186]. Since December 2016, China has banned the use of chlorpyrifos on vegetables, but it is expected to have little overall impact on total usage^[184]. Chlorpyrifos has already passed the POPs risk assessment review under the Convention in 2023 and will soon be officially listed in the controlled POPs list under the Convention.

2.1.2 Organic Brominated POPs

Brominated flame retardants are organic bromine POPs that are widely detected in the environment and are also typical emerging pollutants. Penta-BDE, octa-BDEs, and deca-BDEs were listed in the Stockholm Convention in 2009, 2009, and 2017, respectively. Global PBDE production began in the 1970s, and by 2020, the cumulative production was approximately 1.9 million tons, including 175,000 tons of penta-BDEs, 130,000 tons of octa-BDEs, and 1.6 million tons of deca-BDEs, as shown in Figure 8. Before 1995, the United States and Israel were the main producers of PBDEs. PBDEs were mainly used in electronics, foams, carpets, buildings, transportation, and textiles, with penta-BDEs primarily used in foams and carpets (50%) and construction (20%), octa-BDEs mainly used in electronics (40%) and construction (25%), and deca-BDEs mainly used in electronics (30%), foams and carpets (25%), and construction (20%). North America was the main region for penta-BDEs usage, mainly for polyurethane foam plastics; North America and Asia were the main regions for octa-BDEs and deca-BDEs usage. By 2018, the global atmospheric emissions of ∑5PBDEs (BDE28, 47, 99, 153, and 183) and deca-BDEs were 0.6 (0.03–1.3) thousand tons and 1.1 (0.9–1.2) thousand tons, respectively, reaching a peak in 2004 [187]. China's PBDE production began in the 1980s, mainly producing deca-BDEs, never producing octa-BDEs, and ceased the production of penta-BDEs in 2004 with a total amount less than 1,000 tons [188-189]. After 2000, China became the main producer of deca-BDEs. From 1993 to 2018, China’s cumulative production of deca-BDEs reached 465,000 tons; the usage was 402,000 tons, mainly for plastic production (99%); the environmental emission was 27,000 tons, mainly from chemical manufacturing and flame-retardant plastic modification plants, with 7,000 tons from packaging waste, 11,000 tons discharged into rivers through wastewater, and 9,000 tons from dust in the atmosphere [189]. From 2015 to 2018, the average annual atmospheric emission of deca-BDEs was 412.1 tons, with the largest contribution from production sources (70.4%), followed by waste treatment sources (14.3%), plastic processing sources (5.7%), and product use sources (9.6%) [190]. HBCD was listed in the Stockholm Convention in 2013 [191]. From 1965 to 2015, the global cumulative production of HBCD was 405,000–835,000 tons, including 160,000–280,000 tons of α-HBCD, 115,000–210,000 tons of β-HBCD, and 130,000–345,000 tons of γ-HBCD. North America, China, and Western Europe were the main producing countries or regions, accounting for 40%, 27%, and 25%, respectively. HBCD was mainly used in expanded polystyrene and extruded polystyrene insulation boards (>97%), with a small amount used in textile coatings, expanded polystyrene packaging, high-impact polystyrene in electrical and electronic equipment, etc. From 1965 to 2015, the global cumulative emissions of HBCD reached 340–1,000 tons, including 170–280 tons of α-HBCD, 30–80 tons of β-HBCD, and 140–650 tons of γ-HBCD [192]. China began large-scale production and use of HBCD around 2000 and completely eliminated it in 2021, during which the cumulative production reached 238,000 tons and the usage reached 188,000 tons, mainly applied in EPS insulation boards for building construction (71.5%) and XPS insulation boards (26.5%), with a small amount used in textile coatings [192,193]. Given that HBCD is mainly used in building insulation materials and has a long product use and emission cycle, Li et al. [192] estimated the full life-cycle emissions of HBCD in China using dynamic material flow analysis combined with multimedia environmental fate simulation methods. The results showed that the estimated cumulative environmental emissions of HBCD in China from 2016 to 2100 would reach 156.3 tons, as shown in Figure 9, with soil being the main receiving medium (59%), followed by air (25%) and water (16%) [193].

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图8 1970—2020年全球多溴二苯醚生产和使用趋势:(a)多溴二苯醚产量;(b)十溴二苯醚使用量;(c)八溴二苯醚混合物使用量;(d)五溴二苯醚使用量,改绘自Abbasi 等^[187]

Fig. 8 Global production and use of PBDEs in 1970-2020: (a)production of PBDEs, (b) use of deca-BDE, (c) use of octa-BDE, and (d) use of penta-BDE, redrawn from Abbasi et al^[187]

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图9 基于动态物质流分析与多介质环境归趋模拟的中国HBCD的全生命周期排放估算:(a)生产、加工、使用和废物过程的排放;(b)向大气、水和土壤的排放^[192]

Fig. 9 Estimation of emissions of HBCD in China based on dynamic material flow analysis and multimedia environmental fate models: (a) emissions from production, processing, use and waste, (b) emissions to atmosphere, water and soil, redrawn from Li et al ^[192]

Although traditional brominated flame retardants such as PBDEs and HBCD have been phased out worldwide, other brominated flame retardants such as DBDPE, which is a major alternative to PBDEs, as well as TBBPA, have also been proven to possess PBT or POPs characteristics. With the continuous increase in their production and use scale, the impact on regional and global environments and human health in the future needs to be continuously monitored and studied^{[194⇓⇓~197]}.

2.1.3 Organic Fluorinated POPs

PFAS is a class of chemical substances widely present in the environment, with diverse types and sources, and are considered permanent chemicals (forever chemicals), making them a major research hotspot in the field of emerging pollutants^[198,199]. Among them, PFOS and PFOA were listed in the controlled POPs list of the Convention in 2009 and 2019, respectively. PFOS production began in the 1950s, developed and produced by 3M Company in the United States. It was mainly used as a surfactant and for water-proofing agents, oil-proofing agents, dust-proofing agents, etc., and was widely applied in various industries such as firefighting, electroplating, textiles, leather, papermaking, coatings, electronics, and pesticides. According to statistics from 3M Company, the global output from 1985 to 2002 was 13,670 tons, with the highest annual output being 3,700 tons in 2000^[200]. Another study showed that the cumulative global basic PFOS production (excluding derivatives) from 1970 to 2002 ranged between 96,000 and 122,500 tons, with total emissions from various products and processes amounting to approximately 45,250 tons^[201]. The environmental emission amounts of PFOS during the periods 1951–2004 and 2003–2015 were 2,700–5,450 tons and 1,960–4,020 tons, respectively^[202,203]. China’s PFOS production started in the early 21st century, with an output of about 100 tons/year in 2008, mainly used in electroplating (30–40 tons), water-based fire extinguishing foam (25–35 tons), and pesticide flubendiamide (4–8 tons). The estimated environmental emissions of PFOS in the electroplating, firefighting, and pesticide industries in various regions are shown in Figure 10^[204]. With the implementation of the international compliance process and the national new pollutant action plan, China has already ceased the production and use of PFOS.

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图10 中国各地区电镀、消防和农药行业PFOS环境排放量分布^[204]

Fig. 10 The distribution of environmental releases of PFOS in metal plating, aqueous film forming foams (AFFFs) and sulfluramid in China^[204]Note: Northeast China includes Liaoning, Jilin, Heilongjiang Provinces, Central-north China includes Beijing, Tianjin, Hebei, Shanxi, Shaanxi Provinces, Central China includes Henan, Hubei, Hunan Provinces, Southwest China includes Chongqing, Sichuan, Guizhou, Yunnan Provinces, East China includes Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong Provinces, South China includes Guangdong, Guangxi, Hainan Provinces.

PFOA production began in 1947 and was mainly used as an emulsifier and processing aid in the manufacture of fluoropolymers such as polytetrafluoroethylene; it was also widely applied in industries such as firefighting, textiles, leather, papermaking, coatings, electronics, and photography. Existing studies show that between 1951 and 2004, the global cumulative production was 3600 to 5700 tons; between 1995 and 2002, the average annual production was 200 to 300 tons. PFOA production and its application in the manufacturing process of fluoropolymers are the main direct sources in the environment^[205]. From 1951 to 2030, the estimated environmental emissions of PFOA are 2078 to 18366 tons^[206]. China's PFOA production started at the end of the 1960s, as shown in Figure 11, from 2004 to 2012, the annual production scale was 20 to 60 tons with a cumulative production of approximately 480 tons; PFOA is mainly used in the manufacture of fluoropolymers such as PTFE (about 420 tons) in China, and the cumulative environmental emission amount is about 250 tons^[207].

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图11 2004—2012年间中国PFOA生产和排放状况^[207]

Fig. 11 Production and emission of PFOS in China from 2004 to 2012^[207]

Following PFOS and PFOA, PFHxS has also been nominated for inclusion in the Convention. PFHxS mainly appears as a byproduct or substitute product of PFOS products, with relatively small production volumes and a similar range of applications. 3M is considered one of the largest global producers of PFHxS in history, with its production reaching approximately 227 tons in 1997 but ceased production between 2000 and 2002^[208].Existing reports indicate that China had a PFHxS production scale of about 30 tons per year around 2010, but production has since stopped. With the implementation of international conventions and the "Action Plan for the Governance of New Pollutants," China has now ceased the production and use of PFOS and PFHxS, restricting the use of PFOA to specific exempt purposes such as semiconductor manufacturing and film coating. However, PFAS is a vast family of industrial chemicals encompassing numerous homologous compounds. Due to their stable chemical structures, they generally possess strong environmental persistence, thus being referred to as "forever chemicals." Among them, long-chain PFAS (LC-PFASs) with carbon chain lengths greater than 8 are typically considered to have certain bioaccumulative properties. In 2023, LC-PFASs with carbon chain lengths ranging from 9 to 21 were identified by the POPs Review Committee of the Convention as POPs with global environmental health risks and are expected to be listed in the Convention. However, information regarding the production, use, and emissions of LC-PFAS remains scarce. As more chemicals are included in the PFAS substance category, continuous research on the production, use, and emissions of PFAS will be needed in the future.

2.1.4 Flame Retardant-free POPs

In May 2023, UV-328 became the first halogen-free POP to be listed in the Convention. UV-328 is a ultraviolet absorber mainly used to protect various types of surfaces from ultraviolet radiation. UV-328 began production in the 1970s, and according to the OECD Existing Chemicals Database, UV-328 was classified as a high-production chemical with an annual output exceeding 1000 t^[209]; the registration quantity of UV-328 in the EU is 100–1000 t/a, with an average annual production exceeding 1000 t^[210]; the production in the US from 2012 to 2015 is 450–4500 t/a, and the production or usage in Japan from 2012 to 2014 is 1–1000 t/a^[211]. UV-328 is primarily used as a light stabilizer in surface coatings for automobiles and transparent plastics, food packaging plastic additives, and is also used in printing inks and adhesives for food contact materials^[211]. UV-328 may be released into environmental media during the production process of chemicals, product use, and disposal of waste products, and has been widely detected in air, dust, sludge, river water, sediments, etc., but currently, research on emission estimation related to UV-328 is still very limited.

2.2 Production, Use, and Discharge of Antibiotics

2.2.1 Production and Use of Antibiotics

Broadly speaking, antibiotics are a class of natural or synthetic chemical substances that inhibit or kill microorganisms. The initial antibiotics were secondary metabolites produced by microorganisms (including bacteria, fungi, and actinomycetes) or higher animals and plants during their metabolic processes, which could inhibit or kill other microorganisms. With the advancement of technology, the types of antibiotics discovered from the environment and artificially synthesized have gradually increased, forming more than 20 categories and over 300 kinds of antibiotics (China Food and Drug Administration, National Veterinary Drug Basic Database), mainly including sulfonamides, tetracyclines, quinolones, β-lactams, macrolides, lincomycins, chloramphenicols, polyether ionophores, peptides, aminoglycosides, nitroimidazoles, and nitrofurans. They are increasingly applied in human disease treatment and agricultural pest control. The global antibiotic drug usage increased from 54,083,964,813 defined daily doses (DDDs) in 2000 to 73,620,748,816 DDDs in 2010, rising by 36%. Meanwhile, the resident antibiotic usage grew from 21.1 billion DDDs in 2000 to 40.1 billion DDDs in 2018, with the average DDD per thousand people increasing from 9.8 in 2000 to 14.3 in 2018 (calculated based on GRAM database)^[214]. Moreover, the use of antibiotics for food animals increased from 63,151 tons in 2010 to 104,490 tons in 2015, and is expected to reach 105,596 tons in 2030^{[215, 216]}. China is a major producer and user of antibiotics, accounting for over 30% of the global market share. As shown in Fig. 12, the annual antibiotic production has been around 200,000 tons between 2013 and 2020. In recent years, due to government guidance on the use of antibiotic drugs, especially the restrictions on antibiotic use in livestock breeding and the control plan for veterinary drug residues issued in 2016^[217], the misuse of antibiotics in the food animal farming industry has been effectively reduced. As shown in Fig. 12, since 2016, there has been a clear downward trend in antibiotic usage for food animals (cattle, pigs, chickens). By 2020, it accounted for only 14.7% of the national antibiotic production, far below the 50%–80% ratio of most developed countries^[218].

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图12 中国抗生素生产和使用量(2013—2020年)

Fig. 12 Production and usage of antibiotics in China from 2013 to 2020

Distribution characteristics of global antibiotic use in humans. The increase in antibiotic usage is related to the level of regional economic development, with middle- and low-income countries being the main driving force behind the increase in drug use^[219]. From 2000 to 2010, five developing countries—Brazil, Russia, India, China, and South Africa—were the primary contributors to the growth in antibiotics, accounting for 76% of the global increase in antibiotic consumption during this period^[213]. Despite this, due to the large population base in developing countries, there are significant differences in per capita antibiotic usage globally, which do not entirely align with per capita GDP. Based on the research findings of Browne et al.^[212], a map of global human antibiotic usage was created, as shown in Figure 13. Using the defined daily dose (DDD/1000 person-days), the global human antibiotic usage in 2018 ranged from 2.80 to 45.9 DDD/1000 person-days. Among them, Central Europe, Eastern Europe, South Asia, and Australia had the highest levels of antibiotic use. The country with the highest usage was Greece (45.9 DDD/1000 person-days), while the lowest was the Philippines (5.0 DDD/1000 person-days). China’s per capita antibiotic usage (8.0 DDD/1000 person-days) was at a relatively low level globally. The European Centre for Disease Prevention and Control reported that the average antibiotic usage (in both community and hospital settings) in European countries in 2018 was 20.3 DDD/1000 person-days^[220]. Data on global usage of different categories of antibiotics are scarce, but Browne et al.^[212] reported a significant increase in fluoroquinolones and third-generation cephalosporins in North Africa, the Middle East, and South Asia. Qu et al.^[221] found that third-generation cephalosporins were the most frequently used antibiotics across all regions in China, accounting for more than 45% of all cephalosporins; levofloxacin, cefuroxime, and cefixime were the most commonly prescribed antibiotics for outpatients^[222]. In the EU, the largest volume of outpatient and community antibiotic use was β-lactam and penicillin (6.5 DDD/1000 person-days), followed by macrolides, lincosamides, and streptogramins (2.4 DDD/1000 person-days). Similarly, in the United States from 2017 to 2019, β-lactam, penicillin, and macrolide antibiotics also accounted for the largest share^[223]. Additionally, from 2000 to 2010, the use of "last-line" antibiotics such as glycylcyclines, oxazolidinones, carbapenems, and polymyxins increased rapidly, with the last two increasing by 45% and 13%, respectively^[213,219]. Overall, there are significant differences in the levels, types, and growth rates of antibiotic use across different regions. To maintain the efficacy of global antibiotic drugs and prevent the surge of antibiotic resistance in middle- and low-income countries, all parties worldwide need to work together to promote measures for rational drug use^[213].

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图13 2018年全球人用抗生素使用量,注:DDD为限定日剂量

Fig. 13 Global antibiotic usage for human use in 2018(defined daily doses)

Distribution characteristics of global antibiotic use in animals. For many years, the frequency of non-therapeutic use of antibiotics in food animals has been higher than therapeutic applications, resulting in a continuous increase in antibiotic use in animals worldwide. It is reported that 2/3 of the global increase in antimicrobial consumption (67%) is due to the increasing number of animals used for food production, while the remaining 1/3 is attributed to changes in agricultural practices^[216]. The growth and consumption of animal antibiotics show important geographical heterogeneity, as shown in Figure 14. Based on the global veterinary antibiotic usage data (including cattle, sheep, chickens, and pigs) reported by Mulchandani et al.^[224], the global usage of antibiotics in animals by country was mapped in Figure 14, with the unit of measurement being mg/PCU (population correction units). PCU is a standardized indicator representing the total number of animals in a country (live or slaughtered), multiplied by the average weight of animals during treatment. It can be seen that the hotspots for antibiotic use in animals are predominantly in Asia (67%), while less than 1% is in Africa. Among them, the countries with the highest standard usage are Thailand (338 mg/PCU), Mongolia (253 mg/PCU), Cyprus (220 mg/PCU), China (208 mg/PCU), Australia (165 mg/PCU), and India (114 mg/PCU). According to this standard calculation, the five countries with the largest total antibiotic usage are China, Brazil, India, the United States, and Australia, which together account for 58% of the global animal antibiotic usage^[224]. Similarly, the 2013–2018 antibiotic use report published by the World Health Organization (WHO) summarized the usage of four types of antibiotics for animals in 65 countries (excluding Asian countries such as China and India), and the country with the highest usage was also Brazil (2225.47 tons)^[225]. Van Boeckel et al.^[216] combined livestock density maps to create a map of higher spatial precision for animal antibiotic use, showing that the hotspot areas for global antibiotic consumption are mainly distributed in southeastern coastal China, Guangdong Province and Sichuan Province, the southern coast of India and Mumbai and Delhi, the Red River Delta in Vietnam, the northern suburbs of Bangkok, the southern coast of India and Mumbai and Delhi, southern Brazil, the suburbs of Mexico City, the Midwest and South of the United States, and the Nile Delta, Johannesburg and its surrounding towns. Overall, the distribution characteristics of antibiotic usage based on PCU and total accounting have similarities, especially in developed countries. For developing countries, the intensification level is lower, and the intensity and spatial heterogeneity of antimicrobial consumption are stronger. Generally speaking, in regions where intensive farming is common and food animals are densely concentrated, the amount of antimicrobial consumption per PCU is moderate. For example, higher model prediction uncertainty was observed in Central Asia, Ethiopia, Canada, and eastern India^[216].

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图14 2020年全球动物用抗生素使用量,PCU(population correction units)是一个标准化指标,表示一个国家的动物总数(活的或屠宰的),乘以治疗时动物的平均质量

Fig. 14 Global antibiotic usage for animal use in 2020

For different kinds of animals, the average usage of antibiotics for sheep, pigs, cattle, and chickens in 2020 was 243.3, 173.1, 59.6, and 35.4 mg/PCU respectively. However, different researchers reported varying levels of average antibiotic usage for various types of animals. For example, Van Boeckel et al. reported that the average annual consumption of antimicrobial drugs per kilogram of animal for cattle, chickens, and pigs was 45 mg/PCU, 148 mg/PCU, and 172 mg/PCU respectively^[216]. It can be seen that there is significant uncertainty in the calculation of the amount of antibiotics used for animals. For different types of antibiotics used in animals, tetracyclines are the largest category by volume, with a global usage reaching as high as 33,305 tons in 2020, followed by penicillins, macrolides, and sulfonamides. The U.S. Department of Agriculture's National Distribution Report on Antimicrobials for Food Animals shows that the antibiotic usage for cattle, pigs, chickens (including layer hens and turkeys), and other categories of animals in the U.S. in 2020 was 2,449, 2,451, 832.6, and 268.6 tons respectively, with the main types of antibiotics being tetracyclines (1,703 tons), tetracyclines (1,944 tons), penicillins (486.3 tons), and penicillins (177.3 tons)^[215]. Additionally, Zhang et al. reported in their list of 36 common antibiotics most frequently detected in the Chinese environment that the largest category of animal antibiotics used in China (8,424 tons) was mainly composed of macrolides and quinolones; while Mulchandani et al. used the online database Our World in Data (https://ourworldindata.org/grapher/antibiotic-usage-in-livestock) to show that the main types of animal antibiotics used in China were tetracyclines (10,003 tons), quinolones (4,288 tons), and penicillins (4,113 tons). Overall, tetracyclines and penicillins are the main categories of antibiotics used for animals, but the intensity of use varies from country to country for each type of antibiotic.

2.2.2 Antibiotic Discharge and Pollution Concentration

Antibiotics used by humans and animals are excreted through urine and feces^[227,228],and a large part of the excreted antibiotics exist in their parent form or as active metabolites. These excreted antibiotics enter the environment with sewage or sludge from wastewater treatment plants, hospitals, farms, etc.^[228,229].Therefore, the amount of antibiotic use is not equivalent to the amount of antibiotic discharge. However, there are only a few reports on the amount of antibiotic discharge so far. Zhang et al.^[226]estimated that the total discharge of 36 major antibiotics in 2013 was 53,800 t, of which 46% were discharged into water environments via wastewater, and the remaining 54% entered soil environments through sewage irrigation and manure application. Additionally, Yang et al.^[230]quantified the agricultural antibiotic emissions in mainland China in 2014 and the key driving factors in the global supply chain, showing that the total agricultural antibiotic emissions in China were 4,131 t, including 2,079 t from animal husbandry and 2,052 t from aquaculture. Animal husbandry includes beef cattle, sheep and goats, horse breeding (407 t), animal product industry (1,414 t), and dairy farming (258 t). In addition, some researchers have monitored the concentrations of antibiotics in wastewater and manure from wastewater treatment plants and farms, combined with regional wastewater discharge volumes, livestock and poultry breeding quantities, etc., to estimate individual daily/yearly discharge levels^[229,231],and then calculated the regional/national antibiotic discharge amounts. However, the results of antibiotic discharge amounts obtained from different source data show significant differences, which may be attributed to regional differences in antibiotic usage intensity, as well as different manure treatment models and treatment efficiencies.

The extensive and frequent use of antibiotics has led to their frequent detection in various environmental media worldwide. Most antibiotic residues, as well as resistant bacteria or resistance genes in the environment, are caused by anthropogenic pressure sources in aquatic systems, such as wastewater and waste from municipal, hospital, pharmaceutical, agricultural production, and aquaculture sources^[232]. This section reviews global reports on antibiotic pollution concentrations in water environments (effluents from wastewater treatment plants, surface water, groundwater, and tap water) from 2006 to the present through ISI keyword searches. Combined with a review by Hanan et al.^[232] on antibiotic concentration levels in water environments in the Western Pacific and Southeast Asia, more than 20,000 sets of antibiotic concentration data were obtained (from 259 articles). The data included for statistical analysis (excluding ND and graphical data that were difficult to extract) consisted of 1580 sets from effluents of wastewater treatment plants, 10,268 sets from surface water, and 2111 sets from groundwater. Figure 15 summarizes the antibiotic concentration levels in major countries/regions with reported data, showing significant differences in antibiotic pollution across different countries globally. The results show that there is similarity in the degree of antibiotic contamination in wastewater treatment plants and surface water between different countries. Among them, the quality concentrations of antibiotics in effluents from wastewater treatment plants, surface water, and groundwater in India are 0.14×10⁷~1.4×10⁷, 0.02×10⁶~6.5×10⁶, and 0.05×10⁴~1.4×10⁴ ng/L, respectively, with average values and maximum monitored quality concentrations far exceeding those of other countries with reported data. In comparison, the antibiotic concentration levels in various water environments in the United States and Japan are relatively low. China is the country with the most reported antibiotic concentrations, accounting for more than 40% of all data, with antibiotic quality concentrations in effluents from wastewater treatment plants, surface water, and groundwater being 0.21×10⁴~6.54×10⁴, 0.01×10⁶~1.79×10⁶, and 0.01~2910 ng/L, respectively, with relatively large differences in concentration ranges. The differences in antibiotic concentrations in various water environments in the EU are the smallest, but the concentrations in groundwater are higher than those in other countries. Overall, monitoring results in Asian countries (mainly India, Vietnam, China, and South Korea) are greater than those in developed countries such as the EU and the United States.

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图15 全球主要国家水环境中抗生素质量浓度水平

Fig. 15 Antibiotic concentrations in water environments worldwide

The monitoring concentration range and average value of various types of antibiotics worldwide are shown in Table 3. It can be seen that the magnitude of environmental concentrations of different classes of antibiotics is not consistent with their usage levels. Tetracyclines are the largest antibiotic class used globally and in most countries, but their detection frequency and pollution concentration levels are lower than those of sulfonamides, quinolones, and macrolides, a feature particularly evident in Asian countries such as China, India, Thailand, and Vietnam. Sulfonamides are the most frequently detected antibiotics, but their average and maximum concentrations are generally lower than those of quinolone antibiotics in most countries. For macrolide antibiotics, their detection frequency is also higher than other categories, but their concentration levels vary inconsistently compared to other categories of antibiotics in different countries and environmental media. For example, in China, the EU, Japan, etc., the concentrations of macrolides in sewage and groundwater are relatively high compared to other classes, but their pollution concentrations in surface water are relatively low. Beta-lactam antibiotics such as penicillins have large usage volumes worldwide, but due to their ease of degradation and transformation, their detection frequencies and pollution concentrations in different environmental media in various countries are relatively low, and there are no reported results in groundwater. In summary, the consistency characteristics between the usage volumes and pollution concentrations of different antibiotic categories vary among different antibiotics. The pollution concentration levels in different water environmental media also differ. However, in countries like Asia where the total usage and intensity of antibiotics are high, the concentrations of all categories of antibiotics are higher than in other countries.

表3 全球水环境中各类别抗生素质量浓度水平

Table 3 Concentrations of different types of antibiotic in the global water environment (average (minimum maximum)) ng/L

Kind	Countries and regions	Macrolides	Quinolones	Sulfonamides	Tetracyclines	β-lactams	Others
Effluent water from WWTPs	China	419 (0.20~6770)	285 (0.29~6840)	518 (0.21~ 65400)	148 (0.56~2210)	463 (3.18~5000)	137 (0.70~3050)
	EU	239 (13.0~930)	168 (6.00~640)	97.2 (7.00~ 950)	22.8	—	46.0 (13.0~102)
	India	55.4 (3.92~187)	526552 (0.14×10⁷~1.40×10⁷)	7468 (10.6~ 81100)	—	29453 (0.22~59500)	1072 (6.96~3800)
	Japan	209 (0.25~836)	145 (3.00~819)	62.4 (0.06~ 470)	58.6 (58.6~58.6)	—	—
	South Korea	766 (0.20~9089)	—	72.7 (0.10~ 483)	575 (0.10~4517)	—	—
	Thailand	15.5 (4.00~42.8)	45.9 (9.28~ 89.0)	108 (5.00~ 1499)	—	34.5 (9.28~97.5)	10
	USA	66.8 (1.00~219)	142 (3.20~900)	227 (1.00~ 4100)	111 (15.4~231)	66.7 (0.21~308)	126 (4.50~598)
	Vietnam	800 (200~ 2200)	11414 (600~ 53300)	3057 (38.0~ 20300)	—	3800 (2600~5000)	16711 (100~130400)
Surface water	Australia	12.8 (1.00~50.0)	443 (10.0~1500)	428 (3.00~ 2000)	219 (3.00~600)	2095 (90.0~4100)	2.25 (1.00~4.00)
	Bangladesh	2.64 (0.10~16.7)	—	2.27 (0.04~ 17.2)	—	—	2.74 (0.05~13.5)
	China	648 (0.01~447000)	3339 (0.003~ 1793000)	1071 (0.01~ 893000)	1371 (0.03~218100)	17.0 (0.06~300)	25.6 (0.03~1042)
	EU	48.3 (0.25~793)	276 (1.50~4390)	46.3 (0.25~ 544)	—	—	—
	India	109 (0.10~991)	141948 (0.16~ 6500000)	383 (0.40~ 4000)	14.9	14.6 (0.18~29.1)	46.0 (0.02~130)
	Indonesia	71.8 (1.00~399)	—	88.7 (1.00~ 779)	3.00 (1.00~8.00)	—	—
	Japan	32.4 (0.002~560)	52.1 (0.50~ 4068)	12.1 (0.01~ 151)	—	—	—
	Korea	98.3 (1.10~2190)	87.6 (20.0~151)	248 (0.70~ 14850)	686 (11.3~2750)	—	38.4 (27.1~53.8)
	Malaysia	78.4 (0.60~166)	193 (112~267)	34.7 (1.20~ 102)	—	2.00 (1.00~3.00)	20.9 (16.6~23.1)
	Singapore	494 (0.70~1949)	3496	371 (2.60~ 1172)	4040 (1233~6434)	3746	20730
	Thailand	1881 (1.00~21442)	1839 (9.00~ 45600)	430 (0.76~ 4605)	1422 (2.00~6290)	—	—
	USA	0.5 (0.0003~15.0)	75.7 (0.0006~ 1227)	12.8 (0.004~ 520)	171 (0.67~690)	22.9 (2.71~43.1)	4.94 (0.0021~68.0)
	Vietnam	459 (0.10~55097)	3947 (0.10~ 85190)	1746 (0.012~ 252082)	94.5 (0.90~900)	556 (10.0~5051)	25.0 (3.00~83.0)
Groundwater and tap water	China	63.3 (0.01~2910)	15.2 (0.10~368)	3.09 (0.02~120)	10.9 (0.03~127)	—	5.62 (0.13~36.7)
	EU	7.2	29.6 (1.00~ 77.2)	6.1	—	—	—
	India	0.17 (0.11~0.24)	726 (0.05~ 14000)	14.7 (0.22~ 55.0)	0.18	—	0.09
	Japan	—	—	12.1 (4.40~ 29.0)	—	—	—
	USA	—	81.4 (1.09~ 1740)	26.8 (2.48~ 178)	—	—	—

Note:The data in the table represents "average (minimum - maximum)".

The global consumption of antibiotics is still increasing slowly and shows significant regional characteristics. Due to the large differences in the types and intensities of antibiotic use among different countries and regions, the calculation of the amount of antibiotics discharged into the environment after use remains a challenge. The uncertainty problem in estimating antibiotic emissions at large or regional scales will be a future difficulty to be solved. Correspondingly, different types and intensities of antibiotics have also led to great differences in the pollution characteristics of various environmental media in different regions. In addition, antibiotics are not only used for human beings and livestock farming, but also for planting agriculture and aquaculture. The amount of this part of use is still unclear, which also affects the prediction of antibiotic pollution. Therefore, it is necessary to conduct investigations, feature analysis, and pollution control on antibiotic pollution, and establish standards for various environmental media. This cannot be achieved by using the same indicator throughout, but through methods such as zoning and segmentation. Currently, the monitoring of antibiotic environmental pollution concentrations worldwide is still mainly focused on the concentration of parent antibiotics, and is concentrated on tetracyclines, macrolides, sulfonamides, and β-lactams. It should be noted that penicillins and cephalosporins are the categories with larger usage and faster growth rates. However, due to their hydrolysis properties, the detection frequency and concentration in the environment are at low levels. How to accurately and reasonably reflect the pollution levels of these two substances in the environment, as well as the relationship between usage and pollution concentration, is an important content for improving the assessment of the relationship between antibiotic usage, emissions, and pollution.

2.3 Production, Use, and Emission of Endocrine Disruptors

EDCs are widely used in various daily products and released into the environment during production, use, and disposal, causing adverse effects on human health and ecological environment [233 ⇓⇓⇓~237]. Understanding the production, use, and release of EDCs is the basis for evaluating their exposure sources, exposure pathways, and exposure levels. Therefore, this section will detail the production, use, and release of some EDCs in various countries and regions.

2.3.1 Use of EDCs

EDCs-class chemicals are widely present in different kinds of products and are applied in many fields of the national economy^[7], including transportation, construction, agriculture, electronic and electrical, food, furniture, personal care products, health care, packaging, toys, and textile industries (Fig. 16).

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图16 EDCs类化学品在各类别产品中的使用^[237,238]

Fig. 16 Categories of Products Containing EDCs^[237,238]

The most typical one is di(2-ethylhexyl) phthalate (DEHP), which is commonly used as a plasticizer and mainly used for processing soft polyvinyl chloride (PVC) products, such as wires and cables, films, floors, artificial leather, pipes, soles, gloves, etc.^[239]. In addition, DEHP can also be used for processing rubber, adhesives, sealants, paints, etc.^[239]. Other EDCs with similar applications include dibutyl phthalate (DBP), butyl benzyl phthalate (BBP), cyclohexyl phthalate (DCHP), diisodecyl phthalate (DiDP), and didecyl phthalate (DuDP)^[240].

In the production and processing of products, EDCs have different functions. As shown in Figure 17, a small part of EDCs are used as monomers and intermediates for synthetic polymers, while some EDCs act as processing aids in the production and processing of products. Most EDCs are used as additives to maintain, enhance, and impart specific properties to products^[238].

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图17 EDCs类化学品在产品中的功用(同一类化学品用同一颜色展示)^[238]

Fig. 17 Functions of EDCs in Various Products (The same type of chemicals are shown in the same color)^[238]

2.3.1.1 Monomers and Intermediates

4tPP (tert-pentylphenol), BPF (bisphenol F), and BPS (bisphenol S) are three important monomers. 4tPP is commonly used for the production of phenolic resins and is widely applied in the manufacture of paints, varnishes, and printing ink resins. BPF and BPS serve as substitutes for bisphenol A (BPA) and are mainly used for the production of polycarbonate and epoxy resins. Polycarbonate is extensively used in construction, optical devices, electronic and electrical equipment, as well as the automotive industry; while epoxy resins are primarily applied in protective coatings, printed circuit boards, semiconductors, molds, flooring, and adhesives. Additionally, BPS is also used as a developer for thermal papers such as receipts and tickets [240].

Resorcinol (RS), as a synthetic intermediate, is used in the rubber industry for the synthesis of resorcinol-formaldehyde adhesive resins to enhance tires; in the wood processing industry for the production of low-temperature rapid wood adhesives; in the medical field for the production of bactericides, anthelmintics, etc.; in agriculture for the production of pesticide intermediates such as anilinephenol; it can also be used for the production of ultraviolet absorbers, azo dyes, synthetic resins, preservatives, analytical reagents, tanning agents, explosives, and flame retardants ^[240].

2.3.1.2 Processing Aids

Carbon disulfide (CS₂) is a commonly used organic solvent, which is mainly applied in the rubber, agriculture, chemical, and metallurgical industries. The largest usage of CS₂ is in the production of viscose fiber and cellophane. Moreover, CS₂ can also be used for the production of rubber vulcanization accelerators, agrochemical intermediates, and mineral flotation agents etc.^[240]

Diethyl phthalate (DEP) is commonly used as a solvent for products such as hair sprays, soaps, and nail polishes, and as a carrier of fragrances in perfumes and personal care products. It can also be used as a denaturant for alcohol in some cosmetics, detergents, and pesticides [240]. The content of DEP in products is typically 1.0% to 70.0% (Fig. 18).

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图18 产品中EDCs类化学品的含量范围^[241]

Fig. 18 Concentrations Ranges of EDCs in Products^[241]

4-Nonylphenol (4NPs) is mainly used for the production of nonionic surfactants and modified phenolic resins, with a small amount used for the production of anionic surfactants, lubricant additives, formaldehyde resins, epoxy resins, and other products. Among these, the largest use of 4NP is for the synthesis of nonylphenol polyoxyethylene ethers (4NPnEOs), which are primarily applied in the textile industry^[240].

Octylphenol polyethoxylate (4tOPnEO) is mainly used as an emulsifier in emulsion polymerization. Other applications include finishing textiles and leather, with a small portion used to prepare water-based coatings and veterinary drugs, etc.^[9]Benzophenone (BPs) are commonly used as photoinitiators for curing inks and varnishes, including 2,4-dihydroxybenzophenone (BP-1), 2,2',4,4'-tetrahydroxybenzophenone (BP-2), 2-hydroxy-4-methoxybenzophenone (BP-3), and 4,4'-dihydroxybenzophenone (4,4'-DHB)^[240].

2.3.1.3 Additive

Most EDCs are commonly used as biocides and preservatives. For example, in the agricultural field, tebuconazole (TCZ) is used as a seed treatment agent and foliar spray, mainly for the prevention and control of various pests and diseases in crops such as wheat, rice, peanuts, and corn and sorghum^[240]. While metam sodium (MS), zineb (ZB), ziram (ZM), and thiram (TM) are primarily used for fungicidal applications on crops such as fruits, nuts, vines, vegetables, and ornamental plants^[240]. Triclosan (TCS) and 4-tert-amylphenol (4tPP) are two broad-spectrum biocides; the former is widely used in daily commodities such as soap and toothpaste, while the latter is often used in cleaning agents^[240]. The content of TCS and 4tPP in products ranges from 1.0% to 3.0% and 0.1% to 5.0%, respectively.

Parabens substances, commercially known as paraben esters, are food preservatives that can extend the shelf life of food and are mainly used for the preservation of alcoholic beverages, grain products, condiments, etc. They are also widely used in cosmetics ^[240]. The content of Parabens substances in products is usually 1.0%-4.0%. PCP is an excellent wood preservative with extensive applications in the preservation of railway sleepers, utility poles, building materials, and mine timber ^[9]. Additionally, there are two common preservatives and antioxidants: 2,6-di-tert-butyl-p-cresol (BHT) and butylated hydroxyanisole (BHA), which are primarily used in food, animal feed, cosmetics, as well as other petroleum products such as rubber and soap ^[240].

3-BC, 4-MBC, EHMC, and BPs can absorb ultraviolet radiation, so they can effectively protect humans and products from sun damage. These substances are widely used in sunscreens, skin care products, hair sprays, shampoos, and other personal care and consumer products^[240].

Di(2-ethylhexyl) phthalate (DOP), dihexyl phthalate (DHP), triphenyl phosphate (TPhP), and DEP are commonly used as flame retardants and plasticizers for cellulose acetate and other plastics (e.g., toothbrushes and tool handles)^[240]. Methyl tert-butyl ether (MTBE), because of its high net octane number and good solubility in gasoline, is mainly used as a gasoline additive^[240]. Quinoline silane (QS), an anti-androgen drug, is used to treat prostate cancer, with the brand name Cisobitan^[240].

2.3.2 Production Situation of EDCs

With the rapid development of the chemical industry, 25 EDCs chemicals have been listed in the high production volume chemical list (OECD HPV) published by the Organization for Economic Cooperation and Development (Fig. 19) and corresponding risk assessment reports have been formed, including 2,4,6-tribromophenol (TBP) and p-nitrophenol (4NiP), etc.^[242].

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图19 EDCs类化学品年产量以及经济合作与发展组织高产量化学品清单中涵盖的物质(图上数字代表生产年份;为方便展示分成4个子图)^[238,242]

Fig. 19 Annual Production Volume of EDCs and EDCs included in the OECD High Production Volume List (The numbers on the graphs represent the years of production; Divided into 4 subgraphs for easy presentation)^[238,242]

Although the industrial production of chemicals in some developing countries started relatively late, in recent years, the annual production of many EDCs has approached or even exceeded that of developed countries. For example, in 2020, China's DEHP production reached 1.39 × 10⁹ kg [239], which was 103 times, 688 times, and 25 times that of the United States, Europe, and Japan during the same period [238]. Additionally, in 2017, China's CS₂ production was 7.00 × 10⁸ kg [240], more than four times that of the United States [238]. Furthermore, the annual production of some other chemicals in different regions has already exceeded 1.00 × 10⁶ kg, such as 4NPnEOs, 4HPbl, PCP, TCZ, and methyl paraben (MP) [238].

Some developed countries have clearly prohibited the production and use of some EDCs. For example, EU Directive 2003/53/EC issued in 2003 requires that products containing 4NPs and 4NPnEOs shall not be manufactured, used or placed on the market. Therefore, the production of 4NPs in the EU region has decreased by at least 50% since 2005^[240].

2.3.3 Release of EDCs

2.3.3.1 Release Source

EDCs will be released into the environment through different pathways in various stages of product production, use and disposal, leading to inevitable exposure of human beings and ecological species to EDCs.

In the production and processing stage, volatile EDCs will be released into the atmosphere, such as CS₂; or enter the water bodies through untreated industrial wastewater^[240].

During the usage stage, EDCs will enter into the environment through various pathways such as tire wear, use of pesticides and fungicides, automobile exhaust emissions, washing of clothes, and usage of personal care and cleaning products. For example, fungicides such as MS, ZB, ZM, and TM will release a large amount into the soil during usage^[240].

During the treatment phase, urban wastewater treatment plants often fail to completely remove EDCs, leading to their release into nearby water bodies through treated wastewater. Contaminated sludge is also applied to land, causing soil pollution. For example, although the average removal rate of wastewater treatment plants for compounds such as MP, ethyl paraben (EP), propyl paraben (PP), and butyl paraben (BP) reaches 96.1% to 99.9%, these substances are still detected in the effluent and sludge^[240]. Moreover, leachate from landfill sites is also a major source of EDC release^[240].

2.3.3.2 Release Amount

As shown in Figure 20, from 2008 to 2011, the European Union region released 9.11 × 10⁵ kg of 4NPs into the environment, with the highest release during the usage phase, mainly from washing textiles (7.00 × 10⁵ kg). Additionally, the release amount of 4NPnEOs was 1.12 × 10⁷ kg, primarily from the use of industrial cleaning agents (1.02 × 10⁷ kg)^[240].

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图20 EDCs类化学品的环境释放量^[239,240]

Fig. 20 EDCs Emissions to Environment^[239,240]

In 2001, the environmental release amounts of 4tOP and 4tOPnEO in the EU were 9.36 × 10⁴ and 2.38 × 10⁴ kg respectively [240]. Among them, the largest release was to soil. The main source of 4tOP release to soil was from tire usage (4.16 × 10⁴ kg), while the main source of 4tOPnEO release to soil was from pesticide usage (1.21 × 10⁵ kg) [240].

In 2020, the environmental release amounts of DEHP in China and Japan were 5.71 × 10⁸ kg and 3.15 × 10⁷ kg^[239], respectively. The environmental release amount of DEHP in the EU region in 2007 was 9.23 × 10⁶ kg^[239]. Among them, the release amounts of DEHP to water bodies and soil were relatively high, mainly originating from the usage stage^[239]. For example, the outdoor use of synthetic rubber and wires and cables was the main source of DEHP release to soil, while the indoor use of coatings and paints was the main source of DEHP release to air and water bodies^[239].

The quantification of EDC releases is a prerequisite for evaluating their environmental exposure. However, data on EDC releases are currently very limited, which restricts the risk management of EDCs. Material flow analysis has been widely used to quantify the release amounts during the life cycle of EDCs but often relies on release factors and other data^[239]. Therefore, it is crucial to obtain release factors of EDCs in products through experimental and computational approaches. Wang et al.^[243] simulated the release of phthalate esters (PAEs) from agricultural films and greenhouse films into the air under real-use conditions and combined it with a first-order kinetic model to estimate the release rates of PAEs, filling the data gap.

3 Identification and Characterization of New Contaminants

The core of new contaminant identification and characterization is the analysis and discovery of pollutant structures, which relies on breakthroughs and developments in fundamental analytical chemistry problems. In the face of complex and variable components and matrices in environmental samples, a classic analytical technology system of "chromatographic purification separation, spectroscopic, mass spectrometric, and nuclear magnetic resonance structural analysis" has been formed for new contaminant identification and characterization, and has been widely applied. In recent years, with the continuous enhancement of chromatography-high resolution mass spectrometry (HRMS) performance, compared to other identification and characterization technologies, it has achieved technical integration of purification separation and structural analysis. The new contaminant identification and characterization technology based on chromatographic mass spectrometric technology is rapidly evolving, and new contaminant structures discovered in real environments are also emerging. Under this background, a new paradigm of non-targeted analysis-guided new contaminant identification and characterization has gradually emerged. This chapter will focus on non-targeted analysis technology centered on chromatographic mass spectrometric technology, reviewing the latest progress in new contaminant identification and characterization, while further understanding of the new contaminant identification and characterization technology system can be referred to relevant literature results^[244,245].

3.1 Non-targeted Analysis Techniques for the Identification and Characterization of New Contaminants

3.1.1 Preprocessing Techniques

The purpose of sample pretreatment is to remove potential matrix interferences, concentrate the analytes, modify the physical and chemical properties of the samples, and make the samples better suited for subsequent analysis or testing, thereby obtaining accurate and reliable results while maximizing analytical sensitivity and specificity. There are obvious differences between non-targeted and targeted analysis techniques in this regard. In targeted analysis, sample preparation usually focuses on the extraction and enrichment of specific target compounds, with the extraction methods typically aimed at maximizing the recovery of target compounds, minimizing the instrument detection limit, and removing matrix interferences as much as possible to ensure the stability of the analytical results. On the other hand, non-targeted analysis often places greater emphasis on the broad and comprehensive coverage of compounds, aiming to analyze the entire compound spectrum while maintaining the original chemical characteristics of the samples. Therefore, exhaustive and broad-spectrum pollutant extraction methods are often used. As a result, in non-targeted analysis, situations such as low recovery rates and high detection limits for certain substances often occur. In non-targeted studies, the pretreatment methods are mostly established based on some representative known pollutants, and the reliability and reproducibility of the methods are represented by the quality control results of these known standard substances.

In studies aimed at non-targeted analysis, the extraction methods for liquid samples (such as environmental water samples) mainly include liquid-liquid extraction^[246] and SPE^[247]. The former achieves the enrichment of specific compounds based on their solubility differences in different solvents. It is easy to operate and has good stability, but it often produces a large volume of solvent that requires subsequent concentration. The solid-phase extraction method uses specific stationary phases as adsorbents to extract target components from liquid samples, with various adsorbent fillers of different materials and principles available for selection. For example, resins^[248], HLB^[249], and C18^[250] fillers are suitable for extracting the majority of pollutants with moderate to non-polarity, while amino^[251], WAX^[251], and MAX^[252] extraction columns can be used for enriching polar and ionic compounds. Soil, sediments, and other complex samples often accumulate a large amount of pollutants. The extraction of these samples typically uses solvent extraction methods. Depending on the properties of the target substances, solvents or mixed solvents with different polarities such as n-hexane, dichloromethane, acetone, methanol, and water can be selected. Among them, dichloromethane, due to its high solubility for a wide range of polarities, is one of the most commonly used broad-spectrum pollutant extraction solvents. Solvent extraction for soil and sediments often uses accelerated solvent extraction instruments^[253], Soxhlet extraction^[254], or microwave^[255] extraction equipment, which can promote the thorough separation of pollutants from the sample matrix and transfer them into the extraction solvent. However, some large organic molecules in the sample may also be extracted together with the above methods^[256], and they can be removed from the extract using purification methods including gel permeation chromatography (GPC)^[257]. Solvent extraction is also widely used in the pretreatment of biological samples, and acetonitrile is the most commonly used due to its excellent solubility and penetration ability. In addition, compared with environmental samples, biological samples contain a large amount of biomolecules such as lipids, pigments, proteins, and carbohydrates, which can be removed by methods such as phospholipid column (e.g., hybridSPE), graphite column purification, organic solvent precipitation, and GPC purification.

The above methods are mostly extractive approaches that can extract a large amount of pollutants, but they do not take into account the actual bioavailability of organic pollutants, which may lead to overestimation of exposure and risk. In recent years, environmental science has paid more attention to real environmental risks. Accordingly, some methods that consider bioavailability have been used in environmental analysis. For example, polydimethylsiloxane (PDMS) membranes are used to simulate biological membranes for extracting pollutants from environmental water samples [258]; artificial sweat is used to simulate skin contact for enriching pollutants in mouse pads [259]; supercritical carbon dioxide is used to extract polycyclic aromatic hydrocarbons from soil to simulate exposure of oligochaetes to such pollutants [260].

3.1.2 Instrumental Analysis Techniques

3.1.2.1 High-Resolution Mass Spectrometry

High resolution mass spectrometry (HRMS) with high resolving power (RP) and high mass accuracy (MA) is crucial for non-targeted analysis, allowing the structural identification of unknown compounds in complex samples without any prior information^[261].RP refers to the ability of an instrument to distinguish between ions with close mass-to-charge ratios (m/z). It is defined as the ratio of the mass of a specific ion to its peak width at half maximum intensity (m/Δm). A high RP facilitates the differentiation of target ions from interfering ones. Generally, at least 30,000 RP is required for the identification of organic pollutants in environmental samples. MA refers to the difference between the measured mass of an ion and its accurate mass, expressed as a ratio of the mass difference to the accurate mass, typically in parts-per-million (ppm) units (1 ppm = 1 × 10^-6). High MA can be used to infer the molecular formula of unknown compounds. Additionally, modern HRMS instruments possess high scan rates, enabling their easy coupling with various chromatographic techniques for the detection of emerging contaminants in complex matrices. Currently, commercial HRMS systems are mainly classified into three types: time-of-flight (TOF), orbitrap, and Fourier transform ion cyclotron resonance (FT-ICR). Depending on the manufacturer and model, the parameters vary, but generally, FT-ICR offers the best performance (e.g., RP ≥ 1,000,000, MA ≤ 2 ppm (1 ppm = 1 × 10^-6)), followed by orbitrap (e.g., RP ≥ 100,000, MA ≤ 5 ppm (1 ppm = 1 × 10^-6)), and TOF has the lowest performance (e.g., RP ≥ 30,000, MA = 10 ppm (1 ppm = 1 × 10^-6)). However, FT-ICR is costly to operate and maintain, and its slow scan rate (0.3–1.0 Hz) makes it less compatible with chromatography, limiting its use in environmental sample analysis. TOF is more affordable, with the fastest scan rate (≥50 Hz), but its lower RP restricts its application in identifying new contaminants.

3.1.2.2 Scanning Method

The scanning mode of high-resolution mass spectrometry has also experienced a development process from basic to functional. Nowadays, the instruments generally have functions such as full scan, data-dependent acquisition (DDA), data-independent acquisition (DIA), and MS/MS scan. Full scan (full-scan) directly scans all ions entering the mass spectrometer to obtain the mass-to-charge ratio (m/z) information of each ion in the sample. Both DDA and DIA are used to obtain secondary information of ions, but the difference is that DDA performs secondary fragmentation on the top n ions (the range varies depending on the instrument, with n generally being 1 to 30) identified in the previous full scan, while DIA performs fragmentation on all ions within a certain mass range of the previous full scan to obtain mixed fragment spectra. DDA is more suitable for analyzing new pollutants in high-contaminant areas, hotspots, and commercial samples, whereas DIA is more applicable to the identification of compounds with specific structures and characteristic fragments (e.g., PO₃H₂⁺ for organic phosphates [OPEs] discovery [262] and C₂F₅^- for perfluorinated compound discovery [263]). MS/MS is similar to the multiple reaction monitoring (MRM) mode in triple quadrupole mass spectrometry, which acquires the secondary spectrum of a specific ion. The difference is that high-resolution mass spectrometry performs a full scan on all fragment ions. This scanning mode is suitable for structural determination of suspected pollutants with known mass numbers. Some instruments are equipped with an all-ion fragmentation (AIF) mode and/or source-induced dissociation (SID) mode, both of which perform fragmentation on all ions entering the instrument. It is a special type of DIA, differing only in the location where ion fragmentation occurs. In recent years, iterative DDA (iterative data-dependent acquisition) with enhanced MS/MS collection capability has appeared in some devices. In this mode, first, a full scan of the sample and blank is performed automatically to acquire all ion information in the full scan. By subtracting ions in the blank sample, a possible contaminant ion list is established. Subsequently, the software automatically imports this list into the inclusion list containing the ion list, performs multiple rounds of DDA analysis based on the list, and automatically deletes ions that have undergone MS/MS spectrum acquisition after each injection analysis, then performs the next round of DDA analysis on the remaining ions. After such operations, not only can the fragment information of high-response ions be obtained, but lower abundance ions can also be covered. Compared with DDA, iterative DDA can improve the efficiency of compound identification by 30% to 70% [264]. Since environmental pollutants are usually at levels far below those of the sample matrix, this mode is more suitable for the detection and discovery of new pollutants in routine environmental samples.

3.1.2.3 Ion Source: Types and Basic Principles of Parameter Setting

The ion source is a key component of mass spectrometry. It converts target molecules into ions and then enters the mass analyzer for separation and obtains the mass-to-charge ratio. For commonly used gas chromatography-mass spectrometry (GC-MS), ionization under vacuum conditions is easy to achieve, with common electron impact (EI) and chemical ionization (CI). For liquid chromatography-mass spectrometry (LC-MS), it is necessary to remove a large amount of liquid mobile phase under atmospheric pressure and realize the ionization of target molecules. This process is mainly based on atmospheric pressure ionization (API) technology, including electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), atmospheric pressure photoionization (APPI), etc.

EI source is the most commonly used ionization method for gas chromatography-mass spectrometry. In the ion source, electrons emitted by the filament with energy of 70 eV "bombard" target molecules to ionize them. Since the ionization energy of most organic compounds is around 10 eV, high-energy electron bombardment usually produces a large number of fragment ions, forming rich "fingerprint" information, which is beneficial for structural analysis of substances^[265].EI is easy to implement and has good reproducibility. The mass spectrum obtained under the same collision energy can be compared with the standard spectral library, thus reducing the workload of mass spectrum analysis. CI is achieved by introducing reaction gas (e.g., methane) into the ion chamber and using high-energy electrons to bombard the reaction gas to generate a series of ions and thermal electrons. These ions or thermal electrons combine with the target substance to ionize it. In environmental analysis, electron capture negative ionization (ECNI, sometimes also referred to as negative chemical ionization, NCI) is a more commonly used form of chemical ionization. This ionization method has extremely high selectivity and sensitivity for substances with electron affinity greater than 0.55 eV (such as organic compounds containing halogens, oxygen, and nitro groups)^[266].

ESI and APCI are the common ionization modes of liquid chromatography-mass spectrometry. ESI generates charged small droplets at the tip of a capillary under strong electric field, and then produces ions by solvent desolvation with the help of a sweeping gas or heating. APCI makes the solution nebulize under a high-speed nitrogen flow, and further vaporizes into gaseous molecules in the heated quartz capillary region. Ultimately, it undergoes ionization under the action of a high-voltage corona discharge needle. Both ESI and APCI are soft ionization sources, mainly producing molecular ion peaks, but source-induced fragmentation may also occur. For environmental samples, through comprehensive detection of protonated ions or adduct ions in positive and negative ionization modes, different kinds of compounds with various polarities in complex matrices can be analyzed^[267].

3.2 New Contaminants Identification and Characterization: Data Analysis Techniques

3.2.1 Data Processing of Mass Spectrometry

3.2.1.1 Screening Strategy for the List

Non-target screening is an efficient method for new contaminant screening and can rapidly screen a large number of contaminants in the list. The main process is to set reasonable adduct ions to obtain the accurate mass and isotopic distribution characteristics of contaminants in the list, and then quickly search high-resolution mass spectrometry data within a given error range to obtain the non-target screening results. The key step in non-target screening is the establishment of the screening list. Commonly used database lists include Table 4. Currently, the lists mainly fall into three categories. The first category is chemical production or control lists, including the EU REACH controlled chemical list and various national chemical production lists. These chemicals may enter the environment after production and use. Gago-Ferrero et al. ^[268] combined regulatory databases with non-target screening, selecting 160 substances that are widely used and likely to enter the environment from a Swiss production database of 23,000 chemicals. Through non-target screening, they identified 36 pollutants. The second category is toxic substance lists. Lin et al. ^[269] screened 89 pollutants in air particulate samples using the ToxCast toxicity database. The third category is historical research data lists. Trego et al. ^[270] established a list using 327 halogenated compounds identified in previous studies and detected 167 halogenated compounds in dolphins. Ye et al. ^[271] collected a list of 95 OPEs from previous studies for screening OPEs in Taihu Lake sediments. Liu et al. ^[261] reviewed the list of newly identified perfluorinated compounds up to October 2018, providing a suspect database for subsequent perfluorinated compound screening. Currently, the European NORMAN organization also provides and continuously updates various types of environmental contaminant database lists, such as perfluorinated compounds, surfactants, bisphenols, plastic additives, pesticides, drugs, cosmetics, etc. The US EPA also provides 311 forms of different types containing substances of different categories or uses.

表4 常见的数据库清单信息

Table 4 Available database and lists for emerging contaminant

database	quantity/types	website
NORMAN	111769	www.norman-network.com/?q=node/236
REACH2017	68679	zenodo.org/record/4248826
US EPA Chemicals Dashboard	~800 000(311 sub-lists)	comptox.epa.gov/dashboard/chemical_lists
IECSC	45612	www.mee.gov.cn/gkml/hbb/bgg/201301/t20130131_245810.htm
TSCA	86718	www.epa.gov/tsca-inventory/how-access-tsca-inventory
Tox21	7632	ncats.nih.gov/tox21
ToxCast	5231	comptox.epa.gov/dashboard/chemical_lists/CHEMIN

Moreover, the checklist screening is also applicable to the screening of unknown transformation products of new contaminants. This process requires generating a list of potential transformation products based on the parent compound and then performing checklist screening to identify the transformation products. Currently, the common platforms for generating a major list of transformation products in the environment are shown in Table 5. The mainstream platforms are mainly based on databases of phase I and phase II reactions, intestinal microbial metabolism, enzymatic metabolism, and environmental microbial metabolism to extract transformation rules manually or automatically, and then generate primary or multiple transformation products through reaction-based structural modifications or breaks of the parent compound structures. To avoid generating an overly large number of transformation products, which may increase the difficulty of identification, the Biotransformer and Meteor Nexus platforms have performed reaction probability reasoning based on machine learning to balance the sensitivity and selectivity of generating transformation products and filter out low-probability transformation products^[272].

表5 常见转化产物清单生成平台

Table 5 Available software to generate transformation products

software	predicted transformation categories	website
EAWAG-PPS	microbial metabolism	eawag-bbd.ethz.ch/predict/
enviPath	microbial metabolism	envipath.org/
Biotransformer	microbial and mammalian metabolism	biotransformer.ca
Meteor Nexus	mammalian metabolism	www.lhasalimited.org/
CTS	microbial metabolism,abiotic transformation	qed.epa.gov/cts/
QSAR Toolbox	microbial and mammalian metabolism、abiotic transformation	qsartoolbox.org/

3.2.1.2 "Structure Feature-Oriented Analysis Strategy"

Since the screening of the list relies on the available list, the types and scope of pollutants are based on prior knowledge. Compared with numerous unknown pollutants in the environment, the coverage is limited. This also poses a challenge to the in-depth analysis of mass spectrometry data. Therefore, how to rapidly and efficiently analyze a large number of unknown and new pollutants in complex environmental media has become a research hotspot. Structure feature-oriented analytical strategy is a relatively mature method to solve the above problems and has been well validated in practical applications.

The homologous series method screens homologous series with specific structural units through characteristic mass tolerance and retention time trends, and the characteristic mass difference of a series of precursor ions indicates the repetitiveness of the structural unit, which may be potential homologous series. For each series of homologous series, an increasing trend in retention time with increasing mass should be observed^[273,274]. In recent years, the homologous series method analysis strategy has been gradually improved in PFAS identification, achieving full-process automation for data extraction, recognition, and identification. FluoroMatch 2.0 is the first PFAS automated analysis software based on the homologous series method. On the basis of structure identification in the previous version, FluoroMatch 2.0 adds homologous series detection, characteristic fragment screening, and establishes a new set of confidence standards, which increases the PFAS recognition rate by more than 10 times^[275]. Li et al.^[276]developed a non-targeted analytical strategy for automated identification of PFAS homologous series under SWATH acquisition mode - SWATH-F. This method can effectively improve the identification effect in multi-sample studies, and compared with the traditional IDA acquisition mode, the identification rate increased by 276%. In addition, Loos et al.^[277]also established an open-source homologous series screening platform, providing tools for deep mining of complex homologous series in new pollutants using mass spectrometry data.

The characteristic fragment ion method is another approach besides the homologous series method. Liu et al.^[263]combined large volume injection with high-performance liquid chromatography and ultra-high-resolution Orbitrap mass spectrometry, using the source-in fragmentation labeling method to indicate precursor ions with characteristic ions, and applied this method to industrial wastewater, discovering 36 new PFAS. Liu et al.^[278]also applied this method to detect more than 200 new organohalogen compounds in polar bear serum, including new PCB metabolites and many previously undetected fluorinated or chlorinated substances. In addition to PFAS, Peng et al.^[279]identified bromine- or iodine-containing organic compounds based on their characteristic bromine or iodine ions in secondary spectra, ultimately identifying a large number of halogenated organic compounds in the environment. Ye et al.^[271]also constructed a non-target screening method for organophosphate flame retardants (OPFRs) based on common sub-fragment ions of OPFRs, and finally identified 35 OPFRs in lake sediment samples.

In addition to the above two methods, there is another method for screening potential halogenated compounds in samples based on the isotope distribution pattern in mass spectrometry, namely the isotope distribution method. LÉon et al.^[280] developed the HaloSeeker platform based on the characteristic isotope distribution of chlorinated and brominated compounds for rapid screening of such compounds and applied it to sediments, identifying 165 series of polyhalogenated compounds. Wang et al.^[281] also used the HaloSeeker platform to identify three new brominated organophosphate triesters in indoor dust samples.

The structural feature recognition methods introduced above still have certain limitations, i.e., the three methods are only applicable to the identification of compounds with repetitive structural units, characteristic ions, and halogen elements, and are not suitable for the identification of other non-halogenated pollutants. Besides halogenated organic compounds, new pollutants also include other persistent organic pollutants, antibiotics, and endocrine disruptors. The aforementioned methods are not applicable to the non-target screening of these pollutants. Researchers have found from the perspective of mass spectrometry analysis that there is an association between the structural features and spectral characteristics of molecules, i.e., structurally similar substances have similar fragmentation patterns. A networked recognition strategy based on spectral similarity can screen new pollutants beyond organic halogen compounds. By calculating the similarity between pairs of organic mass spectra, it can be extrapolated to the structural similarity between pairs of organic molecules, ultimately clustering organic molecules with similar structures into clusters in molecular networks (MNs), forming "molecular families"^[282], thereby expanding the scope of application of structural feature recognition methods. The widely used mainstream platform of molecular networks currently is the global natural products social molecular networking (GNPS)^[283]. GNPS is an interactive online tandem mass spectrometry data analysis platform aimed at exploring non-target mass spectrometry data from as many chemical perspectives as possible and associating them with actual biological problems. Later, related researchers successively introduced feature-based molecular networking (FBMN)^[284] and ion identity molecular networking (IIMN)^[285] as supplementary tools for GNPS, thereby improving the classical molecular network. In the past two years, research has applied molecular network technology to the environmental field, optimizing non-target screening strategies based on molecular networks by integrating various substructure analysis, category annotation, and mass spectrometry identification software, significantly enhancing the efficiency of discovering new pollutants from environmental samples^[286].

3.2.1.3 Knowledge-Oriented Analysis Strategy

In the practical environmental applications for the identification of new contaminants, it is not usually necessary to identify all substances in a sample, but rather to screen for relevant substances from tens of thousands of chromatographic peaks based on the issues of concern. This knowledge-directed analytical strategy is typically implemented through the following two methods.

1) The data feature-oriented analysis strategy is based on the features of mass spectrometry data itself, such as selecting substances with high signal and detection rates for identification to characterize pollutants with higher content^[287,288].The data feature-oriented method is helpful for discovering pollutants with high concentration and high detection rates. The implementation difficulty of this method is low. However, due to its relatively single approach and dependence on subsequent identification processes, it is usually used in combination with methods such as checklist screening and isotope distribution sorting^[287].

2) The experimental design orientation combines experimental design and statistical analysis to identify target substance peaks with statistical differences among different experimental groups. Currently, statistical techniques such as principal component analysis, hierarchical clustering analysis, and time-series analysis have been successfully applied in the identification of new contaminant transformation products in ozone processes^[289], the recognition of new contaminants with stable increases in sediment cores^[290], and the source tracing analysis of new contaminants in surface water^[291]. In summary, the experimental design orientation performs differential testing, time-series analysis, and spatial distribution analysis on samples according to specific experimental purposes through statistical testing methods, selecting and identifying substance peaks with significant changes. It usually shows good results in practical environmental applications.

3.2.2 Structural Identification of Mass Spectrometry Data

3.2.2.1 Confidence Level of Structural Identification

The structural annotation of new contaminants is currently a bottleneck in the identification and characterization of new pollutants. The study of new contaminant identification usually does not perform accurate structural identification for all substances in the sample. Additionally, without standards, qualitative judgment of substances based on non-targeted analysis has uncertainty, so confidence levels are introduced for explanation. The general applied confidence level is the five-level confidence hierarchy proposed by Schymanski et al.^[292]. Substances identified as Level 5 are interest peaks extracted after high-resolution mass spectrometry analysis, with only unique information about exact mass, unable to make further judgments. Substances identified as Level 4 are interest peaks that can be determined to have a unique molecular formula through analysis of isotope information and adduct information. Substances identified as Level 3 are interest peaks that can be used to identify part of the structure after analyzing spectra and fragmentation patterns, and these peaks can be confirmed as belonging to a certain class of substances. The Level 2 confidence level is subdivided into Level 2a and Level 2b, where: substances identified as Level 2a are interest peaks identified by comparing the spectrum of the target feature peak with the spectrum of known substances in the database; substances identified as Level 2b are interest peaks deduced from the spectrum of the target feature peak combined with fragmentation rules to infer the complete substance structure. Substances identified as Level 1 are those whose accurate structures have been determined by standards through first and second level spectra and retention times.

Charbonnet et al.^[293]proposed a confidence system for PFAS by adding mass defect, retention time, and homologue identification evidence to the five levels of confidence proposed by Schymanski et al.^[292]The mass defect refers to CF₂ normalized mass defect values ranging from 0 to 0.15 or 0.85 to 1. Retention time refers to the increase in retention time of the analyte with the lengthening of the perfluoroalkyl chain and its comparison with the reference standard being reasonable. This identification system further refined the original five levels of confidence. Level 5 was subdivided into two levels based on screening evidence for suspect compounds and non-targeted analysis evidence. Level 3 was subdivided into four levels based on different types of diagnostic fragments and homologue evidence. Level 2 introduced Level 2c, which refers to substances identified as at least two homologues with a confidence level of 2a or higher in the same sample. Level 1 was divided into Levels 1a and 1b. Level 1a requires confirmation by matching the exact mass number, isotope pattern, retention time, and MS/MS spectrum of the characteristic peak with standards analyzed on the same instrument and with similar matrices. Level 1b refers to terminal-substituted or branched isomers of some PFAS, which are almost indistinguishable based on the second spectrum, and thus were determined to be at Level 1b.

As the necessary condition for the highest level of confidence assessment, the verification of non-target screening results by purchasing or synthesizing standards can reduce the false positive of non-target identification, thereby ensuring the reliability of the results.

3.2.2.2 Secondary Spectrum Library Retrieval

In fact, the validation of compound structures by standard reference materials to reach level 1 is still unrealistic for current high-throughput non-targeted analysis technology. The time and economic cost of synthesizing standard reference materials are key factors limiting the throughput of compound identification. In view of this, the strategy of compound identification based on library search of secondary mass spectra has become the main method for the identification of new contaminants and an important basis for accurate structural annotation without standard reference materials (level 2a).

Currently, the compound mass spectral database available for matching covers research directions such as metabolomics, environmental science, forensics, and food safety. They mainly include: NIST(https://www.nist.gov), GNPS(https://external.gnps2.org/), MSDIAL(http://prime.psc.riken.jp/compms/msdial/), MassBank(https://massbank.eu/MassBank), HMDB(https://hmdb.ca), PubChem(https://pubchem.ncbi.nlm.nih.gov), and Metlin(http://metlin.scripps.edu).

In the actual identification process, some researchers compare the secondary spectra of samples with those in the database. Besides the match of the parent ion, identification is considered successful if two or more fragment ions match successfully^[294]. However, more researchers adopt spectral similarity scoring algorithms for rapid database searching. The most commonly used spectral similarity algorithm is the dot product (DP) algorithm, and the matching result of spectra is judged by setting a scoring threshold. To assist in the annotation of unknown structures, some studies attempt to introduce fragmentation mechanisms, neutral loss matching principles, and other methods, developing various spectral similarity algorithms^[295,296], retrieving known compounds with similar structures from the mass spectrometry database to assist in the inference of unknown compound structures.

In addition to the above conventional similarity algorithms, Treen et al. ^[297] innovatively introduced a base sequence alignment algorithm into the spectrum-graph similarity calculation in their SIMILE algorithm (significant interrelation of MS/MS ions via laplacian embedding), while ensuring statistical significance and robustness to allow multiple chemical differences. With the development of deep learning, some new spectrum-graph similarity algorithms have also emerged in recent years. For example, Yang et al. ^[298] constructed FastEI, which learned vector representations related to molecular structural information from spectrum-graph data through the Word2Vec model, and combined the hierarchical navigable small world (HNSW) algorithm to build a vector database, achieving ultra-fast matching for secondary spectrum-graph databases.

3.2.2.3 Structure Prediction Software

The existing open-source mass spectrometry databases are often developed from endogenous metabolites and natural product databases, and the number of spectra belonging to contaminants is limited. Therefore, even with the help of mass spectrometry database searching, a large number of new contaminants that are difficult to structurally identify still exist during non-targeted analysis. Compared with the number of compounds that may exist in typical environmental samples, the number of annotatable structures obtained through mass spectrometry database searching remains relatively small. To address such issues, some compound structure prediction models currently exist, which have been successfully applied to the structural identification of unknown new contaminants (level 2b or 3).

Sirius is a prediction model from spectrum to structure. This model can obtain the specific structures corresponding to the mass spectrum in existing structure databases such as PubChem^[299]. The Sirius platform has added the function of predicting unknown pollutant categories from spectra, providing more and more accurate structural information for annotating the structures of unknown new pollutants, and accelerating researchers in identifying the structures of unknown new pollutants^[300]. CFM-ID is a prediction model from structure to spectrum. This model can predict MS/MS spectra generated by fragmentation at different energies based on the substance structure, and can annotate and predict the mass spectral peaks or substance structures in the samples through the predicted spectra^[301]. In the latest version 4.0 of CFM-ID, machine learning methods have been combined to predict fragmentation spectra, further improving the predictive performance of the model^[302]. MetFrag is similar to the Sirius model. This model introduces multi-index evaluation systems such as retention time and related literature, improving the accuracy of substance structure identification^[303]. In the latest version, MetFrag optimizes the existing structure database and supplements a large number of new pollutants in the environmental field, enhancing the identification effect of environmental pollutants^[304].

At present, all kinds of platforms have deficiencies and limitations in pollutant structure prediction performance. Researchers are also cautious about the predicted structures. Some studies select the optimal prediction results by simultaneously predicting with multiple platforms. However, the development of platforms has greatly reduced the difficulty of non-target identification, which is of great significance to shorten the non-target analysis cycle. With the increase of pollutant mass spectrometry data, the future expected efficiency of the structure prediction model will continue to improve.

3.3 Application of New Contaminant Identification and Detection Technologies

3.3.1 Discovery of New Structures of Pollutants

Unlike traditional targeted analysis, non-targeted studies for new contaminants are not limited to known compounds. Instead, they expand the understanding of environmental pollutants by screening for unknown pollutants from the environment and elucidating the structures of new pollutants with environmental and health significance^[305]. Even for classic contaminant types such as organochlorine pesticides, organobromine flame retardants, and PAHs, non-targeted studies have identified a large number of previously overlooked compounds in environmental media^[306~307]. Taking PAHs as an example, traditional targeted research often focuses on the 16 priority pollutants recommended by the U.S. Environmental Protection Agency (EPA). However, the actual number of PAHs in the environment far exceeds the number of priority-controlled species. Kuang et al.^[308] used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS) to detect 141 possible methylated PAHs and PAHs in PM_2.5 samples from the Hong Kong region. Xu et al.^[309] used FT-ICR-MS and two-dimensional gas chromatography - time-of-flight mass spectrometry (GC×GC-TOF-MS) to detect polycyclic aromatic compounds (PACs) in Beijing's submicron particles (PM₁) and identified 386 PACs. Manzano et al.^[310] focused on heterocyclic PACs and detected 259 heterocyclic PACs in multiple samples including air, snow, and sediments using GC×GC-ToF-MS. Meanwhile, PACs outside the priority control have also been found to have equivalent or even higher environmental health risks compared to the 16 PAHs^[311]. These non-targeted studies not only expand the number of PAC species but also provide support for subsequent source apportionment, toxicity assessment, and exposure evaluation.

For new types of pollutants, non-targeted analysis is a key technical approach that can systematically interpret the occurrence of these compounds in environmental media. The nitro group is an important hazardous component in atmospheric fine particles. For example, after PAHs are nitrated to generate nitro-polycyclic aromatic hydrocarbons (NPAHs), their mutagenicity can increase by up to five orders of magnitude^[312]. This type of substance mainly includes organic nitrates (ONs, where the nitro group is connected to the carbon skeleton through an oxygen atom) and nitroaromatic hydrocarbons (NACs, where the nitro group is directly connected to the benzene ring). Shi et al.^[313] found that under ECNI conditions, organic nitrates can fragment into characteristic fragments NO₂^- and [M-NO₂]^-/[M-NO₂-H₂]^-, and based on this, they screened 78 organic nitrate compounds from PM_2.5 samples collected in Beijing, identified 12 of them with standard substances, which mainly originated from the atmospheric transformation of anthropogenic volatile organic compounds (VOCs) such as olefins. According to the electrophilic characteristics of the nitro group and the conjugation feature of the nitro-aromatic structure, Shi et al.^{[314, 315]} used gas chromatography-triple quadrupole tandem mass spectrometry (GC-QQQ-MS) with ECNI ion source, fixed monitoring of the NO₂^- fragment ion, and detected nearly 3000 NACs, achieving systematic detection of this type of compound without standards.

Among numerous new contaminants, PFAS as substitutes and environmental transformation products have become a key issue in global environmental research. Yu et al.^[316] used an all-component air sampler to simultaneously collect gaseous and particulate PFAS in the air, and reported for the first time 11 chlorinated perfluoroalkyl ether alcohols (3 types) and 4 chlorinated perfluoroalkyl ether carboxylic acids (2 types). Wang et al.^[317] proposed an integrated screening method based on suspicious screening, homologues, and characteristic ions, collected wastewater samples from sewage treatment plants in 17 cities in China, and identified 13 categories (25 subcategories) of 63 PFAS, including 14 traditional PFAS and 49 emerging PFAS. On the Qinghai-Tibet Plateau, Zhou et al.^[318] investigated the occurrence, spatial distribution, and sources of PFAS in soil, identifying a total of 34 PFAS, including 19 newly emerging PFAS such as fluorotelomer sulfonates (FTS), chlorinated polyfluoroalkyl ether sulfonic acids (Cl-PFESAs), and hexafluoropropylene oxide (HFPO) homologues. These substances are not only derived from local pollution but also from atmospheric migration influenced by the Indian monsoon and westerlies. As PFAS continue to be detected in various environmental matrices, their exposure to humans has received increasing attention. Kang et al.^[319] measured PFAS in human follicular samples and identified 15 newly emerging PFAS, including Cl-PFESAs such as 4∶2, 5∶2, 6∶2, and 8∶2 Cl-PFESAs, and studied the blood-follicle transfer of these PFAS, revealing their potential exposure risks. Li et al.^[320] established and applied a suspicious and non-targeted screening strategy based on CF₂, CF₂O, and CH₂CF₂ unit mass differences, screening 19 high-confidence novel PFAS from 117 paired maternal and umbilical cord sera. Novel PFAS account for a significant proportion of total PFAS in both pregnant women and fetuses.

In recent years, due to the regulation of traditional polybrominated diphenyl ether flame retardants, organophosphate esters (OPEs) flame retardants have been widely used in consumer and industrial products. Concerns about environmental and health risks have made OPEs flame retardant transformation products a new class of pollutants that have drawn international attention. Liu et al.^[321]reported the distribution, concentration, and risk of transformation products of OPEs flame retardants in the atmosphere of 18 global cities by combining laboratory simulation, field observation, and suspected material screening, finding that 10 transformation products were widely detected in all cities, reflecting the widespread existence of transformation products in the global atmosphere due to the use of OPEs flame retardants, and emphasizing that the overall risk of the mixture of transformation products is higher than that of the parent compounds. Gong et al.^[322]identified 26 OPEs and their potential transformation products in soil using an integrated strategy of suspect and non-targeted screening, among which 16 were reported for the first time in soil samples. Ye et al.^[271]developed a targeted, suspect, and functional group-dependent screening strategy to effectively identify 35 OPEs and their transformation products in Taihu Lake sediment samples, of which 23 were newly reported pollutants in the Taihu region, and found that meta-hydroxytriphenyl phosphate (meta-OH-TPhP) had high abundance and detection rate and greater biological toxicity than its parent compound triphenyl phosphate (TPhP). Choi et al.^[323]applied suspect and non-targeted screening to identify transformation products of OPEs flame retardants in aquatic systems, identifying 29 transformation products of TPhP based on producer-consumer-decomposer relationships, considering biotransformation mechanisms in organisms, and referencing the EAWAG-BBD pathway prediction system.

In addition, some new types of compounds, such as organosulfates^[324,325], liquid crystal monomer compounds^[326,327], organosilicon compounds^[328], and even nanomaterials and microplastic degradation products^[329,330], can all establish systematic non-targeted screening identification processes to comprehensively detect these compounds in environmental media. With the development of society, the number of new pollutants is increasing, and their impacts on the environment and health are still unknown. Therefore, effectively screening them using continuously improved non-targeted techniques will be a key step in achieving reasonable management.

3.3.2 Analysis of the Transformation Process of New Pollutant Molecules

During the process of new contaminants being discharged into the environment, degradation of the parent compound and the formation of transformation products often occur due to factors such as photolysis, hydrolysis, and microbial metabolism. Due to their structural similarity, the two often exhibit the same toxicity action mode, and synergistic effects may enhance overall toxicity^[331]. In addition, numerous research results indicate that after transformation, some new contaminants generate transformation products with higher toxicity^[332]. For example, after wastewater treatment, some antibiotics' transformation products are more persistent, migratory, and toxic than the parent compounds^[286,333]. New contaminant identification and detection technologies, especially non-targeted analysis, can identify new contaminants in a high-throughput manner, which is helpful for studying the transformation processes of new contaminants. Helbling et al.^[334] identified the transformation products of 30 compounds containing amide structures, observed amide hydrolysis and N-dealkylation, hydroxylation, oxidation, and other transformation processes, and determined the preferences for the transformation pathways of different amide structures. Wang et al.^[335] provided a comprehensive method for identifying organic disinfection by-products through non-targeted analysis, proposed 11 possible transformation product structures for each of the three sulfonamide antibiotics, and determined the common reaction pathways of sulfonamides under chlorination reactions. Wu et al.^[336] identified 9, 12, and 14 transformation products of diclofenac in activated sludge, nitrifying sludge, and heterotrophic sludge systems through non-targeted analysis, and found that hydroxylation is a key transformation step. In recent years, non-targeted analysis coupled with molecular network technology has been proven to be a powerful and efficient tool for studying transformation processes^[337]. Molecular networks can be used to assist in identifying transformation products of new contaminants, greatly increasing the throughput of identification^[338]. Sleight et al.^[339] extracted thousands of potential transformation products of four polycyclic aromatic hydrocarbons through the integration of pathway prediction and network theory. Yu et al.^[286] proposed a non-targeted screening strategy combining molecular networks with computer simulation tools, determining 52 antibiotics and 49 transformation products, among which 47 are potential PMT substances. In addition, molecular network approaches can also assist in studying transformation mechanisms. Wu et al.^[340] identified 14 unreported transformation products of sulfonamide drugs based on molecular networks and found that pteridine chelation and formylation are the dominant transformation pathways of sulfonamide drugs. However, these newly identified potential transformation pathways and transformation products of new contaminants still require further verification through more chemical, physical, and biological experiments. Non-targeted analysis and molecular network technology increase the throughput of identifying transformation products of new contaminants, enhancing our understanding of the transformation processes of new contaminants, and helping to reduce and control risks associated with new contaminants and their transformation products.

3.3.3 Identification and Diagnosis of New Contaminants Based on Effect-Directed Analysis

Health risks of compounds originate from exposure and toxicity. Non-target screening based on high-resolution mass spectrometry only focuses on the structure of compounds, and further toxicity evaluation is still needed to confirm high-risk pollutants, which is a bottom-up research approach. Effect-directed analysis (EDA), as a top-down method, directly identifies key toxicants in environmental samples through sample toxicity assessment, component separation, and analysis of highly toxic components. EDA was developed in the 1980s, but due to limitations in chemical analysis techniques, it could only be used to evaluate the contribution of known toxic substances to sample toxicity for a long time. Many toxicities of the samples remained unexplained. In the past 20 years, the rapid development of non-targeted technologies has brought a new upsurge in EDA research. By combining non-targeted chemical identification with EDA, researchers have conducted numerous studies on various environmental matrices such as sediments, wastewater, and biological samples, identifying many previously unknown toxic contaminants. For example, the sudden death of large numbers of Pacific salmon before returning to spawn was attributed to the oxidation product of antioxidant 6-PPD (N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine) in automobile tires, and the massive deaths of bald eagles were found to be related to a neurotoxin produced by blue-green algae called AETX (anatoxin-a epimer). This toxin is produced when blue-green algae are exposed to brominated substances originating from human activities^[341]. Yang et al.^[343] combined estrogen-related receptor γ protein affinity with non-targeted analysis and screened out a class of new contaminants without commercial sources in indoor dust - nitro-bisphenol A compounds.

A profound understanding of the toxicity of environmental samples is a prerequisite for conducting accurate health risk assessment and formulating effective risk control measures. However, in current EDA research, the selection of target toxicity for samples is relatively arbitrary and lacks standards. In most studies, researchers tend to choose toxicities that are easy to measure under laboratory conditions. This does not necessarily represent the most sensitive or significant toxicity of the sample. The pollution control and risk reduction policies formulated based on these studies may not effectively reduce actual health risks and may be difficult to achieve maximum benefits. To address this issue, some explorations and attempts have been made. For example, Jiang Guibin's research group at the Research Center for Eco-Environmental Sciences of the Chinese Academy of Sciences proposed the concept of "non-targeted toxicology." By exposing environmental samples from the molecular level to the cellular, tissue, organ, and individual levels, changes in proteins, transcriptomes, metabolomes, etc., are measured and comprehensively projected onto adverse outcome pathways (AOPs) to clarify the categories and intensities of toxic effects that the sample can cause, and to screen out the most sensitive and critical toxicities. Target screening based on non-targeted toxicology is expected to provide precise toxicity targets for various risk pollutant screening studies, including EDA, making related research more targeted.

3.4 Outlook

Non-targeted analysis based on high-resolution mass spectrometry enables us to identify and characterize organic emerging contaminants in the environment more comprehensively and rapidly, greatly expanding our understanding of these contaminants and providing critical scientific basis and technical means for their control and prevention. However, this technology and its applications still face many challenges. For example, different pretreatment methods and instruments may lead to significant differences in results, and it is necessary to standardize the methods so that different studies can be comparable. Emerging contaminants are diverse in types, and a single platform and method are difficult to cover them all. The comprehensive use of different ion sources and data acquisition modes may bring new perspectives. High-resolution mass spectrometry generates large amounts of data, and the current data processing strategies are still time-consuming and labor-intensive. Developing intelligent identification models for mass spectrometry by combining deep learning models may be an important direction for future research. A large number of emerging contaminants have been identified through non-targeted techniques. It is of great significance to develop a rapid, inexpensive, and effective prioritization system to determine which contaminants should be further studied, which can improve chemical management. As a qualitative analytical technique, non-targeted analysis cannot characterize the concentration of contaminants or further analyze their potential risks. Developing non-targeted quantitative methods can further improve this technology.

Besides the widely concerned organic pollutants, there are other types of emerging contaminants in the environment, such as ARGs, micro- and nanoplastics, and inorganic pollutants. Among them, ARGs are mainly identified and analyzed by high-throughput quantitative polymerase chain reaction and metagenomic sequencing methods. However, the complex analysis technology, high cost, and lack of standardized methods remain to be solved problems^[344~345]. Various visual, spectroscopic, and electron microscopy methods have been developed to identify microplastics in the environment^[346], but analyzing micro- and nanoplastics with smaller sizes remains a challenging problem. Pyrolysis-gas chromatography-mass spectrometry and surface-enhanced Raman spectroscopy have been proven effective for identifying micro- and nanoplastics^[346,347], but how to improve the effectiveness and accuracy of analysis still poses a challenge. Emerging contaminants continue to emerge, and new forms and properties of emerging contaminants may appear in the future. Developing identification methods for emerging contaminants requires interdisciplinary integration and collaboration.

Finally, the identification and characterization techniques of new pollutants also provide a basis for new pollutant source tracing. Combining the results of new pollutant identification with receptor models such as chemical mass balance models and multi-factor variable analysis methods, the simulation of pollutant diffusion and transport processes can achieve the source tracing of atmospheric new pollutants^[347]. For the source tracing of pollutants in surface water and groundwater, hydraulic parameters and other factors are usually considered, and a model is constructed to describe the transport and transformation process of newly identified pollutants in the environment, and solved by optimization-based methods, stochastic-based methods, and mathematical-based methods to achieve source apportionment^[348]. Currently, the technology and means of source tracing are still developing, and a unified and sound source tracing system has not yet been formed. The existing source tracing methods mainly target single pollutants, and the source tracing analysis of a large number of new pollutants is still in the exploratory stage. In recent years, the rapidly developing artificial intelligence algorithms may provide a new perspective for the precise source tracing analysis of new pollutants.

4 Environmental Levels and Distribution Characteristics

4.1 Regional Distribution Characteristics of New Contaminants

4.1.1 Show obvious regional aggregation.

The distribution of new contaminants is closely related to human activities such as industrialization and urbanization. Many new contaminants have been found in sites with frequent human activity. The structural formulas of typical new contaminants involved in this section are shown in Figure 21. For example, PFAS were detected in leachate from municipal solid waste landfills, soil and surface water near firefighter training sites, and wastewater and sludge from wastewater treatment plants^[349]. The detection results of 13 PFCAs and 4 PFSAs in the leachate from six municipal solid waste landfills in Chongqing, China, show that PFBS and PFBA are the main substances. The quality concentration of PFAS in landfill leachate shows a trend of being higher in active landfills (average quality concentration: 12194 ng/L) than in closed landfills (average quality concentration: 2747 ng/L)^[350]. Liu et al.^[351] found that the incineration of garbage before landfilling also affects the distribution of PFAS. After incineration, the quality concentration of PFAS in leachate from domestic waste landfills (290 ng/L) was significantly lower than that in leachate from directly landfilled domestic waste (11000 ng/L). Moreover, the composition of PFAS in the leachate of incinerated landfills was similar to the composition of combustion ash in that field. Benskin et al.^[352] monitored the changing trends of 24 PFAS in urban landfill leachate for five consecutive months and found that perfluoropentanoic acid (PFPeA) and perfluorohexanoic acid (PFHxA) were the main PFAS in the leachate. The levels of several PFAS precursor compounds, such as PFOA and PFOS, were 2-10 times higher in March-April than at other times. A pollution survey of urban drinking water in Sweden found that local drinking water contamination was related to the use of PFAS-containing foam at firefighter training sites. Among them, PFOS had the highest mass fraction in soil samples from firefighter training sites (accounting for 87% of ΣPFAS, with a maximum mass fraction of up to 560 ng/g (dw)), and the ΣPFAS mass concentration in groundwater near firefighter training sites was as high as 1000 ng/L^[353]. The highest concentration of PFOS in freshwater lake foam in northern United States could reach 97000 ng/L^[354]. In addition, Ruan et al.^[355] found the existence of traditional and novel PFAS in the sludge of wastewater treatment plants in 20 provinces and cities in China, among which PFOS was the primary pollutant, with an average mass fraction of 3.2 ng/g (dry weight, dw). The average mass fractions of 6∶2 chlorinated polyfluoroalkyl ether sulfonic acid (6∶2 Cl-PFESA) and its homologous compound 8∶2 Cl-PFESA were 2.2 ng/g (dw) and 0.50 ng/g (dw), respectively.

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图21 主要新污染物的化学结构式

Fig. 21 Chemical structure of typical new pollutants

In recent years, human exposure to PFAS has been very common and shows obvious regional characteristics. In the third national breast milk survey, researchers analyzed breast milk samples from 24 provinces in China and found that PFOA and PFOS were mainly detected, followed by 6:2 Cl-PFESA. The results of the regional characteristic study showed that the exposure concentration of PFOA in breast milk in Shandong Province was the highest. Compared with the detection results in 2007, the concentrations of PFAS in Shanghai and Liaoning Province have significantly decreased, while the concentrations of PFAS in Shandong Province and Hubei Province have significantly increased^[356]. At the same time, new PFAS have also been found in human serum. A study on the whole blood and serum of 1516 people from 7 cities in China showed that 6:2 Cl-PFESA ranked third (8.7%) among all PFAS monomers, with an average mass concentration of 4.42 ng/mL^[357]. Multiple new PFAS were detected in 1151 breast milk samples from 21 cities in China, with detection rates of 6:2 Cl-PFESA, perfluoro-2-methoxyacetic acid (PFMOAA), and perfluoro(3,5,7,9,11-pentaoxatridecanoic) acid all higher than 70%^[358].

Brominated flame retardants are among the largest volume and most widely used organic flame retardants in the world. PBDEs have been widely used in electronics, chemicals, construction and other fields since the 1970s due to their high flame-retardant efficiency and low addition amount. Due to their potential environmental hazards such as persistence, bioaccumulation and long-range transportability, commercial pentabromo-, octabromo-, and decabromo diphenyl ethers have been included in the Convention. NBFRs, with better compatibility and excellent flame-retardant properties, have been widely used globally as substitutes for PBDEs. From 1990 to 2006, the annual production of bis(2-ethylhexyl) tetrabromophthalate (BEHTEBP) in the United States increased from 450 tons to 4500 tons^[359]. China is the main consumer and producer of NBFRs^[360]. In 2006, the total production of DBDPE in our country was 12,000 tons, reaching about 70,000 tons in 2007^{[361, 362]}.

Brominated flame retardants exhibit significant accumulation characteristics in human activity sites such as e-waste dismantling and recycling, landfilling, and building decoration. Research at an Australian e-waste recycling plant has shown that even under conditions compliant with legal regulations and environmental emission standards, the generated brominated flame retardants can still contaminate surrounding soils (∑PBDEs mass fraction: 0.10–98000 ng/g (dw), median mass fraction: 92 ng/g (dw); ∑NBFRs mass fraction: ^[363]. Poudel et al.^[364] detected the presence of PBDEs in environmental and human samples from Vietnamese e-waste recycling plants. The highest concentrations of PBDEs were found in dust from the e-waste recycling plants, with exposure concentrations 2 to 48 times higher than those at reference points. Mothers and children from electronic waste processing areas showed higher carcinogenic risks compared to the reference population. In Brazilian landfills, DBDPE and BTBPE were found to have the highest concentrations in soil samples from the e-waste landfill area, while dust samples had the highest levels in the e-waste storage zones^[365]. Due to their widespread use in the construction sector, brominated flame retardants have been detected in indoor environments in many countries and regions, with developed countries and regions where they are produced showing higher pollution levels. For example, Bjorklund et al.^[366] found that the maximum concentration of PBDEs in Swedish office air could reach up to 7300 pg/m³. The average concentration of PBDEs in the air of Seattle, USA households was 12100 pg/m³^[367]. The average concentration of PBDEs in PM₁₀ in offices near the e-waste dismantling area in Shenzhen, China was as high as 30600 pg/m³^[368]. By contrast, the levels of brominated flame retardants in indoor air in ordinary households in China were relatively low. Continuous monitoring for 15 months of the concentration of PBDEs and NBFRs in the air of a household resulted in values ranging from 0.60 to 14 pg/m³ and 9.3 to 686 pg/m³, respectively^[369].

CPs are widely used in flame retardants, plasticizers, high-temperature lubricants, and adhesives^[370]. In the Pearl River Delta region where industry and urbanization are highly developed, the mass concentrations of SCCPs in seawater were 180–460 ng/L, in sediments were 180–620 ng/g (dw), and in marine biological samples were 870–36000 ng/g (lw)^[371]. The mass concentrations of SCCPs and MCCPs in soils from the Pearl River Delta region were 2.0–236 ng/g (dw) and 2.0–530 ng/g (dw), respectively. The average concentrations of SCCPs and MCCPs in outdoor air locally were 18 ng/m³ and 14 ng/m³^[372]. Wang et al.^[373] found that the mass concentration of SCCPs in Beijing urban atmospheric environments was 1.9–332 ng/m³. In recent years, developed countries have strictly controlled the production, use, and emissions of CPs, resulting in a continuous decrease in their environmental content. However, the usage of CPs in China has shown an increasing trend, with concentration levels in environmental media several orders of magnitude higher than those in developed countries. For example, the average mass fractions of SCCPs in UK forest soils and atmospheres were only 22 ng/g (dw) and 0.19–3.4 ng/m³^{[358, 374]}, whereas in sediments of the Pearl River Delta region, the mass fraction of SCCPs was 320–6600 ng/g (dw), which is higher than in Barcelona (210–1170 ng/g (dw)), and the concentrations of SCCPs in sediments of the North Sea and Baltic Sea were 5.0–377 ng/g (dw)^[375]. Human activities also significantly influence the environmental behavior of CPs in remote areas. Studies have shown that urban landfill sites in Tibetan cities are sources of local SCCPs, with mass fractions of SCCPs in urban and rural landfill site soils reaching 57–1348 ng/g (dw)^[376]. SCCPs from anthropogenic emissions have also been found in polar regions, with the mass concentration of SCCPs in the atmosphere at Great Wall Station, Antarctica, ranging from 70 to 4210 pg/m³, with an average value of 1170 pg/m³^[377].

The processes of production, storage, transportation, and use of CPs, as well as waste disposal and solid waste treatment processes and surface runoff can cause environmental pollution by CPs^[378]. For example, wastewater containing SCCPs still has 7% to 10% of SCCPs even after activated sludge treatment, and the effluent entering the river will again become a source of CPs pollution^[379]. The concentrations of SCCPs in soils from typical industrial areas (paper mills, metal smelting plants, and solid waste treatment plants) in China are 2 to 9 times higher than those in areas without pollution sources, while the concentrations of MCCPs are 4 to 81 times higher than those in areas without obvious pollution sources. Among them, the concentration of SCCPs in paper mills is the highest, and the concentration of MCCPs in solid waste treatment plants is the highest^[380].

In summary, new pollutants are of various types and their regional agglomeration is closely related to production characteristics. New pollutants with clustered development have more obvious regional agglomeration; while new pollutants with dispersed production and use have relatively weaker regional agglomeration.

4.1.2 Related to the distribution of industry types

PFAS are characterized by hydrophobicity, oleophobicity, high surface activity, and excellent chemical stability, and are widely used in industries and commercial products such as electroplating, paper coatings, cookware, and firefighting foams^[381,382]. Direct emissions from fluorinated chemical enterprises are an important source of PFAS in the environment. He et al.^[383] investigated eight long-chain PFCAs and 40 newly emerging PFAS in environmental media from a fluorinated chemical plant in Hubei Province, China, and found that a total of 52 PFAS were detected in indoor dust, with median mass fractions of 276 ng/g for long-chain PFCA and higher concentrations of short-chain PFAS in total suspended particles (median mass concentration of 416 ng/m³) and factory effluents (Σ₄₈ PFAS at 212 μg/L). High concentrations of PFAS were also detected in rural areas near a fluorochemical industrial park in the Yangtze River Delta region of China, with total PFAS mass fractions in soil, surface water, groundwater, and rainwater ranging from 0.60 to 65 ng/g (dw), 16 to 481 ng/L, 4.8 to 615 ng/L, and 13 to 542 ng/L, respectively. In recent years, the fluorochemical industry in Fuxin has rapidly developed. Bao et al.^[384] detected PFAS contaminants dominated by PFOA in serum samples and drinking water samples from residents in Fuxin City in 2015. Compared with the results in 2009, the pollution levels of PFOA in serum samples and drinking water samples from residents in Fuxin City have increased. From 2009 to 2015, the total burden of PFOA intake from drinking water by local residents has increased nearly threefold. PFAS pollutants may enter vegetables through groundwater irrigation from fluorochemical industrial parks, and PFBA and PFBS were mainly detected in self-produced vegetables around the Fuxin fluorochemical industrial park^[385].

PFAS have important applications in the electroplating industry. 6:2 Cl-PFESA (commercial name F-53B) and PFOS are currently the two widely used mist agents. After the ban on PFOS, 6:2 Cl-PFESA has become the main chromium mist suppressant^[386].China first synthesized 6:2 Cl-PFESA for use as a chromium mist suppressant in 1975. Although it has been used in China for several decades, it has only recently gained attention as a newly emerging contaminant^[387].Although 6:2 Cl-PFESA is only produced and used in China^[388], global surveys of emerging PFAS in surface water have found that 6:2 Cl-PFESA can be transported globally through ocean currents and has become a global contaminant, with detection rates of 95% and 70% in seawater of the northwest Pacific and the eastern Indian Ocean, respectively^[389].6:2 Cl-PFESA is a PFAS with relatively strong biological persistence, with a half-life of 15.3 years. 6:2 Cl-PFESA has been widely detected in environmental media, wildlife, and humans in China, with a detection rate of nearly 80% in human blood^[390].

According to statistics, in 2006, the production of bromine-based flame retardants in our country was about 2.6×10⁵ t, among which Decabromodiphenyl Ether (BDE-209) was the most widely produced with an output of 3.6×10⁴ t [391]. The production of bromine-based flame retardants is mainly concentrated in Shandong Province and Jiangsu Province in China. The mass fractions of 8 PBDEs in the serum of residents in Laizhou, Shandong Province were 613 ng/g(lw), with BDE-209 as the main homolog; compared with non-occupationally exposed populations, the concentration level of PBDEs in occupationally exposed populations was 2~3 orders of magnitude higher [392]. Zhao et al. [393] found that PBEB, PBT, HBBz, BTBPE and DBDPE were widely detected (detection rate greater than 90%) in the serum of residents in the bromine-based flame retardant production area in Shandong Province. Another survey on the serum of people in the bromine-based flame retardant production factory in Shandong Province showed that the highest mass concentration of DBDPE among the three detected NBFRs was 0.38 ng/mL (average mass concentration) [394]. Compared with PBDEs, the detection rate and detection mass concentration of NBFRs in human blood were at a relatively low level. In 2014 and 2015, the average mass fractions of ΣNBFRs (PBBz, PBT and HBBz) in the serum of residents in Shandong Province were 2.2 ng/g(lw) and 3.5 ng/g (lw) respectively, and the mass fraction level of NBFRs was 1~2 orders of magnitude lower than that of PBDEs [395].

China used to be the main country for global e-waste recycling and dismantling. About 80% of electronic waste from developed countries such as Europe and America was transported to Asia, with 90% of it flowing into China^[396]. The regions for e-waste dismantling were mainly concentrated in Guiyu, Guangdong Province, Qingyuan, Guangdong Province, and Taizhou, Zhejiang Province. Brominated flame retardants released and continuously accumulated in the environment by the e-waste dismantling industry have caused an undeniable human burden^[397]. Studies show that the serum of e-waste dismantling populations in Guiyu, Guangdong Province, presents high levels of PBDEs, with concentrations ranging from 140 to 8500 ng/g(lw), where the concentration of BDE-209 is as high as 3100 ng/g(lw)^[398]. The average concentration of DBDPE in the serum of workers at e-waste dismantling plants in rural areas of Taizhou, Zhejiang Province, is 125 ng/g(lw). In contrast, the average concentrations of DBDPE in the serum of non-occupationally exposed residents and urban residents in this region are 56 ng/g(lw) and 14 ng/g(lw), respectively^[399]. The average concentration of eight PBDEs in the serum of residents in the e-waste dismantling area of Wenling, Taizhou, is 357 ng/g(lw), while the concentration of eight PBDEs in the serum of the e-waste dismantling population in Taizhou Luqiao is 118 ng/g(lw)^[400]. The median concentration of ΣPBDEs in the serum of workers in e-waste recycling workshops in Taizhou, China, is 125-622 ng/g(lw), with octa-to-deca-BDEs accounting for more than 80% of the total PBDEs concentration. Additionally, three hydroxylated diphenyl ethers (OH-BDEs) were widely detected (the median concentration of ΣOH-BDEs is 93 ng/g(lw))^[401]. These results indicate that brominated flame retardant production and e-waste dismantling industries have high human exposure levels, and their health risks need to be taken seriously.

The northeastern, eastern, southeastern and central regions of our country have many CPs processing factories, and the spatial distribution of CPs content is highly consistent with the distribution of processing factories. Studies have shown that the concentration of CPs in the soil near the processing plants is also higher, and the emission characteristics show a trend of being higher in the eastern region than in the western region^[380,402]. In the soil of the CPs production plant in Dalian, the total mass fractions of SCCPs and MCCPs were 3093-3870 ng/g (dw) and 64-1884 ng/g (dw), respectively, and the spatial distribution of CPs in this area was related to the distance from the factory^[403]. The study on SCCPs in aquatic food products in China showed that the quality fraction of SCCPs in food of residents in the eastern and southern areas was higher than that in the western areas, with an average quality fraction of 1472 ng/g (ww), which was much higher than similar results in other countries^[404]. Huang et al.^[405]found an average quality fraction of 129 ng/g (ww) of SCCPs in meat and meat products in 20 provinces of China, and its distribution characteristics were similar to the distribution situation of SCCPs production plants in China.

The electronic waste recycling industry is another important source of CPs pollution. Research results from different countries and regions have shown that CPs generated during the dismantling of electronic devices can enter the surrounding soil through wastewater or waste residues. The quality fraction of SCCPs in the soil of the Taizhou electronic waste recycling area in China was 69 to 2.2 × 10⁵ ng/g (dw), and the quality fraction of MCCPs was 507 to 4.4 × 10⁶ ng/g (dw)^[406]. The detection results of surface particulate matter in four electronic waste recycling workshops in China (Taizhou, Guiyu, Dalu, and Qingyuan) indicated that the CPs had a non-negligible quality fraction (SCCPs: 3.0 × 10⁴ ~ 6.1 × 10⁴ ng/g (dw); MCCPs: 1.7 × 10⁵ ~ 8.9 × 10⁵ ng/g (dw))^[407]. Higher CPs contents were also found in air and soil samples from urban waste dumping sites in Tanzania^[408]. The chemical structures of the main new pollutants are shown in Fig. 21.

4.2 Environmental Occurrence Characteristics of Emerging Contaminants

With the implementation of the "Action Plan for the Governance of Emerging Pollutants," PFAS, EDCs, PPCPs, DBPs, and other emerging pollutants are receiving increasing attention. These pollutants are frequently detected in environmental media such as air, soil, and water. On one hand, these emerging pollutants can be transported into the soil environment via point sources and non-point sources. On the other hand, new pollutants in the soil may also enter surface water, groundwater, and other water environments through runoff, diffusion, leaching, percolation, and rainwater flushing. It is particularly noteworthy that most emerging pollutants, such as PFAS, EDCs, PPCPs, and DBPs, differ from traditional POPs in physicochemical properties, with relatively strong water solubility. Therefore, water environments may be the main "sink" for emerging pollutants. For a long time, the high-intensity production, use, and discharge of industrial chemicals or daily-use products in China have resulted in pollution residues of emerging pollutants in water environmental media such as surface water and groundwater.

PFAS is a typical category of emerging pollutants, and many reports have focused on PFOS and PFOA. Not only do they exist in soil, atmosphere, and organisms, but they can also be found in large quantities and for a long time in water bodies, causing serious pollution to the aquatic environment. Benskin et al.^[352] detected high concentrations of PFOS, PFOA, and their precursors in urban landfill leachate, with ΣPFAS reaching up to 36 μg/L. It was estimated that approximately 8.5 to 25 kg of PFAS enters WWTPs annually through landfill leachate. Since WWTPs have low removal efficiency for PFAS in influent water, they are considered an important source of PFAS pollution in the aquatic environment. Researchers detected PFOA and PFOS concentrations in WWTP effluents ranging from 58 to 1050 ng/L and 3.0 to 68 ng/L, respectively, and found higher concentrations of long-chain PFAS in sludge and more short-chain PFAS in water^[409]. Studies on the spatiotemporal distribution of PFAS in China's water environment have found that the pollution level of short-chain PFAS is increasing year by year. For example, the quality concentration of PFAS in the water environment of the Yangtze River Delta region is 8.6 to 737 ng/L^[410], and the total concentration of ∑PFAS in drinking water in Sichuan Province is 4.2 to 41 ng/L^[411], where medium and short-chain PFAS such as PFOA and PFHxA predominate. Zhou et al.^[412] discovered high-quality concentrations of PFAS (4.6 to 12 μg/L) in surface water near a fluorochemical plant in Wuhan, with short-chain PFAS migrating further along the flow direction and penetrating deeper vertically. Another study also showed that medium and short-chain PFAS are more commonly found in the aqueous phase and migrate along the flow direction, while long-chain PFAS tend to bind with suspended particles or sediments^[413]. Sediment serves as both a "sink" and "source" for PFAS, with residual levels typically at ng/g levels. Contaminated sediment can cause long-term pollution to overlying water and groundwater. Compared to sewage, surface water, and drinking water, PFAS contamination in groundwater has stealth and persistence. Researchers detected PFAS concentrations of 1.1 to 24 ng/L in groundwater in irrigated areas in Beijing, which significantly decreased with increasing distance from landfills^[414]. Atmospheric dry and wet deposition is also an indispensable non-point source of PFAS pollution in the aquatic environment. Huang Yu et al. analyzed the presence characteristics of PFAS in the Gongga Glacier (Hailuogou area) water environment and detected total ΣPFAS concentrations of 7.1 to 106 ng/L in glacial meltwater, lake water, and rainwater samples. The Gongga Hailuogou area has no direct discharge sources, but high-quality concentrations of PFAS were detected in rainfall, indicating that PFAS in this region's water environment originates from atmospheric dry and wet deposition^[415].

EDCs are a class of exogenous substances that can interfere with the biological endocrine system, and the environmental pollution caused by them has become the third-generation environmental issue urgently requiring management after the ozone layer hole and global warming. So far, many studies have been conducted on the occurrence status of EDCs in water environments. Li Jinrong et al.^[416] reviewed various typical EDCs such as PAEs and phenols, which are widely present in various environmental media including water bodies, soils, and air, and are particularly prevalent in areas with high levels of urbanization and industrialization. Steroid estrogens include natural estrogens like estradiol (E2) and synthetic estrogens like ethinylestradiol (EE2) and diethylstilbestrol. The detection rate of estrone (E1), E2, and EE2 in groundwater in Wuxi-Changzhou region is above 90%, followed by estriol (E3), with a detection rate of 67.7%^[417]. The quality concentration of EDCs in the drainage ditch entering the Yellow River in Ningxia ranges from 82 to 1730 ng/L, among which the highest detected concentration of E2 is 1367 ng/L^[418]. PAEs are one of the widely used plasticizers^[419], Xu et al.^[420] detected 15 kinds of PAEs in surface water of the Yangtze River Basin, with a total quality concentration of 1594 to 5156 ng/L, mainly composed of DEHP, DBP, and DIBP. The Σ₁₅ PAEs quality concentration in the water body of the Yangtze River Basin increases from upstream ((2342 ± 429) ng/L) to midstream and downstream (respectively (3892 ± 843) and (2504 ± 356) ng/L). Phenolic EDCs have structures similar to those of natural hormones, with strong hormone effects. Peng et al.^[421] analyzed the occurrence of phenolic substances in urban rivers in Guangzhou, finding that 4NPs are the main pollutants, with quality concentrations and mass fractions of 5050 ng/L and 14400 ng/g in the aqueous phase and sediments, respectively. Xiong Shimao et al.^[422] investigated the main sources of drinking water, surface water, and WWTPs in the downstream of Beijiang (the second largest river system of Pearl River), finding that bisphenol A (BPA) and 4NPs are the main EDCs in the downstream of Beijiang, with average quality concentrations of 360 ng/L and 382 ng/L, respectively. Overall, there are significant spatial differences and quality concentration changes in EDCs in water environments, but all regions have an undeniable ecological risk, and China still lacks comprehensive and effective regulations to restrict and reduce the production and emission of EDCs^[423].

PPCPs have drawn much attention due to their large usage volume, “pseudo-persistent” nature, and strong toxic effects. PPCPs are closely related to human activities, and they exhibit significant spatiotemporal distribution characteristics in terms of types and concentrations in the environment. Bu et al. [424] reviewed the occurrence status of 112 kinds of PPCPs in China's water bodies and sediments. Their mass concentration and mass fraction levels were at ng/L and ng/g, respectively. Among them, antibiotics such as SMX and ETM, as well as anti-inflammatory drugs such as IBU and diclofenac, had relatively high potential risks in surface water. Moreover, ARGs pollution caused by antibiotic abuse poses a threat to ecological safety and human health [425]. Studies have analyzed the characteristics of ARGs in China's drinking water sources, detecting a total of 265 unique ARGs and mobile genetic elements (MGEs). The abundance of ARGs in rivers (6.2 × 10⁵ copies/mL) was higher than that in reservoirs (4.8 × 10⁵ copies/mL) and groundwater (8.1 × 10⁴ copies/mL) [426]. It is estimated that the total annual usage of 36 common antibiotics in China reached 92,700 tons in 2013, with approximately 25,000 tons eventually entering the aquatic environment [226]. Due to high-density aquaculture, the highest detected mass concentration of antibiotics in the coastal areas and estuary rivers of Qinzhou Bay was 12 ng/L, with SMX having the highest detection rate and average mass concentration, which were 100% and 4.1 ng/L, respectively [427]. In five typical surface water environments in the Yangtze River Delta, Jianghan Plain, Pearl River Delta, Yellow River Delta, and Chaohu Watershed, the most concerning antibiotics were SMX and ETM, with the highest detected mass concentrations being 260 ng/L and 382 ng/L, respectively [428]. Su et al. [429] detected four categories and 22 types of antibiotics, including quinolones, macrolides, sulfonamides, and tetracyclines, in the mainstream of the Yellow River, with the highest mass concentrations ranging from 0.27 to 30 ng/L. Among them, SMX and ETM posed the highest potential risks to aquatic organisms. From a spatial distribution perspective, tetracycline contamination was mainly found upstream, quinolone contamination in the midstream, and sulfonamide and macrolide contamination downstream [429]. Non-steroidal anti-inflammatory drugs are another category of PPCPs widely detected in the water environment. Researchers detected PPCPs in groundwater and reservoirs near a municipal solid waste landfill site in Guangzhou. The mass concentration level in groundwater was low (ng/L), while the detection frequency and mass concentration in the reservoir were relatively high, with the highest mass concentration of IBU reaching 1417 ng/L [430]. Chen Xian et al. [431] detected PPCPs such as naproxen and IBU in the surface water of the Yellow Sea and East China Sea. The overall spatial distribution showed a trend of higher values near the river mouth and coast and lower values in the offshore sea areas. The mass concentration of PPCPs in the Yellow Sea was higher than that in the East China Sea, which was related to the multiple pollution sources and weaker water exchange capacity in the Yellow Sea.

DBPs are the by-products generated from the chemical reactions between disinfectants and precursor organic matter in water under certain conditions. With increasing water pollution, the types and concentrations of organic matter in water bodies have both increased, leading to a higher production of DBPs during the disinfection process. Currently, more than 700 types of DBPs have been detected and confirmed. The main types of DBPs include trihalomethanes (THMs), haloacetic acids (HAAs), haloacetonitriles (HANs), haloquinones (HBQs), nitrosamines (NAs), halonitromethanes (HNMs), and haloacetamides (HAcAms), with their mass concentrations ranging from ng/L to μg/L^[432]. THMs, HAAs, and HANs are the primary regulated DBPs, with higher contents in disinfected water. In urban drinking water in China, their mass concentrations are 0.011-99, 0.020-67, and 0.030-14 μg/L respectively^[433]. HBQs are a new type of unregulated DBPs, with toxicity far exceeding that of conventional DBPs such as THMs and HAAs. Wang et al.^[434] found that the concentration of 2,6-dichloro-1,4-benzoquinone in swimming pool water was 19-299 ng/L, which is 100 times higher than that in tap water (1.0-6.0 ng/L). Higher dissolved organic carbon (DOC), chlorine dosage, and water temperature in swimming pools promote the formation of HBQs, with concentrations of 2,3,6-trichloro-1,4-benzoquinone, 2,3-dibromo-5,6-dimethyl-1,4-benzoquinone, and 2,6-dibromo-1,4-benzoquinone being 0.10-11, 0.050-0.70, and 0.050-3.9 ng/L respectively. Hu et al.^[435] identified three novel iodinated HBQs in drinking water, including 2-chloro-6-iodo-1,4-benzoquinone, 2-bromo-6-iodo-1,4-benzoquinone, and 2,6-diiodo-1,4-benzoquinone, with concentrations of 0.70-1.3, 1.8-8.0, and 0.40-16 ng/L respectively. NAs, HNMs, and HAcAms, due to their high toxicity and detection rates, have drawn significant attention and are considered priority targets for investigation and regulation among DBPs. Bei et al.^[436] investigated the occurrence of nine NAs in the effluent, tap water, and source water of 44 cities across 23 provinces in China. Among them, the concentration of N-dimethylnitrosamine (NDMA) was the highest, ranging from 0 to 189 ng/L, with average concentrations of 27 and 29 ng/L in tap water in the Yangtze River Delta region. Zhou et al.^[437] found that the concentration of HNMs in drinking water in eight counties (cities) in Jinhua, Zhejiang Province, ranged from 0.20 to 2.9 μg/L, with a median value of 0.70 μg/L, and the summer levels were generally higher than those in winter and spring. Chu et al.^[438] measured the concentrations of HAcAms in the effluents of seven drinking water treatment plants in three provinces and seven cities in China, finding total concentrations ranging from 0.07 to 8.2 μg/L. The total concentration of the three dihalogenated HAcAms, including dichloroacetamide, bromochloroacetamide, and dibromoacetamide, accounted for more than 60% of all HAcAms. The concentrations of HAcAms in drinking water and swimming pool water in Taiwan Province were 0.43-3.0 μg/L and 0.77-4.03 μg/L respectively^[439]. Additionally, hundreds of organic DBPs at ng/L to μg/L levels and inorganic DBPs at mg/L levels have been detected in reclaimed water^[440]. To ensure human water use or water ecological safety, it is urgent to strengthen risk control of DBPs in water environments.

4.3 Bioconcentration and Accumulative Effects of New Contaminants

Per- and polyfluoroalkyl substances (PFAS) and chlorinated paraffins (CPs), which are emerging pollutants, exhibit distinct differences in physical and chemical properties compared to traditional halogenated persistent organic pollutants (POPs) such as polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), and polybrominated diphenyl ethers (PBDEs). Perfluorocarboxylic acids (PFCAs) and perfluorosulfonic acids (PFSAs) are ionic compounds with hydrophobic and lipophobic characteristics. CPs possess an extraordinarily complex chemical composition with numerous homologues and isomers. Meanwhile, PFAS and CPs are aliphatic organic compounds, whereas PCBs, OCPs, and PBDEs are predominantly aromatic organic compounds. The former exhibit proteinophilic characteristics, while the latter display lipophilic traits. Therefore, the bioaccumulation and accumulation of these emerging pollutants may differ significantly from those of traditional POPs.

Indoor exposure experiments reported the bioconcentration factor (BCF) of PFASs in fish. The determined BCF values of different perfluorocarboxylic acids with various carbon chain lengths in different tissues of fish are shown in Table 6. Taking BCF > 5000 (log BCF > 3.7) as the criterion, it can be seen that perfluorocarboxylic acids with carbon chain length > 9 have obvious bioaccumulation potential. For perfluorosulfonic acid compounds, currently only a few perfluorosulfonic acids with different carbon chain lengths (mainly PFBS, PFHxS and PFOS) have been measured for BCF values. The results show that the BCF of PFOS in bighead carp and grass carp is > 5000 ^[441], while in rainbow trout ^[442], black-headed dumbfish ^[443] and zooplankton ^[444] the BCF is < 5000. PFHxS has BCF values higher than 5000 only in some tissues of bighead carp and grass carp.

表6 室内暴露实验测定的不同碳链全氟羧酸在鱼不同组织中的生物浓缩因子(log BCF)

Table 6 Bioconcentration factors of PFCAs of different carbon chains in different tissues of fish as measured by indoor exposure experiments

Compounds	Silver carp					Grass carp					Rainbow trout	Fathead minnow
Compounds	Muscle	Liver	Kidney	Blood	Bile	Muscle	Liver	Kidney	Blood	Bile	Rainbow trout	Fathead minnow
PFBA	1.5	2.0	1.9	2.3	1.7	1.7	2.1	2.2	2.7	1.7
PFPeA	1.0	1.8	1.8	1.8	2.5	1.1	1.4	1.9	1.8	2.0	-0.2
PFHxA	1.7	2.6	2.8	2.5	2.5	1.4	2.7	2.9	2.6	2.5	-0.2
PFHpA	1.8	2.6	2.8	2.8	2.7	2.1	2.6	2.8	2.8	2.8	-0.2
PFOA	1.3	1.7	1.7	2.1	2.0	1.2	1.8	1.9	2.4	2.1	0.6	0.3
PFNA	2.8	3.5	3.5	3.9	3.6	2.5	3.5	3.6	4.1	3.7
PFDA	3.4	4.1	4.2	4.5	3.6	3.6	4.4	4.5	5.1	3.9	2.7
PFUnDA											3.4
PFDoDA	3.8	5.1	5.2	5.5	4.2	4.1	5.3	5.5	6.0	4.4	4.3
PFTrDA	4.1	5.3	5.5	5.7	4.4	4.2	5.4	5.6	6.1	4.6
PFTeDA	3.8	5.0	5.1	5.4	4.6	3.7	4.9	5.1	5.6	4.8	4.4

The bioaccumulation factor (BAF) of PFAS measured in aquatic organisms was related to the carbon chain length^{[445⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓~456]}. As shown in Figure 22, similar to the BCF values measured indoors, long-chain perfluoroalkyl carboxylic acids (carbon chain length > 9) showed significant bioaccumulation potential. The BAF of perfluorosulfonic acids was positively correlated with the carbon chain length. For perfluoroalkyl carboxylic acids, the BAF increased with the increase in carbon chain length and then decreased. However, no decreasing trend was observed for perfluorosulfonic acids, possibly because the longest perfluorosulfonic acid measured so far has only 10 carbon atoms. Laboratory studies have shown that when the carbon chain length exceeds 12, the binding affinity of PFCAs with serum albumin and hepatic fatty acid-binding proteins slightly decreases. The compound's geometric shape folds to adapt to the hydrophobic binding pocket, leading to electrostatic repulsion, indicating that large molecular volume may cause spatial hindrance effects, limiting the ability of PFAS to bind with proteins^[457,458].

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图22 文献报道的PFCs中的log BAF与碳链长度的关系^{[445⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓~456]}

Fig. 22 Relationship between log BAF and carbon atom number for PFCs reported in literatures^{[445⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓~456]}

Currently, there are many reports on the biomagnification effect of PFAS in aquatic food chains. The main food chains (webs) include freshwater aquatic food chains^[459]; polar marine mammals, birds and fish food chains^{[460, 461]}; estuarine coastal aquatic food webs^[454] etc. The main compounds involved include perfluorocarboxylic acids with different carbon chain lengths, perfluorooctane sulfonate (PFOS), etc. Research results show that PFAS exhibits a food chain magnification effect in most food chains/webs. However, some individual compounds have not been observed to exhibit food chain magnification effects in certain food chains, such as the bioaccumulation factor (BMF) of PFOA in Lake Ontario lake trout^[459] and ringed seals and whales in Canada^[461] being less than 1; polar cod^{[460, 461]} does not show biological magnification for PFOS; BMF values of PFOA in birds and cod^[460] range from 0.04 to 0.3. Studies on the food chain magnification of PFAS in terrestrial food chains are relatively scarce. Muller et al.^[462] demonstrated that wolves and reindeer in northern Canada exhibit biological magnification capability for perfluoroacids and PFOS with 8-13 fluorinated carbons. Fremlin et al.^[463] used a method to standardize the concentration of perfluoroalkyl substances in the biological community relative to its biochemical composition (polar lipids, neutral lipids, total protein, albumin, and water) to study the trophic magnification potential of PFAS in terrestrial food webs composed of Cooper's hawks, several bird species, beetles, and earthworms in Vancouver, Canada. The results showed that most PFAS compounds exhibit biological magnification in food webs; however, PFOA, PFHxDA, and PFHxS do not show biological magnification effects; PFBS shows biological dilution effects.

Several studies have compared the bioaccumulation potential of PFAS and lipophilic POPs within the same organism or food chain (web). Tomy et al.^[460] found that the biomagnification factors (BMFs) of PFASs were higher than those of PBDEs for polar marine mammals (ringed seals and beluga whales), whereas cod showed much lower BMFs for PFASs than for PBDEs. Kelly et al.^[464] simultaneously investigated the biomagnification effects of PFASs, PBDEs (BDE 47 and BDE 99), PCBs (PCB153 and PCB180), Mirex, DDTs, and HCHs in the polar food web. The results showed that, except for PFOS, which had a similar biomagnification ability as PCBs in high trophic level wildlife (seals, whales, and polar bears), most other PFASs exhibited lower biomagnification abilities than PCBs, Mirex, and DDTs but higher than PBDEs and HCHs.

Two hypotheses currently exist regarding the enrichment of PFAS in organisms. The first hypothesis suggests that phospholipids in cell membranes play a decisive role in PFAS bioaccumulation. Phospholipids have high affinity for ionic molecules and thus serve as an important carrier in the PFAS bioaccumulation process. Numerous indoor exposure experiments and field monitoring experiments have shown a positive correlation between PFAS content in tissues and phospholipid content [sup>[465 ⇓~467], supporting this hypothesis. The second hypothesis posits that PFAS have high affinity with specific proteins in tissues such as serum albumin, liver fatty acid-binding protein, and organic anion transporters [sup>[468,469], making them more likely to accumulate in tissues or organs containing these proteins. Feng et al. [sup>[470]

The bioaccumulation potential of CPs in mussels labeled with ¹⁴C showed that the BAF values of MCCPs (C16, 34% Cl) and SCCPs (C12, 69% Cl) were 7000 and 13900, respectively^[471]. The log BCF values of five CPs in Daphnia magna were 6.7-7.0^[472]. There are many reports on the BAF values of CPs in field aquatic organisms (mollusks, crabs, shrimps, fish, Chinese soft-shelled turtles, and zooplankton)^[473⇓~475]. Most of the BAF values of SCCPs obtained from field monitoring were higher than 1000. The above results indicate that CPs have the potential for biological enrichment. Regarding the correlation between carbon chain length and bioaccumulation factor, existing results are inconsistent (Fig. 23). For example, in fish and zooplankton in Lake Ontario and Lake Michigan^[473], the BAF values showed a parabolic relationship with the increase of carbon chain length, with C12 as the inflection point, where the BAF values first decreased and then increased with the increase of carbon chain length (r²= 0.71, p<0.0001). However, there was no obvious relationship between the BAF values of reptiles, fish, and invertebrates near the discharge outlet of the Gaobeidian sewage treatment plant^[474] and in the Liaodong Bay sea area^[475] and the carbon chain length. Since most of the existing data focus on SCCPs, although some literature has reported the enrichment of medium-chain and long-chain CPs in organisms^{[472,476,477]}, the relationship between enrichment potential, carbon chain length, and chlorination degree is still not very clear.

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图23 文献报道的SCCPs中的log BAF与碳链长度的关系^[473⇓~475]

Fig. 23 Relationship between log BAF and carbon atom number for SCCPs reported in literatures^[473⇓~475]

Many studies have reported the biomagnification and trophic magnification of short-chain chlorinated paraffins (SCCPs) in aquatic and terrestrial food chains. For example, biomagnification has been observed in the aquatic food chain of the Pearl River estuary [478], the Indo-Pacific humpback dolphin food chain in Hong Kong waters [479], the water snake-fish food chain in Qingyuan ponds [379], and the red-spotted keelback snake-black-spotted frog food chain [480]; however, some food chains exhibit a trophic dilution effect for SCCPs, such as the aquatic food chain dominated by fish in an e-waste pond in Qingyuan [379], the grass-rabbit-hawk food chain in the Qinghai-Tibet Plateau [481], and the Arctic amphipod-gadoid food chain [482]. The relationship between biomagnification factor (BMF) and carbon chain length or degree of chlorination is inconsistent. Most food chains or food webs show that BMF increases with increasing carbon chain length or degree of chlorination [478, 483, 484]. However, some results indicate that the bioaccumulation potential is related to the degree of chlorination rather than the carbon chain length [379], while other results suggest that BMF decreases with increasing carbon chain length [483].

Recently, the reports on the bioaccumulation of MCCPs and LCCPs have been increasing. Du et al.^[476] analyzed the mass fractions of SCCPs, MCCPs, and LCCPs in nine wild animals in paddy fields in the Yangtze River Delta, China, which were 91–43000, 96–33000, and 14–10000 ng/g respectively. Except for owls and common cuckoos, most species mainly contained MCCPs (average 44%). Yuan et al.^[477] studied the bioaccumulation of SCCPs, MCCPs, and LCCPs in low trophic levels and high trophic levels in marine and terrestrial ecosystems in the Scandinavian region: although SCCPs and MCCPs dominated in most species, the concentrations of LCCPs in many terrestrial species were generally higher than those in marine species. Terrestrial raptors accumulated particularly high concentrations of LCCPs, with the highest concentration and dominance in peregrine falcons (accounting for 55% of total chlorinated paraffins). In the food chain of Rana temporaria and Elaphe climacophora, the BMF values of SCCPs, MCCPs, and LCCPs were 2.2, 1.8, and 1.7 respectively, showing a biomagnification effect, indicating that these CPs may also exhibit a biomagnification effect^[480].

The highly inconsistent results of studies on the biological enrichment of CPs are closely related to the complexity of their composition. Generally, CPs are considered as low-polarity lipophilic compounds, and they are mainly enriched in tissues with high fat content in organisms. However, their properties vary with different carbon chain lengths and degrees of chlorination. Therefore, the enrichment mechanisms and tissue distribution of congeners with different carbon chain lengths and degrees of chlorination may not be the same. A recent study using laying hens exposed to long-, medium-, and short-chain CPs with different degrees of chlorination showed that highly chlorinated LCCPs were primarily enriched in liver tissue, while low-chlorinated LCCPs were mainly distributed via blood circulation to different tissues and entered eggs mainly; whereas SCCPs and MCCPs were relatively uniformly distributed among various tissues^[485].Currently, there is a view that CPs may also have protein-binding characteristics^[486,487]. Thus, some biological enrichment rules of pure lipophilic compounds may not fully apply to CPs. The lipophilicity and protein-binding property of CPs are obviously related to both carbon chain length and degree of chlorination. However, due to the complexity of analyzing this type of compound, people have little understanding of how their lipophilicity and protein-binding property change with carbon chain length and degree of chlorination. This also limits the understanding of their biological enrichment and bioaccumulation amplification patterns.

In summary, PFAS and CPs, as a new class of contaminants different from traditional lipophilic aromatic compounds, have unique bioaccumulation mechanisms. At present, human understanding of the bioaccumulation patterns of these two types of substances is far less than that of traditional lipophilic aromatic compounds, and more research is needed to understand the bioaccumulation, tissue distribution, and metabolic processes of these two types of compounds in different organisms.

5 New Contaminants' Environmental Migration and Transformation, Source-Sink Mechanisms

5.1 Multimedia Processes and Fate of New Contaminants in Aquatic Environments

Approximately 30% of renewable freshwater resources worldwide are used for industrial production and human activities. Industrial and domestic sewage under high-intensity human activity backgrounds usually contains tens of thousands of toxic chemical pollutants, mainly including industrial raw materials, pesticides, drugs, personal care products, and other types of new chemicals^[237]. The Ministry of Ecology and Environment of China has registered more than 45,000 chemical substances in production and use, many of which have carcinogenic, teratogenic effects, as well as genetic and developmental toxicity, posing potential hazards to aquatic organisms and human health. Sewage discharge is the main pathway for chemical substances to enter natural water bodies. Due to the fact that most chemical pollutants are not yet included in national relevant water quality standards, the treatment effect of existing sewage treatment processes on them is poor, resulting in different degrees of chemical pollution in major river basins in China^[488]. In addition, due to the close hydraulic connection between surface water and groundwater, an increasing number of chemical pollutants have also been detected in groundwater^[489].

5.1.1 Sources of New Contaminants in Water

Due to the widespread use of PPCPs, EDCs, and PFAS in family, hospital, and industrial sectors, these emerging contaminants have appeared extensively in the aquatic environment. Taking pharmaceuticals as an example, not only is the medical industry dependent on drugs, but antibiotics and other drugs are also widely used in aquaculture and animal husbandry^[490,491]. These chemicals can be excreted through feces or urine, entering the sewage pipeline system, and eventually discharged into WWTPs. Wastewater from different sources such as family, industry, and hospital contains tens of thousands of emerging contaminants, and conventional wastewater treatment technologies do not perform well in removing some of these new pollutants. It is worth noting that persistent, mobile, and toxic substances (PMTs) can "penetrate" the wastewater treatment process and enter natural water bodies^[492]. Based on this new understanding, in October 2020, the European Commission released the "Chemical Strategy for Sustainability Towards a Toxic-Free Environment" with a vision for 2050, proposing to include PMT in the EU REACH list and implement classification and labeling systems^[493].

Despite the emergence of various advanced sewage treatment technologies, due to high costs and difficulties in large-scale operation, sewage discharge remains the main source of various new pollutants in water environments. Additionally, new pollutants also enter the water environment through multiple pathways, including surface runoff, atmospheric deposition, urban surface runoff, agricultural runoff and other non-point sources, as well as point sources such as sewage pipeline leakage, overflow from sewage treatment plants, and direct discharge of rural sewage^[494]. Groundwater accounts for about 1/4 of the total accessible fresh water resources on Earth and is one of the important fresh water resources for humans. In arid and semi-arid areas, it even becomes the only water supply source. With the rapid process of industrialization and urbanization, groundwater quality degradation has become a global issue. Recent studies have shown that the composite pollution of groundwater in our country is severe, with new pollutants such as pharmaceuticals and personal care products, perfluorinated and polyfluorinated compounds, pesticides, etc., all detected^[495,496].

5.1.2 Migration and Distribution of Emerging Contaminants in Water

New pollutants enter the surface runoff and form a "mixed zone of sewage and receiving river" under the coupling action of physical, chemical, and biological natural processes. Observational studies have shown that conventional water quality indicators such as biochemical oxygen demand, dissolved oxygen, and ammonia nitrogen near the sewage discharge outlet downstream for several kilometers have approached those of natural water bodies, but the concentrations of some new pollutants remain high, forming an important "diffusion zone" for conveying chemical pollutants to the downstream and groundwater^[497]. During the process of pollution transport and migration, hydrodynamic conditions, adsorption behavior, and multi-phase distribution processes directly affect the spatiotemporal distribution of new pollutants and their migration between media^[498].

5.1.2.1 Hydrodynamic Conditions

The migration of emerging contaminants in surface water and groundwater is mainly controlled by physical processes such as advection, diffusion, and dispersion. Among them, advection is the main process for contaminant transport, controlling long-distance transport in the flow direction; diffusion is driven by concentration gradients, causing the slow movement of contaminants from high concentration to low concentration; dispersion consists of molecular diffusion and mechanical dispersion processes, transporting contaminants in both the flow direction and the vertical direction^[499]. The hydrodynamics of emerging contaminant transport in surface water are influenced by various factors, such as terrain, riverbed roughness, and confluence of tributaries. In low-velocity and slow-flowing water bodies, emerging contaminants may accumulate. For example, in areas passing through cities, due to characteristics such as human activities, sluggish flow, and weak mixing, new contaminants may be retained and accumulated during passage^[500]. The distribution and migration of emerging contaminants in groundwater are mainly affected by adsorption/desorption, pore diffusion, and dispersion mixing processes under different hydrogeological conditions. In low-velocity groundwater environments, pore diffusion in porous media is the dominant process controlling contaminant migration. The diffusion coefficient of emerging contaminants in water is an important parameter for predicting their diffusion and dispersion behavior, but there is currently a lack of relevant data. Future research urgently needs to conduct diffusion experiments to provide important basic parameters for predicting the migration behavior of emerging contaminants in the aquatic environment.

5.1.2.2 Adsorption and Allocation

Adsorption is a key process that influences the migration of emerging contaminants. In aquatic environments, adsorption of emerging contaminants mainly occurs at the water-sediment interface. The organic carbon-based adsorption coefficient (K_oc) and the octanol-water partition coefficient (K_ow) are two important distribution coefficients, primarily used to evaluate the strength of contaminant adsorption and aqueous phase mobility. Specifically, when the pH value is between 4 and 9, compounds with a log K_oc value below 4.0 or 3.0 are respectively classified as "mobile" and "very mobile." If experimental K_oc values for a compound cannot be obtained, for neutral and non-polar molecules, the smallest log K_ow value within the pH range of 4 to 9 is used as an alternative indicator; for ionic or ionizable substances, the pH-corrected octanol-water partition coefficient (log D_ow) is used as an alternative indicator. For neutral molecules, if their minimum log K_ow value at pH 4 to 9 is ≤4.5, it indicates that the substance may have mobility [501].

The solid-water distribution coefficient (K_d) is commonly used to evaluate the migration potential of pollutants in the underground environment. After reaching adsorption equilibrium, adsorption isotherms are often used to quantitatively analyze the distribution of pollutants at the solid-water interface. Common adsorption isotherm equations include linear isotherms, Freundlich isotherms, and Langmuir isotherms. Groundwater is a porous medium composed of minerals such as quartz sand, carbonates, clay, etc. Studies have shown that the physicochemical properties of the solid medium (mineral content and type, etc.) can influence the migration of various organic pollutants. For example, PFOA has a higher retention rate in limestone than in quartz sand, because the surface of limestone has fewer negative charges and a larger surface area^[502]. In addition, the adsorption behavior of new contaminants in groundwater is also related to water chemical factors such as pH value, ionic strength, and ion types. Furthermore, the physicochemical properties of new contaminants, including functional group types and carbon chain lengths, will also affect the adsorption behavior at the solid-water interface. The study of adsorption behavior of ionic new contaminants compared to relatively neutral new contaminants will be a difficult and hot topic for future work^{[502, 503]}.

5.1.3 Biological and Abiotic Transformation of Emerging Contaminants in Water

The natural transformation pathways of new contaminants mainly include processes such as biodegradation, photodegradation, and hydrolysis. Microorganisms can decompose new contaminants through hydrolysis, redox, and mineralization, during which new contaminants can serve as carbon sources and nitrogen sources for microbial growth. In surface water, microbial degradation usually occurs at the water-sediment interface. Gilevska et al. ^[504] combined passive sampling technology with single-isotope analysis to in situ trace the transformation process of acetochlor at the water-sediment interface. In groundwater, the degradation of new contaminants is comprehensively influenced by factors such as temperature, redox conditions, and nutrient concentrations. Studies have shown that the biodegradation of low-concentration new contaminants is slow, allowing them to migrate over long distances. Natural light-induced pollutant transformation is one of the important pathways for the degradation of new contaminants in surface water. The photodegradation of new contaminants in water includes direct photodegradation and indirect photodegradation. Direct photodegradation refers to the chemical bond-breaking reaction caused by pollutants absorbing sunlight, generating different photodegradation products. Indirect photodegradation occurs when natural photosensitizers (such as nitrate, nitrite, and dissolved organic matter) absorb sunlight, producing reactive species like hydroxyl radicals, which then react with the pollutants ^[505]. The photodegradation rate and transformation pathways of new contaminants in natural water bodies are affected by environmental factors such as water chemistry, water temperature, water depth, and sunlight duration ^[506].

The transformation of new contaminants in the aquatic environment can produce different metabolites, and the toxicity of some metabolites even exceeds that of the parent compounds. Taking the natural photodegradation of triclosan as an example, the photodegradation products include chlorophenols and chlorodioxins, etc., whose toxicity and hazards have exceeded those of the parent compound^[507].The metabolite dichlorobenzamide of the pesticide dichlorobenzonitrile has weaker adsorption and a longer half-life. Compared with the parent compound, it shows stronger migration ability in groundwater, significantly affecting the regional groundwater quality^[508].Based on this new understanding, such pesticides have been banned in Denmark and several other European countries. In addition, specific biodegradation products of new contaminants may also limit microbial growth, thereby affecting the degradation mechanism. Therefore, to fully understand the natural attenuation of new contaminants in water, the environmental hazard characteristics of their transformation products should be examined, including persistence, bioaccumulation, aqueous phase migration, and toxicity, etc., so as to accurately assess the aquatic environmental risk of new contaminants.

5.2 Migration and Transformation of New Pollutants in the Soil-Plant System

PFAS, OPFRs, and MPs, as new contaminants, can enter the soil through various ways such as direct discharge, atmospheric dry and wet deposition, and sewage irrigation. Once in the soil, they adsorb, age, and accumulate, making the soil an important reservoir for new contaminants in the environment. As primary producers, plants are the most crucial component of terrestrial ecosystems. They can alter the adsorption-desorption process of new contaminants in the soil, enrich and metabolize them through absorption and transformation, and promote the biotic and abiotic degradation of new contaminants via root exudation. The soil-plant system is an important factor influencing the fate of new contaminants in the environment. The migration and transformation of new contaminants in the soil-plant system are shown in Figure 24.

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图24 新污染物在土壤-植物系统中迁移转化示意图

Fig. 24 Schematic diagram of the migration and transformation of emerging contaminants in soil-plant systems

5.2.1 Adsorption and Desorption of Emerging Contaminants in the Soil-Plant System

In soil-plant systems, new contaminants that are tightly bound to the soil have low activity, poor mobility, and poor bioavailability, having little impact on the environment. The biologically available forms of new contaminants freely existing in the soil are active in the environment, with strong migration ability. They are key components that can be absorbed, transported, and metabolized by plants, and their contents are determined by the adsorption-desorption capacity between soil and new contaminants, which is currently a research hotspot.

The adsorption-desorption process between soil and new pollutants is controlled by the properties of new pollutants and the physicochemical characteristics of soil. Among these, the chemical structure of new pollutants and the composition of soil organic matter (SOM) play important roles in this process. For example, the hydrophobic groups in new pollutants interact with hydrophobic regions such as aliphatic or aromatic functional groups in SOM to form stable structures^[509]. The oxygen atoms on carboxyl, hydroxyl, sulfonic acid, and other groups of new pollutants can interact with hydroxyl groups in SOM to form hydrogen bonds^[510]. π-π stacking interactions can also occur between new pollutants and SOM, enhancing their stability in the soil^[511]. Electrostatic forces are also an important mode of interaction between new pollutants and SOM. New pollutants like PFAS, which have extremely low dissociation constants, often exist as anions in soil, experiencing electrostatic repulsion from negative charge groups such as carboxyl and phenolic hydroxyl in SOM, but electrostatic attraction from positive charge groups such as amino and amide groups^[510,512]. Conversely, some cationic pollutants exhibit opposite effects. Additionally, after carboxyl and sulfonic acid groups on new pollutants are ionized, they can form ion-dipole bonds with certain functional groups in SOM^[513].

Similar to SOM, soil minerals can also bind with new contaminants through hydrophobic interactions, electrostatic interactions, and other ways. Moreover, the carboxyl, sulfonate, and other functional groups of new contaminants can undergo ligand exchange reactions with the hydroxyl groups on soil mineral surfaces, while SOM can competitively influence the ligand exchange process of new contaminants by occupying the adsorption sites on mineral surfaces^[514]. Metal ions such as Mg²⁺ and Ca²⁺ in soil solution can form SOM-metal-PFAS cation bridges with SOM and PFAS pollutants, thereby reducing the water solubility of the contaminants^[515]. The size of soil pores is also a key factor affecting the adsorption and desorption processes of new contaminants. Contaminants adsorbed in micropores have strong adhesion to the soil and weak desorption ability, whereas contaminants adsorbed in mesopores or macropores have relatively faster desorption rates^[516]. In addition to the physicochemical properties of soil, the cellular residues from dead organisms in soil can also adsorb new contaminants through electrostatic interactions and hydrophobic interactions^[517].

Plant residues are an important source of SOM in soil and can affect the composition and abundance of SOM, as well as alter the adsorption-desorption processes between soil and new contaminants. The soluble organic matter secreted by roots can also regulate soil pH, the speciation of new contaminants, the number of competitive adsorption sites on soil minerals, and reshape the binding ability between soil and new contaminants. Previous studies have shown that oxalate secreted by roots can promote the dissolution of metal ions, iron and aluminum oxides, and organic matter in the soil to form oxalate-metal complexes, thereby reducing the adsorption retention of soil components for new contaminants^[518]. In addition, the surface charge of plant roots can influence the electrostatic force between soil and new contaminants, and the changes in soil porosity caused by root development can also alter the adhesion ability of new contaminants in the soil.

5.2.2 Uptake of Emerging Contaminants by Plants

At present, the research on the absorption mechanism of plants for new pollutants has been relatively thorough. There are mainly two ways for new pollutants to enter plants: the apoplastic pathway and the symplastic pathway^[519]. The apoplastic pathway refers to the transmission of new pollutants through the gaps between plant cells or the cell wall, while the symplastic pathway is the process in which new pollutants move from the cytoplasm of one cell through plasmodesmata into another cell. After plants absorb new pollutants from the soil, they enter the internal part of the plant via the symplastic or apoplastic pathway, and can eventually be loaded into xylem or phloem, transferring and distributing within the plant body. Among them, due to no need for transmembrane processes, the apoplastic pathway has a significantly higher transfer speed than the symplastic pathway that requires transmembrane transport^[520]. The transmembrane process of new pollutants can be divided into passive transport and active transport depending on whether energy consumption is required. Passive transport does not require energy and mainly relies on the concentration difference of new pollutants on both sides of the cell membrane to drive. Passive transport can be divided into two types: 1) new pollutants diffuse directly through the phospholipid bilayer; 2) new pollutants diffuse with the assistance of carrier proteins. Active transport requires the consumption of energy produced by plant metabolism and uses transport proteins to complete transmembrane transport. Protein channels also exist on the plant cell membrane, through which new pollutants can be transported, such as water channels and anion channels^[521,522]. Transport processes that require protein assistance all have saturation effects, and this process conforms to the Michaelis-Menten equation. Although the possible pathways of new pollutant plant absorption and transportation have become very clear, it remains extremely difficult to analyze the absorption and transportation mechanisms of specific new pollutants in specific plants and the contribution rate of single mechanisms. Researchers^[521,523] analyzed possible mechanisms by adding metabolic inhibitors, channel protein inhibitors, data fitting Michaelis-Menten equations, and other methods, finding that the plant absorption process of new pollutants involves coupled multi-mechanisms. In addition, the possibility of transporter proteins participating in plant absorption and transportation can be revealed through chiral characteristics^[524]. However, inhibitors affect normal physiological metabolism of plants, Michaelis-Menten equation fitting can only explain the saturation effect, and chiral characteristics apply to a limited range of new pollutants. Therefore, there is an urgent need to develop non-destructive new technologies to analyze the plant absorption mechanism of new pollutants and their relative contribution rates.

The absorption and transport of new contaminants in plants are influenced by factors such as contaminant concentration, structure, plant species, and growth stage. Studies have shown that as the concentration of new contaminants increases, the concentration of contaminants within the plant also rises^[525]. The reasons are: 1) the increase in contaminant concentration in the soil effectively increases the concentration gradient between the inside and outside of plant root cells, promoting diffusion; 2) high concentrations of contaminants enhance the utilization efficiency of transport proteins. However, excessively high concentrations of contaminants can inhibit plant growth, and transport proteins have a saturation effect, so the promotion effect of concentration has an upper limit. The structure of new contaminants not only determines lgK_ow, affecting their entry efficiency into plants^[526], but can also form special electron cloud structures, regulating plant absorption ability through interactions with surface charges and groups on plant roots^[527]. It can also affect contaminant spatial dimensions, changing the difficulty of entering and transporting within plants^[528]. Plant species determine the root tissue structure, with significant differences in the types and quantities of transport channel proteins related to the absorption of new contaminants on the cell membrane surface. All these factors can alter the plant's ability to absorb and transport new contaminants. The number of leaf stomata and growth status vary among different plant species, leading to distinct transpiration capabilities, while transpiration pull is an important driving force for the passive absorption of new contaminants and their transport in the xylem^[529]. Plant species are also closely related to protein and phospholipid content in various tissues, influencing the distribution of new contaminants within the plant: tissues with high protein content preferentially accumulate hydrophilic new contaminants, while tissues with high lipid content tend to accumulate hydrophobic new contaminants^[530]. Additionally, there are significant differences in the structure and integrity of the Casparian strip in different plant species and at different growth stages, and the Casparian strip is one of the most critical plant factors controlling the absorption of new contaminants^[531].

Unlike general emerging pollutants, MPs are large in size, diverse in type, and complex in structure. Therefore, how to characterize their absorption and transport processes in plants is a great challenge. In recent years, the application of MP fluorescence labeling technology has been widely used. With the help of this technology, researchers have successfully proved that small-sized MPs can be absorbed by plants and transported to the aboveground parts, medium-sized MPs are mainly enriched in roots, while large-sized MPs cannot enter plants^[532]. However, fluorescence labeling has problems such as interference from spontaneous fluorescence in biological tissues and leakage of dye molecules. The rare earth metal organic fluorescent complex doping technique overcomes these problems^[533], and based on this technology, it has been confirmed that MPs are mainly aggregated in the intercellular spaces of plant cells and are transported from roots to leaves through the apoplastic pathway and vascular bundles^[534]. Previous studies have shown that the apoplastic pathway is the key pathway for plants to absorb MPs, and MPs can be transported upward along the cell wall of the vascular bundle cells through incomplete Casparian strips^[535]. However, the latest research has found that the symplastic pathway can also participate in the absorption of MPs. Evidence shows that algae and tobacco cells can absorb MPs through endocytosis, and water channels can also enhance the absorption of MPs^[535].

5.2.3 New Contaminants' Plant Transformation

Once new pollutants enter the plant body, they can not only accumulate and stabilize but also be metabolized by enzymes within the plant body. The metabolic process of plants towards new pollutants can be divided into two major categories: one is the direct degradation and mineralization of new pollutants; the other is the modification of new pollutants to increase their water solubility, which can promote contact with other enzyme classes, improve detoxification efficiency, and facilitate their transfer to regions such as vacuoles for fixation. Plants contain a variety of enzymes that can participate in pollutant degradation, such as cytochrome P450, peroxidase, laccase, acid phosphatase, etc., which degrade new pollutants through oxygenation, hydrolysis, and other methods. Among them, cytochrome P450^[536], laccase^[537], etc., have large structural variations and low substrate specificity in different plants, possessing multiple organic pollutant degradation capabilities and are widely studied; whereas enzymes such as acid phosphatase^[538] prefer to hydrolyze specific pollutants with single substrates, receiving less attention in research. Glutathione S-transferases, glucosyltransferases, etc., can connect glutathione or glucose to pollutants, increasing their polarity, helping pollutants to be transferred to vacuoles for fixation or further degradation^[539].

5.2.4 Rhizosphere Degradation of Emerging Contaminants

The rhizosphere is a special microdomain of the soil-plant system, with abundant microbial populations and active metabolism, making it a hotspot for the microbial metabolism of new pollutants. A large number of studies have shown that sulfonamides [540], DOP [541], OPEs [542], and other new pollutants undergo efficient degradation and transformation in the rhizosphere. The reasons are as follows: 1) Plants can improve soil aeration and water retention through root systems, promote the formation of soil aggregate structures, providing microhabitats and good growth environments for microorganisms; 2) Large amounts of root exudates can provide carbon sources, nitrogen sources, and other nutrients needed for microbial growth, enhancing the metabolic activity and abundance of functional microorganisms, and alleviating survival pressures caused by external environmental changes [543, 544]; 3) Specific compounds secreted by plants can enrich specific degrading microorganisms to colonize in the rhizosphere [545]; 4) Some exudates alter microbial community diversity, enhance network connectivity among microorganisms, and promote interactions between degrading microorganisms and other microorganisms [546, 547]; 5) Plant-secreted monosaccharides increase the number of degrading microorganisms and promote the transfer of degradative plasmids between different microorganisms [548]; 6) Plant-secreted organic acids reduce oxidative stress on microorganisms, enhancing microbial activity [548]; 7) Structural analogs of pollutants in root exudates induce the expression of microbial degradation genes [549].

Unlike degradable pollutants, PFAS possess high chemical stability and are difficult to be degraded by microorganisms. Currently, there is much controversy regarding the microbial degradation ability of traditional fluorinated compounds such as PFOA and PFOS, and some studies suggest that they cannot be degraded by microorganisms^[550]. However, other research reports have shown that microorganisms can achieve defluorination and degradation^[551]. In comparison, the microbial transformation of precursor compounds of fluorinated substances has been widely recognized, and under microbial action, multiple PFAS can be generated^[552,553]. Similar to PFAS, non-biodegradable MPs also face great difficulty in microbial degradation. Although Oda Kohei's research team from Japan successfully isolated a microorganism with polyethylene terephthalate resin degradation ability in 2016^[554], due to the complex structure and diverse types of MPs, they are generally considered difficult to be degraded by microorganisms, and microorganisms mainly participate in the aging process of MPs^[555]. Unfortunately, no research has yet explored the role of plants in the microbial degradation and transformation processes of PFAS and MPs. The diversity of plant rhizosphere effects may play a role in the metabolic processes of these recalcitrant emerging contaminants, and relevant research needs to be carried out.

5.2.5 Non-biological Degradation Transformation of New Contaminants Mediated by Plants

Mineral components in the soil can reduce halogenated contaminants under anaerobic conditions and participate in free radical generation under aerobic conditions to promote the degradation of new contaminants. Plants have oxygen-releasing ability, which can carry oxygen to the roots, leading to an increase in the redox potential in the rhizosphere and forming an aerobic/anaerobic interface to react with mineral components, thereby accelerating the reduction of pollutants. For example, rice roots release oxygen during daytime photosynthesis, interacting with Fe/Mn oxides to produce ·OH^[556]; ryegrass produces root exudates that stimulate fungal and bacterial growth, regulate the Fe(III)/Fe(II) cycle, and generate O₂·-, while water-soluble phenols in root exudates can also reduce O₂to produce O₂·-. O₂·- undergoes dismutation or hydrolysis to form H₂O₂, which then reacts with Fe(II) via a Fenton-like reaction to produce ·OH^[557]. Moreover, root exudates can act as electron shuttles to assist in electron transfer during the degradation of new contaminants, enhancing the degradation and transformation capacity of pollutants^[558]. It is evident that plants can mediate various abiotic processes in the soil through multiple mechanisms to alter the abiotic degradation and transformation capacity of new contaminants.

After years of research, we have gained preliminary knowledge about the migration and transformation processes of new contaminants in soil-plant systems. However, due to the complex structures and numerous types of artificially synthesized new contaminants, there are still many challenges in the research: 1) Current studies are often based on specific contaminants and specific plants, making it difficult to form general conclusions. How to predict the behaviors of different types of new contaminants in soil-plant systems by integrating plant genome characteristics, soil environmental factors, and the intrinsic properties of new contaminants, and quantify the interaction relationships between soil-plant systems and new contaminants, remains to be explored; 2) The complex types of new contaminants in natural soil-plant systems make it difficult to distinguish parent compounds from metabolites, and their high diversity increases the difficulty of analysis. The coupled occurrence of migration and transformation processes of new contaminants in the system leads to biased conclusions regarding the behavior patterns of in-situ environmental new contaminants; 3) When multiple new contaminants coexist, various interactions may occur, and their interactions are not easy to quantify, increasing the difficulty of analyzing the migration and transformation rules of new contaminants in soil-plant systems. In summary, the study of the environmental fate of new contaminants in the multi-faceted and complex soil-plant system is just beginning.

5.3 Atmospheric Processes of Emerging Contaminants

New pollutants are mainly released into the atmosphere through primary and secondary emissions, and atmospheric environmental processes are an important link in their biogeochemical cycle. These processes typically include four types: gas-particle partitioning of pollutants, atmospheric migration and transformation, dry and wet deposition, etc. At present, relevant studies are mainly carried out through two technical routes: fixed site observation and numerical model simulation. In recent years, global scholars have focused their research on large cities and background areas regarding the atmospheric environmental processes of new pollutants. Meanwhile, breakthroughs have also been made in basic theories, analytical techniques, and numerical models.

5.3.1 Gas-particle partitioning

New contaminants exhibit significant differences in their gaseous and particulate components during processes such as dry and wet deposition, atmospheric chemical reactions, and pathways into the human body, indicating that gas-particle partitioning significantly influences the atmospheric environmental processes of new contaminants. The traditional thermodynamic equilibrium theory suggests that typical new contaminants, such as POPs, reach thermal equilibrium partitioning between the gas phase and particle phase, with the partitioning coefficient being linearly related to the octanol-water partition coefficient (lg K_oa) or the saturated vapor pressure (P_L). According to this, less volatile POPs like BDE-209 are mainly present on particles and undergo long-range atmospheric migration via particles^[559,560].However, Li et al.^[561] found through analysis of global observational data that thermodynamic equilibrium gas-particle partitioning of POPs is only a special case in real environments, and proposed a steady-state model to describe the gas-particle partitioning behavior of semi-volatile organic pollutants (SVOCs) in the atmosphere. This model indicates that even for extremely low volatility POPs like BDE-209, they are primarily transported over long distances in the atmosphere in gaseous form rather than reaching the Arctic via atmospheric particles as commonly believed^[562]. Subsequently, Li et al.^[55,563] applied the steady-state gas-particle partitioning theory to third and fourth-order multimedia fugacity models, using polybrominated diphenyl ethers and polycyclic aromatic hydrocarbons as examples, to simulate the gas-particle partitioning behavior and spatiotemporal distribution characteristics of typical new contaminants in the atmosphere^[564,565]. They discovered that the proportion of particles at the time of pollutant emission significantly affects its gas-particle partitioning behavior.

Some typical new contaminants (such as OPFRs with relatively high polarity) exhibit super-enrichment phenomena in particulate matter, deviating from the predictions of the classical gas-particle partitioning model^[566]. Due to the lack of systematic observations, the reasons for this phenomenon are still inconclusive^[567]. Previous studies have mainly focused on the gas-particle partitioning behavior of nonpolar or weakly polar SVOCs (e.g., PBDEs and PAHs)^[568,569], with little attention paid to polar SVOCs. Since 1990, it has been generally believed that the adsorption behavior of particles for nonpolar or weakly polar SVOCs is dominated by organic matter (OM)^[570]. Subsequently, elemental carbon (EC) and mineral surface adsorption were also found to play a role. Arp et al.^[571] divided particles into different polar adsorption phases and proposed a multiparametric linear free energy relationship model (pp-LFERs), considering multiple possible adsorption phases and molecular interactions between compounds^[572], but did not address this special phenomenon of polar OFPRs. Meanwhile, environmental humidity (RH) promotes the enrichment of some OFPRs in particulate matter. Studies show that the relative proportion of OFPRs in the particle phase/gas phase correlates differently with RH depending on the polarity and water solubility of the compounds^[566]. However, the classical theory of gas-particle partitioning models does not support the view that increased humidity promotes the distribution of organic pollutants in the particulate state^[573].

Most of the previous studies on gas-particle partitioning have focused on total suspended particles (TSP), with less attention paid to PM_2.5. However, there are significant differences in origin and composition between PM_2.5 and TSP, which are particularly evident in the high secondary component feature of urban atmospheric PM_2.5. The newly formed secondary components are mostly polar compounds with strong hygroscopicity. Currently, fine particulate matter in urban air in China is mainly composed of secondary aerosols (SA), which can account for more than 50% of the mass of PM_2.5 in some cases. SA includes two major categories: secondary organic aerosol (SOA) and secondary inorganic aerosol (SIA). Simply applying the gas-particle partitioning models based on TSP to PM_2.5 will undoubtedly lead to significant recognition deviations. Moreover, SA is often polar/hygroscopic due to its new formation. Additionally, some smog chamber simulation experiments have also confirmed that compounds such as PAHs can be "encapsulated" by newly formed SOA, making it difficult to release them back into the atmosphere again^[575,576]. In major cities in China, especially southern cities, SOA has accounted for about 50% of PM_2.5^[574].

5.3.2 Atmospheric Transformation

The main removal pathway of new contaminants in the atmosphere is the reaction with hydroxyl radicals and chemical transformation^[577,578]. Since these atmospheric chemical transformation products all contain polar groups and have significantly reduced volatility, they will inevitably become part of SOA in the atmosphere through a series of atmospheric physical and chemical processes such as hygroscopicity, condensation, collision, and heterogeneous reactions^[579]. For example, volatile methylsiloxanes (VMS) can react with ·OH and ·Cl radicals in the atmosphere to form low-volatility silanols and formates, which are components of SOA on PM_2.5^[580]; FTOH can react with radicals in the atmosphere to produce highly water-soluble PFCAs^[581], which then undergo atmospheric deposition and affect the surface environmental quality in remote areas^[582].

At present, there is limited knowledge about the reaction rates, transformation mechanisms, and oxidation products of new contaminants in real atmospheric environments^[583]. Quantum chemical calculations indicate that HCB can react in the ambient air to generate relatively stable POPs-like compounds PCP, and PCP also tends to bind into fine particulate matter; PCBs have been studied more extensively, which can react in the atmosphere to form OH-PCBs, and then react with NO₂ to produce nitro-biphenyls with SOA characteristics^[585]. Currently, research on the atmospheric transformation of new contaminants is mainly conducted through smog chamber and flow tube simulation experiments under laboratory-controlled conditions. Smog chamber simulations confirm that FTOH can be converted into PFCAs^[586], and PCBs can be converted into OH-PCBs^[587]. Based on ·OH oxidation experiments using a flow tube, it has been confirmed that OPFRs and liquid crystal monomers (LCMs) existing in particulate form undergo atmospheric transformation. The calculation results of heterogeneous oxidation rates show that their persistence in the atmosphere is much higher than that based solely on gas-phase calculations, and both have the potential for long-range transport^[588⇓~590].

The toxicity of transformation products of new contaminants in the atmosphere has drawn high attention from academia^[591]. Liu et al.^[591] evaluated the atmospheric environmental risks of OPFRs using a research paradigm that combines laboratory studies, field observations, and model simulations. They found that OPFRs and their transformation products are widely distributed in the ambient air of 18 large cities worldwide. More importantly, the toxicity of individual OPFRs' transformation products is stronger than that of the parent compounds, and their persistence is orders of magnitude higher. Therefore, there is an urgent need to conduct risk assessments on transformation products of new contaminants^{[591, 592]}.

5.3.3 Long-range transport in the atmosphere

Atmosphere is the most active medium for the diffusion and migration of new pollutants and also the main carrier for the occurrence of new pollutant migration. During the migration process, continuous deposition and re-volatilization migration occur, which is also known as the "grasshopper effect". On a global scale, temperature and global atmospheric circulation are the main driving forces for the transfer of new pollutants. The long-distance atmospheric migration ability of new pollutants is closely related to their own physicochemical properties. The research on the atmospheric migration of new pollutants is mainly carried out through comprehensive investigation of source area material composition and its tracers, long-term or dense observations at positioning stations, and the development of applicable numerical models. The positioning observation of long-distance atmospheric migration of new pollutants is mainly conducted at background points far away from human activities. Due to the strong mobility of the atmosphere, the composition characteristics and changing trends of new pollutants at background points usually have good consistency with the emissions in the source areas. Background point observations can collect pollution event information and combine with airflow trajectory analysis, source area pollutant composition spectrum, and corresponding isomer ratios for source location identification. This method reacts quickly, has large information volume, high resolution, and is one of the important means to study the long-distance atmospheric migration of new pollutants.

Long-term monitoring at background stations such as the North and South Poles and the Qinghai-Tibet Plateau has provided important evidence for confirming the long-range transport of new pollutants. In China’s Great Wall Station in Antarctica, OPFRs increased by a factor of 2 between 2014 and 2018, demonstrating their potential for long-distance transport^[593]; polybrominated diphenyl ethers (PBDEs) showed a significant downward trend between 2011 and 2019, while new brominated flame retardants showed a significant upward trend, with concentrations in the same samples far exceeding those of PBDEs^[594]. The Arctic Monitoring and Assessment Programme (AMAP), which was launched in 1991, has been running steadily for more than 30 years. AMAP's atmospheric observations over the past 20 years have shown that PAH concentrations increase with latitude, mainly due to forest fire emissions. Since global warming promotes secondary volatilization of PAHs, the concentration of PAHs in the Arctic atmosphere has not decreased as expected^[595]. Researchers^{[596, 597]} observed that POPs in different environmental media on the Qinghai-Tibet Plateau are mainly from inputs from South Asia and Europe. Microplastics have also been widely detected in remote background sites far from human activities, such as the Pyrenees in France^[598] and the Qinghai-Tibet Plateau^[599], and can reach the free troposphere for long-range atmospheric transport^[598].

Many new pollutants, such as POPs, have the characteristic of persistence. The source information retained during their long-distance transport may also be used to trace atmospheric circulation processes. Wang et al.^[600] obtained the spatial distribution characteristics and molecular fingerprint spectra of perfluoroalkyl acids (PFAAs) in plateau snow and ice based on three consecutive years of observation, and defined the influence ranges of the monsoon and westerly wind transport.^[601] Based on the characteristic molecular fingerprints of PFAAs, the plateau can be clearly divided into three zones: the monsoon zone (short-chain PFAAs as characteristic pollutants), the transition zone (complex pollution characteristics), and the westerly wind zone (long-chain PFAAs as characteristic pollutants). These correspond to the three climatic modes of Indian monsoon-westerly interaction (monsoon mode, transition mode, and westerly mode). This study confirms that POPs themselves can be used as tracers for surface process research.

Based on observations, we can also trace source regions and estimate emissions through numerical models, as well as evaluate the factors affecting the atmospheric transport of new pollutants. Using potential source contribution analysis, it can be determined that various POPs in the Tengchong background point atmosphere in China can be traced back to the Pearl River Delta region of China and the Indian subcontinent^[602]; the POPs in the Ningbo background point atmosphere in the eastern region mainly originate from northern China and Japan^[603]. The "top-down" method, which combines pollutant field observation and model inversion, is increasingly being used to estimate emissions of pollutants in regional atmospheres. This technical approach has already been applied to the estimation of global and regional-scale POPs, VOCs, and greenhouse gas emissions and source tracing^[604⇓~606]. In the field of POPs research, Martin Scheringer's team at ETH Zurich, based on long-term atmospheric observation data from the Zeppelin station in the Arctic, used the FLEXPART model combined with cluster analysis to trace the main source areas of typical POPs such as PCBs and methyl siloxanes in this area^[607,608]; Tian et al.^[609] coupled regional observations with reverse numerical simulation models in the Pearl River Delta to determine the regional atmospheric emission flux of OCPs, confirming that current OCP emissions are less than 1‰ of historical usage, indicating that control measures have been very effective. Yang et al.^[610] recently estimated industrial source emissions of HCBD in industrial parks in Jinan using concentration differences of HCBD upwind and downwind of source areas, with the highest emission reaching 90 t/a.

5.3.4 Dry-wet deposition

The removal of new pollutants, especially their transformation products, through atmospheric deposition marks the final chapter in the atmospheric fate of new pollutants. Both dry and wet deposition play crucial roles in the atmospheric clearance of pollutants at regional and global scales, linking the atmosphere with surface environments and representing trans-medium substance migration. Transformation products of new pollutants often possess polar functional groups that increase their solubility and may bind to PM_2.5 particles. Therefore, atmospheric deposition mechanisms such as precipitation and dustfall significantly shape their ultimate fate in the atmosphere and, consequently, influence surface environmental quality, particularly in remote areas (e.g., drinking water sources). A striking example is the widespread detection of PFAS in global rainfall, with one major source being atmospheric transformation products of FTOHs^[611⇓~613]. Ian Cousins from Stockholm University recently summarized findings showing that concentrations of PFOS and PFOA in rainwater worldwide far exceed US EPA and Danish drinking water standards, even in remote regions like Tibet and the Arctic^[614]. Notably, the concentration of trifluoroacetic acid (TFA), a typical ultra-short-chain perfluoroalkanoic acid, has increased sixfold in surface waters over the past 20 years^[615]. Additionally, model simulation studies indicate that rainfall is the primary mechanism for removing PFAS from the atmosphere^[616,617].

Atmospheric deposition of microplastics is also a current research hotspot. The atmospheric environment is an important component of the global circulation of microplastics. Micro- and nanoplastics in the atmosphere have the characteristics of small particles and low density, which are conducive to their long-distance transport with the atmosphere and subsequent deposition in polar and remote areas. Studies have shown that the deposition of microplastics in Paris, France, London, UK, and China ranges from 175 to 1008 per (m² · d) [ 618 ⇓~620 ]. Even in the Pyrenees Mountains of France, far from human activity interference, the deposition flux of microplastics is as high as 365 per (m² · d), comparable to the concentrations in some urban areas, mainly influenced by precipitation, wind speed, and wind direction [ 621 ]. Observational studies on microplastic deposition across all natural reserves in the United States suggest that the annual deposition in this area is equivalent to 120 million to 300 million plastic bottles, primarily originating from resuspended particles from cities, soils, and water bodies [ 622 ]. Additionally, micro- and nanoplastics may act as cloud condensation nuclei (CCN) and ice nucleation particles (INPs), affecting the formation of clouds and fog [ 623 ]. Meanwhile, cloud internal removal, photochemical aging, and rainfall processes also influence the circulation of micro- and nanoplastics in the atmosphere. However, there are currently few related research reports. Xu et al. [ 624 ] found a large number of microplastics in the fog water of Mount Tai in China, with concentrations influenced by fog water content, air mass sources, and airflow height.

5.4 Numerical Simulation of the Regional Environmental Fate of New Contaminants

The numerous types and quantities of new pollutants with different properties can be rapidly and effectively described by models for quantitatively portraying the environmental geochemical processes (such as spatial transport, interfacial migration and degradation transformation) of various new pollutants, thereby clarifying their spatiotemporal evolution laws, multi-medium distribution characteristics, as well as source-sink relationships. Additionally, it is possible to quantify the impact of human activities on regional environmental concentrations and fates of new pollutants and identify the main influencing factors.

Due to the significant differences in emission pathways and physicochemical properties among various new contaminants—for instance, dissociable organic compounds represented by PPCPs and PFASs, non-dissociable semivolatile organic compounds represented by OPEs, CPs, and PBDEs in non-salt forms, rigid particles such as microplastics, and macromolecular organic substances including antibiotic-resistant bacteria and resistance genes—their environmental behaviors and key environmental geochemical processes may exhibit great variations. Currently, models available for regional environmental fate studies include multimedia environmental fate models, atmospheric models, marine models, water quality and hydrological models, as well as some large coupled models. With the rapid development of artificial intelligence, the application of machine learning model methods in the study of pollutant environmental pollution and fate simulation has gradually increased in recent years. The summaries of different models and their applications are as follows.

5.4.1 Multi-medium Model

Donald Mackay and Sally Paterson first proposed the multimedia environmental fate models in the late 1970s to early 1980s and introduced the fugacity approach, which refers to the tendency of a chemical to escape from one medium/phase [625, 626]. The original purpose of developing such models was to study the behavior of chemicals in the environment and assess their potential risks to ecological environments and human health. Traditional multimedia models are also known as box models, typically referring to models that consider different environmental media such as atmosphere, soil (or differentiated soil types), surface water and sediments, and seawater and sediments, with a focus on inter-media migration and transport processes. Many multimedia models also take into account vegetation (such as MUM, SESAMe, BETR, CoZMo-POP, SimpleBox, CHEMGL, G-CIEMS, and ELPOS models, see Table 7). Most multimedia models only consider topsoil and surface water, while very few include groundwater (such as the CHEMGL model). Early developed multimedia models were general models, used only for theoretical research on the environmental behavior patterns of pollutants, without focusing on specific regions. However, with the increase in research needs, region-specific multimedia models gradually emerged, such as ChemCan (Canada), ChemFrance (France), BETR North America (North America), BETR Global (global), Globo-POP (global zonal), and SESAMe (China) models, etc., greatly enriching the variety, functions, and applications of the models.

表7 多介质模型汇总

Table 7 Summary of multimedia models

Model Name	Steady state/ Dynamic	Method	Scale	Existing studies applications	Medium	Source of literature
QWASI	Dynamic	Fugacity	Regional scale (lake and river systems)	Polycyclic aromatic hydrocarbons, antibiotics, per- and polyfluoroalkyl substances, microplastics	Air, water, soil, sediment	627 ⇓⇓⇓⇓⇓⇓~634
MUM(SO-MUM)	Dynamic	Fugacity	Regional scale (urban system)	Polychlorinated biphenyls, polybrominated diphenyl ethers, phthalates, organophosphate esters	Air, upper air, soil, vegetation, surface water, sediment, organic film	635 ⇓⇓⇓⇓~640
MUM-Fate	Steady state	Fugacity	Regional scale (urban system)	Polycyclic aromatic hydrocarbons	Low-layer and upper-air, surface water, sediments, soil, vegetation, and organic films covering impervious surfaces	641
BETR/BETR-Urban-Rural/BETR North America/BETR Global/Evn-BETR	Steady-state/dynamic	Fugacity	Global, continental, and regional scales	Toxaphene, γ-hexachlorocyclohexane (lindane), polybrominated diphenyl ethers, polycyclic aromatic hydrocarbons, hexachlorobenzene, decabromodiphenyl ether, polychlorinated biphenyls, perfluorooctane sulfonate, pentadecafluorooctanesulfonic acid, decamethylcyclopentasiloxane (D5)	Air (high-altitude, low-altitude), vegetation, soil, freshwater, freshwater sediment, seawater, marine sediment (BETR-UR urban soil, rural soil, urban air, rural air)	642 ⇓⇓⇓⇓⇓⇓⇓⇓~651
Globo-POP	Dynamic	Fugacity	Global scale	Toxicfens, polychlorinated biphenyls, polybrominated diphenyl ethers, DDT, α-hexachlorocyclohexane	Air, water, soil, sediment	652 ⇓~654
POPsME	Dynamic	Fugacity	Regional scale	Polycyclic aromatic hydrocarbons, polychlorinated dibenzo-p-dioxins/dibenzofurans	Air, water, soil, sediment	655,656
ChemRange	Steady state	Fugacity	Global scale	Polychlorinated biphenyls	Air, soil, water	657,658
ChemCAN	Steady state	Fugacity	Regional scale	2,2',5,5'-Tetrachlorobiphenyl (PCB 52), tetrachloroethylene, polychlorinated biphenyls, α-hexachlorocyclohexane, benzene [apyrene, hexachlorobenzene, atrazine	Air, water, soil, sediment	659
CHEMFrance	Steady state	Fugacity	regional scale	γ-hexachlorocyclohexane (lindane), atrazine	Air, surface water, soil, bed sediments, groundwater, coastal water	660 ⇓⇓~663
CliMoChem	Dynamic	Fugacity	Global scale	Polychlorinated biphenyls, carbon tetrachloride, α-hexachlorocyclohexane, endrin, atrazine	Air, water, soil	657,664 ⇓ ~666
ChimERA	Dynamic	Fugacity	Universal model	Polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs)	Water, sediment, large plants, total suspended particulates, dissolved organic matter	667,668
EQC	Steady state	Fugacity	Regional scale	Polychlorinated biphenyls, hexachlorobenzene	Air, soil, water, sediment	669
TaPL3	Steady state	Fugacity	regional scale	Polychlorinated biphenyls (PCBs), hexachlorobenzene (HCB)	Air, soil, water, sediment	670
CoZMo-POP 2	Dynamic	Fugacity	regional scale	POPs	Air, forest canopy, forest soil, soil, water, sediment	671,672
NEM (based on CoZMo-POP 2 and BETR-Global)	Dynamic	Fugacity	Global scale	Polychlorinated biphenyls	Air, water, soil, sediment, vegetation	673
G-CIEMS	Dynamic	Fugacity	continental scale	Benzene, dioxin, 1,3-butadiene, etc. Herbicide	Air, fresh water, forest canopy, soils under seven land-use types, sea water, and sediments	674,675
PeCHREM	Dynamic	Fugacity	continental scale	Pesticide	Air, water, soil	676,677
DynAPlus	Dynamic	Fugacity	Regional scale	Insecticide, fungicide	Air, waste, soil, water	678,679
CHEMGL	Steady state/ Dynamic	Fugacity	continental and regional scales	Atrazine, benzopyr [a]pyrene, benzene, hexachlorobenzene	Stratosphere, troposphere, air boundary layer, surface water and sediments, soil, vegetation, groundwater	680
SoilFug	Dynamic	Fugacity	regional scale	Herbicides (acetochlor, terbutryn, and thiobencarb) and insecticides (atrazine, carbofuran, dimethoate, isoproturon, γ-hexachlorocyclohexane (lindane), semiamidines and trifluralin)	Soil and surface water	681 ⇓~683
SoilPlus	Dynamic	Fugacity	Universal model	Pesticide	Air, soil, water	684,685
RSEMM	Dynamic	Fugacity	Regional scale (Beijing-Tianjin river system)	Antibiotics	Air, water, soil, sediment	686
SMURF	Steady state	Fugacity	Regional scale (urban system)	Polybrominated diphenyl ethers, phthalates	Air, water, soil, sediments, urban surfaces, indoor air, indoor vertical surfaces including ceiling and floor	687,688
IMPACT North America/IMPACT 2002	Steady state	Fugacity	continental and regional scales	Pentachlorodibenzofuran, polycyclic aromatic hydrocarbons, hexachlorobenzene	Air, water, soil, sediment, vegetation	689
Pangea	Steady state	Concentration	continental scale	Triclosan, anionic surfactant linear alkylbenzene sulfonate, preservative methyl paraben for personal care products, skin conditioner decamethylcyclopentasiloxane	Air, freshwater sediment, seawater, freshwater, natural land, and farmland	690,691
SimpleBox (updated to v4.0)	Steady state/ Dynamic	concentration	Global, continental, and regional scales	Polychlorinated biphenyls, α-hexachlorocyclohexane, benzo[apyrene	Air, freshwater and seawater, freshwater and seawater sediments, natural, agricultural and other soils	692 ⇓⇓~695
SimpleBox4Nano/SimpleBox4Plastic(SB4P)	Dynamic	Concentration	Global, continental, and regional scales	Nanoplastics and microplastics	Air, surface water, sediment, soil	696
ELPOS	Steady state	concentration	Regional scale	Polybrominated diphenyl ethers, polycyclic aromatic hydrocarbons, polychlorinated biphenyls	Air, soil, water, vegetation, sediment	697
SESAMe/SESAMe-Veg	Steady state	Concentration	continental and regional scales	PPCPs, organophosphates, benzene [apyrene, acenaphthene, triclosan, dibenzofuran	Air, fresh water, sediments, three soils, and two vegetation types	698 ⇓⇓~701
MAMI	Dynamic	Activity	General-purpose model	Pesticide (2,4-dichlorophenoxyacetic acid), aromatic amine aniline, antibiotics (trimethoprim, TMP)	Air, three types of soil, fresh water, sea water, fresh water sediment, and sea water sediment	702

Based on the model construction principle and the nature of the output results,multimedia models can be divided into fugacity models,concentration models and activity models.However,the construction of any model follows the principle of mass conservation. Since the development of fugacity models is the earliest and it is very suitable for semivolatile,non-ionizable POPs (such as PAHs,PCBs and OCPs) which are the main concerns in the early environmental field,fugacity models have developed greatly in both function and quantity.As Table 7 shows,conservatively speaking,there are about 23 different fugacity models that have been developed,of which some kinds also have different variants or versions such as BETR and IMPACT.Some models that have been developed and applied do not have a name,but are simply called Level III or Level IV multimedia fugacity models.

The number of concentration models and activity models is relatively small, with 5 and 1 model respectively (Table 7). Among them, except for SimpleBox4Nano and SimpleBox4Plastic, which simulate particle matter such as nanomaterials and microplastics, the other models are applicable to organic substances ^[696]. Most concentration models and fugacity methods do not have significant differences in key principles. However, due to their inability to simulate the environmental behavior of dissociated organic compounds, these models have limitations when studying some newly concerned dissociable emerging pollutants including PPCPs, PFAS, and some pesticides, etc. They can only correct the calculation of medium/phase partitioning of dissociable organic compounds by adjusting partition coefficients (such as K_OW, etc.), and lack data support for correction under different environmental pH conditions, resulting in large simulation errors. MAMI and SESAMe models, due to the introduction of the activity method, enable the models to simulate phase partitioning of different dissociation forms of organic compounds, improving simulation accuracy, and have unique advantages in solving the simulation of the environmental behavior of such organic compounds.

However, as shown in Table 7, the MAMI model is a general model that does not target specific environmental regions and considers limited environmental media and processes [702]. The SESAMe model (the latest version is SESAMe v3.4) is the first high spatial resolution concentration model to introduce activity algorithms. Its latest version embeds 0.5° resolution national-scale environmental parameters of China into the model, enabling it to conduct high-resolution multi-media environmental migration and fate simulations of organic pollutants for real large-scale regional spatial differentiation. Meanwhile, the development team of this model is also building more SESAMe model versions, such as adding a vegetation phase and realizing the transmission simulation of dissociable organic matter within crops (SESAMe-Veg) [703]. Moreover, unlike other multi-media models, the SESAMe model incorporates irrigation, a human activity, for the first time into multi-media models, connecting environmental geochemical processes with human activities to study the impact and relationship of the anthroposphere on pollution [704]. This model is increasingly approaching the direction of improvement and development of multi-layered earth system models mentioned later in terms of environmental geochemical processes, but it focuses more on new pollutants and studies their environmental behavior.

Compared with atmospheric models and hydrological/water quality models embedded with more complex physical and chemical processes, the environmental processes in most current multimedia models are relatively simple. Although there are limitations in describing complex and fine environmental geochemical processes, they have a unique advantage in being easily extended to new pollutants, making them have a distinct advantage in rapidly promoting and applying to the study and prediction of the environmental behavior patterns of a large number of new pollutants.

5.4.2 Atmosphere and Ocean Models

Developed with the joint support of the US EPA and multiple institutions around the world, atmospheric models have been greatly developed in the past 50 years and are widely used for the study of "typical" air pollutants such as atmospheric particles, nitrogen oxides, sulfur oxides and ozone. The initial development of ocean models was aimed at studying the impact of oceans on meteorology and climate. The application of atmospheric models to the study of trace persistent organic pollutants and other new pollutants began in the early 21st century. The use of ocean models for pollutant migration and transport simulation started later but has developed rapidly in the past 5-6 years, becoming a new research and application field.

The atmospheric models CMAQ and CanMETOP have already been applied in regional atmospheric transport simulation of new organic pollutants. CMAQ is a variable-scale air quality model developed by USEPA, and it was used to simulate the migration and fate of pesticides (atrazine) in the United States as early as 2002^[705,706], which is one of the earlier atmospheric models for simulating new pollutants. In recent years, different studies have respectively constructed the CMAQ-PFAS new model by adding 26 kinds of PFAS into this model, and constructed the WRF-CMAQ-SMOKE model by coupling with the climate model WRF and the emission inventory processing system, to simulate the pollution near PFAS production enterprises in North Carolina and the behavior of OPFRs in the food web of the Bohai Sea and Yellow Sea in China^[707⇓~709]. The CMAQ-PFAS model does not consider gas-phase and heterogeneous chemical reactions, nor does it consider the aerosol surface interaction between PFAS and non-PFAS components. The WRF-CMAQ-SMOKE model considers the oxidation reaction of hydroxyl radicals and OPFRs to simulate the removal of OPFRs in the atmosphere. CanMETOP is an atmospheric, water, and soil coupled atmospheric transport model developed by Environment Canada. This model has been used to study the migration, fate, and long-range transport of various organic pollutants such as polycyclic aromatic hydrocarbons, organochlorine pesticides, PCBs, OPFRs, and CPs in the atmosphere, as well as the corresponding environmental health risk assessment^{[710⇓⇓⇓~714]}. This model does not consider the chemical reactions of pollutants. Additionally, another DEHM model (Danish Eulerian Hemispheric model) is a specialized air quality model for predicting the transport of atmospheric pollutants to the Arctic. Currently, its application in new pollutants is relatively limited. Hansen et al.^[715]applied it to the research on the atmospheric migration of 13 previously focused POPs to the Arctic and the impact of climate change on their fate in the Arctic environment. These atmospheric models can all be applied to more volatile and semi-volatile new pollutants.

For the simulation of microplastic migration and fate, the HYSPLIT model, which is currently applied more frequently, is the hybrid single-particle Lagrangian integrated trajectory model developed by the Air Resources Laboratory of the National Oceanic and Atmospheric Administration (NOAA). This model is mainly used to simulate backward and forward trajectories of microplastic atmospheric transport. The WRF-PM model developed based on the WRF model and the GC-MP model developed based on the GEOS-Chem model are more convenient for quantitative research on the atmospheric transport and migration process of microplastics^[720,721]. By adding a microplastic module, the development team of the GC-MP model also constructed the NJU-PM model for simulating the migration and transportation of marine microplastics based on the MITgcm model developed at the Massachusetts Institute of Technology^[722,723]. The MITgcm model, centered on the ocean, covers the atmosphere, sea ice, and corresponding biogeochemical processes, and is one of the more mainstream ocean circulation models. Therefore, the construction of the NJU-PM model has brought convenience for the simulation of microplastic migration and transportation with global or regional-scale ocean and atmospheric circulation. In addition, the MITgcm model has also been used to study the transport of PFAS and PCBs with ocean currents^[724,725], so it can also be widely applied to other emerging organic pollutants.

5.4.3 Water Quality and Hydrological Models

Water quality and hydrological models are currently mainly used to simulate the distribution of new contaminants (such as pesticides, antibiotics, and pathogenic microorganisms) in surface water and soil on land surfaces within a watershed. Common water quality models include MIKE SHE, MHYDAS, IBER, TELEMAC, SWAT, GREAT-ER, CE-QUAL-W2, Delft3D, and a series of surface water quality models developed by USEPA such as HSPF, AQUATOX, WASP, and EFDC^[726,727].Among them, SWAT, GREAT-ER, AQUATOX, and HSPF models are commonly used for the simulation of new pollutants such as pesticides and antibiotics^{[728⇓⇓⇓~732]}. Moreover, SWAT and HSPF are also widely used for the simulation of pathogens in watersheds. SWAT is a hydrological model developed by the U.S. Department of Agriculture based on geographic information systems, which is used to simulate runoff and water quality at the watershed scale^[733]. HSPF is an integrated model of hydrology and water quality that can simulate the overland flow and pollutant transport processes^[734]. In recent years, these two models have been extensively used in Europe and America, particularly in the United States, for simulating the transport, deposition, and resuspension of Escherichia coli within watersheds^{[733⇓⇓⇓~737]}.

However, the HSPF is a lumped parameter model, which treats the entire watershed as a whole and does not consider the spatial variation of watershed characteristic parameters within the basin. In contrast, SWAT is a distributed model, which divides the watershed into several hydrological units, and the environmental and meteorological parameters of different units are distinct, thus allowing for a more accurate reflection of the spatial differences in watershed pollution. With the development of computers and the improvement of computing power, as well as the increase in research needs, the distributed model will have a greater development prospect and may be increasingly applied to the simulation of new pollutant environmental fate in watersheds.

In addition, there are other water quality or hydrological models used for the transport behavior of Escherichia coli, Campylobacter, Cryptosporidium, antibiotic-resistant bacteria, and viruses in watersheds, such as WAM^[738], WATFLOOD^[739], PCB (pathogen catchment budgets)^[740], SimHyd-EG coupled model^[741], ASP, WALRUS^[742], VIRTUS (Virus Transport in Unsaturated Soils)^[743] and SPDE model etc.^{[744, 745]}. However, their usage is not as widespread as the SWAT model and HSPF model mentioned above.

5.4.4 Machine Learning Patterns

With the development of artificial intelligence, machine learning methods have also been applied to predict the environmental concentrations and distributions of emerging contaminants such as nanomaterials, PFAS, and antibiotic resistance genes. Among them, the most commonly used are different machine learning methods to predict the concentrations and exposure risks of PFAS in public water supply wells and drinking water sources in multiple regions of the United States^{[746⇓⇓⇓~750]}. Secondly, there are applications based on macrogenomic data to predict the global spatial distribution or horizontal transfer of antibiotic resistance genes in soil^[751,752]. In addition, there are also applications for predicting the regional distribution of nano-engineered materials^[753]. The more commonly used machine learning algorithms are Bayesian network method and random forest method, and there are also applications of boosted regression tree method, spatial regression method, and conditional autoregressive method. Machine learning models differ from the above mechanistic models and can be classified as statistical models. When the environmental biogeochemical processes are unclear and the transformation mechanisms are not understood, machine learning models show unique advantages in predicting the concentration distribution of pollutants. However, this method cannot describe the process and mechanism, so it cannot be used to study the driving mechanisms of pollutant environmental fate, etc.

5.4.5 Multi-layer Coupled Model

Unlike the above multimedia models, coupling existing mature atmospheric, water quality or hydrological, marine, and ecosystem models, even human exposure models, to construct a multimedia model for studying the migration, transmission, distribution, transformation, and exposure of pollutants in multiple environmental media can also be called a multi-layer coupling model or Earth system model. Such models have always been important tools for studying climate and environmental changes, and in recent years, they have begun to be used for simulating the biogeochemical processes of pollutants (mainly mercury and methylmercury)^[754].Lohman et al.^[755] coupled the atmospheric fate and transport model TEAM, watershed fate and transport model R-MCM, and human exposure model to identify the main controlling factors of mercury and methylmercury human exposure. The research team at Nanjing University's Environmental Biogeochemistry Modeling has, in different studies, used its developed coupler (NJUCPL) to couple climate models (GISS ModelE2, IGSM), ecosystem models (DARWIN), atmospheric models (GEOS-Chem), land surface models (GTMM), and ocean models (MITgcm) to study the health impacts of global atmospheric mercury emissions and further refine mercury emission calculations^{[756, 757]}. Furthermore, this team also constructed the CLM5-Hg model based on the land surface model (CLM5) in the Earth system model CESM2 to study the role of vegetation in the air-land exchange of mercury^[758]. Currently, there is a lack of application of multi-layer coupling models or Earth system models in the study of new pollutant environmental fate. Although mercury and methylmercury are not new pollutants, they reflect the possibility of applying such models to new pollutants.

5.4.6 Outlook, Limitations, and Challenges

Many new contaminants may migrate and distribute among different environmental media, while their emissions and environmental behaviors are greatly influenced by human activities. Therefore, the future development direction of this type of model is to couple the human sphere with a multi-layer coupled Earth system model to consider the interaction between human activities and the Earth system. However, such models lack modules that can simulate the migration, distribution, and transformation of different new contaminants. The multi-medium model has been widely used in the simulation research of regional environmental fate of various new contaminants and already has some algorithms for cross-medium migration and phase distribution of new contaminants, especially new organic pollutants. The current development trend also tends to describe the environmental migration and fate of pollutants in the real Earth system environment and the regional differences. In the newly developed models, the environmental geochemical processes of pollutants also tend to be more complete, and coupled multi-medium models with human activities, such as SESAMe v3.4, have emerged. Therefore, coupling these two types of models to varying degrees may be a feasible method and development direction for future research on the regional environmental fate simulation of new contaminants.

However, unlike "typical" atmospheric or water quality index pollutants with relatively clear contaminant types and more concentrated targeted studies in the past, new pollutants are numerous, have significant property differences, and are currently an emerging research direction. The attention to them is relatively short, and different research teams focus on different new pollutants, leading to scattered research efforts. As a result, the chemical reaction mechanisms, product types, and generation efficiencies of many new pollutants in the natural environment, as well as their (micro)biological processes and mechanism, remain unclear. Relevant parameters for emission calculations, such as emission factors, are also lacking. This poses significant challenges to the development of numerical methods for regional environmental fate simulation of new pollutants and makes it difficult to simulate new pollutants and their products simultaneously. Therefore, there is an urgent need to strengthen research on new pollutants, especially those of high concern, to support the improvement of environmental biogeochemical processes in models. Additionally, for processes whose mechanisms are still unclear, coupling machine learning models into the above mechanistic models for predictions is a feasible method to improve model accuracy. However, this approach requires relevant big data accumulation and support.

In summary, developing models that connect human activities with the environmental biogeochemical processes of new pollutants will be an important means to comprehensively understand the behavior characteristics and rules of new pollutants in the environment, including their emission, spatial transport, interfacial migration distribution, degradation transformation, and source-sink relationships. This is an indispensable research direction in the field of new pollutant studies.

6 Ecotoxicological Effects of Emerging Contaminants

6.1 Ecotoxicology of Perfluoroalkyl and Polyfluoroalkyl Substances

6.1.1 Research Background and Significance

As early as the 19th century, humans synthesized the simplest PFAS, i.e., tetrafluoromethane (CF₄), which is still used in plasma etching processes for various integrated circuits. Since the 20th century, long-chain perfluorinated compounds such as PFOA, perfluorooctanoate (PFO), PTFE, and other perfluorinated compounds containing functional groups, as well as perfluoropolymers, have been manufactured and widely applied^[202,759]. In 2021, OECD and UNEP provided a new and broader definition of PFAS: "Fluorinated substances containing at least one perfluoromethyl (-CF₃) or perfluoromethylene (-CF₂-) carbon atom are called PFAS"^[760]. PFAS are usually composed of nonpolar carbon chains and polar head groups, which endows them with hydrophobic and oleophobic properties similar to surfactants^[761]; moreover, due to the extremely high bond energy of C—F bonds^[762], PFAS possess excellent stability and are widely used in all aspects of production and life. In the aerospace field, PFAS can be used to coat reactive metal powders to protect self-igniting components, alter their combustion behavior, and enhance their reactivity and hydrophobicity. Aluminum nanoparticles coated with PFAS have been used as propellants in solid rocket propellants and aircraft jet fuels^[763]. In the decoration and construction field, PFAS have been used in various coating products to improve the gloss, surface coverage, antistatic, and stain-resistant properties of materials such as marble, tiles, and cement. In the daily chemical industry, PFAS can be used as emulsifiers in cosmetics and also as additives in conditioners to improve hair lubricity and oleophobicity. They can also be used as anti-dental plaque additives in toothpaste^[759]. Furthermore, PFAS are applied in many fields including textiles, architecture, plastic and rubber production, semiconductor manufacturing, etc.^[764].

Studies have shown that PFASs have been widely detected in various environmental media such as water, air, soil^[765⇓~767], wildlife and plants^[768,769], and even in humans^[770], and they exhibit multiple toxic effects such as hepatotoxicity, neurotoxicity, embryonic developmental toxicity, and endocrine disruption^{[771⇓⇓~774]}. Epidemiological studies suggest that exposure to PFAS may be associated with diseases such as diabetes^[775], reproductive dysfunction^[776], and various cancers^[777]. Considering the potential ecological toxicity and health risks of PFAS, as well as their persistence, bioaccumulation, long-range transport, and multiple toxicities in the environment, countries around the world have implemented restrictions and controls on the use of PFAS. In 2006, the U.S. Environmental Protection Agency (USEPA) signed an agreement with eight international companies to reduce emissions and product content of PFOA and related chemicals by 95% by 2010^[778]; perfluorooctane sulfonate (PFOS), PFOA, and perfluorohexane sulfonate (PFHxS) were successively listed in the Stockholm Convention on Persistent Organic Pollutants by UNEP in 2009, 2019, and 2022 respectively; in 2023, environmental protection agencies from several European countries submitted a proposal to the European Chemicals Agency (ECHA) calling for a ban on the use of industrial PFAS chemicals^[779].

In recent years, with the restriction of long-chain PFAS production and application, various novel PFASs, such as perfluoropolyether carboxylic acids, perfluoropolyether sulfonic acids, etc., have been developed as potential alternatives. The number of PFAS species detected in the environment has also gradually increased. Considering the harm to organisms in nature, the threat to ecological balance, and the health risks posed to humans through dietary pathways^[780], it is particularly necessary to study the environmental distribution and toxicology of PFASs. This section mainly summarizes the distribution status of PFASs in the environment and their ecological toxicity, and discusses and looks ahead to the difficulties in current research and future research directions, aiming to provide reference for the ecological toxicology research of PFASs and the assessment of environmental pollution risks.

6.1.2 Distribution of PFAS in the Environment

6.1.2.1 Water body

Large numbers of studies have confirmed that PFAS are widely distributed in various types of water bodies globally. Guo et al.^[781] detected the presence of 10 PFAS including PFOA and PFHxS in surface water in the Baiyangdian area, with a total mass concentration of 140.5 to 1828.5 ng/L. Breitmeyer et al.^[782] detected PFAS in all 122 rivers in Pennsylvania, with detection rates of PFOA and PFHxA reaching as high as 70% and 60%, respectively. At the same time, PFAS are also distributed in global marine water bodies. Seven substances, including PFBS, PFHxS, and PFHxA, were detected in the East China Sea, with a mass concentration of 181 to 2658 pg/L^[36]; perfluoroalkyl acids containing a total mass concentration of 346.9 to 3045.3 pg/L were also detected in seawater samples from the North Pacific to the Arctic Ocean, among which PFBA was most widely distributed^[783]. This phenomenon suggests that PFAS have the ability to migrate from continental freshwater systems to the ocean and diffuse over long distances with ocean currents.

In addition to traditional PFAS, various novel PFAS have also been detected in water bodies in recent years. In 2015, Heydebreck et al.^[784] first reported the existence of hexafluoroepoxypropane dimer (HFPO-DA) in the Xiaoqing River water in China, with an average concentration as high as 271.4 ng/L. At the same time, HFPO-DA was also detected in Arctic seawater, suggesting that it has similar long-range mobility to traditional PFAS^[785]. In 2016, Sun et al.^[786] reported seven novel PFAS in the Cape Fear River Basin in the United States, including PFMOAA, perfluoro-3,5-dioxohexanoic acid (PFO2HxA), perfluoro-3,5,7-trioxooctanoic acid (PFO3OA), etc. In 2022, Yao et al.^[787] first detected homologues of hexafluoroepoxypropane trimer (HFPO-TA), C7-HFPO-TA and C8-HFPO-TA, in the fluorinated chemical industrial zone upstream of Lake Tai. The former existed in the effluent of the plant area at a high concentration of 447 μg/L and was detected at a concentration of 5.6 ng/L in Lake Tai.

6.1.2.2 Atmosphere

Per- and polyfluoroalkyl substances (PFAS) in the atmosphere can exist in the form of being attached to particulate matter. Yu et al.^[788] collected samples of atmospheric particulate matter (APM) from five cities in China. While detecting various traditional PFAS such as PFOA and PFHxA, they first reported the presence of novel PFAS including 1: n perfluoroalkyl ether carboxylic acids (1: n PFECAs), perfluoroalkyl dicarboxylic acids (PFdiOAs), hydrogen-substituted perfluoroalkyl dicarboxylic acids (H-PFdiOAs), and unsaturated perfluoroalcohols (UPFAs) in APM. FTOH was confirmed to be widely present in the German atmosphere, with mass concentrations reaching up to 546 pg/m³ in urban areas and 311 pg/m³ in suburban areas^[789]. Two volatile PFAS, N-methyl perfluorooctane sulfonamide (NMeFOSA) and 4:2 FTOH, were also detected in atmospheric samples^[789]. Yao et al.^[790] detected FTOH and perfluoroalkyl phosphoric diesters (DiPAPs) in outdoor dust samples collected from multiple provinces, confirming that atmospheric particulate matter can accumulate PFAS through adsorption and return them to the ground via dry deposition.

6.1.2.3 Soil and Sediments

The analytical data of soil samples from residential areas in 30 administrative regions of China indicate that PFOA still dominates in the soil, with an average mass fraction of 354 pg/g and a detection rate of 96.6%; while PFOS has been replaced by its alternatives, chlorinated perfluoroalkyl sulfonates (Cl-PFESAs) and short-chain homologs, among which the detection rate of PFOS homologs with carbon chain lengths less than 8 is as high as 100%^[791].

Since long-chain PFAS have a high sediment-water distribution coefficient^[792], sediments tend to become a sink for PFAS. Sixteen traditional and emerging PFAS, including PFBA, 8∶2 Cl-PFESA, 6∶2 Cl-PFESA, etc., were detected in the sediments along the Red Sea coast of Arabia^[793]. Benskin et al.^[794] detected a new PFOS precursor, perfluorooctane sulfonamide ethanol phosphate (SAmPAP), in the sediments of Vancouver Harbor, Canada. The concentration of SAmPAP was significantly correlated with that of PFOS, suggesting it may be an important source of PFOS pollution.

6.1.2.4 Plant

In 2023, Griffin et al. ^[795] collected aquatic plant samples from various groups such as large algae, wetland plants, and floating aquatic plants in Florida, detecting 12 types of PFAS including PFOA, PFOS, and PFBA, with a total mass fraction of 0.18–55 ng/g. For terrestrial ferns and seed plants, studies have shown that traditional PFAS such as PFOA and PFOS and new PFAS such as HFPO-DA and 6∶2 fluorotelomer sulfonate (6∶2 FTS) tend to accumulate in the roots of plants, and long-chain PFAS are prone to being adsorbed and retained on the root epidermis, while short-chain PFAS are more easily absorbed and transferred upward ^{[796, 797]}.

6.1.2.5 Wildlife

Several studies have shown that various PFAS, especially long-chain PFAS with carbon chain lengths greater than 7, have the characteristics of accumulating in living organisms and being amplified through the food chain^[798,799]. PFOA, PFOS, and PFHxS were detected in the liver, muscle, and other tissues of fish such as Parabramis pekinensis in the Yangtze River Basin, with a bioaccumulation factor (BAF) of all > 5000 L/kg, indicating high bioaccumulation^[800]. The concentration of PFOS in the serum of Northern Cardinals (Cardinalis cardinalis) in the Atlanta metropolitan area can reach up to 180 ng/mL^[801], while in the serum of golden monkeys at the Shanghai Wildlife Park, the mass concentrations range from 0.06 to 0.52 ng/mL^[802]. Meanwhile, the mass concentrations of various PFAS in the serum of golden monkeys are led by PFOA, with the highest reaching 4.08 ng/mL^[802].

In recent years, the existence of several novel PFAS has also been reported in wildlife (Fig. 25). For example, monohydrogen perfluoroalkyl carboxylic acids (H-PFCAs), perfluorohexane sulfonamide (FHxSA), and perfluoropentane sulfonamide (FPeSA) were detected in beluga whale livers in Canada, with average mass fractions of 1.4, 5.5, and 2.3 ng/g, respectively ^[803]. However, some newly detected substances could not be accurately quantified due to a lack of reference standards.

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图25 不同营养级生物体内检出的新型PFAS及最大检出浓度^{[799,803,807⇓⇓⇓⇓⇓ ~813]}

Fig. 25 Novel PFAS detected in organisms with different trophic levels and their maximum detectable concentration^{[799,803,807⇓⇓⇓⇓⇓ ~813]}

In summary, both traditional long-chain PFAS and new types of PFAS such as polyether and fluorinated alcohol-based PFAS have been widely distributed in various environmental media. PFAS have been detected from urban areas to remote regions such as the poles, indicating that they have become global environmental pollutants. Currently, land remains the largest source of PFAS pollution. PFAS from places such as chemical manufacturing plants and wastewater treatment plants enter the environment through sewage, exhaust gas, and biosolids emissions^[804], and are transported between different media via atmospheric deposition, ocean currents, aerosol adsorption, and biological migration^[805], achieving long-distance diffusion^[806], eventually entering the ocean, which is the most important sink^[806](Fig. 26), and the contamination status of PFAS in marine ecosystems deserves particular attention.

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图26 PFAS的环境分布

Fig. 26 Environmental distribution of PFAS

6.1.3 Biotoxicity of PFAS

6.1.3.1 Plant

In terms of aquatic plants, the mixed exposure of PFBA and perfluorobutane sulfonamide (FBSA) significantly inhibited the growth of Chlorella pyrenoidosa, and adverse effects were observed in photosynthesis, reactive oxygen metabolism, and DNA replication processes^[814]. Gonzales et al.^[815] found that low concentrations of PFOA caused variations in the number of leaves in Lemna minor, but chlorophyll synthesis and reactive oxygen metabolism were not significantly affected. Li et al.^[816] investigated the toxicity of PFOS on two wetland plants, Eichhornia crassipes and Cyperus alternifolius. They discovered that PFOS concentrations below 0.1 mg/L could promote plant growth and chlorophyll synthesis, but at high concentrations of 10 mg/L, chlorophyll synthesis was inhibited, leaf membranes and cell structures were damaged, and the mitochondrial outlines in root cells became incomplete.

In terrestrial plants, higher concentrations of PFOA and HFPO-DA can inhibit the growth of seedlings in Arabidopsis thaliana and Nicotiana benthamiana, disrupt the antioxidant system, hinder root absorption of trace elements, and affect the diversity and structure of root microbial communities^[817]. Lin et al. ^[818] found that under exposure to Cl-PFAESs, wheat showed lower tolerance compared to the same concentration of PFOS, with greater damage to root membranes, and suffered more negative effects on pigment content, oxidative damage, and antioxidant capacity.

6.1.3.2 Invertebrates

Invertebrates are usually at the bottom of the food chain and are crucial to the material circulation and energy flow of the entire ecosystem. The accumulation and toxicity of PFAS in these organisms largely determine the health status of other animals.

Several studies have shown that PFOS can cause oxidative stress responses in bivalves such as Perna viridis^[819⇓~821] and crustaceans such as Macrophthalmus japonicus^[822,823], with up-regulation of the expression of related genes such as superoxide dismutase. In addition, when the water flea Daphnia magna is exposed to PFHxA, PFHxS, and PFNA, the concentrations of several amino acids (such as proline and glycine), nucleosides (such as adenosine monophosphate), and neurotransmitters (such as acetylcholine) change significantly. These disturbances disrupt Coenzyme A metabolism, histidine metabolism, and protein synthesis^[824].

For terrestrial invertebrates such as earthworms and insects, PFAS also exhibit certain toxic effects. Exposure to contaminants such as PFOA can induce the production of excessive reactive oxygen species in earthworms, leading to oxidative stress and subsequently causing DNA damage^[825]. Studies using Eisenia fetida as the subject indicate that after 14 days of exposure to 120 mg/kg PFOA, the activity of antioxidant-related enzymes in earthworms increases, but significantly decreases after 42 days, with maximum DNA damage observed^[826]; while Aporrectodea caliginosa exhibited DNA damage after PFOS exposure but showed no signs of oxidative stress, suggesting that PFOS may cause DNA damage through other mechanisms such as inhibiting DNA repair pathways^[827]. European honeybees () experienced increased mortality, halted larval development, and abnormal behaviors including cluster behavior, hive maintenance behavior, and defense behavior after consuming sugar syrup containing a certain concentration of PFOS^[828].

6.1.3.3 Vertebrates

Vertebrates generally have high trophic levels in the food chain. During predation on organisms with low trophic levels, PFAS accumulate in their bodies, resulting in biomagnification and potentially causing obvious toxic effects.

Gebreab et al. [829] conducted exposure experiments of perfluorooctanoic acid (PFOA) and various perfluoro ether carboxylic acids (PFECA) on wild-caught dolphinfish (Coryphaena hippurus). They found that the embryonic mortality rate was positively correlated with the carbon chain length, hydrophobicity, and exposure time of the compounds, which was consistent with the traditional zebrafish model. Dale et al. [830] treated the liver of Atlantic cod separately and in combination with PFOA, PFOS, and PFNA. By assessing transcriptomics, they discovered that the liver exhibited varying degrees of differential gene expression, and these genes were mostly related to cellular processes such as oxidative stress, sterol metabolism, and nuclear receptor pathways.

Besides fish, amphibians such as frogs and salamanders have also been frequently used as research subjects in ecological toxicology in recent years. This is because their life cycle is relatively complex, requiring metamorphosis from aquatic to terrestrial development, and their skin generally has good permeability, making them more sensitive to environmental pollutants. Flynn et al.^[831] found that after 96 hours of exposure, PFOS had a lethal effect on the tadpoles of the American bullfrog (Rana catesbeiana) with the lowest median lethal concentration (LC₅₀) being 144 mg/L. Additionally, PFOA, PFOS, PFHxS, and other substances caused effects such as reduced body weight, declining physical condition, and developmental delay in various amphibians^[832]. The toxicity not only varies depending on the type of PFAS and species but also differs by developmental stage^[833]. For example, salamanders are typically more sensitive to PFAS than frogs and toads, and they are affected more significantly during early stages of larval development.

Regarding the effects of PFAS on birds, some progress has been made. Studies have shown that great tits (Parus major) living near a fluorochemical plant have plasma containing PFOS, PFOA, PFDA, and PFDoDA, and their non-enzymatic antioxidant levels and peroxidase activity are significantly related to PFAS concentrations, proving that PFAS has triggered oxidative stress responses in great tits^[834]. Additionally, double-banded kingfishers (Tachycineta bicolor) living near John Lake contaminated with PFOS have significantly elevated concentrations of this contaminant in their plasma and eggs, and there is a significant negative correlation between egg hatching success and PFOS concentration in the eggs. When the PFOS mass fraction in the eggs reaches 150 ng/g (wet weight), the hatching success rate decreases significantly^[835].

At present, there are relatively few reports on the toxic effects of PFAS on wild mammals. Pedersen et al.^[836] determined the concentrations of PFAS and steroid hormones in the brain tissues of polar bears in East Greenland. The results showed that there was a significant positive correlation between the levels of pro-gesterone, progesterone, testosterone, and other hormones and the total concentration of perfluoroalkyl acids such as PFOA, PFNA, and PFHxA, indicating that PFAS can cause endocrine disorders in polar bears to a certain extent. Northeast tigers, which are also large carnivores, were found to have a significant positive correlation between serum PFAS and blood glucose levels and ALT levels, the latter being a marker for liver damage, suggesting that PFAS may lead to abnormal liver function in northeast tigers^[837]. The biological toxicity effects of PFAS are shown in Figure 27.

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图27 PFAS的生物毒性效应

Fig. 27 Biological toxicity effects of PFAS

6.1.4 Conclusion and Outlook

At present, extensive research has been conducted both domestically and internationally on the distribution of PFAS in various environmental media and their ecological toxic effects. In summary, various types of PFAS are widely distributed in natural media such as water, air, and soil, accumulate in biological media such as plants and animals, and produce multiple toxic effects such as hepatotoxicity, developmental toxicity, and endocrine disruption on organisms, which deserves people's attention. Of course, there are still some pressing difficulties that need to be overcome in existing studies. First, there is species difference^[832] in the toxicity of the same pollutant to organisms, and even gender differences^[838] within the same species. When assessing the toxicity of PFAS, the scope of consideration needs to be expanded, and more emphasis should be placed on studying the underlying mechanisms. Starting from the mechanism, species-specific and gender-specific hazard extrapolation can be achieved through approaches such as AOPs and computer simulation. Second, compared with conducting toxicity studies on model plants/animals under standard experimental conditions, studying the toxicology of PFAS on organisms in a natural state may inevitably be affected by various factors. This is because biological samples obtained from the natural environment may simultaneously be exposed to other pollutants such as heavy metals, and the conclusions drawn may not establish a clear causal relationship with PFAS exposure^[839].

Finally, the continuous emergence of PFAS alternatives brings greater challenges to toxicity research. At present, developing corresponding predictive models based on computational toxicology methods has become a key technology to solve the above problems. One major direction is to construct models according to the quantitative structure-activity relationship (QSAR) of pollutants, that is, to quantitatively study the interaction between organic small molecules and biological macromolecules and the physiological processes such as absorption, distribution, metabolism, and excretion in organisms by using mathematical and statistical means with physicochemical property parameters or structural parameters of pollutant molecules. Through QSAR modeling, researchers have successfully predicted the cytotoxicity of various PFAS and mixtures to amphibian fibroblasts and the ecological risks to water fleas^[840,841]. Cheng et al.^[842] also constructed the first PFAS-specific database through machine learning based on model construction, classifying the biological activities of more than 3000 PFAS.

Nowadays,some PFAS alternatives have been proved to be more toxic to organisms,and it is urgently needed to carry out the research on the hazards of novel PFAS,especially potential alternatives. Relevant industries should accelerate the development of production processes or control methods for alternatives, and relevant departments should also speed up the efforts on governance and control measures of PFAS to minimize its harm to the ecological environment as far as possible.

6.2 Ecotoxicology of organophosphorus esters

6.2.1 Organophosphate Overview

OPEs are a class of synthetic organophosphate ester derivatives, and their structural general formula is shown in Fig. 28. The OPEs molecule has a phosphate group at its center. During the esterification process, the hydrogen atoms of the phosphate are replaced by different groups. According to the differences in functional groups of the substituents, OPEs can be divided into halogenated OPEs, alkane-based OPEs, and aromatic OPEs (Table 8, Table 9) [843]. With traditional brominated flame retardants (such as PBDEs and HBCD) with persistent pollutant characteristics being banned by legislation in many countries, OPEs, as substitutes for traditional brominated flame retardants, have been widely used in plastics, furniture, textiles, building materials, automobiles, and electronic equipment worldwide to increase material plasticity and flame retardancy [844 ⇓⇓~847]. According to statistics, from 2001 to 2018, the global consumption of OPEs increased sharply from 186,000 tons to 1 million tons. China is one of the main production areas of OPEs globally, and by 2022, the production of OPEs in China had reached 300,000 tons [848 ⇓~850]. Since OPEs often exist in products in the form of physical addition, they tend to be released into the environment through volatilization, leaching, wear, and dissolution during production and use, leading to varying degrees of detection of OPEs in various environmental media worldwide, causing environmental pollution [851 ⇓⇓~854]. As the usage of OPEs increases year by year, their environmental concentrations also continue to rise, and in some regions, the pollution level has even exceeded that of traditional brominated flame retardants [855, 856]. OPEs, as a new type of pollutant, have drawn widespread attention to their ecological health risks. OPEs exposure has multiple toxic effects on organisms, including growth and developmental toxicity, endocrine disruption, liver and metabolic toxicity, neurotoxicity, etc. This section will focus on the recent five years' progress in the pollution status and toxicology research of OPEs.

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图28 有机磷酸酯的化学结构通式

Fig. 28 Chemical structure formula of OPEs

表8 传统有机磷酸酯(T-OPEs)基本信息

Table 8 Basic information of conventional OPEs(T-OPEs)

classification	English name	Chinese name	abbreviation	CAS
Alkanes OPES	Trimethyl phosphate	Trimethyl phosphate	TMP	512-56-1
	Triethyl phosphate	Triethyl phosphate	TEP	78-40-0
	Tripropyl phosphate	Tricresyl phosphate	TPrP	513-08-6
	Triisopropyl Phosphate	Triisopropyl phosphate	TIPP	513-02-0
Alkanes OPES	Tributyl phosphate	Tributyl phosphate	TnBP	126-73-8
	Triisobutyl phosphate	Triisobutyl phosphate	TIBP	126-71-6
	Tris(2-butoxyethyl) phosphate	Tris(2-butoxyethyl) phosphate	TBOEP	78-51-3
	Tris(2-ethylhexyl) phosphate	Tris(2-ethylhexyl) phosphate	TEHP	78-42-2
Chlorination OPEs	Tris(2-chloroethyl) phosphate	Tris(2-chloroethyl) phosphate	TCEP	115-96-8
	Tris(2-chloroethyl) phosphate	Tris(2-chloroethyl) phosphate	TCEP	115-96-8
	Tri(2-chloroisopropyl) phosphate	Tris-(2-chloroisopropyl) phosphate	TCIPP	13674-84-5
	Tris(1,3-dichloro-2-propyl) phosphate	Tris(1,3-dichloro-2-propyl) phosphate	TDCIPP	13674-87-8
Aromatic OPEs	Triphenyl phosphate	Triphenyl phosphate	TPhP	115-86-6
	Tritolyl phosphate	Tricresyl phosphate	TCrP	1330-78-5
	2-Ethylhexyl diphenyl phosphate	Diphenyl isoctyl phosphate	EHDPP	1241-94-7

表9 新型有机磷酸酯(NOPEs)基本信息

Table 9 Basic information of novel OPEs (NOPEs)

Classification	English name	Chinese name	abbreviation	CAS number
	2,2-Bis(chloromethyl)trimethylene bis(bis(2-chloroethyl) phosphate)	Bis[(2-chloroethyl) phosphate] of 2,2-bis(hydroxymethyl)-1,3-propanediol	V6	38051-10-4
Halogenated OPEs	Tris(2,3-dibromopropyl) phosphate	Tris-(2,3-dibromopropyl) phosphate	TDBPP	126-72-7
	Tris(tribromoneopenthyl)phosphate	Tris(tr bromoneopentyl)phosphate	TTBP	19186-97-1
	Cresyl diphenyl phosphate	Diphenyl p-tolyl phosphate	CDP	26444-49-5
Aromatic OPEs	2-Isopropylphenyl Diphenyl Phosphate	Diphenyl phosphate of 2-isopropylphenyl	IPDP	64532-94-1
	Naphthalen-2-yl diphenyl phosphate	Phosphoric acid 2-naphthyl diphenyl ester	NAPHP	18872-49-6
	tert-butylphenyl diphenyl phosphate	Tertiary butylated diphenyl phosphate	BPDP	56803-37-3
	Isodecyl diphenyl phosphate	Isodecyl phenylphosphate	IDPP	29761-21-5
	Bis(2-ethylhexyl) phenyl phosphate	Diphenyl bis(2-ethylhexyl) phosphate	BEHPP	16368-97-1
	2-Biphenylylphenyl phosphate	Phosphoric acid 2-phenyldiphenyl ester	BPDPP	132-29-6
	Bis(2-isopropylphenyl) Phenyl Phosphate	Diphenyl(2-isopropylphenyl) phosphate	RBDPP	69500-29-4
	Bis(4-Tert-Butylphenyl) Phenyl Phosphate	Diphenyl(4-tert-butylphenyl)phosphate	B₄TBPPP	115-87-7
	Tris(2-isopropylphenyl) phosphate	Tris(2-propylphenyl) phosphate	TIPPP	64532-95-2
	Tri-(4-Tert-Butylphenyl) Phosphate	Tris( p - tert - butylphenyl ) phosphate	TBPP	78-33-1
	Resorcinol bis(diphenylphosphate)	Resorcinol tetrakis (diphenyl phosphate)	RDP	57583-54-7
	Tris(2,4-di-tert-butylphenyl) phosphate	Tris(2,4-di-tert-butylphenyl)phosphate	TDtBPP	95906-11-9
	Bisphenol-A bis (diphenyl phosphate)	Bisphenol A bis(diphenyl phosphate)	BPA-BDPP	5945-33-5

6.2.2 Status of Environmental Pollution by OPEs

6.2.2.1 Indoor Dust and Atmosphere

Since OPEs can easily migrate and release into indoor environments during use, the indoor environment is considered an important microenvironment for human exposure to OPEs^[872,873]. In recent years, the main OPEs in indoor dust are still traditional OPEs (T-OPEs), but the detection rate and concentration of novel OPEs (NOPEs) are increasing year by year (Table 10). The total mass fraction (ΣOPEs) of OPEs in indoor air and dust in the Midwest of the United States was as high as 736330 ng/g^[857]. In UK indoor dust, the ΣOPEs mass fraction reached 1099519 ng/g, with major detected OPEs being EHDPP, TCEP, TBOEP, TDCIPP, and TCIPP^[860]. In Greek indoor dust, high-quality fractions of OPEs were also detected (189962 ng/g), mainly TDCIPP, TCIPP, TBOEP, and TPhP. Additionally, in Greek indoor dust, the detection rate of NOPEs was 100%, and a high-quality fraction of IDPP (401 ng/g) was detected^[859]. In indoor dust from Nepal, the ΣOPEs mass fraction was 12100 ng/g, mainly TCIPP, TPhP, and EHDPP^[861]. In 2019, American scholars found that compared with developed countries (South Korea: 31300 ng/g; Japan: 29800 ng/g; USA: 26500 ng/g), the mass fraction levels of OPEs in indoor dust from developing countries were lower (Vietnam: 1190 ng/g; China: 1120 ng/g; India: 276 ng/g)^[862]. In Guangzhou, China, the total mass fraction of OPEs in indoor dust reached 95230 ng/g, with major detected OPEs being TCIPP, TDCIPP, TPhP, TEHP, TBOEP, and BPA-BDPP. It is noteworthy that the total mass fraction of NOPEs was as high as 16550 ng/g, which was close to the mass fraction of PBDEs in indoor dust in South China (27950 ng/g)^{[857,874,875]}, indicating a serious risk of NOPEs exposure for people in South China. In 2020, Chinese scholars found that the main detected OPEs in indoor dust samples from 25 provinces across the country were TCIPP, TCEP, TEHP, TDCIPP, and TPhP. Although the average mass fraction of NOPEs was only tens of ng/g, their detection rate exceeded 75%^[858].

表10 2018—2023年有机磷酸酯在室内粉尘和大气环境中的分布情况

Table 10 Distribution of OPEs in indoor dust and atmospheric environments, 2018—2023

environmental medium	country or region		minimum value	maximum value	main OPEs	ref
indoor dust	China	Guangzhou	3040 ng/g	47240 ng/g	TDCIPP, TEHP, TCIPP, TPhP, EHDPP, BPA-BDPP^a	857
		Northeastern region	700 ng/g	9536 ng/g	TEP, TEHP, TCEP, TCIPP, TDCIPP, EHDPP	858
		East China Region	293 ng/g	7150 ng/g	TEHP, TCEP, TCIPP, TDCIPP
		North China Region	958 ng/g	7915 ng/g	TEHP, TCEP, TDCIPP, TCIPP
		South and Central China	702 ng/g	8900 ng/g	TEHP, TCEP, TCIPP
		Northwest region	364 ng/g	5060 ng/g	TCEP, TCIPP, TDCIPP, TPhP
		Southwest China	900 ng/g	3700 ng/g	TCEP, TCIPP, TDCIPP
	Greece		2034 ng/g	189962 ng/g	TDCIPP, TCIPP, TBOEP, TPhP	859
	United Kingdom		19911 ng/g	1099519 ng/g	EHDPP, TCEP, TBOEP, TPhP, TCIPP, TDCIPP	860
	Nepal		153 ng/g	12100 ng/g	TCIPP, TPhP, EHDPP	861
	Global	China	149 ng/g	4740 ng/g	TCEP, TPhP	862
		Columbia	54.6 ng/g	8130 ng/g	TBOEP, TCIPP, TPhP
		Greece	1690 ng/g	90200 ng/g	TnBP, TBOEP, TCEP, TCIPP,
		India	52.5 ng/g	9650 ng/g	TBOEP, TEHP, TDCIPP
		Japan	7720 ng/g	238000 ng/g	TBOEP, TCEP, TCIPP, TDCIPP,
		South Korea	3090 ng/g	249000 ng/g	TBOEP, TCEP, TCIPP, TPhP, IDDP^a
		Kuwait	633 ng/g	44400 ng/g	TBOEP, TCEP, TCIPP, TDCIPP,
		Pakistan	49.4 ng/g	473 ng/g	TBOEP, TPhP
		Romania	775 ng/g	54900 ng/g	TnBP,TBOEP, TCEP, TCIPP, TPhP
		Saudi Arabia	791 ng/g	35000 ng/g	TBOEP, TCEP, TCIPP, TDCIPP, TPhP
		United States	1930 ng/g	101000 ng/g	TBOEP, TCEP, TCIPP, TDCIPP, TPhP
		Vietnam	228 ng/g	79600 ng/g	TBOEP, TCIPP, TPhP, RDP^a
	United States		22690 ng/g	736300 ng/g	TBOEP, TDCIPP, TCIPP, TPhP, BPDPP^a	857
atmosphere	China		1228 ng/g	612668 ng/g	TPhP, TCEP, TCIPP, TDBPP	863
		Northeastern region	257 ng/g	2630 ng/g	TCEP, TCIPP, TDCIPP	858
		East China Region	99.8 ng/g	5960 ng/g	TCEP, TCIPP, TDCIPP, TPhP	858
		North China Region	140 ng/g	4070 ng/g	TCEP, TCIPP
		South and Central China	154 ng/g	5700 ng/g	TCEP, TCIPP, TDCIPP, TnBP
		Northwest region	120 ng/g	1030 ng/g	TCEP, TCIPP, TDCIPP
		Southwest China	147 ng/g	972 ng/g	TCEP, TCIPP, TEHP
		Hong Kong	1294 pg/m³	8481 pg/m³	TCIPP, TDCIPP, TnBP, TBOEP	864
		Guangzhou	4.01 ng/m³	75.2 ng/m³	TCIPP, TPhP	865
		Taiyuan	3.1 ng/m³	544 ng/m³	TCEP, TCIPP, TBP	865
		Guangzhou	262 pg/m³	421914 pg/m³	TCEP, TCIPP, TnBP, RDP^a	866
		Pearl River Delta	91 pg/m³	2055 pg/m³	TCEP, TCIPP, TnBP,TDCIPP	867
	United States of America	The Great Lakes	41.2 pg/m³	1320 pg/m³	TCIPP, TnBP, TEHP, TCEP	868
	United States of America	New York	1320 pg/m³	20700 pg/m³	TCEP, TCIPP, TDCIPP, TPhP, EHDPP	869
	Antarctica		164.82 pg/m³	3501.79 pg/m³	TCIPP, TCEP	870
	Arctic		231.56 pg/m³	1884.25 pg/m³	TCIPP, TCEP	871

Note:^a for NOPEs

OPEs are also widely present in the atmosphere. The median mass concentration of ΣOPEs in the atmosphere over the Great Lakes region in the United States was 280 pg/m³, with the main OPEs being TCIPP, TnBP, TEHP, and TCEP^[868]. In the air at New York Airport, the mass concentration of ΣOPEs reached as high as 20700 pg/m³, dominated by TCEP, TDCIPP, TCIPP, and EHDPP. Notably, high concentrations of NOPEs were detected in the air at New York Airport (CDP: 182 pg/m³, BPDP: 622 pg/m³, TBPP: 1790 pg/m³, V6: 796 pg/m³)^[869]. Additionally, OPEs have been detected in polar atmospheric environments. For example, in the atmosphere of Antarctica, the mass concentration of ΣOPEs was 165-3501 pg/m³, with TCIPP and TCEP as the main OPEs^[870]. In the Arctic Circle atmosphere, the pollution level of OPEs is comparable to that of Antarctica (232-1884 pg/m³), with TCIPP and TCEP as the main components^[871]. In 2018, Chinese scholars found that OPEs were the most abundant compounds in the atmosphere of 10 major cities in China, with a mass fraction of 612668 ng/g, which is several orders of magnitude higher than PBDEs and NBFRs. Among them, TCEP, TDCIPP, and TCIPP were the main OPEs in the atmosphere, and the mass fraction of TDBPP (866 ng/g) was already higher than that of TDBPP in household indoor dust in the United States^[863]. In 2016, a full-year monitoring result of the atmosphere in Hong Kong, China, showed that the annual average mass concentration of ΣOPEs in the atmosphere in the Hong Kong region was 4936.1 pg/m³, with traditional chlorinated OPEs accounting for 82.7% of the total content^[864]. In the atmosphere of Guangzhou, the average mass concentration of ΣT-OPEs was 4591 pg/m³, with TCIPP, TCEP, and TPhP as the main T-OPEs; the average mass concentration of ΣNOPEs was 600 pg/m³, with RDP (381 pg/m³) as the dominant component^[866]. In the atmosphere of the Pearl River Delta, the average mass concentration of ΣOPEs was 834 pg/m³, with TCEP, TCIPP, TnBP, and TDCIPP as the main OPEs^[867]. These environmental monitoring data indicate that indoor air and atmospheric environments in China are generally contaminated by OPEs, with TCEP and TCIPP as the main pollutants. It is worth noting that as T-OPEs are replaced by NOPEs, the detection rate and content of NOPEs in the atmosphere of China are increasing year by year, and Chinese residents are facing an increasing exposure risk from NOPEs. Therefore, in the future, it is necessary to pay attention to the atmospheric pollution problem of NOPEs in China and its potential human exposure risks.

6.2.2.2 Water Bodies and Sediments

OPEs in the atmosphere usually enter water environments through precipitation or deposition. Additionally, rainwater runoff, surface runoff, and discharge from treated sewage are also important sources of OPEs pollution in natural water bodies^{[876⇓⇓~879]}. Currently, OPEs have been detected in various types of water environments globally (Table 10). The concentration of ΣOPEs in San Francisco Bay seawater in the United States is 130 ng/L, with TDCIPP, TBOEP, and TPhP being the main OPEs^[880]. In the surface water of Hanoi, Vietnam, the average concentration of ΣOPEs is 1412 ng/L, with TCIPP being the main OPEs^[881]. In the seawater of Marseille Bay in the Mediterranean, the concentration of ΣOPEs reaches up to 1013 ng/L, with TCIPP and TCEP being the main OPEs^[882]. In remote environments such as the Arctic Ocean in Canada, OPEs were also detected but at lower concentrations, with the median concentration of ΣOPEs being only 8.3 ng/L, mainly TEHP, TCIPP, and TCEP^[883]. Researchers in China tested 50 urban sewage samples from 25 cities across the country in 2023. The results showed that the concentration of ΣOPEs in treated urban sewage in central and eastern China was higher than in other regions. Among them, the average concentration of ΣOPEs in treated sewage from Wuhan wastewater treatment plant reached as high as 9030 ng/L^[884]. Moreover, in natural water bodies such as rivers in the middle and lower reaches of the Yellow River, the average concentration of ΣOPEs is 1280.06 ng/L, with TCEP, TEP, and TCIPP being the main OPEs^[885]. In Taihu Lake water body, the average concentration of ΣOPEs is 800 ng/L, with TEP (620 ng/L) being the main one^[886]. The average concentrations of ΣOPEs in surface water and drinking water in Nanjing are 3671 ng/L and 719.83 ng/L respectively^[887]. The average concentration of ΣOPEs in surface water in Hong Kong, China, is 637 ng/L^[888]. In the western region of China, the average concentration of ΣOPEs in water environments is much lower than in other regions. For example, Chinese scholars conducted an OPEs detection throughout the Yangtze River Basin. The concentration of OPEs in the upstream river water (Qinghai, Yunnan, Sichuan, and Chongqing) is significantly lower than in the midstream and downstream areas. However, in different river sections, TEP, TCIPP, and TCEP are still the main OPEs^[889]. The average concentration of ΣOPEs in surface water in the Tibet region is 289.28 ng/L, with TCEP and TCIPP being the main ones^[890]. The average concentration of ΣOPEs in surface water in rural areas of Chongqing is 52.6 ng/L^[891]. The average concentration of ΣOPEs in rivers in Chengdu is 204 ng/L^[892].

In the sediments of rivers in Nigeria, the mass fraction of ΣOPEs can be as high as 2110 ng/g(dw), with TBOEP being the predominant OPEs^[893]. Chinese scholars detected the sediments of major lakes in the Taihu Lake Basin in 2019. The results showed that the average mass fraction of ΣOPEs in the sediments of the Taihu Lake Basin was as high as 1430 ng/g(dw), with aromatic OPEs and alkane OPEs being the main types. Notably, TDtBPP and BEHPP were detected in all sediment samples, indicating that these two NOPEs are widely present in the sediments^{[894, 895]}. OPEs were detected in the sediments of the Liao River in China at a concentration range of 19.7 to 234 ng/g(dw) (dw)^[896]. The average mass fraction of ΣOPEs in the sediments of the middle and lower reaches of the Yellow River was 136.05 ng/g(dw), with TEP, TCEP, and TCIPP being the main types. Although NOPEs (RDP, BPA-BDPP, and V6) were detected in the sediments of the Yellow River, their average mass fractions were all below 1 ng/g(dw)^[885]. The mass fraction of ΣOPEs in the sediments of the Pearl River estuary was 23.5 to 287 ng/g(dw), with TCEP, TCIPP, and TPhP being the main types of OPEs^[897]. In general, OPEs in China's water environment are mainly T-OPEs, with TCEP, TCIPP, and TEP being the three types with the highest proportions, which require close attention. Although NOPEs have been detected, their pollution levels are relatively low.

6.2.2.3 Wildlife

OPEs can enter organisms through ways such as inhalation or water exposure, bioaccumulation and food chain transmission. In recent years, many studies have reported the accumulation of OPEs in aquatic organisms, poultry and mammals at various trophic levels (Table 10). Currently, only a few studies have reported on the accumulation status of OPEs in low-trophic level aquatic organisms. For example, high concentrations of OPEs (660–922 ng/g(dw)) were detected in plankton in the South China Sea, with TCIPP, TCEP and TBOEP being the main contaminants^[898]. In Taihu Lake, the OPEs contents in plankton and invertebrates are comparable, and TCrP, TPhP and TEHP are the predominant ones^[899].

Fish, as an important link in the aquatic food web, have been the focus of long-term monitoring by researchers. For example, in the fish from Laizhou Bay in China, relatively high concentrations of ΣOPEs (average: 1630 ng/g lw) were detected. Among them, alkane OPEs were the main pollutants. It is worth noting that the average concentration of ΣOPEs in benthic fish (2120 ng/g lw) was significantly higher than that in mid-upper layer fish (1200 ng/g lw)^[900].In the fish from the Pearl River Delta, high concentrations of OPEs (average ΣOPEs concentration: 431–771 ng/g lw) were also detected, with TEHP, TCIPP, TBOEP, and TCEP being the predominant compounds^{[901, 902]}.In the fish from the North Canal, the average concentration of ΣOPEs was 822 ng/g lw, with TnBP, TCEP, TCIPP, and TEHP being the major OPEs^[903].In the fish from Lake Taihu, the average detected concentration of ΣOPEs was 6.68 ng/g ww. Notably, unlike other regions, the highest concentrations of OPEs in the fish from Lake Taihu were TDtBPP (2.72 ng/g ww) and TBOEP (8.66 ng/g ww), which was consistent with previous OPEs detection results in sediments of the Taihu Basin. This indicates that TDtBPP is a widely distributed NOPE in all environmental media in the Taihu Basin^[904].In foreign countries, a long-term monitoring study conducted by Canadian scholars showed that the concentration of ΣOPEs in lake trout in the Great Lakes decreased from 122 ng/g ww in 2001 to 9.06 ng/g ww in 2017^[905].In remote areas, such as Antarctic cod in Antarctica, 173 ng/g lw of OPEs were detected, with TCEP and TDCIPP being the major OPEs, which may be related to their POPs characteristics^[906].

Currently, limited research has been conducted on OPEs in high trophic level organisms in aquatic food webs. Recent studies have shown that the contamination levels of OPEs in aquatic mammals are much higher than those in fish. It was reported that the mass fraction of OPEs in the tissues of short-finned pilot whales in Spanish waters reached up to 24.7 μg/g (lw), which is comparable to the previously reported concentrations of PBDEs in dolphins, with IDPP, TnBP, and triisopropyl phosphate ester (IPPP) being the main OPEs. Notably, high mass fractions of NOPEs were detected in the tissues of common bottlenose dolphins (IDPP: 516 ng/g (lw); IPPP: 142 ng/g (lw))^[907]. The average mass fraction of ΣOPEs in the muscles of three species of dolphins in the western Indian Ocean was 1.045 μg/g (lw), with TBOEP being the predominant OPEs compound. Additionally, the study results indicate that the mass fraction of ΣOPEs in dolphins in the western Indian Ocean is two orders of magnitude higher than that of PBDEs^[908]. In the muscles of fin whales captured in Icelandic waters, seven types of OPEs were detected, with an average mass fraction of ΣOPEs at 985 ng/g (lw), dominated by IPPP, TnBP, and triphenyl phosphine oxide (TPPO)^[909]. From 1990 to 2018, Spanish scholars conducted long-term monitoring of OPEs pollution characteristics in striped dolphins. The study results show that the concentration of ΣOPEs in striped dolphins in the Mediterranean region has remained relatively stable, but the predominant OPEs compounds differ across different periods. In 1990, the main OPEs in striped dolphins were TBOEP. From 2004 to 2018, the main OPEs in striped dolphins were all TnBP. This reflects to some extent the historical OPEs exposure situation of aquatic organisms in the Mediterranean region^[910]. Moreover, high mass fractions of TEHP (164 ng/g (lw)) and TCIPP (41 ng/g (lw)) were detected in cetaceans in the northern Norwegian waters^[911].

In addition, there is a lack of related research on OPEs in terrestrial food webs, especially for high trophic level organisms. Currently, only a few studies have reported the OPEs contamination characteristics in birds and mammals. Nine kinds of OPEs were detected in the eggs of different species of herons in the upper reaches of the Yangtze River, with an average ΣOPEs mass fraction of 48.8 ng/g(lw). The main OPEs were TnBP, TIBP, and TCIPP^[912]. OPEs were detected in the eggs and sera of bald eagles in the Great Lakes^[913]. High mass fractions of OPEs (78-1200 ng/g(lw)) were detected in the feces of primates in Latin America and Uganda, with TBOEP and TCIPP being the main OPEs compounds^[914].

6.2.2.4 Human Body

OPEs can enter the human body through multiple pathways such as respiration, drinking water, food intake, and skin absorption in daily life, accumulating in the body and causing human exposure to OPEs^[915⇓~917]. In recent years, OPEs have been detected in human samples from many countries. Due to ethical regulations of human medical research, there are limitations in obtaining human samples, and serum (liquid), hair, breast milk, and urine are commonly used for OPEs detection. The average concentration of ΣOPEs in pregnant women's serum in Hong'an, Hangzhou, and Mianyang (8.28~14.5 ng/mL) is comparable to that in their fetal umbilical cord blood (5.822~11 ng/mL), which is higher than the average concentration of ΣOPEs in Beijing adults' serum (5.29 ng/mL). There are differences in the composition of OPEs in human serum from different regions, which may be related to the degree of industrialization in the region^[918⇓~920]. Moreover, the average concentration of ΣOPEs in elderly people's (60-69 years old) serum in Jinan was 6.739 μg/L, with TnBP, TPhP, and TCIPP being the main OPEs in elderly people's serum. It is worth noting that NOPEs (CDP, BPA-BDPP, and RDP) were detected in all serum samples, with an average concentration of 0.008~0.117 μg/L^[921]. The average concentration of ΣOPEs in breast milk of Chinese women in Beijing was 26.6 ng/mL, with the average concentration of TPhP reaching 15.1 ng/mL, followed by V6 (6.04 ng/mL)^[922]. The average concentration of ΣOPEs in breast milk samples from American women was 3.61 ng/mL^[923]. The accumulation of OPEs in the aquatic environment and organisms from 2018 to 2023 is shown in Table 11.

表11 2018—2023年OPEs在水环境及生物体中的蓄积情况

Table 11 Accumulation of OPEs in aquatic environment and organisms, 2018—2023

medium	country or region			minimum concentration	maximum concentration	main OPEs	ref
Water environment
Surface water	China			602 ng/L	9030 ng/L	TEP, TCIPP	884
		the middle and lower reaches of the Yellow River		97.66 ng/L	2433 ng/L	TEP, TCEP, TCIPP, TDCIPP	885
		Taihu Lake		100 ng/L	1700 ng/L	TEP	886
		Nanjing		4.36 ng/L	195269 ng/L	TDCIPP, TCEP, TDBPP	887
		Hong Kong		58.8 ng/L	3090 ng/L	TCIPP, TBOEP	888
		Yangtze River Basin		27.9 ng/L	2531 ng/L	TEP, TCIPP, TCEP	889
		Tibet		46.45 ng/L	1744.73 ng/L	TCEP, TCIPP	890
		Chongqing		24.8 ng/L	65 ng/L	TnBP, TCIPP, TCEP, TBOEP	891
		Chengdu		19.1 ng/L	533 ng/L	TCEP, TCIPP, TBOEP	892
	United States of America			35 ng/L	290 ng/L	TDCIPP, TBOEP, TPhP	880
	Arctic Ocean			6 pg/L	440 pg/L	TCEP, TCIPP	924
	Vietnam			46 ng/L	3644 ng/L	TCIPP	881
	Canada			2.9 ng/L	67 ng/L	TEHP, TCIPP, TCEP	883
	Mediterranean Sea			9 ng/L	1013 ng/L	TCIPP, TCEP	882
Sediment	China	Taihu Lake		12.8 ng/g(dw)	9250 ng/g(dw)	TEHP, TDtBPP^a, TCIPP	894,895
Sediment		Liao River		19.7 ng/g(dw)	234 ng/g(dw)	TnBP, TBOEP	896
		the middle and lower reaches of the Yellow River		47.33 ng/g(dw)	234 ng/g(dw)	TCEP, TCIPP, TEP	885
		Pearl River estuary		23.5 ng/g(dw)	187 ng/g(dw)	TCEP, TCIPP, TPhP	897
	Nigeria			13.1 ng/g(dw)	2110 ng/g(dw)	TBOEP	893
Organism
Aquatic life Matter	China	South China Sea	Plankton	660 ng/g(dw)	922 ng/g(dw)	TCIPP, TCEP, TBOEP	898
		Taihu Lake	Plankton	9.4 ng/g(dw)	10.8 ng/g(dw)	TEHP, TPhP, TCrP^a	899
		Laizhou Bay	Benthopelagic fish	833 ng/g(dw)	3150 ng/g(dw)	Alkane-type OPEs	900
			Mesopelagic fish	296 ng/g(dw)	2120 ng/g(dw)	Alkane-based OPEs
		Pearl River Delta	Shrimp	32.1 ng/g(dw)	102 ng/g(dw)	TCIPP, TBOEP, TDCIPP	902
			Crab	30.3 ng/g(dw)	88.4 ng/g(dw)	TCIPP, TBOEP, TDCIPP, EHDPP
			Fish	8.35 ng/g(dw)	40.9 ng/g(dw)	TCIPP, TBOEP, TDCIPP, TCP
			Plankton	660 ng/g(dw)	922 ng/g(dw)	TCIPP, TCEP, TBOEP
		Taihu Lake	Grass carp	1.61 ng/g(ww)	7.66 ng/g(ww)	TDtBPP^a	904
			Crucian carp	2.11 ng/g(ww)	13.2 ng/g(ww)	TDtBPP^a
			Common carp	0.833 ng/g(ww)	6.95 ng/g(ww)	TDtBPP^a
			Bighead carp	2.87 ng/g(ww)	7.75 ng/g(ww)	TDtBPP^a
			Silver carp	3.7 ng/g(ww)	32.3 ng/g(ww)	TDtBPP^a, TBOEP
	Canada	The Great Lakes	Lake trout		122 ng/g(ww)		905
	Antarctica		Cod		173 ng/g(lw)	TCEP, TDCIPP	906
			Dolphin				907
			Muscle	69.5 ng/g(lw)	2939 ng/g(lw)	TBOEP, IDPP^a
			Liver	9.7 ng/g(lw)	712 ng/g(lw)	TBOEP, EHDPP, IDPP^a
	Spain		Kidney	nd	789 ng/g(lw)	TBOEP
			Adipose tissue	27.2 ng/g(lw)	2450 ng/g(lw)	TCIPP, TBOEP
			Brain	nd	24729 ng/g(lw)	TCIPP,TBOEP,TnBP,IDPP^a,IPPP^a
	Western Indian Ocean		Dolphin	1630 ng/g(lw)	31861 ng/g(lw)	TBOEP	908
	Iceland		Rorqual	31.9 ng/g(lw)	10232 ng/g(lw)	IPPP^a, TnBP, TPPO	909
	China	Pearl River Delta	Snakehead fish	50 ng/g(lw)	829 ng/g(lw)	TEHP, TCIPP, TCEP	901
			Tilapia	145 ng/g(lw)	1086 ng/g(lw)	TEHP, TCIPP, TCEP
			Schilbe mystus	133 ng/g(lw)	2321 ng/g(lw)	TEHP, TCIPP, TCEP
		North Canal River	Loach	309 ng/g(lw)	1973 ng/g(lw)	TCIPP, TCEP, TnBP	903
			Crucian carp	274 ng/g(lw)	1042 ng/g(lw)	TCIPP,TCEP,TnBP,TEHP,TPhP
			Ricefish	265 ng/g(lw)	1586 ng/g(lw)	TnBP, TCIPP, TEHP
Lives wildlife Matter	China	Upper and middle reaches of the Yangtze River	Egret-Egg	18 ng/g(ww)	185 ng/g(ww)	TnBP, TIBP^a, TCIPP	912
	United States of America	The Great Lakes	Bald Eagle				913
			Egg	4.76 ng/g(ww)	760 ng/g(ww)	TCIPP, TnBP, TPhP	913
			Serum	2.68 ng/g(ww)	16.9 ng/g(ww)	TCIPP, TnBP
	Latin America and Uganda		Primate feces	78 ng/g(lw)	1200 ng/g(lw)	TBOEP, TCIPP	914
Human body	China	Hangzhou	Maternal serum	3.3 ng/mL	51.4 ng/mL	TCEP, TCIPP, TPhP	918
	China	Mianyang	Maternal serum	1.66 ng/mL	23.8 ng/mL	TnBP, TCEP, TPhP	918
		Hubei	Maternal blood	1.06 ng/mL	59.6 ng/mL	TCrP, TIBP, TnBP, TCIPP	919
		Hubei	Umbilical cord blood	nd	45.5 ng/mL	TCrP, TIBP, TnBP, TCIPP	919
		Jinan	Serum of the elderly	0.923 ng/mL	26.55 ng/mL	TnBP, TPhP, TCIPP	921
		Beijing	Breast milk	14.2 ng/mL	59.2 ng/mL	EHDPP, TPhP, V6^a	922
	United States of America			0.67 ng/mL	7.83 ng/mL	TIBP^a, TnBP, TBOEP	923

Note:^a are NOPEs

6.2.3 Current Research Status of OPEs' Toxicology

6.2.3.1 Growth and Developmental Toxicity

A study showed that TDCIPP caused defects in the gastrula development and abnormal envelopment of zebrafish embryos by affecting estrogen receptor alpha (ERα) and peroxisome proliferator-activated receptor gamma (PPARγ), demethylation at CpG sites, reducing the content of acetyl carnitine and cytidine-5-diphosphocholine in the embryo, ultimately leading to developmental defects and envelopment abnormalities of zebrafish embryos^[925,926]. After carp were exposed to environmentally relevant concentrations of TDCIPP (2752, 8064, 47824 ng/L) for a long time, it caused developmental delay and death of their F1 generation embryos^[927]. In addition, another study showed that after carp were exposed to 5 μg/L or 50 μg/L TDCIPP for 90 days, TDCIPP downregulated the expression of growth hormone (ghs), growth hormone receptor (ghr), and insulin-like growth factor-1 (igf1) genes in carp; and by binding to growth hormone-releasing hormone receptor protein, it interfered with the regulation of growth hormone-releasing hormone on ghs, thereby reducing the transcription levels of ghr1 and ghr2 in the pituitary gland, ultimately leading to growth retardation of carp^[848]. After zebrafish embryos were exposed to 25 μmol/L or 50 μmol/L TPhP for 72 hours, TPhP reduced the expression of cyp26a1 in zebrafish embryos, antagonized nuclear retinoic acid receptor (RAR) activity, thus causing developmental delays and deaths of zebrafish embryos^[928]. After zebrafish were exposed to environmentally relevant concentrations of diphenyl phosphate (DPhP) (0.8, 3.9, 36.5 μg/L) for 120 days, DPhP inhibited oxidative phosphorylation in zebrafish, downregulated fatty acid oxidation, upregulated phosphatidylcholine degradation, reduced succinate dehydrogenase activity in the liver and carnitine O-palmitoyltransferase 1 protein content, thereby reducing body length and weight in male zebrafish, leading to growth retardation^[929]. In addition, after zebrafish embryos were exposed to high concentrations of DPhP (250, 500, 1000 μmol/L) for 72 hours, DPhP significantly reduced the level of in situ hemoglobin by disrupting mitochondrial function and heme biosynthesis-related pathways, thereby increasing the distance between the venous sinus and the bulbus arteriosus in zebrafish embryos, inducing cardiotoxicity, and causing developmental abnormalities of zebrafish embryos^[930]. Another study showed that after silver carp were exposed to environmentally relevant concentrations of TnBP (100, 1000 ng/L) for 60 days, TnBP significantly inhibited the growth and development of silver carp by downregulating the expression of igf1 gene^[902].

In addition to fish, Caenorhabditis elegans has also been widely used in ecotoxicology research. For instance, after being exposed to environmentally relevant doses of TBOEP (50, 500, and 5000 ng/L) for 21 days, multi-omics analysis results showed that TBOEP disrupted the expression of genes encoding heat shock proteins, lipases, and stearoyl-CoA desaturase, increased the content of L-arginine in Caenorhabditis elegans, and significantly upregulated the expression of micro-RNAs in the lethal-7 family, thereby accelerating the aging and death of Caenorhabditis elegans [931]. Moreover, after exposure to TDCIPP (0.1, 1, 10, 100, and 1000 μg/L) for 72 hours, TDCIPP can inhibit the tumor suppressor DAF-18/PTEN in Caenorhabditis elegans, activate the insulin/insulin-like growth factor signaling pathway (IIS), reduce the dephosphorylation level of PIP₃, and cause accumulation of phosphatidylinositol 3,4,5-trisphosphate (PIP₃), further promoting the activation of the serine/threonine kinase Akt/protein kinase B (PKB) family, thereby inducing the phosphorylation of DAF-16/FoxO and promoting its sequestration in the cytoplasm, leading to a shortened lifespan of Caenorhabditis elegans [932].

At present, there are few studies on the correlation between human development and OPEs. Epidemiological investigations have shown that OPEs have adverse effects on infant development. In Wuhan, China, researchers analyzed the relationship between OPEs metabolites in pregnant women's urine and their infants' birth weight, and the results showed that the content of DPhP in pregnant women's urine was positively correlated with the risk of low birth weight in newborn girls^[896]. An analysis of OPEs contamination characteristics in Shanghai pregnant women's serum and infant developmental status indicated that TnBP and tris(methylphenyl) phosphate (TMCP) had negative impacts on infant growth and development, with more significant effects on male infants^[933]. Canadian scholars analyzed the relationship between children's asthma incidence and OPEs in their sleep area dust, and the study results showed that TBOEP could significantly increase the incidence of childhood asthma^[934].

6.2.3.2 Endocrine Disruption Effect

Screening endocrine-disrupting chemicals with cell-based and yeast two-hybrid techniques is a convenient, rapid, and efficient method. Recently, Chinese scholars evaluated the thyroid endocrine-disrupting effects of eight typical OPEs (TCEP, TDCIPP, TCP, TPhP, TBOEP, TEHP, TEP, and TnBP) using an in vitro exposure and computational toxicology combined research approach. Recombinant receptor gene yeast experiments showed that under co-exposure conditions with T3, TDCIPP, TEP, TPhP, and TnBP competitively bind to thyroid hormone receptor β (TRβ)^[935]. Furthermore, through E-screen and MVLN tests, it was confirmed that OPEs generally have anti-estrogenic activity. Further molecular docking experiments indicated that OPEs may cause anti-estrogenic effects by disrupting the positioning of helix 12 in ER. Additionally, OPEs with bulky substituents (such as benzene rings) or long alkyl chains exhibit stronger anti-estrogenic activity^[935]. Exposure to KGN human ovarian granulosa cells with OPEs (1–50 μmol/L, TPhP, TMPP, IPPP, BPDP, and TBOEP) promoted the synthesis of E2 and progesterone, increased the expression of key genes for steroid synthesis (STAR, CYP11A1, CYP19A1, HSD3B2, and NR5A1), and inhibited steroid biosynthesis. The study results indicate that OPEs can disrupt steroid synthesis in KGN human ovarian granulosa cells by targeting the regulation of steroidogenic enzymes and steroid transport proteins^[936].

In vivo experiments showed that after being exposed to EHDPP (29.9, 104, and 434 ng/L) for 100 days, the incidence of feminization in all male fish in the exposure groups significantly increased. Among them, the content of testosterone (T) and 11-ketotestosterone (11-KT) in the serum of male fish in the 434 ng/L exposure group significantly decreased, while the expression levels of 17β-E2, vtg1, and vtg2 in male fish of the 104 ng/L and 434 ng/L exposure groups significantly increased. Additionally, yeast two-hybrid experiments indicated that EHDPP and its metabolites (5-OH-EHDPP and 3-OH-EHDPP) have anti-androgenic effects and play an important role in the process of male fish feminization development^[937]. After exposing zebrafish to environmentally relevant concentrations of TCEP (0.8, 4, 20, and 100 μg/L) for 120 days, the expression levels of genes related to thyroid hormones (T4) and the hypothalamic-pituitary-thyroid (HPT) axis in the serum of their offspring (F1) were significantly downregulated, leading to thyroid dysfunction in zebrafish^[938].

Epidemiological studies have shown that four OPEs (TPrP, TBOEP, TCIPP and TDCIPP) are associated with the incidence of thyroid cancer in humans^[939].Researchers in China investigated the Dalian maternal and infant populations and found that OPEs exposure could cause thyroid dysfunction in pregnant women and infants through 8-hydroxy-2'-deoxyguanosine (8-OHdG)^[940].

6.2.3.3 Hepatotoxicity and Lipid Metabolism Disruption Effects

The liver is an important site for detoxification and lipid metabolism in the body and is considered a major target of toxic chemicals. In recent years, researchers have gradually paid attention to the toxicity of OPEs on the liver. Studies have shown that exposure to tris(hexyl) phosphate (THP) (800 mg/kg (body weight)) can induce vacuolar degeneration in mouse hepatocytes and increase alanine transaminase levels. Further studies found that THP induces acute liver injury by affecting endoplasmic reticulum stress, apoptosis, cell cycle, and glycolysis signaling pathways^[941].In addition, Chinese scholars conducted research on the hepatic toxicity of TPhP and its metabolite DPhP. The results showed that exposure to TPhP and DPhP significantly inhibited the uptake of extracellular glucose and glycogen synthesis in human liver cells, causing insulin resistance. After 8 weeks of exposure to TPhP (40 and 80 mg/kg (body weight)), mice exhibited cytoplasmic vacuolation, central venous and sinusoidal congestion, and inflammatory cell infiltration in the liver, accompanied by a significant increase in postprandial blood glucose levels. Co-exposure with the endoplasmic reticulum stress antagonist 4-PBA partially reduced the postprandial blood glucose levels, indicating that TPhP induces endoplasmic reticulum stress in the mouse liver, disrupts the normal function of pancreatic islet cells, and leads to insulin resistance and liver toxicity^[942].High concentration exposure (0.5 and 5 μg/L) of TCEP for 28 days in zebrafish led to a significant decrease in aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and superoxide dismutase (SOD) activities in the liver, inducing liver inflammation. Additionally, TCEP exposure caused intestinal dysbiosis in zebrafish, with a significant increase in the proportion of Bacteroides in the intestine, activating pro-inflammatory factor expression in the liver through the liver-intestine axis, further promoting liver damage^[943].

Research has shown that endocrine disruptors can disrupt the energy balance and basal metabolic rate of organisms, promoting weight gain and the occurrence of related metabolic diseases. Previous research results have indicated that OPEs are a widely present class of endocrine disruptors^[944]. Therefore, in recent years, the impact of OPEs on organism metabolism has attracted significant attention from researchers. Studies have shown that OPEs can significantly promote lipid deposition and cause lipid metabolism disorders. For example, in vitro experiments, TCrP promotes lipid accumulation in human cells by activating the pregnane X receptor (PXR) and peroxisome proliferator-activated receptor gamma (PPARγ)^{[945, 946]}. Researchers conducted studies on the effects of two aromatic OPEs (TCrP and TPhP) and three chlorinated OPEs (TDCIPP, TCIPP, and TCEP) on lipid metabolism. The results showed that target OPEs at a concentration of 10 μmol/L could all lead to lipid deposition in AML-12 cells by affecting mitochondrial function. Additionally, aromatic OPEs caused more significant lipid deposition, indicating that aromatic OPEs have stronger lipid metabolism interference effects^[947]. Foreign scholars found through research using human 3D liver cell spheroids that after exposure to EHDPP at 1 μmol/L and 10 μmol/L for 7 days, EHDPP caused lipid metabolism disorders in human 3D liver cell spheroids, including steroid lipids, sphingomyelin, triglycerides, glycerophospholipids, and fatty acyls, by altering the expression of genes such as ACAT1, ACAT2, CYP27A1, ABCA1, GPAT2, PNPLA2, PGC1α, and NRF2^[948]. After zebrafish were exposed to high concentrations of TCIPP (5~25 mg/L), TCIPP led to lipid accumulation in zebrafish livers by inducing overexpression of genes related to fat synthesis, inhibiting transcription of genes related to fatty acid β-oxidation, and affecting lipid metabolism and apoptosis signaling pathways^[949]. Researchers exposed 6-month-old zebrafish to EHDPP (5, 35, and 245 μg/L) for 21 days, and the zebrafish exhibited abnormal feeding behaviors, including significantly increased average food intake, feeding frequency, and feeding rate, leading to significant increases in body weight, plasma total cholesterol, triglycerides, and body fat content^[950]. After mice were exposed to TCEP (20 mg/kg body weight and 60 mg/kg body weight) for 9 weeks, TCEP inhibited FXR activity by directly binding to two amino acid residues, lysine 335 and lysine 336, on FXR, resulting in hypertriglyceridemia, hepatic steatosis, and weight gain in mice^[949]. C57 mice exposed to subchronic concentrations (1 mg/(kg·d)) of TCrP for 12 weeks experienced interference with unsaturated fatty acids and steroid hormone synthesis in the liver, leading to abnormal lipid deposition in the liver, liver dysfunction, elevated triglycerides, and total cholesterol. Additionally, in the high-fat diet group, TCrP exposure impaired mitochondrial and endoplasmic reticulum functions in mouse livers by altering the activity of cytochrome P450 enzyme subfamilies, further exacerbating lipid metabolism disorders^[951].

6.2.3.4 Neurotoxicity

Several studies have shown that exposure to TPhP, TnBP, TBOEP, and TCEP can lead to abnormal motor behavior in zebrafish^{[952⇓⇓~955]}. Research results from Chinese scholars indicate that OPEs can cause changes in neurotransmitters such as dopamine and γ-aminobutyric acid in zebrafish, inhibit acetylcholinesterase activity, and induce abnormal neurological behavior in zebrafish^[956,957]. TCEP and TCIPP can induce neurodevelopmental toxicity by downregulating the expression of genes and proteins related to neurodevelopment in zebrafish larvae, thereby reducing their motor vitality^[958].

Moreover, recent studies have shown that OPEs exhibit gender-specific neurotoxicity in organisms. For example, zebrafish embryos exposed to TDCIPP until adulthood showed a gender-specific reduction in dopamine levels in female brains, downregulation of the transcription of genes related to dopaminergic signaling, and increased DNA methylation levels in the promoter regions of key genes for neural development, leading to anxiety-like behaviors in adult female zebrafish^[959]. Another study found that long-term exposure of Japanese medaka larvae to environmentally relevant concentrations (29.9, 104, and 434 ng/L) of EHDPP during sexual maturation caused feminization in male fish and specifically inhibited behaviors such as chasing, courtship, and mating in male fish^[937]. Researchers using mice as experimental subjects discovered that pregnant female mice orally exposed to a mixture of TDCIPP, TCrP, and TPhP exhibited abnormal gene transcription related to neuronal development in the hypothalamus of F1 generation male mice and displayed anxiety-like behaviors^{[960, 961]}. A study conducted on pregnant women and their newborns in Wuhan indicated a negative correlation between the level of chlorinated OPEs in maternal blood and neonatal neurodevelopmental indicators in male infants^[962].

6.2.4 Outlook

In summary, the pollution of OPEs has drawn extensive attention. Due to the large amount of OPEs used, they are still widely present in products, and during use and recycling, OPEs will continue to release into the environment, causing persistent environmental pollution. Moreover, with the introduction of NOPEs, many scientific problems remain to be further clarified. Based on the existing research results, the following prospects are proposed.

1) As research has advanced, some T-OPEs (such as TCEP, TCIPP, TDCIPP, and TPhP, etc.) have been confirmed to possess POPs characteristics, which has led to the extensive use of NOPEs. This has resulted in a continuous increase in their concentrations in environmental media, wildlife, and human bodies. Compared with T-OPEs, NOPEs are characterized by high molecular weight, high hydrophobicity, and high bioaccumulation coefficients, indicating potentially stronger bioaccumulation capacity, environmental persistence, and more complex metabolic transformation processes. However, current research on the environmental behavior and toxicology of NOPEs is extremely limited. Therefore, we need to pay close attention to the environmental pollution problems caused by NOPEs and their potential risks to ecosystems and human health, conducting research on the source tracing, transformation, metabolism, and other environmental behaviors of NOPEs, as well as their toxicological effects and mechanisms. Given that the acute toxicity of NOPEs, as substitutes, is likely to be relatively low, the research focus should shift from acute exposure damage to long-term exposure risks. It is necessary to focus on the potential toxicity effects and mechanisms that may cause metabolic disorders and long-term or transgenerational toxic effects, exploring toxicity effects and mechanisms that are difficult to detect through traditional conventional toxicological methods such as molecular targets, cellular-level damage, and metabolic disruption.

2) Some OPEs enter living organisms and undergo complex metabolic processes in vivo. The toxicity of the resulting metabolites may be stronger than that of the parent compounds. Therefore, it is necessary to focus on the biotransformation process of OPEs in vivo, the analysis and verification of their metabolites, and the effects and mechanisms caused by the metabolites, so as to further reveal their toxic mechanisms. In terms of scale, it is necessary to carry out ecological research to explore the rules of toxicity transmission along the food chain.

3) A wide variety of OPEs are continuously emerging, posing significant challenges for their environmental health risk assessment. Previous studies have accumulated a large amount of omics, phenotypic and biochemical indicator data regarding OPEs exposure, but there is a lack of tools for deep data mining and analysis, resulting in low data utilization and restricting our risk evaluation of OPEs. In recent years, with the development of big data and artificial intelligence technologies, the transition from traditional statistics to machine learning has become an inevitable trend. In future research, we need to use machine learning to accurately identify the morphological characteristics of important tissues or organs in model organisms under OPEs exposure conditions, and couple with omics big data sets to precisely identify specific target toxicity effects of OPEs. Establish deep learning and complex network analysis methods and technologies to integrate environmental big data and toxicology big data, and comprehensively assess their environmental health risks.

6.3 Integrative Exposure Assessment of Neonicotinoids in Honeybee-Visit Plants

6.3.1 Research Background and Significance

The protection of insect pollinators contributes to biodiversity conservation and promotes food production, being a key component in the complex network of species interactions within healthy ecosystems^[963,964]. Bees are the most important pollinating insects^[965], with approximately 25,000 species of wild or domesticated bees worldwide involved in flower pollination and aiding plant reproduction^{[963,964,966]}. However, since 2006, the increasing phenomenon of global honeybee colony losses and rising bee diseases has been observed, with many commercial beekeepers attributing the widespread use of systemic pesticides as one of the most significant factors contributing to colony collapse disorder (CCD)^{[967⇓⇓⇓⇓⇓~973]}. Research has shown that neonicotinoid pesticides pose potential risks to non-target organisms, including pollinators^[974⇓~976]. Additionally, studies have confirmed a direct correlation between residues of neonicotinoid pesticides in environmental media and declines in pollinator populations such as bees, as well as occurrences of colony collapse disorder (CCD)^{[977⇓⇓⇓⇓~982]}.

Neonicotinoids are new contaminants that have drawn extensive attention from scholars at home and abroad^[983,984]. Their systemic properties allow them to be applied in various ways, such as seed coatings, foliar sprays, or direct application to the soil^{[985⇓⇓~988]}. It is estimated that over half of U.S. soybeans and virtually all non-organic corn are planted using neonicotinoid-treated seeds^{[986,989,990]}. Since Bayer in Germany successfully developed the first neonicotinoid pesticide, imidacloprid, in 1984, a fourth-generation neonicotinoid insecticide, flonicamid, has been developed^[991]. The emergence of neonicotinoid pesticides has led to a shift in the types of pesticides sold in the market towards this category^{[992⇓⇓~995]}. It is expected that by 2025, the market value of this class of pesticides will reach nearly $10 billion annually^[974,996].

How to assess the risk of pollinators contacting pesticides remains an ongoing problem^[997]. Rarely are insecticides found alone in the environment^[997⇓~999]; six types of neonicotinoid pesticides commonly detected in various environmental media. Mitchell et al.^[1000] showed that 50% of honey samples in North America contained at least three types of these pesticides (including imidacloprid, thiamethoxam, and clothianidin). Combined exposure to multiple pesticides in the environment significantly affects individual and colony traits of bees^[1001]; however, current studies often lack evaluation of integrated exposure to neonicotinoid pesticides. The International Programme on Chemical Safety (IPCS) under WHO proposed that cumulative exposure can be quantified through integrated exposure to multiple chemicals^[1002,1003]. This paper reviews the main ways bees are exposed to neonicotinoid pesticides via nectar-producing crops and the characteristics of pesticide residues worldwide, aiming to quantify bee exposure levels through integrated exposure concentration (IMI_RPF) and provide a theoretical basis for ecological risk assessment and rational application of this pesticide.

6.3.2 Literature Sources and Data Extraction Process

This article revolves around the following questions: 1) How do bees get exposed to neonicotinoid pesticides through nectariferous plants? 2) To what extent do neonicotinoid pesticides accumulate in pollen, nectar, and honey? 3) How can the exposure risk of neonicotinoid pesticides to bees be reasonably assessed? The retrieval process refers to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ^[1004], as well as the studies by Impellizzeri ^[1005] and Zhang et al. ^[1006]. Using Web of Science, a systematic literature search was conducted on articles published before September 1, 2023. By using "Honey or Nectar or Pollen" and "Neonicotinoid" as keywords, a total of 1783 relevant documents were obtained. All related documents were screened by reading their titles and abstracts to determine whether they are suitable for this review. The specific exclusion and screening process is shown in Figure 29.

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图29 2023年之前发表的纳入和排除研究的流程图。在Web of Science上用于搜索的关键词包括“花粉、花蜜或蜂蜜”和“新型烟碱类化合物”,研究数量显示在括号中

Fig. 29 Flow diagram of included and excluded studies published prior to 2023.Keywords used to search in the Web of Science included“Pollen, Nectar, or Honey”and “Neonicotinoid”. The numbers of studies are shown in parentheses

Each study was reviewed to extract and record the data, which included sampling location (continent, country), sample size, concentration mean or concentration range (minimum and maximum values), limit of quantitation (LOQ) or limit of detection (LOD). At the same time, the different units used in various studies to express concentration (e.g., ppb, μg/g, or ng/g) were all converted into ng/g to unify the calculation unit^[1007,1008]. When the detected concentration was less than LOQ or LOD, 1/2LOD was used for calculation^[1008,1009]. The data analysis and graphing software were Excel and GraphPad Prism 8.

6.3.3 Based on the Relative Performance Factor (RPF) Method for Integrated Exposure Assessment

6.3.3.1 Relative Performance Factor Method (RPF)

After screening, the included studies reported residues of neonicotinoid insecticides in pollen, nectar and honey. Some of these studies also found co-occurrence of multiple neonicotinoids, but none of them evaluated the total exposure of neonicotinoids. When highly toxic and less toxic neonicotinoids co-exist in the same sample, simple arithmetic addition would underestimate the real exposure risk^[1006]. Therefore, it is important to select a method that integrates all neonicotinoid residues by considering the toxicity differences of individual neonicotinoids.

US EPA has developed a relative potency factor method (RPF)^[1010], which is used to evaluate the health risks associated with exposure to mixtures of chemicals that have similar molecular structures and the same mode of action (or toxicological endpoint)^{[1011⇓⇓⇓~1015]}. According to Equation (1), the RPF values for seven novel neonicotinoid compounds are calculated based on the lowest observed adverse effect levels (LOAELs, mg/(kg·d)) of each individual novel neonicotinoid pesticide^[977] (Table 12), and then the integrated exposure concentration is calculated using Equation (2)^[1016].

(1)

Q R P F, i = Q L O A E L, I M I / Q L O A E L, i

(2)

$C I M I, R P F (n g / g) = ∑ i (C n e o n i c s, i × Q R P F, i) =$

$C I M I + C A C E × 0.97 + C C L O × 0.54 + C D I N ×$

0.001 + C T H I A C × 6.76 + C T H I A M × 9.39 + C I M I D × 1

wherein: C_IMI,RPF represents the integrated exposure concentration, ng/g; i represents different types of neonicotinoid pesticides; Q_RPF,i represents the relative potency factor of different types of neonicotinoid pesticides; Q_LOAEL,i represents the lowest observed adverse effect level (LOAEL) value of different types of neonicotinoid pesticides, mg/(kg·d); Q_LOAEL,IMI represents the LOAEL value of IMI, mg/(kg·d); neonics is the abbreviation for neonicotinoid pesticides; IMI is the abbreviation for Imidacloprid, and we selected Imidacloprid (IMI), the most commonly used and well-researched neonicotinoid pesticide in the world, as the indicator compound [1006,1016,1017]; the abbreviations of other neonicotinoid pesticides, LOAEL values, and RPF values of seven neonicotinoid pesticides are shown in Table 12.

表12 7种新型烟碱类化合物的CAS号、化学结构式、无可见有害作用水平(LOAEL)及相对应的相对效价因子(RPF)

Table 12 CAS numbers, Chemical structures, No observed adverse effect levels (LOAELs) and Relative potency factors (RPF) of 7 neonicotinoid insecticides

CAS Number	Neonicotinoid		LOAEL/[mg/(kg·d)]	RPF
135410-20-7	Acetamiprid (ACE)	Acetamiprid	17.5	0.97
210880-92-5	Clothianidin (CLO)	thiamethoxam	31.2	0.54
165252-70-0	Dinotefuran (DIN)	Dinotefuran	991	0.001
138261-41-3	Imidacloprid (IMI)	Imidacloprid	16.9	1.00
111988-49-9	Thiacloprid (THIAC)	Thiamethoxam	2.5	6.76
153719-23-4	Thiamethoxam (THIAM)	Thiamethoxam	1.8	9.39
105843-36-5	Imidaclothiz (IMID)	Chlorfenapyr	—	1.00

6.3.3.2 Residue Study of Neonicotinoid Pesticides in Pollen, Nectar, and Honey Worldwide

The initial database search yielded a total of 1783 articles (Fig. 29), and after relevance screening of the titles and abstracts, 35 articles were included in our study after removing 12 duplicate articles (Table 13, Table 14, and Table 15). The first article was published in 2011, and from 2016 onwards, the number of articles monitoring the residues of novel neonicotinoid pesticides in pollen, honey, and nectar gradually increased. However, after 2018, the number of related studies showed a downward trend (Fig. 30).

表13 在全球范围内研究中不同蜜源作物花粉中六种新型烟碱类农药残留概述及IMI_RPF值

Table 13 Overview of neonicotinoids in pollen reported in the peer-review literature and the calculated IMI_RPF

Location		N sample⁷	Compound (Mean or Range)						LOQ⁸/ (ng/g)	IMI_RPF⁹/ (ng/g)	ref¹⁰
Continent	Country	N sample⁷	IMI¹	THIAM²	ACE³	CLO⁴	THIAC⁵	DIN⁶	LOQ⁸/ (ng/g)	IMI_RPF⁹/ (ng/g)	ref¹⁰
Asia	China	22	20.12	-¹¹	4.36	3.93	—	9.41	0.6~1.5(LOD)	26.48		1053
	China	483	20.3	39.6	ND	ND	ND	ND	0.47~2.13	392.14		1025
	China	69	ND~3.2	ND~2.03	ND~3.26	ND~0.28	ND~1.56	ND~2.9	0.031~0.074	0~36.12		1016
	Thailand	6	22	—	—	—	—	—	1	22.00		1054
		3	ND¹²	—	—	—	—	—	(LOD)	0.00
		11	3.9	—	—	—	—	—		3.90
Africa	Egypt	33	6.15	12.35~15.50	13.63	4.53	—	7.61	0.1~2.1	137.79~167.37		1055
Europe	U.K.	13	ND	≤0.12¹³	—	≤0.12	0.06±0.22	—	0.12~0.48	0.58¹⁴		1021
		13	ND	≤0.12	—	0.16±0.58	0.15±0.36	—		1.27
		19	≤0.16	0.58±1.64	—	≤0.10	1.47±4.41	—		15.42
		23	≤0.16	4.96±11.29	—	≤0.12	0.08±0.31	—		47.15
		7	0.31±0.82	≤0.12	≤0.04	ND	1.34±3.52	—		9.54
		13	ND	≤0.12	ND	ND	ND	—		0.17
		11	ND	ND	≤0.04	ND	≤0.04	—		0.05
		11	1.13±3.34	≤0.12	0.14±0.42	ND	≤0.04	—		1.48
		22	≤0.16	≤0.12	≤0.04	ND	≤0.04	—		0.24
	U.K.	11	<0.36	5.7	<0.02	3.8	19	—	0.07~2.2	184.07		1023
		8	<0.36~<1.1	2.8	<0.02	<0.72	0.6	—		30.46~30.57
		10	<0.36	0.13	<0.02	0.5	0.3	—		3.58
		25	0.2	0.15	<0.02~<0.07	<0.72~<2.2	0.9	—		7.75~7.88
		19	<0.36~<1.1	<0.12~<0.36	<0.02	<0.72	<0.07	—		0.36~0.80
	U.K.	18	6.9±16	11.0±16	0.45±0.23	11.0±9.3	0.78±1.1	—	0.14~5.9	121.84		1056
	Greece	10	72	ND	ND	6.1~69.04	ND	—	0.2~0.6	75.29~109.28		1057
		4	73.9	14.4	ND	308.3~1273	<0.4	—		376.00~896.94
	Germany	16	ND	—	ND	ND	2	—	0.1~2.0	13.52		1058
		39	ND	—	3.42	ND	42.37	—		289.74
		24	ND	—	2.08	0.425	99.8	—		676.90
		22	ND	—	ND	ND	18.13	—		122.56
		30	ND	—	ND	ND	43.18	—		291.90
		20	0.11	—	ND	ND	57.4	—		388.13
		9	ND	—	ND	ND	3.24	—		21.90
		20	ND	—	1.76	ND	24.94	—		170.30
		12	0.17	—	ND	0.24	55.18	—		373.32
		19	ND	—	ND	ND	21.94	—		148.31
		27	ND	—	ND	ND	26.68	—		180.36
		16	ND	—	0.13	ND	ND	—		0.13
		27	ND	—	ND	ND	17.7	—		119.65
	Ireland	12	—	—	—	<0.13	ND	—	0.07~0.29	0.02		1033
		8	—	—	—	ND	4.43±0.06	—		29.95
	Italian	238	2	2	—	—	—	—	0.25	20.78		999
		152	2	1	—	—	—	—		11.39
		164	2	1	—	—	—	—		11.39
	Italy	<67	17.9~32	ND	10.5~33.1	ND	6±2	—	5.69~6.12	104.67~64.11		1059
	Luxembourg	154	0.44~0.79	0.36~0.84	—	0.39~1.40	0.57~133.05	—	0.28~0.42	7.88~908.85		1060
	Poland	53	3.1	ND	—	—	61.3	—	0.8~8.9	417.49		1061
N. Am¹⁵	Canada	6	4.96	—	—	—	—	—	0.02~0.063	4.96		1062
		6	18.4	—	—	—	—	—	(LOD)	18.40
	Canada	86	6.0±7.2	6.5±2.0	9.9±9.2	—	1.4	—	ND	86.10		1063
	USA	219	<0.1~43.1	<0.1~2.5	<0.1~4.36	<0.1~8.09	<0.1~40.8	<0.1~4.94	0.1	0.28~350.99		1021
	USA	38	2.5	53.9	ND	17.3	—	—	2	517.96		1064
		35	3.9	ND	ND	ND	—	—	(LOD)	3.90
		31	2.9	3.9	1.6	4.4	—	—		43.45
	USA	170	0.41±0.05	3.65±0.36	—	0.80±0.05	—	—	0.33	35.12		1065
		144	1.37±0.17	2.30±0.13	—	1.24±0.08	—	—		23.64
	USA	13	0.2~2.2	ND	ND	ND	0.1~0.3	ND	0.1	0.88~4.23		1032
	USA	25	1.2	2.1	14.3	—	—	ND	0.2~0.5	34.79		1066
		24	1.1	2.6	2.1	—	—	0.4		27.55
	USA	32	ND	ND	27	ND	ND	—	0.5~20	26.19		1067
	USA	275	1.66	—	1.09	—	0.52	—	1~2	6.23		1068
		273	8.6	—	ND	—	ND	—	1	8.60
		190	5.79	—	ND	—	1.1	—		13.23
		124	2.8	—	ND	—	ND	—		2.80
	USA	7	0.4~2.3	ND	ND	ND	ND	ND	0.1~0.5	0.40~2.30		1069
Oceania	New Zealand	6	0.2~1.2	ND	ND	0.2~2.6	0.1~3.3	ND	0.1~0.5	0.98~24.91		1069

Note:1.Imidacloprid; 2. Thiamethoxam; 3. Acetamiprid; 4. Clothianidin; 5. Thiacloprid; 6. Dinotefuran; 7. Number of samples; 8. Limit of Quantitation; 9. Imidacloprid equivalent relative potency factor(ng/g); 10. Reference; 11. Not mentioned in the references cited; 12. No detected; 13. Less than limit of quantitation (LOQ); 14. Calculated using 1/2LOD for data less than LOQ; 15. North America.

表14 在全球范围内文献中报道的花蜜中六种新型烟碱类农药残留概述及IMI_RPF值

Table 14 Overview of neonicotinoids in nectar reported in the peer-review literature and the calculated IMI_RPF.

Location		N sample	Compound (Mean or Range)						LOQ/ (ng/g)	IMI_RPF/(ng/g)	ref
Continent	Country	N sample	IMI	THIAM	ACE	CLO	THIAC	DIN	LOQ/ (ng/g)	IMI_RPF/(ng/g)	ref
Asia	China	34	2.40	ND	7.81	2.16	ND	2.83	0.6~1.5	11.14	1053
	China	391	30.3	31.2	16.9	—	—	—	0.80~2.37	339.66	1025
Europe	U.K.	8	ND	≤0.10	ND	ND	ND	—	0.08~0.4	0.14	1021
		7	ND	0.26±0.68	ND	ND	ND	—		2.44
		14	≤0.14	≤0.10	ND	ND	ND	—		0.17
		13	ND	0.2±0.51	ND	ND	ND	—		1.88
		5	≤0.10	0.76±1.52	ND	ND	ND	—		7.15
		13	ND	≤0.10	ND	ND	ND	—		0.14
		10	ND	≤0.10	ND	ND	ND	—		0.14
		12	ND	ND	≤0.14	ND	0.05±0.13	—		0.36
		19	ND	≤0.10	≤0.14	ND	0.09±0.15	—		0.77
Europe	Ireland	12	—	—	—	< 0.09~0.95	ND	—	0.09	0.01~0.51	1033
		8	—	—	—	< 0.09	ND	—		0.01
N. Am	USA	224	0.85	1	1.08	ND	0.52	—	1~2	14.80	1068
		264	0.53	ND	ND	ND	ND	—	(LOD)	0.53
		193	0.62	ND	ND	ND	ND	—		0.62
		87	ND	ND	ND	ND	ND	—		0.00

表15 在全球范围内文献中报道的蜂蜜中6种新型烟碱类农药残留概述及IMI_RPF值

Table 15 Overview of neonicotinoids in honey reported in the peer-review literature and the calculated IMI_RPF

Location		N sample	Compound (Mean or Range)							LOQ/ (ng/g)		IMI_RPF/ (ng/g)	ref
Continent	Country	N sample	IMI	THIAM		ACE	CLO	THIAC	DIN	LOQ/ (ng/g)		IMI_RPF/ (ng/g)	ref
Asia	Azerbaijan	1	<0.03	<0.02		<0.008	<0.03	<0.002	—	0.008~0.03		0.04	1000
	Burma	2	0.021~0.135	<0.02~0.092		<0.008~0.084	<0.03~0.044	<0.002	—	0.008~0.03		0.05~1.11
	China	3	<0.03~0.151	<0.02~0.298		<0.008~0.89	<0.03	<0.002~0.094	—	0.008~0.03		0.04~4.45
	China	639	5.01	1.75		4.78	4.36	0.896	—	0.1~0.15		34.49	1040
	China	483	1.8~21.5	2.0~55.9		2.0~64.2	ND	ND	—	0.67~2.22		22.52~608.68	1025
	China	10	ND~2.59	ND~0.63		ND	ND~0.14	ND	ND~1.71	0.031~0.074		8.58	1016
	India	3	<0.03~0.171	<0.02~0.044		<0.008~0.109	<0.03	<0.002	—	0.008~0.03		0.04~0.69	1000
	Indonesia	6	<0.03~0.037	<0.02~0.046		<0.008	<0.03	<0.002	—	0.008~0.03		0.04~0.47
	Iran	2	0.081~0.089	<0.02		<0.008~0.057	<0.03	<0.002	—	0.008~0.03		0.11~0.18
	Israel	2	2.516~2.652	<0.02		0.011~0.443	<0.03	0.064~0.375	—	0.008~0.03		2.99~5.65
	Israel	2	0.7~0.8	ND		0.2	ND	ND	ND	0.1~0.5		0.89~0.99	1069
	Japan	3	0.030~2.198	<0.02~1.195		0.125~21.786	0.018~1.829	<0.002~0.002	—	0.008~0.03		0.19~35.55	1000
	Lebanon	2	0.037~0.041	<0.02~0.020		0.019~0.231	<0.03	<0.002	—	0.008~0.03		0.09~0.46
	Malaysia	1	0.095	<0.02		<0.008	<0.03	<0.002	—	0.008~0.03		0.13
	Nepal	2	0.047~0.635	0.026~0.095		<0.008~0.524	<0.03	<0.002	—	0.008~0.03		0.30~2.04
	Philippines	1	<0.03	<0.02		<0.008	<0.03	<0.002	—	0.008~0.03		0.04
	Saudi Arabia	2	0.086~0.148	<0.02~0.061		0.045~0.405	<0.03	<0.002	—	0.008~0.03		0.16~1.12
	Sri Lanka	1	<0.03	<0.02		<0.008	<0.03	<0.002	—	0.008~0.03		0.04
	Thailand	2	0.182~0.383	<0.02~0.117		0.049~0.501	<0.03	<0.002	—	0.008~0.03		0.26~1.97
	Turkey	2	<0.03~0.124	<0.02		0.080~0.788	<0.03	<0.002~0.660	—	0.008~0.03		0.11~5.38
	Vietnam	1	0.079	0.111		<0.008	<0.03	<0.002	—	0.008~0.03		1.13
	Yemen	2	<0.03	<0.02~0.099		<0.008~0.041	<0.03	<0.002	—	0.008~0.03		0.04~0.98
Africa	Burkina Faso	1	<0.03	<0.02		<0.008	0.099	0.003	—	0.008~0.03		0.11
	Cameroon	2	<0.03	0.067~0.598		<0.008	<0.03	<0.002	—	0.008~0.03		0.64~5.63
	Central African Republic	1	<0.03	<0.02		<0.008	<0.03	<0.002	—	0.008~0.03		0.04
	Egypt	2	<0.03~0.159	<0.02		<0.008~0.102	<0.03	<0.002	—	0.008~0.03		0.04~0.29
	Egypt	45	0.5~1.7	ND~18.8		1.7~4.9	ND	ND	—	0.1~2		178.68~192.38	1070
	Egypt	37	0.87	18.84		4.5	ND	—	0.57	0.1~2.1		182.14	1055
	Eritrea	1	<0.03	<0.02		<0.008	<0.03	<0.002	—	0.008~0.03		0.04	1000
	Ethiopia	1	<0.03	0.046		<0.008	<0.03	<0.002	—	0.008~0.03		0.44
	Ghana	2	<0.03~0.029	<0.02~0.027		<0.008	<0.03	<0.002	—	0.008~0.03		0.04~0.29
	Ivory Coast	1	<0.03	0.097		<0.008	<0.03	<0.002	—	0.008~0.03		0.92
	Kenya	3	<0.03	<0.02~0.080		<0.008	<0.03	<0.002~0.003	—	0.008~0.03		0.04~0.78
	Madagascar	3	<0.03	<0.02~0.033	<0.008		<0.03	<0.002	—	0.008~0.03		0.04~0.32
	Morocco	3	<0.03~0.104	<0.02	<0.008~0.066		<0.03	<0.002	—	0.008~0.03		0.04~0.20
	Mozambique	2	<0.03~0.096	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03		0.04~0.13
	Nigeria	1	<0.03	0.211	<0.008		<0.03	<0.002	—	0.008~0.03		1.99
	Guinea	1	<0.03	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03		0.04
	Malawi	1	0.328	0.142	<0.008		0.091	<0.002	—	0.008~0.03		1.71
	Senegal	2	<0.03	0.136~0.411	<0.008		<0.03	<0.002	—	0.008~0.03		1.29~3.87
	South Africa	2	2.388~2.445	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03		2.42~2.48
	Tanzania	2	<0.03~0.166	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03		0.04~0.20
	Tunisia	1	0.072	<0.02	0.139		<0.03	0.5	—	0.008~0.03		3.62
	Uganda	1	<0.03	0.047	<0.008		<0.03	<0.002	—	0.008~0.03		0.45
Europe	Austria	32	ND	<0.6	2.2~15.2		ND	5.0~27.4	ND	0.6~2.0		36.78~200.81	1071
		9	ND	ND	ND		ND	<0.6~2.1	ND	0.6~2.0		0.61~14.20
	Belgium	1	0.280	0.266	2.898		0.134	15.458	—	0.008~0.03		110.16	1000
	Bulgaria	2	<0.03	<0.02~0.033	<0.008~0.128		<0.03	<0.002~0.259	—	0.008~0.03		0.04~2.19
	Croatia	1	0.075	0.03	0.015		<0.03	0.194	—	0.008~0.03		1.69
	Cyprus	1	<0.03	<0.02	0.056		<0.03	0.087	—	0.008~0.03		0.68
	Czech Republic	2	0.054~0.055	0.083~0.252	0.864~1.126		0.029~0.037	1.307~5.874	—	0.008~0.03		10.52~43.24
	England	2	<0.03~0.0168	<0.02~0.350	<0.008~0.512		<0.03~0.216	<0.002	—	0.008~0.03		0.04~3.92
	Finland	1	<0.03	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03		0.04
	France	12	<0.03~0.301	<0.02~0.038	<0.008~0.445		<0.03	<0.002~0.770	—	0.008~0.03		0.04~6.30
	Georgia	1	0.021~0.135	<0.02	0.013		<0.03	0.012	—	0.008~0.03		0.15~0.26
	Germany	2	<0.03	<0.02	<0.008~4.579		0.197~0.951	0.647~46.767	—	0.008~0.03		4.51~321.13
	Greece	3	<0.03~0.029	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03		0.04~0.06
	Greece	5	ND	ND	ND		ND	ND	—	0.4~1.1		0.00	1057
		1	ND	ND	ND		ND	ND	—			0.00
	Hungary	1	<0.03	<0.02	0.688		0.051	3.378	—	0.008~0.03		23.56	1000
	Irish	30	11	ND	ND		<3	<3	ND	3		14.29	1041
	Italy	4	<0.03~1.199	ND~0.021	<0.008~0.080		<0.03	<0.002~1.677	—	0.008~0.03		0.01~12.81	1000
	Latvia	1	<0.03	0.136	<0.008		0.421	0.142	—	0.008~0.03		2.47
	Liechstenstein	1	<0.03	0.035	<0.008		0.421	<0.002	—	0.008~0.03		0.56
	Lithuania	1	<0.03	0.291	<0.008		0.197	0.019	—	0.008~0.03		2.97
	Norway	1	0.074	<0.02	<0.008		<0.03	0.647	—	0.008~0.03		4.48
	Poland	2	0.05~0.153	0.087~1.420	1.583~29.312		<0.03~0.128	0.704~25.340	—	0.008~0.03		7.16~213.29
	Portugal	3	<0.03~0.149	ND~<0.02	<0.008		<0.03	<0.002~0.012	—	0.008~0.03		0.01~0.26
	Macedonia	1	<0.03	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03	0.04
	Romania	2	0.081~0.104	<0.02~0.030	0.380~0.442		<0.03	0.001~0.066	—	0.008~0.03	0.49~0.82
	Russia	6	<0.030.108	<0.02~0.048	<0.008~0.087		<0.03	<0.002~0.006	—	0.008~0.03	0.04~0.69
	Slovakia	2	<0.03	<0.02~0.390	<0.008		<0.03~0.106	0.157~8.263	—	0.008~0.03	1.1~59.58
	Slovenia	1	<0.03	<0.02	<0.008		<0.03	0.010	—	0.008~0.03	0.10
	Slovenia	60	—	ND	ND		—	<0.005~0.018	—	0.005	0.01~0.12		1072
	Slovenia	51	ND	ND	<0.008~2.0		ND	<0.06~9.6	—	0.19~2.84	0.06~66.84		1073
	Spain	7	<0.03~0.529	ND~0.065	<0.008~0.255		<0.03	<0.002~0.026	—	0.008~0.03	0.01~1.56		1000
	Switzerland	3	<0.03	<0.02~0.123	<0.008~0.008		<0.03~0.327	<0.002	—	0.008~0.03	0.04~1.35
	U.K.	21	0.05	0.1	—		0.28	—	—	0.10~0.38	1.14		1074
		109	0.03	0.04	—		0.11	—	—	0.06~0.17	0.47
	U.K.	22	<0.1	ND	<0.01~0.70		<0.02~0.74	ND	ND	0.01~0.1	0.02~1.09		1075
	Ukraine	1	0.036	<0.02	<0.008		<0.03	0.997	—	0.008~0.03	6.81		1000
N. Am	Canada	7	<0.03~0.043	0.125~1.280	<0.008~0.061		0.069~0.932	<0.002~0.060	—	0.008~0.03	1.22~13.03
	Curaçao	1	0.035	<0.02	0.016		<0.03	<0.002	—	0.008~0.03	0.08
	Mexico	3	<0.03~0.610	<0.02~0.040	<0.008~0.021		<0.03	<0.002	—	0.008~0.03	0.04~1.01
	United States	15	<0.03~6.325	<0.02~2.674	<0.008~0.034		<0.03~0.917	<0.002	—	0.008~0.03	0.04~31.96
	USA	8	0.1~1.3	0.4	ND		0.1~0.5	ND	ND	0.1~0.5	3.91~10.73		1069
	USA	53	<0.1~14.7	<0.1~0.5	<0.1~0.3		<0.1	<0.1~0.1	<0.1~14.5	0.1	0.28~20.38		1017
Oceania	Australia	7	<0.03~0.091	<0.02~0.045	<0.008		<0.03	<0.002~0.190	—	0.008~0.03	0.04~1.80		1000
	Fiji	1	<0.03	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03	0.04
	France (New Caledonia)	1	<0.03	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03	0.04
	New Zealand	4	<0.03~0.071	<0.02~0.344	<0.008~0.034		<0.03	<0.002~0.018	—	0.008~0.03	0.04~3.46
	Niue	1	<0.03	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03	0.04
S. Am	Peru	3	<0.03~1.618	<0.02~0.103	<0.008~2.573		<0.03	<0.002~0.003	—	0.008~0.03	0.04~5.10
	Uruguay	1	0.076	0.091	0.009		<0.03	0.023	—	0.008~0.03	1.10
	Argentina	4	<0.03~0.398	<0.02~0.077	<0.008~0.016		<0.03~0.032	<0.002~0.200	—	0.008~0.03	0.04~2.51
	Brazil	8	<0.03~1.114	<0.02~0.282	<0.008		<0.03~0.048	<0.002	—	0.008~0.03	0.04~3.79
	Chile	2	<0.03	<0.02~0.271	<0.008		<0.03	<0.002	—	0.008~0.03	0.04~2.55
	Colombia	1	<0.03	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03	0.04
	Costa Rica	2	<0.03	<0.02	<0.008		<0.03	<0.002	—	0.008~0.03	0.04
	Peru	3	<0.03~1.618	<0.02~0.103	<0.008~2.573		<0.03	<0.002~0.003	—	0.008~0.03	0.04~5.10
	Uruguay	1	0.076	0.091	0.009		<0.03	0.023	—	0.008~0.03	1.10

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图30 2023年之前出版的纳入本综述数据分析的论文在每个出版年份中的数量分布图。图中,P表示花粉,H表示蜂蜜,N表示花蜜

Fig. 30 The distribution of the number of the number of peer-review literatures published before 2023 that were included in the data analysis of this review is shown. In the figure, P means pollen, H means honey, and N means nectar.

Novel neonicotinoid pesticides with systemic properties, when applied to crops via any method (e.g., seed treatment, foliar spraying, and soil application etc.^[995]), will be absorbed by crop tissues^{[988,995,1018,1019]}. They can be transported through the xylem and phloem to the flowers of flowering plants^[974] and exist in pollen and nectar^[1020,1021]. Pollen is a major food source for bees^[1022,1023], and pesticide residues found in pollen and nectar are key pathways for bee exposure^[1024]. In addition, bees may also be exposed to pesticide residues by consuming contaminated hive products and inhaling dust produced during pesticide spraying^[1025,1026]. Given the uncertainty of bees' actual contact with pesticides^[1027,1028], it is generally recommended to assess the exposure risk of pesticide residues in honeybee-related products (pollen, nectar) and hive products (honey)^[1025,1029].

The foraging behavior of pollinators on flowers is an important biological basis for assisting plant pollination, evolution, and reproduction [1030]. Adult worker bees carry the collected pollen back to the hive as the main food source for the bee colony inside the hive [1031]. The earliest study on neonicotinoids in pollen included in this article comes from North America. Chen et al. [1032] detected imidacloprid and thiacloprid in pollen samples with detection rates of 85% and 15%, respectively, using a developed high-performance liquid chromatography-tandem mass spectrometry method. A study from Europe reported that only metabolites of thiacloprid and thiamethoxam were detected in pollen samples collected in 2019, but the residue levels were lower than those reported in previous similar studies [1023, 1034]. This may be due to the fact that the European Food Safety Authority banned the outdoor use of thiacloprid and other new-generation neonicotinoids in 2018 [1035].

Nectar is an effective mechanism for attracting pollinators to assist in pollen transfer and output in plants that rely on biological pollination, serving as a reward to compensate pollinators for their energy consumption during flower visits^[1036]. However, there has been little research on the residues of neonicotinoid pesticides in nectar, and the number of studies has only slowly increased in recent years, possibly due to the difficulty in collecting nectar samples. Elizabeth et al.^[1021] reported changes in the residue concentrations of this pesticide in pollen and nectar before (2013), during (2014), and after (2015) the EU ban on neonicotinoid pesticides. The results showed a decrease in residue concentrations in rural-related media but no significant change in suburban concentrations.

Previous studies have fully validated the ecotoxicological impact of pesticide residues accumulating in beehive products (honey, beeswax, bee bread, etc.)^{[1037⇓~1039]}. A global survey with the most comprehensive results on the residues of neonicotinoid pesticides in honey^[1000] showed that 75% of honey samples contained at least one type of neonicotinoid pesticide, with the highest rates in North America (86%), Asia (80%), and Europe (79%). Imidacloprid had the highest detection rate (51%), while thiamethoxam had the lowest (16%). A study from China^[1040] indicated that 67.9% of samples contained one type of neonicotinoid compound, and 31.8% of samples contained two or three types of neonicotinoid compounds, with acetamiprid having the highest detection rate (23.1%), and imidacloprid showing the highest concentration. Kavanagh et al.^[1041] detected residues of neonicotinoid pesticides in European honey in 2014, and the results showed that 70% of rural samples contained at least one type of this pesticide, with imidacloprid again being the most frequently detected (43%), even though the EU had already imposed a ban at that time.

6.3.3.3 Integrated exposure concentrations (IMI_RPF) and their distribution of neonicotinoid pesticides

The Table 13, Table 14, and Table 15 summarize the research data on residues of neonicotinoid pesticides in pollen and honey, including Asia, Africa, Europe, North America, South America, and Oceania. Subsequently, we performed statistical calculations on the collected research data and integrated them into crop pollen, honey, and nectar IMI_RPF, as shown in Figure 31. It can be seen that most studies originated from a few countries in Europe, Asia, and North America. In terms of pollen, the IMI_RPF in Europe (0.05–908.85 ng/g) was much higher than that in Asia (maximum value of 392.14 ng/g) and North America (0.28–350.99 ng/g). Due to the lower attention given to nectar compared to honey and pollen by researchers, there were relatively fewer studies on nectar, with the highest IMI_RPF in nectar in Asia reaching 11.14–339.66 ng/g. When considering studies from countries outside Europe and North America, potential problems exist in the analysis of geographical distribution bias ^[1042]. Tang et al. ^[1043] analyzed data from the Food and Agriculture Organization of the United Nations (FAOSTAT) database and concluded that many high-risk areas are located in regions with high biodiversity and insufficient freshwater supply, and four out of five countries requiring special attention belong to high-income and upper-middle-income economies. For honey, which has the broadest coverage of research areas, the IMI_RPF in Asian honey ranges from 0.04 to 608.68 ng/g, in African honey from 0.04 to 3.87 ng/g, in European honey from 0.01 to 321.13 ng/g, in North American honey from 0.04 to 31.96 ng/g, in Oceanian honey from 0.04 to 3.46 ng/g, and in South American honey from 0.04 to 5.10 ng/g. It can be seen that there are regional differences in the integrated exposure concentration of IMI_RPF in honey. Honey from Asia, Europe, and North America, where agriculture is relatively developed and the population is large, has a relatively high IMI_RPF, while the IMI_RPF in African honey is lower, possibly related to local development levels and agricultural scale. Similar to some research results ^{[1042, 1044, 1045]}, pesticide use tends to be more intensive in middle- and high-income countries compared to high-income countries, and regulation of some pesticides is weaker in low-income countries, which may also be related to education levels ^[1045]. These may all be reasons for the geographical differences in the integrated exposure concentration of neonicotinoid pesticides worldwide.

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图31 在世界范围内所研究的花粉、蜂蜜和花蜜中新型烟碱类化合物的总分布。图中括号内数字分别表示各洲花粉、蜂蜜和花蜜的样本量。参考文献及数据在Table 13~15中。地图审批号:GS(2014)2081,引用时仅进行颜色的改动未对洲边界线进行改动

Fig. 31 Distributions of total neonicotinoid insecticides in pollen, honey, and nectar reported in studies conducted worldwide. Which (n=a:b:c) means (n=number of pollen samples: number of honey samples: number of nectar samples).References were included in Tables 13~15

In previous studies, most of the assessments focused on the risks of individual new neonicotinoid pesticides, with a lack of risk assessment for integrated exposure concentrations (cited references in Tables 13 to 15). Few studies have used the RPF method to evaluate the integrated exposure concentrations of new neonicotinoid pesticide residues in bee-pollinated crops. Tang et al. [1016] used this relative potency factor-based method to calculate the integrated exposure concentrations of new neonicotinoid pesticides in pollen and honey samples collected over three months in Zhejiang Province, China. The results showed that the highest integrated concentrations of IMI_RPF in pollen and honey were 34.93 ng/g and 8.51 ng/g, respectively. Wood et al. [1046] calculated the expected residues in pollen and nectar of study crops using 75 field study data provided by the European Food Safety Authority (EFSA) (including thiamethoxam, clothianidin, and imidacloprid) as well as the authorized maximum and minimum application rates [1047, 1048-1049]. Their results were similar to those of other studies [1016, 1034, 1050, 1051], showing different detection levels of various types of new neonicotinoid pesticides in different countries and regions, with detection levels in pollen always higher than those in nectar. The statistical results of this article are also similar: the highest integrated exposure concentration in pollen is 908.85 ng/g, in nectar is 339.66 ng/g, and in honey is 608.68 ng/g.

The demand for agricultural pest control varies with geographical regions, crop types, and seasonal changes^[1039], so the statistics on the global residues of neonicotinoid pesticides predicted in this article may differ from the real situation. Considering that the use of neonicotinoid pesticides may continue to increase globally, it can be expected that they will be ubiquitous in bee-pollinated crops. Nevertheless, the residue data of neonicotinoid pesticides in pollen, honey, and nectar (especially in regions such as Africa, South America, and Oceania) reported in existing studies are still insufficient for a more accurate integration exposure assessment to reflect the basic intake of bees during foraging. To address this potential important ecological environment issue, there is an urgent need for more residue data of neonicotinoid pesticides in relevant matrices to conduct reasonable and accurate assessments of their potential ecological health risks.

6.3.4 Research Prospects for New Neonicotinoid Pesticides on Bees and Other Organisms

1) Since the practical application of three novel neonicotinoid pesticides in the field to the ban on outdoor use in Europe several years later, these pesticides have been continuously detected in honeybee forage crops (pollen and nectar) and beehive products. However, few studies have investigated the correlation between pesticide residues in honeybee forage crops and beehive substrates. Moreover, no study has evaluated the differences in residues in pollen and nectar of honeybee forage crops after the application of novel neonicotinoid pesticides under the same agricultural conditions over a long time scale. Therefore, it is necessary to continuously monitor the residues and fate of novel neonicotinoid pesticides in the above media.

2) Selecting more reasonable biomarkers to evaluate the toxicity mechanism of neonicotinoid pesticides is of great significance for finding their antidotes. A recent study showed that mitochondrial dysfunction is a reasonable approach to explain the mechanism of CCD generation^[978]. At the same time, a cross-sectional study indicated that the relative mitochondrial DNA copy number (RmtDNAcn) in oral epithelial cells can be used as a reasonable biomarker for human exposure to neonicotinoid pesticides^[1014]. Therefore, mitochondrial-targeted antidotes have certain application prospects^[1052].

3) Despite the scientific evidence indicating that the use of neonicotinoid pesticides has negative impacts on ecosystems, most pest management still relies on the use of these pesticides. Therefore, it is necessary to conduct a more detailed and comprehensive assessment of the real residue risks when these pesticides coexist, in order to create a healthier living environment for pollinators such as bees. This requires more global research collaboration and development to provide fair and rigorous research results for regulatory agencies and policymakers.

6.4 Ecotoxicology of PPCP Class Pollutants

PPCPs, as an emerging contaminant, can persist in aqueous environments after entering the environment due to their physicochemical properties, and traditional water treatment methods cannot effectively remove these compounds. Through migration and transformation and bioaccumulation, they cause different degrees of eco-toxicological effects on organisms and humans.

6.4.1 Molecular Level

Domestic research has found that PPCPs exhibit significant toxicological effects at the molecular level (Table 16), specifically manifested as the impact of PPCP pollutants on the expression and synthesis of multiple proteins and genes. In a study using clams as experimental models, it was discovered that caffeine, carbamazepine, and fluoxetine can significantly reduce the activity of acetylcholinesterase (AchE). Among these, carbamazepine demonstrated the strongest toxicity, showing negative effects at a concentration as low as 0.1 μg/L ^{[1076⇓~1078]}. Various anti-inflammatory painkillers and beta blockers, such as IBU, indomethacin, acetaminophen, tiaprofenic acid, bisoprolol, and propranolol, have shown significant regulatory effects on the activities of various enzymes in invertebrates and algae ^{[1079⇓~1081]}. Additionally, tramadol (40 mg/kg) and codeine (80 mg/kg) can induce the production of malondialdehyde/nitrite and cause SOD deficiency in mice ^[1082], while codeine can increase testicular enzyme levels in male rabbits at a concentration of 4 mg/kg, inhibiting testosterone levels and circulation ^[1083]. Studies also found that when the concentration of levofloxacin exceeds 50 mg/L, it can significantly reduce nitrate reductase activity in mung bean plants while increasing antioxidant enzyme activity ^[1084]. Recently, Zhang et al. ^[1078] discovered through research on groundwater microorganisms that only 10 μg/L of tetracycline significantly reduced denitrification enzyme activity at the protein level. Secondly, some PPCPs also exhibit certain inducing effects on protein synthesis. Yamamoto et al. ^[1085] conducted oocyte vitellogenin tests on Japanese medaka exposed to methylparaben and found that exposure to 630 μg/L of methylparaben significantly induced yolk protein production in male medaka. Galaxolide also caused an increase in total triiodothyronine (TT3) and a significant decrease in total thyroxine (TT4) in zebrafish ^[1086]. Triclosan at concentrations of 0~100 μmol/L can inhibit fatty acid synthesis, cause protein aggregation, and increase estradiol and progesterone levels ^[1087,1088]. Moreover, various PPCPs exhibit other toxicological effects, such as benzophenone and fluoxetine affecting hormone levels including testosterone and estradiol ^{[1089⇓~1091]}, codeine and alprazolam exhibiting interference with neuroactive substances ^[1092], IBU influencing the arachidonic acid pathway ^[1093], and diclofenac altering chlorophyll a content, lipid accumulation, and antioxidant enzyme function in algae, thereby affecting growth ^[1094].

表16 PPCPs 在分子水平的毒理效应

Table 16 Toxicity effect of PPCPs in molecular level

Classification	Species	PPCPs chemicals and effective concentration	Toxicity effect	ref
Enzyme effect	Clams	Caffeine (50 μg/L)	AChE acetylcholinesterase activity decreased by 56%	1076
	Carbamazepine (0. 1 μg/L)	AChE acetylcholinesterase activity decreased by 56%
	Clams	Fluoxetine (1 μg/L & 5 μg/L)	Acetylcholinesterase (AChE) activity decreased significantly	1077
	Groundwater microorganism	Tetracycline (10 μg/L)	Decreased denitrifying enzyme activities at the protein level	1078
	Daphnia magna	Indomethacin (1 mg/L); Ibuprofen (1 mg/L)	Reduced feeding rate and modulated activities of key enzymes like Alkalineand acid phosphatases, lipase, peptidase, β-galactosidase, and glutathione Stransferase	1079
	Chlorella vulgaris, Desmodesmus armatus.	Bisoprolol (100 mg/L), Ketoprofen (100 mg/L)	Regulation of antioxidant enzyme activity and chlorophyll content	1080
	Male rabbits	Codeine (4 mg/kg)	Elevated testicular enzymes inhibit testosterone levels and circulation in the testicles	1083
	Rat	Tramadol (40mg /kg); Codeine (80 mg/kg)	Induces malondialdehyde/nitrite production and superoxide dismutase (SOD) deficiency	1082
	Vigna radiata	Levofloxacin (≥50 mg/L)	The activity of nitrate reductase decreased significantly, and the activity of antioxidant oxidase increased	1084
	Mytilus galloprovincialis	Propranolol (11 μg/L); Acetaminophen (23 & 403 μg/L)	Modulates antioxidant enzyme activity	1081
Protein synthesis induction	Vallisneria natans	Sulfadiazine (-)	Alternation of α- and β-D-glucopyranose polysaccharides and the increased content of autoinducer peptides and N-acylated homoserine lactones	1102
	Human ovarian granulosa cells	Triclosan (0~10 μmol/L)	TCS increased estradiol and progesterone levels with upregulated steroidogenesis gene expression	1087
	Groundwater microorganism	Tetracycline (10 μg/L)	Decreased the concentration of electron donors (nicotinamide adenine dinucleotide, NADH), electron transport system activity, at the protein level	1078
	Rat	Oxytetracycline (200 mg/kg)	Erum urea and creatinine increased significantly, and creatinine clearance decreased	1103
	Chlorococcum sp	Triclosan (100 μmol/L)	Affect algae primarily by inhibiting fatty acid synthesis and causing protein aggregation	1088
	Medaka (Oryzias latipes)	Methylparaben (630 μg/L & 10 μg/L)	Induction of significant vitellogenin in male medaka at 630 μg/L, the expression levels of genes encoding proteins such as choriogenin and vitellogenin increased in concentrations (10 μg/L)	1085
	Danio rerio	Galaxolide (HHCB) (0.005 mmol/L)	Cause an increase in total triiodothyronine (TT3) and a significant decrease in total tetraiodothyronine (TT4)	1086
DNA damage	Danio rerio	Ethylparaben (20 mg/L)	Inhibition of gene expression related to myocardial contraction	1095
Oxidative stress and antioxidation	Human	Sulfamethoxazole (40 μmol/L)	Significantly reduced the low level of TAthione, resulting in decreased immunity	1096
	Rat	Triclocarban (300 nmol/L)	Non-protein mercaptan (glutathione) was significantly reduced	1097
	Amino acid	Tonalid (500 μmol/L)	Acts as a photosensitizer to significantly increase photo-induced oxidative damage to amino acids.	1100
	Rat hepatocytes	Acetaminophen (10 mmol/L 24 h)	Decreased the levels of both intracellular ATP and GSH, and GSH-conjugated APAP (APAP-GSH) were formed.	1098
	Mytilus galloprovincialis	Propranolol (11 μL); Acetaminophen (23 & 403 μg/L)	Induces oxidative stress	1081
	Brachionus rotundiformis	Acetaminophen, oxytetracycline	Production of reactive oxygen species (ROS) and increased glutathione S-transferase activity trigger oxidative stress	1099
	Danio rerio	Fluoxetine (5、16 and 40 ng/L)	Resulted in elevated levels of protein carbonyl content (PCC) and hydrogen peroxide (HPO) in various organs of Danio rerio, including the liver, gut, brain, and gills, following oxidize proteins and alter their functionality by forming new low molecular weight aggregates through oxidative stress.	1101
Others	Rat	Benzophenone-2 (100 mg/kg)	Significant reduction in testosterone levels and an increase in 17β-estradiol levels in the blood	1089
	Rat	Benzophenone-2	Reduces circulation of triiodothyronine (T3) and thyroxine (T4)	1090
	Gobiocypris rarus	Alprazolam (10 ng/L)	Interfering effect of GPC, CHOP, Met and other neurosubstances	1092
		Lorazepam (100 ng/L)	Interfering effect of Cho, 5-HT, Trp, 5-HIAA and other neurosubstances
	Oryzias latipes	Fluoxetine (100~500 μg/L 28 d exposure)	Affects estradiol concentrations in fish.	1091
	Chlorella pyrenoidosa	Diclofenac (>100 mg/L)	Alteration of chlorophyll a, lipid accumulation, and antioxidant enzyme function, thereby affecting growth	1094
	Mytilus galloprovincialis	Ibuprofen (250 ng/L)	Affects the arachidonic pathway	1093

At the same time, PPCPs can cause abnormal gene expression and DNA damage. Ethylparaben was found to inhibit the expression of genes related to myocardial contraction^[1095]. Acetaminophen, tetracycline, sulfamethoxazole, and triclocarban can each reduce the expression level of glutathione genes at certain concentrations^{[1096⇓⇓~1099]}, thereby reducing immunity. Propranolol, acetaminophen, tonalide, and fluoxetine can induce the production of reactive oxygen species (ROS), hydrogen peroxide (HPO), and other substances, which can induce oxidative stress in fish and invertebrates to a certain extent, causing oxidative damage and altering the expression of genes associated with various disease indicators^{[1099⇓~1101]}. A fathead minnow DNA microarray analysis of parabens showed that at a concentration of 10 μg/L, parabens could increase the expression levels of genes encoding proteins (vitellogenin and phosvitin)^[1085]. Triclosan significantly upregulated the expression of steroidogenic genes at concentrations ranging from 0 to 100 μmol/L^[1087,1088].

6.4.2 Cells and Subcellular Levels

According to previous studies, PPCPs can interfere with and affect cells and subcellular structures to a certain extent (Table 17). At the cellular level (Fig. 32), studies on mammals have found that catecholamines can damage myocardial cells and increase the risk of myocardial infarction ^[1104], while high concentrations of caffeine and dapsone can cause or exacerbate cell death ^{[1096, 1105]}. Musk xylene and 17β-estradiol can cause changes in cell morphology, such as forming irregular spindle-shaped, protruding, and multinucleated cells, or cell hypertrophy ^{[1106, 1107]}. Meanwhile, musk xylene can also lead to unlimited cell proliferation ^[1106]. Research on zebrafish experiments has found that caffeine exposure during pregnancy can cause hair cell damage ^[1108]; tetracycline and enrofloxacin can significantly reduce the number of macrophages and granulocytes ^{[1109, 1110]}; enrofloxacin at 50–200 μg/mL can induce apoptosis and reduce mitochondrial membrane potential. Additionally, some PPCPs can cause stress in microalgal cells, thereby altering cell components ^[1111]; tolmetin and bisoprolol can regulate algal cell morphology ^[1080]; sulfamethoxazole can inhibit algal growth by reducing cellular energy allocation ^[1112]. Studies using invertebrates as experimental models have shown that caffeine, IBU, carbamazepine, novobiocin, amoxicillin, and sulfamethoxazole exhibit toxic effects such as reducing blood cell viability, causing extracellular acidosis, and increasing apoptosis at different concentration levels (micrograms per liter) ^{[1113⇓~1115]}.

表17 PPCPs 在细胞和亚细胞水平的毒理效应

Table 17 Toxicity effect of PPCPs in cellular and subceullar level

Classification	Organism	Species	PPCPs chemicals and Effective concentration	Toxicity effect	ref
Cellular level	Mammal	Human	Catecholamines	Damage heart muscle cells and increase the risk of heart attack	1104
			Aminophenylsulfone (0.5 mmol/L)	Cause epidermal keratinocyte death	1096
			Caffeine (1 mmol/L)	Abrogates the cell cycle arrest and increases cell death	1105
			Musk xylene (10, 100, and 1000 μg/L)	Cause some irregular fusiform, protuberances and multinucleated cells, indefinite cell proliferation, ability of anchorage-independent proliferation and increase of migration and invision	1106
		Rat	17β-Estradiol (10~50 mg/kg)	Central lobular hepatocyte hypertrophy	1107
	Fish	Danio rerio	Caffeine (25 μmol/L)	Induced significant hair cell damage during gestation period	1108
			Enrofloxacin (10 μg/L, 100 μg/L)	Significant reductions in macrophage and neutrophil populations and biomarkers of immunosuppressive effects	1109
			Tetracycline (100 μg/L)	Neutrophil counts were significantly reduced in offspring zebrafish	1110
		Grass carp	Enrofloxacin (50, 100 and 200 μg/mL)	Increased the lactic dehydrogenase release and malondialdehyde concentration, induced cell apoptosis, reduced the mitochondrial membrane potential	1120
	Algae	Chlorella vulgaris, Desmodesmus	Chlorella vulgaris, desmodesmus (100 mg/L)	Regulation of cell morphology	1080
		Raphidocelis subcapitata	Sulfamethoxazole (8.3 μmol/L)	Inhibited growth by causing a decrease in cellular energy allocation (CEA)	1112
		Microalgae	PPCPs	Cause the stress in microalgal cells, thereby altering cellular composition	1111
	Invertebrate	Clam	Amoxicillin (400 μg/L)	Cause extracellular acidosis	1113
		Corbicula fluminea	Sulfamethoxazole (1, 10 and 100 μg/L)	Enhance cell apoptosis	1114
		Manila Clam	Caffeine (15 μg/L); Ibuprofen (10 μg/L); Carbamazepine (1 μg/L); Neomycin (1 μg/L)	Decrease in blood cell viability	1115
Subcellular level	Mammal	Male rabbits	Codeine (4 mg/kg)	Testicular DNA breakage and caspase-dependent apoptosis	1083
		Rat	Tramadol (40 mg /kg); Codeine (80 mg/kg)	The activity of mitochondrial respiratory chain complex I was inhibited in the prefrontal cortex and midbrain of mice	1082
		Human	Homosalate (1000 μmol/L)	Induced micronucleus formation and cleavage inducing to cells	1116
			Triclosan (1~10 μg/mL)	Depolarizes the mitochondria and increases the rate of glucose consumption in the cells, inducing metabolic acidosis	1117
	Plant	Duckweed	Diclofenac (1, 10, 100 and 1000 μmol/L)	Induced oxidative stress in isolated duckweed chloroplasts	1121
	Algae	Chlorella spp	Triclosan (≥0.2 mg/L)	Inhibiting the increase of photosynthetic pigment content in Chlorella cells	1118
	Invertebrate	Tetrahymena thermophilus	Triclosan (1000 μg/L)	Damaged cell membrane and affected the lysosomal activity	1121

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图32 PPCPs 细胞和亚细胞水平的毒性效应

Fig. 32 Toxicity effects of PPCPs on cellular and subcellular levels

At the subcellular level, tramadol and cocaine, analgesic and anti-inflammatory drugs, can inhibit the activity of respiratory chain complex I in the prefrontal cortex and midbrain mitochondria of mice^[1082]. Meanwhile, cocaine can cause DNA fragmentation and caspase-dependent apoptosis in the testes of male rabbits^[1083]. Based on human experiments, methyl salicylate significantly induces micronucleus formation and has a cell division-inducing effect^[1116], while triclosan causes mitochondrial depolarization, increases glucose consumption in cells, and subsequently induces metabolic acidosis^[1117]. Triclosan can also inhibit the increase in photosynthetic pigment content in Chlorella cells^[1118] and cause obvious damage to the cell membrane of invertebrates and affect their lysosomal activity^[1119].

6.4.3 Organismal Level

Similarly, PPCPs can affect the tissues and organs of organisms to a certain extent (Fig. 33 and Table 18). First, in studies based on mammals, it was found that 4-octylphenol and tetracycline could cause testicular size reduction and dysfunction, as well as decreased sperm motility, quantity, and morphological abnormalities at certain levels, leading to adverse lesions ^[1122,1123]. 17β-estradiol and benzophenone could lead to increased uterine weight, endometrial hyperplasia, and enlargement of uterine glands in mice ^{[1107,1123,1124]}, and 17β-estradiol could also induce pituitary and mammary gland hyperplasia, increasing the number of cystic follicles in the ovary ^[1107]. Triclosan significantly inhibited fatty acid synthase activity in rat liver and adipose tissue, affecting fat synthesis ^[1125], while Galaxolide and Tonalide also caused increased relative liver weight and hepatomegaly, resulting in liver damage ^[1126,1127]. Propranolol, atenolol, and mesalazine were also found to induce excessive production of reactive oxygen species (ROS) and mitochondrial damage, causing cardiac toxicity in rats ^[1128,1129]. Second, studies on poultry and fish found that diclofenac could cause gout-like symptoms in poultry, with urate deposits detected in kidneys, liver, heart, and spleen ^[1130]. It could also affect the integrity of fish kidneys and gills, induce hepatic and gonadal antioxidant defense, and trigger hematopoietic toxicity ^{[1131⇓~1133]}. Thifluzamide could significantly reduce cholesterol and triglyceride levels, inducing liver toxicity in fish ^[1134]. Triclosan could increase pericardial fluid in larvae and alter cardiac structure ^[1135]. Astemizole caused blood cell accumulation, pericardial edema, and bloodless systemic phenomena in zebrafish ^[1136]. Studies on multiple PPCP exposures also showed that IBU and ETM had the highest bioaccumulation in female fish brains ^[1137]. Acetaminophen, carbamazepine, gemfibrozil, and venlafaxine could significantly reduce fish embryo production, increase oocyte apoptosis in female ovaries, and cause histological changes in kidneys ^[1138]. At the same time, mixed exposure to multiple PPCPs (10 μg/L) could significantly cause morphological changes in intestinal tissue ^[1139]. Sulfamethoxazole, triclosan, and ETM could interfere with thyroid endocrine function, causing thyroid disruption ^{[1140⇓~1142]}. Naproxen and Tonalide were also found to cause lipid peroxidation in renal tissue and increased hematopoietic proliferation, resulting in significant kidney toxicity ^[1143,1144]. Finally, phytotoxicity studies showed that mixed exposure to 17 PPCPs (acetaminophen, caffeine, meprobamate, atenolol, trimethoprim, carbamazepine, diazepam, gemfibrozil, primidone, sulfamethoxazole, phenytoin sodium, diclofenac, naproxen, IBU, atorvastatin, triclosan, triclocarban) could change leaf chlorophyll content, increase leaf necrosis, and significantly reduce root activity ^[1145]. Oxytetracycline, sulfadiazine, and tetracycline were found to have a significant impact on plant roots, including reducing fresh root weight, altering root orientation, promoting lateral root growth, and downregulating genes related to homologous recombination in the root meristem zone, inhibiting mitosis ^{[1146⇓~1148]}.

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图33 PPCPs 对组织器官水平的毒性效应

Fig. 33 Toxic effects of PPCPs on tissue and organ level

表18 PPCPs 在组织器官水平的毒理效应

Table 18 Toxicity effect of PPCPs in tissue and organ level

Organism	Species	PPCPs chemicals and effective concentration	Toxicity effect	ref
Mammal	Human	4-octylphenol (1000 μg/L)	After 1~22 days of exposure, the average testicular size decreased slightly but significantly.	1122
	Rat	Benzophenone 2 (BP-2) (1000 mg/kg)	Increased uterine weight	1123
		17β-Estradiol (10~50 mg/kg)	Diffuse Pituitary hyperplasia; breast hyperplasia: increased number of cystic follicles in the ovaries; endometrial andendometrial glandular hypertrophy	1107
		17β-Estradiol (-)	Uterine response: increased uterine weight due to water retention and cell proliferation	1124
		Triclosan (30~250 mg/kg)	Decreased fatty acid synthase activity in liver and adipose tissue, inhibited fatty acid synthesis, and lowered triglyceride of serum, liver and adipose tissue	1125
		Propranolol and atenolol (5, 10, and 20 μg/mL)	Induction of reactive oxygen species and mitochondrial damage to cardiac tissue	1128
		Tetracycline (28.6 mg/kg)	Causing testicular dysfunction and spermatogenesis disorders in animals and humans	1149
		Mesalazine (25, 50 and 100 μmol/L)	Triggers overproduction of mitochondrial ROS, releases cytochrome c, and causes cardiotoxic effects	1129
		Galaxolide fragrance (HHCB) (150 mg/kg)	The liver became swollen	1126
		Toxalide fragrance (AHTN) (100 mg/kg)	Increased liver weight and liver injury was formed	1127
Birds	Domestic fowl	Diclofenac (9.8 mg/kg)	Showed signs of gout with deposits of urates in the kidney, liver, heart and spleen	1130
Fish	Danio rerio	Triclosan (40~400 μg/L)	Increased pericardial effusion and changes in heart structure in fish	60
		PPCPs (10 μg/L)	The similar histomorphological changes in the gut tissues of male and female fish in terms of considerably reduced VL length and size	1139
		PPCPs (acetaminophen, carbamazepine, gemfibrozil and venlafaxine) (0.5 μg/L and 10 μg/L)	Significant decrease in embryo yield, increased oocyte atresia in female ovaries, and histological changes in kidneys	1138
		Astemizole (1, 4, 10 and 20 μmol/L)	Occurred blood cell accumulation, pericardial edema and no blood flow in the whole body	1136
		PPCPs (1 and 10 μg/L)	Ibuprofen and erythromycin exhibited the highest bioaccumulation in the brain tissues of female fish	1137
		Erythromycin (0.1, 1, 10 and 100 μg/L)	Thyroid disruption	1140
		Sulfamethoxazole (5.6 μg/L)	Thyroid endocrine disruption	1141
		Thifluzamide (0.019, 0.19 and 1.90 mg/L)	Decreased cholesterol and triglyceride levels, induced hepatotoxicity	1134
	Rhamdia quelen	Diclofenac (0.2, 2 and 20 μg/L)	Hepatic & gonadal antioxidant defenses	1131
	Salmo trutta	Diclofenac (0.1~100 μg/L)	Hematopoietic toxicity	1132
	Salmo trutta	Diclofenac (5~50 μg/L)	Affect the integrity of kidneys and gills.	1133
	Gasterosteus aculeatus	Naproxen (299 and 1232 μg/L)	Increased renal hematopoietic hyperplasia	1143
	Oncorhynchus mykiss	Toxalide fragrance (AHTN) (854 and 8699 μg/kg)	Lipid peroxidation in caudal kidney tissue	1144
	Cyprinodon variegatus	Triclosan (20, 50 and 100 μg/L)	Thyroid disruption	1142
Plant	Cucumber	PPCPs (acetaminophen, caffeine, Mirton, atenolol, trimethoprim, carbamazepine, diazepam, gemfibrozil, primidone, sulfamethoxazole, phenytoin sodium, diclofenac, naproxen, ibuprofen and atorvastatin, triclosan and triclocarban) (50 μg/L; 5 μg/L and 50 μg/L)	Increase of leaf necrosis; Root activity decreased by 15.4% and 28.2%	1145
	Wheat	Oxytetracycline (0.01~0.08 mmol/L)	The fresh weights of wheat roots decreased.	1146
	barley	Tetracycline (10, 100, and 200 mg/L)	Down-regulated genes related to homologous recombination in the root meristem zone and inhibited the mitosis index	1147
	Corn	Sulfadiazine (10 mg/kg)	Promoted abnormal root apical orientation in maize	1148
	Willows	Sulfadiazine (200 mg/kg)	A large increase in the number of lateral roots

6.4.4 Individual level

When PPCPs enter living organisms, exposure at a certain concentration will cause negative impacts of varying degrees and types at the individual level (Table 19). Their toxic effects are primarily obtained through acute and chronic experimental studies.

表19 PPCPs在个体水平的毒理效应

Table 19 Toxicity effect of PPCPs in individual level

Effects	Toxicity	Organism	Species	PPCP chemicals and effective concentration	Toxicity effect	ref
Sublethal effect	Acute toxicity	Algae	Pseudokirchneriella subcapitata	Methyl-paraben (80000 μg/L 72 h); Ethyl-paraben (52000 μg/L 72 h); n-propyl-paraben (36000 μg/L 72 h); i-propyl-paraben (48000 μg/L 72 h); n-butyl-paraben (9500 μg/L 73 h); i-butyl-paraben (4000 μg/L 73 h); Benzyl-paraben (1200 μg/L 74 h)	EC₅₀ in growth inhibition	1085 ⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓~ 1150
			Chlorella vulgaris	Acetophenone (1000 μg/L); Benzophenone (6.855 mg/L 96 h); Diethyltoluamide (270.72 mg/L 96 h)	EC₅₀ in growth inhibition/EC₅₀	1152,1153
			Anabaena sp.	Erythromycin (0.022 mg/L 72 h); Ciprofloxacin (0.01 mg/L)	EC₅₀	1154,1155
			P. subcapitata	Ciprofloxacin (2.97 mg/L 72 h)	EC₅₀	1156
				Erythromycin (0.35 mg/L 72 h)	EC₅₀	1154
			Dunaliella tertiolecta	Carbamazepine (53.2 mg/L)	EC₅₀ in growth inhibition	1157
			Phaeodactylum tricornutum	Nano-TiO2 (167.71 mg/L)	EC₅₀ in growth inhibition	1158
			Ulva pertusa	Isobutylparaben (15650 μg/L 96 h)	EC₅₀ in sporulation inhibition	1151
			Synechococcus sp.	Erythromycin (0.23 mg/L 144 h)	EC₅₀	1159
		Invertebrate	Daphnia magna	Methyl-paraben (34000 μg/L 48 h); Ethyl-paraben (7400 μg/L 48 h); n-propyl-paraben (2000 μg/L 48 h); i-propyl-paraben (3500 μg/L 48 h); n-butyl-paraben (1900 μg/L 48 h); i-butyl-paraben (3300 μg/L 48 h); Benzyl-paraben (2100 μg/L 48 h)	EC₅₀ in the number of immobilized bodies	1085, 1150
				Tetracycline (617.2 mg/L 48 h); Diclofenac (68.0 mg/L); Ibuprofen (101.2 mg/L); Naproxen (166.3 mg/L); Erythromycin (0.94 mg/L 48 h)	EC₅₀	1160 ⇓~ 1162
			Ceriodaphnia dubia	Erythromycin (0.22 mg/L 7 d); Propranolol (1.51 mg/L)	EC₅₀	1163,1164
			Brachionus calyciflorus	Propranolol (2.59 mg/L)	EC₅₀	1164
		Fish	Danio rerio	Ciprofloxacin (100 mg/L)	EC₅₀	1165
			Pimephales promelas	Ciprofloxacin (>100 mg/L)	EC₅₀
		Floating plant	Lemna gibba	Chlortetracycline (114 μg/L); Lomefloxacin (38 μg/L); Sulfamethoxazole (37 μg/L)	EC25 in growth inhibition	1166
	Reproductive toxicity	Fish	Juvenile	Estradiol (0.05~0.5 mg/kg)	Affects spawning	1167
				Diclofenac (10 mg/L)	Reduced egg hatchability	1168
				Ibuprofen (0.0001 mg/L)	Reduced hatchability, yolk proteins content	1169
			Pimephales promelas	Fluoxetine (1 μg/L)	Impacted mating behavior in male fish, specifically nest building and defending	1171
				Fadrozole (2 μg/L 21 d exposure)	Decline in reproduction rate	1172
			Dicentrarchus	Estradiol (10 mg/kg)	Affect reproduction	1170
		Invertebrate	Daphnia magna	Propranolol (0.128 mg/L)	Reduce the fecundity and reproductive rate	1173
			Chironomus riparius	Carbamazepine (0.625 mg/kg 28 d exposure)	Inhibit pupa to be formed	1174
	Developmental toxicity	Plant	Lactuca sativa	Erythromycin (0.1~300 mg/kg)	Inhibition of bud development	1175
			Solanum lycopersicum	Sulfadiazine (0.1~300 mg/kg)	Inhibition of bud development
			Brassica rapa chinensis	Chlortetracycline (2.5~20 mg/kg)	Inhibition of bud development	1176
			Oenanthe javanica	Oxytetracycline (0.5~10 mg/kg)	Inhibition of plant height	1177
		Fish	Danio rerio	Musk xylene (33 μg/L)	Significant impact in early life stages	1178
				Muscone (33 μg/L)	Reduced fish mass and length of females and reduced fecundity	1179
				Amitriptyline (0.001~1000 μg/L)	Inhibition of growth and development; alteration of ACTH concentration level	1180
			Pimephales promelas	Propranolol (3.4 mg/L)	Reduced body weight and egg hatchability	1181
		Algae	Scenedesmus obliquus	Ibuprofen (107.91 mg/L); aspirin (103.05 mg/L); ketoprofen (4.03 mg/L)	Inhibition of algal growth	1182
		Amphibian species	Bufo americanus.	Acetaminophen (100 μg/L 28 d exposure)	Survival rates are affected	1183
	Endocrine disruption	Invertebrate	Mytilus spp.	Gemfibrozil (1000 μg/L)	Endocrine disruption	1184
		Fish	Danio rerio	Galaxolide(-)	Endocrine disruption	1086
	Neurotoxicity	Fish	Oryzias latipes	Acetaminophen (30 + 30 mg/kg, 4 h apart)	Decline in memory, learning ability and cognitive flexibility	1186
			Gambusia affinis	Fluoxetine (0.05~5 μg/L)	Sleeping time expanded	1187
		Mammal	mice	Triclosan (1000 mg/kg, 2000 mg/kg, 4000 mg/kg)	Cause certain behavioral disorders in mice	1185
		Amphibian species	Rana pipiens	Acetaminophen (1 μg/L 14 d of exposure)	Behavior affected	1183
	Cardiovascular toxicity	Mammal	Human	Caffeine (-)	Correlation with cardiovascular disease (CVD)	1188
			Beagle dogs	Sibutramine (30 mg/kg)	Increasing Heart Rate and Blood Pressure in Beagles	1189
Lethal effect		Fish	Medaka (Oryzias latipes)	Methyl-paraben (63000 μg/L 96 h); Ethyl-paraben (14000 μg/L 96 h); n-propyl-paraben (4900 μg/L 96 h); i-propyl-paraben (4500 μg/L 96 h); n-butyl-paraben (3100 μg/L 96 h); i-butyl-paraben (4600 μg/L 96 h); Benzyl-paraben (730 μg/L 96 h)	LC₅₀ in lethality	1085, 1150
			Danio rerio	Galaxolide (0.037 mmol/L 96 hpf); Tetracycline (406.0 mg/L 96 h); Diethyltoluamide (109.67 mg/L 96 h); Benzophenone (14.73 mg/L 96 h)	LC₅₀ in lethality	1086, 1153, 1160
			Carassius auratus	Ttetracycline (322.8 mg/L 96 h)	LC₅₀ in lethality	1160
	Invertebrate		Daphnia magna	Diethyltoluamide (40.74 mg/L 24 h); Cefoperazone (141.11 mg/L); Amoxicillin (129.3 mg/L); Aztreonam (29.3 mg/L); Benzophenone (7.63 mg/L 24 h)	LC₅₀ in lethality	1190
			Lumbriculus variegatus	Benzylidene-camphor (44.2 μmol/L)	Substantial reduction in fertility and significant increase in mortality	1191

6.4.4.1 Sublethal Effects

Various PPCPs exhibit significant sublethal effects at certain concentrations. Some PPCPs with higher concentrations show corresponding acute toxicity to algae, fish, invertebrates, and plants. Yamamoto et al.^[1085,1150] conducted conventional acute/chronic toxicity tests on seven parabens using Daphnia magna and Selenastrum capricornutum, finding that the lowest EC₅₀ values for inhibiting algal growth and causing Daphnia magna immobilization were 1200–80000 μg/L, with benzyl paraben at 1200 μg/L for Daphnia magna and methyl paraben at 80000 μg/L for Selenastrum capricornutum, while the EC₅₀ value for inhibiting algal spore formation by isobutyl paraben was 15650 μg/L after 96 h exposure^[1151]. DEET, benzophenone, acetophenone, ETM, ciprofloxacin, carbamazepine, and Nano-TiO₂ also exhibit growth inhibition effects on various algae, with EC₅₀ values ranging from 0.01 to 270.72 mg/L^{[1152⇓⇓⇓⇓⇓⇓~1159]}. Many antibiotics, anti-inflammatory painkillers, and beta-blockers also have EC₅₀ values in the milligram per liter range for invertebrates^{[1160⇓⇓⇓~1164]}. Ciprofloxacin can cause a 50% effect in multiple fish species at concentrations ≥100 mg/L^[1165]. Tetracycline, lomefloxacin, and sulfamethoxazole achieve a 50% inhibitory effect on duckweed growth at concentrations of 114, 38, and 7 μg/L, respectively^[1166].

In addition to acute toxicity, PPCPs also bring various types of chronic toxicity to organisms. First, several PPCPs exhibit reproductive toxicity in fish and invertebrates. Ibuprofen (IBU), diclofenac, and 17β-estradiol can affect fish spawning, reduce egg hatching rates, and influence reproduction [1167 ⇓⇓~1170]; fluoxetine can directly affect the mating behavior of male fish, especially nesting and defense [1171]; fenpropazole at 2 μg/L for 21 days exposure can significantly reduce reproductive rates [1172]. Propranolol and carbamazepine have also been proven to decrease the reproductive capacity and rate of Daphnia magna or inhibit the formation of Chironomus pupae [1173,1174]. Second, some PPCPs have certain developmental toxicity. Antibiotics and anti-inflammatory drugs, such as oxytetracycline, ETM, chlortetracycline, and sulfadiazine, can inhibit the development of plant teeth and plant height [1175 ⇓~1177]. Musk xylenes, muscone, amitriptyline, and propranolol can suppress and reduce fish weight and length, causing significant effects on early life stages [1178 ⇓⇓~1181]. Furthermore, analgesic and anti-inflammatory drugs IBU, aspirin, and tiaprofenic acid showed inhibitory effects on algal growth at concentrations of 107.91, 103.05, and 4.03 mg/L, respectively [1182]. Research has also found that acetaminophen can affect the survival rate of amphibians, American toads [1183]. Moreover, some PPCPs can produce endocrine-disrupting effects on individuals. For example, gemfibrozil can cause obvious endocrine disruption in invertebrates [1184]; galaxolide can cause thyroid hormone secretion and regulation disorders in fish embryos [1086]. Additionally, many PPCPs also show neurotoxicity and cardiovascular toxicity. Acetaminophen and triclosan can affect animal behavior, leading to behavioral disorders [1183,1185]; acetaminophen can also lead to a decline in fish memory, learning ability, and cognitive flexibility [1186]; fluoxetine at concentrations of 0.05~5 μg/L can prolong sleep time in fish [1187]. Furthermore, sibutramine and caffeine can affect heart rate and blood pressure in mammals, showing a certain correlation with cardiovascular diseases [1188,1189].

6.4.4.2 Lethal Effect

Based on the results of acute toxicity tests, exposure to high concentrations of PPCPs can cause lethal toxic effects in fish and invertebrates. Yamamoto et al. [1085,1150] found that the lowest LC50 values for 7 kinds of parabens to medaka (Oryzias latipes) were 730–63,000 μg/L, among which methylparaben had the lowest toxicity. Galaxolide, benzophenone, DEET, and tetracycline showed decreasing toxicity to fish, with LC50 values of 0.046 mmol/L, 14.73 mg/L, 109.67 mg/L, and 322.8 mg/L (or 406.0 mg/L), respectively, after 96 hours of exposure [1086,1153,1160]. Moreover, DEET, benzophenone, cefepime, amoxicillin, and aztreonam had LC50 values for 24-hour or 48-hour exposure to Daphnia magna ranging from 7.63 to 129.3 mg/L, with benzophenone being the most toxic [1153,1190]. Schmitt et al. [1191] also found that at a concentration level of 44.2 μmol/L, sunscreen ingredient 3-benzylidene camphor significantly reduced the reproductive rate and increased the mortality rate of earthworms.

6.4.5 Population, Community, and Ecosystem Levels

At the individual level, PPCPs can also cause toxic effects to populations, communities, and ecosystems to a certain extent (Table 20). Studies based on populations have shown that when exposed to high concentrations of cimetidine, the growth and biomass of the invertebrate Gammarus fasciatus will be significantly reduced, while the survival rate of Psephenus herricki will decrease ^[1192]. Sunscreen can induce coral bleaching ^[1193], and triclosan not only reduces algal growth but also inhibits and interferes with crustacean reproduction ^{[1194, 1195]}. It was found that exposing fish, algae, and large water fleas to caffeine at an environmental wastewater quality concentration of 232 μg/L in an Indian city showed that caffeine poses a high risk to all three populations. Among them, compared with water fleas and fish, caffeine poses the highest risk to algae ^{[1196, 1197]}. 17α-estradiol and fluoxetine were found to reduce the growth rate of snail populations ^[1198]. At concentrations of 1.8~2 μg/L, norazepam can alter the behavior and feeding rate of wild European perch, but it has no direct impact on the behavior of mayflies ^{[1199, 1200]}. In addition, tebuconazole at a concentration of 0.25 μg/mL has a certain inhibitory effect on the population size of zebrafish and also shows inhibitory growth phenomena. After exposure to a concentration of 0.5 μg/mL, masculinization occurred in the zebrafish population ^[1201].

表20 PPCPs 在种群、群落和生态系统水平的毒理效应

Table 20 Toxicity effect of PPCPs in population, community and ecosystem level

Classification	Species or community	PPCPs chemicals and effective concentration	Toxicity effect	ref
Population	Gammarus fasciatus,Psephenus herricki	Cimetidine(0.07~70 μg/L)	Reduced growth and biomass of G. fasciatus, low survivorship of P.herricki when exposed to high concentrations	1192
	Millepora complanata,Stylophora pistillata,Acropora sp.	Sunscreen UV filter (10~100 μg/L 2~48 h)	Induction of coral bleaching	1193
	Algal	Triclosan(400 and 200 ng/L)	Algal growth toxicity	1194
	Crustacean	Triclosan(600 and 340 ng/L)	Crustacean reproduction toxicity	1195
	Fish,alge,daphnid	Caffeine(232 μg/L )	RQ value of caffeine against algae was calculated to be very high, i.e., 5800, whereas against fish and daphnid, it was 25.4 and 8.2, respectively	1196
	Physa pomilia	17α-ethynylestradiol (1.0 μg/L or 100 μg/L); Fluoxetine (1.0 μg/L or 100 μg/L)	Lower population growth rate	1198
	Perca fluviatilis)	Oxazepam (1.8 μg/L)	Alters behavior and feeding rate of wild Perca fluviatilis at concentrations encountered in effluent-influenced surface waters	1199
	Perca fluviatilis,Coenagrion hastulatum	Oxazepam (2 μg/L)	Increased predator activity of perch, no behavioral effects on mayflies (direct)	1200
	Danio rerio	Triadimefon(0.25 μg/mL,0.5 μg/mL)	Triazolone at 0.25 μg/mL had an inhibitory effect on zebrafish population and growth;The zebrafish population appeared to be androgynous after treatment at a concentration of 0.5 μg/mL	1201
Community	Stream benthic communities	Amphetamine(1 μg/L)	Suppression of gross primary production on autotrophic biofilms, compositional shift of bacterial and biofilm communities, increased dipteran (stream insect) emergence	1202
		Triclosan(0.1~10 μg/L)	Increase in abundance of triclosan resistant bacteria and stimulation of periphyton growth	1203
	Benthic bacterial communities	Triclosan(17.3 μg/g)	Triclosan Exposure Increases Triclosan Resistance and Influences Taxonomic Composition of Benthic Bacterial Communities	1204
	Algal and invertebrate benthic communities	Fluoxetine(20 μg/L); Citalopram(20 μg/L)	Suppression of primary productivity and community respiration on biofilms (algae). Increased stream insect emergence	1205
	Algae	Caffeine(5 μg/L); Acetaminophen(5 μg/L); Diclofenac(5 μg/L)	Decreased biofilm (algal) biomass (indirect)	1206
	Helisoma trivolvis,Elimia livescens	Carbamazepine(2000 ng/L)	Change biomass	1207
	Ammonia-oxidizing Archaea	Enrofloxacin(1 μg/L)	Reducing the abundance of ammonia-oxidizing archaea in a lake system	1208
	Sediment bacterial community	Ciprofloxacin(200 μg/L)	Altering the composition of sediment bacterial communities and reducing the ability to metabolize pyrene	1209
	Chlorella vulgaris,Microcystis	Ser-HCL(25~200 μg/L)	Altered the composition of photosynthetic community	1224
	Algae	Nano-TiO₂	Decrease the abundance of Acutodesmus dimorphus, Gomphonema clavatum, and Gomphonema lagenula, and increase the abundance of Oscillatoria and Chaetophora sp.	1210
	Phytoplankton	Norfloxacin	Norfloxacin can inhibit community formation and thus reduce phytoplankton abundance. These changes can disrupt trophic relationships between zooplankton and plants	1211
	Soil microbial community	Sulfamethoxazole	When 100 mg/kg sulfamethoxazole was added for 7 d, the soil microbial population density decreased significantly (p<0.05), and the utilisation rate of sugars decreased by 20.4%	1212
		Sulfamethazine; Sulfamethoxazole;Chlortetracycline;Tetracycline;Tylosin	The maximum inhibition of soil microbial respiration by sulfadiazine, sulfamethoxazole, chlortetracycline, tetracycline, tylosin, and methicillin reached 34.3%, 34.4%, 2.71%, 3.08%, 7.13%, and 38.1%	1213
		Difloxacin	Soil denitrification rates were inhibited by 10 mg/kg of difloxacin, and this inhibition lasted for more than 30 days, suggesting that the presence of difloxacin altered the function of the soil microbial community	1214
		Oxytetracycline	5 μmol/L oxytetracycline or 20 μmol/LCu alone can reduce the diversity of soil microbial populations by about 25% and 15%, respectively	1215
Ecosystem		Amphetamine;Methamphetamine; Ketamine;Ephedrine; Cocaine;Benzoylecgonine; Methadone;Morphine; Heroin;Codeine; Methcathinone, etc	11 drugs and their metabolites were detected in the water samples, with RQs ranging from 0 to 0.047	1217
		Enoxacin;Chloramphenicol; Lincomycin, etc	RQ value of enrofloxacin was >1, which was at high ecological risk, while the RQ values of chloramphenicol and lincomycin were >0.1	1218
		Enoxacin;Ciprofloxacin; Sulfamethoxazole, etc	RQ values of enrofloxacin, ciprofloxacin, and sulfamethoxazole were >1, which showed high ecological risks to aquatic organisms	1219
		Gale Musk,;Tuna Musk	Relevant studies have shown that the biodegradation coefficients of Galle musk and Tuna musk are 0.071 and 0.023, respectively	1220
		Xylene Musk;Ketone Musk	The bioconcentration factors (BCF) for xylene musk, ketone musk and galax musk were 3800, 760 and 1584, respectively	1221
		Germicides;Disinfectants	Germicides and disinfectants are widely added to all types of household cleaning products and can enter the food chain from the environment and accumulate in plants, animals and humans	1225
		Sulfadimethoxine(50 μg/kg)	50 μg/kg sulfadimethoxine reduces soil nitrification	1222
		Caffeine;Carbamazepine; Ibuprofen	Three pharmaceuticals posed risk to aquatic organisms, with PI values of 10.3, 1.3, and 0.3, respectively. The maximum RQ value for caffeine was 10.3, RQ values of carbamazepine and ibuprofen ranged from 0.63~1.86, 0.25~1.48, respectively	1223

The results of community-based studies indicate that PPCPs exhibit toxic effects on many biological communities. Studies on stream benthic communities have found that amphetamine can inhibit the total primary production of autotrophic biofilms, induce changes in the composition of bacterial and biofilm communities, and increase the emergence rate of dipterans (stream insects)¹²⁰²; exposure to triclosan increases triclosan resistance and affects the taxonomic composition of benthic bacterial communities, such as increasing the abundance of triclosan-resistant bacteria and stimulating the growth of surrounding plants^[1203,1204]. Fluoxetine and citalopram can inhibit primary productivity of biofilms (algae) and community respiration in algal and invertebrate benthic animal communities, and increase the emergence rate of stream insects^[1205]. Caffeine, acetaminophen, and diclofenac significantly reduce the biomass of biofilms (algae)^[1206], while carbamazepine can alter the biomass of snails in invertebrate communities^[1207]. Enrofloxacin and ciprofloxacin can reduce the abundance of ammonia-oxidizing archaea in lake systems, alter the composition of sediment bacterial communities, and decrease the ability to metabolize pyrene^[1208,1209]. Norfloxacin and Nano-TiO₂ have toxic effects that inhibit community formation, reduce population abundance, and disrupt trophic relationships between planktonic organisms^[1210,1211]. Additionally, oxytetracycline, difloxacin, sulfadimidine, sulfamethoxazole, chlorotetracycline, tetracycline, and tylosin all show inhibitory effects on microbial respiration and changes in microbial community structure in soil microbial communities^{[1212⇓⇓~1215]}.

PPCPs can cause changes in bacterial and diatom communities and disrupt and alter the progression of aquatic invertebrate communities, and these disruptions or alterations can, in turn, disrupt other ecosystem functions^[1216].Research on PPCPs at the ecosystem level is scarce, or it is difficult to clearly define. So far, the toxicological effects of PPCPs in ecosystems are mostly represented by risk quotient (RQ) values. Hu et al.^[1217] studied the residual levels of 11 drugs and their metabolites (such as amphetamine, desoxymethamphetamine, ketamine, ephedrine, cocaine, benzoylprocaine, methadone, morphine, heroin, codeine, and methylphenidate) in the North Canal, with multiple drugs detected in water samples, and their RQ values ranged from 0 to 0.047, showing low ecological risks to aquatic organisms. Dong Guanguan et al.¹²¹⁸ analyzed the distribution characteristics of 23 antibiotics in the water bodies of the Biliu River Reservoir and its inflow rivers in Dalian City, and through the RQ value study, found that the RQ value of enrofloxacin was >1, indicating high ecological risk, while the RQ values of chloramphenicol and lincomycin were >0.1, showing medium ecological risk. Zhang et al.^[1219] studied the residues of 13 antibiotics in the Laizhou Bay and evaluated their ecological risks based on RQ values, finding that all 13 antibiotics were detected, with RQ values of enrofloxacin, ciprofloxacin, and sulfamethoxazole being >1, showing high ecological risks to aquatic organisms. In addition, studies have shown that various musks, such as galaxolide, tonalide, xylene musk, and ketone musk, have high bioaccumulation coefficients and low biodegradation coefficients^[1220,1221]. Other studies have reported that antibiotic exposure has been proven to potentially affect key ecosystem functions, for example, 50 μg/kg of sulfadimethoxine can reduce soil nitrification^[1222]. In the Taihu Lake ecosystem, caffeine, carbamazepine, and IBU pose risks to aquatic organisms, with priority index (PI) values of 10.3, 1.3, and 0.3, respectively, among which the maximum RQ value of caffeine was 10.3, the highest of the three^[1223].

6.4.6 Summary and Outlook

PPCPs have been a category of emerging contaminants that have drawn high attention in recent years. Although numerous studies have been conducted, our understanding of the ecological toxicological effects and human health risks of PPCPs is still far from comprehensive. The existing large number of studies are mainly focused on the detection and risk assessment of PPCPs in aquatic environments. Based on previous research, future studies need to focus on the following aspects:

1) The group of PPCPs pollutants is gradually expanding. So far, research on PPCPs is still mainly focused on several main types of compounds, and in the future, it is necessary to expand the research on a wider range of PPCPs compounds to make up for the research blind spots.

2) At present, most studies on the biological toxicity of PPCPs select high experimental concentrations for acute and subacute toxicity research, which is inconsistent with the actual environmental exposure situation. Future toxicity studies should be more based on environmental concentrations close to the actual ones.

3) The pollutants in the environment do not exist independently or have single toxicity. Future studies should focus on the joint toxicity and synergistic environmental effects between different kinds of PPCPs and other pollutants.

7 Human Exposure and Health Risks of Emerging Contaminants

7.1 Screening of Environmental Pollutants Based on Human Health Risk Orientation

There are many environmental pollutants. It is crucial to effectively identify and assess harmful new environmental pollutants for the protection of public health and environmental safety. This section focuses on the methods of screening environmental pollutants based on a human health risk orientation, specifically covering the strategies of new pollutant screening in exposure assessment, toxicity assessment, and risk assessment.

7.1.1 Exposure Assessment

Environmental exposure assessment is a critical component of environmental health risk assessment, which involves evaluating the amount of pollutants that humans are exposed to through inhalation, ingestion, and skin contact. Exposure assessment can be divided into external exposure assessment and internal exposure assessment. External exposure assessment primarily focuses on human contact with environmental pollutants, i.e., measuring or estimating the amount of pollutants that humans are exposed to from the environment during a specific time period. Common assessment methods include field environmental monitoring, where human external exposure levels are evaluated by directly measuring pollutant concentrations in air, water, and soil samples. Additionally, mathematical models can indirectly estimate the amount of pollutants to which humans may be exposed, but calculation simulations are mainly based on environmental monitoring data and human activity patterns. Internal exposure assessment focuses on the distribution and accumulation of pollutants within the human body, i.e., measuring or estimating the concentration of pollutants in various organs and tissues of the human body. Among these, biological monitoring, as a commonly used method, characterizes human internal exposure levels by measuring the concentration of pollutants or their metabolites in human tissues or biological samples (such as blood and urine), while physiological physiologically-based pharmacokinetic models are used to quantitatively describe the processes of pollutant absorption, distribution, metabolism, and excretion in the human body.

7.1.1.1 A Typical High-Throughput External Exposure Assessment Model

In recent years, with the increasing variety of chemicals, how to assess the potential risks they pose to human health has drawn extensive attention from all sectors. Exposure prediction models have thus emerged. In the development and evolution of various models, the US Environmental Protection Agency (US EPA) launched the "ExpoCast" exposure prediction project^[1226], aiming to establish a high-throughput exposure (HTE) model to characterize the potential exposure risks caused by thousands of chemicals. The working principle of the HTE model is based on the description of exposure mechanisms and exposure events to quantitatively predict chemical intake, and the model describes multiple exposure pathways from the source of chemicals to humans. For example, one pathway may involve the release of chemicals from household products, followed by interactions on environmental media (such as air, water, indoor dust), and finally reaching the human body. To improve the accuracy of predictions, ExpoCast also incorporates other relevant chemical indicators, such as existing regulatory exposure assessments or chemical production volumes.

To further integrate, evaluate, and calibrate these exposure predictors, a method called "Systematic Empirical Evaluation of Models" (SEEM) was developed based on ExpoCast^[1227], which is used for consensus modeling of exposure model predictors. The core idea is to use an "intermediate encounter" approach, comparing the "forward" HTE model's predicted chemical intake rates with the "reverse" models that use biomarker data from the National Health and Nutrition Examination Survey (NHANES) conducted by the Centers for Disease Control and Prevention (CDC). This approach has the advantage of combining information from different data sources to provide a more comprehensive and accurate exposure prediction. In SEEM, available predictors are combined into a consensus Bayesian regression meta-model to estimate population exposure medians through comparison with human biomonitoring data. This method not only provides exposure predictions for each chemical but also performs uncertainty analysis for each prediction.

There are differences in the starting points and included parameters of the two models. ExpoCast is a model used to predict the exposure levels of chemicals in the environment [1226], while SEEM3 is an environmental exposure model oriented towards human health risks [1227]. The former mainly considers the production, use, and disposal processes of chemicals; incorporates a large number of molecular descriptors in the parameters to characterize the physical and chemical properties of chemicals; and uses a series of mathematical formulas to describe the transport process of chemicals from source to receptor, fully considering the fate of chemicals in the environment, thereby achieving quantitative assessment of potential risks to human health. The latter tends to consider multiple sources of pollutants, including air, water, and soil; as well as human exposure through inhalation, ingestion, and skin contact; simultaneously taking into account the physical and chemical properties of pollutants described by various parameters, as well as human physiological and behavioral characteristics and their impacts on exposure risks; based on this, it describes the transport process of pollutants from source to receptor with a series of mathematical formulas.

Specifically, the SEEM3 framework uses linear regression to integrate relevant exposure predictors into a consensus Bayesian regression meta-model. When given path predictions and other inputs, this meta-model can provide a consensus prediction. This approach is similar to quantitative structure-property predictors. The framework applies Bayesian methods to estimate possible parameters, aligning exposure predictors with intake rates of 114 chemicals inferred from NHANES biomonitoring data. Both exposure predictors and NHANES chemicals are assigned to one or more pathways. For chemicals without NHANES data, machine learning methods are used to train and assign chemicals to pathways based on their structures, followed by using Equation (3) to make predictions for a large chemical structure library.

(3)

l o g R i = a 0 + ∑ j δ i, j × (a j + ∑ k w j, k × l o g π i, k)

where: R_i is the absorption rate of chemical substance i; a₀ is the "total average" intake rate for all chemicals, which represents the part not explained by the exposure predictors; δ_i_,_j is a Boolean value (0/1) indicating whether chemical substance i is exposed through pathway j; a_j is the additional average intake rate for all chemicals through pathway j, which is also not explained by the predictors; w_j_,_k is the predictor k's load on chemical substance i through pathway j ("weight") (π_i_,_k), and if an exposure predictor does not correspond to pathway j, then w_j_,_k is zero.

For SEEM3, the structure and physicochemical properties of chemicals were used to predict the probability that a chemical may be associated with four exposure pathways, which lead from sources—consumers (near-field), diet, far-field industry, and far-field pesticide—to the general population. The balanced accuracy of these source-based exposure pathway models ranged from 73% to 81%, with false positive rates for identifying chemicals ranging from 17% to 36%. The SEEM framework created a consensus meta-model in which exposure predictors were combined across pathways and weighted according to their ability to predict chemical intakes, which were inferred from human biomonitoring data for 114 chemicals. In terms of prediction accuracy, the consensus model achieved an R² of 0.82 and a root mean square error (RMSE) of 0.9 (Fig. 34). Detailed descriptions and code for SEEM3 are available at https://github.com/HumanExposure/SEEM3RPackage. However, this model was only calibrated for the entire U.S. population. It can be further optimized for specific target populations as needed in the future.

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图34 SEEM模型预测能力。注:NHANES生物监测数据推断的摄入率与SEEM元模型预测之间的相关性;虚线表示同一性(完美预测变量),而实线表示中位数的最小二乘回归(灰色阴影区域表示标准误差)

Fig.34 SEEM model predictive capacity. Note: This figure illustrates the correlation between intake rates inferred from NHANES biomonitoring data and predictions made by the SEEM meta-model. The dashed line represents identity (perfect prediction variable), while the solid line denotes the median least squares regression (with the gray shaded area indicating standard error)

As the number and variety of chemicals increase globally, the importance of exposure assessment is becoming increasingly prominent. High-throughput exposure prediction models like ExpoCast provide us with an effective tool to estimate the exposure risks of large numbers of chemicals. However, current predictive models still face many challenges. First, the quality and completeness of data are a key issue. Although we have a large amount of environmental and biological monitoring data, these data may have biases or be incomplete, which could affect the accuracy of the models. Second, with the emergence of new chemicals and new exposure pathways, existing models need to be constantly updated and improved to ensure they can accurately predict future exposure risks. Additionally, we need to better understand the behavior of chemicals in the human body and their interactions with other chemicals, which requires interdisciplinary collaboration and research. Overall, while we have made great progress in exposure assessment, there is still much work to be done in the future to optimize the models so that they can overcome the aforementioned problems and meet the needs of predictive exposure assessment.

In the field of environmental health risk assessment, in addition to the aforementioned external exposure assessment methods, scientists have also developed a series of specialized exposure prediction models for precise prediction of exposure risks for specific environments or populations. These models provide a deep understanding and accurate prediction of exposure risks by taking into account various factors such as environmental conditions, human activities, and physiological characteristics. For instance, the indoor PM_2.5 concentration prediction model^[1228], which is specifically designed to predict indoor particle concentrations. Considering factors such as outdoor air quality, ventilation efficiency, building characteristics, and residents' activity patterns, this model has been applied in tropical and subtropical regions like Taiwan and validated using multiple linear regression models. This tool is of great significance for assessing indoor air quality and formulating corresponding improvement measures.

The development and application of models for predicting external exposure have significantly enhanced our ability to assess environmental exposure risks. By precisely simulating and predicting exposure levels, these models not only provide powerful tools for environmental health research but also offer a scientific basis for formulating effective environmental protection and health prevention measures. With the advancement of technology and the continuous development of data analysis methods, it is expected that these models will play an even more important role in the field of environmental health in the future.

7.1.1.2 "Prediction and Evaluation of Human Body Exposure Characteristics Based on Big Data and Toxicokinetics"

How to quantitatively associate the environmental exposure concentration of pollutants with their target concentrations in organisms has long been a challenge for ecological and health risk assessment of pollutants. Traditional compartment models simulate an organism through one or more compartments, and the parameters of these models are often based on fitting results of experimental data, lacking consideration of anatomical information of the organism. Therefore, they cannot accurately describe the specific enrichment process of pollutants in different tissues. Unlike traditional compartment models, PBTK (Physiologically Based Toxicokinetic) models consider physiological characteristics of organisms to a large extent. These models consist of a series of compartments corresponding to actual physiological organs, and the compartments are connected through the circulatory system. Each compartment is precisely described using physiological parameters related to a specific species, such as tissue volume, blood flow, etc. In addition, PBTK models use parameters related to compounds to replace generic rate constants obtained only by fitting experimental data in traditional compartment models, showing broad application prospects. The structure of PBTK models can be found in Figure 35. The physiological parameters of commonly used PBTK models are summarized in Table 21.

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图35 基于生理的全身药代动力学模型。注:Q_pul、Q_ca、Q_re、Q_bo、Q_mu、Q_sp、Q_ha、Q_hv、Q_gu、Q_th、Q_sk、Q_fa分别代表流向肺、心脏、肾脏、骨骼、肌肉、脾脏、肝脏、肝静脉、肠道、胸腺、皮肤和脂肪的血流量。输入部位可以是身体的任何部位。此模型只展示了肝脏和肾脏的清除过程,而某些药物或污染物也可以在其他部位发生清除^[1229]。

Fig.35 Physiologically-based whole-body pharmacokinetic model. This model illustrates blood flow to various organs including the lungs (Q_pul), heart (Q_ca), kidneys (Q_re), bones (Q_bo), muscles (Q_mu), spleen (Q_sp), liver (Q_ha), hepatic vein (Q_hv), intestines (Q_gu), thymus (Q_th), skin (Q_sk), and fat (Q_fa). The site of entry can be any part of the body. The model exclusively shows clearance processes in the liver and kidneys, though clearance of certain drugs or pollutants may also occur at other sites^[1229].

表21 常见的用于PBTK模型的生理参数^[1230]

Table 21 Common physiological parameters for PBTK models^[1230]

Item	Mouse	Rat	Monkey	Human
Ventilation
Alveolar/[L/(h·kg)]	29.0	15.0	15.0	15.0
Blood flows
Total/[L/(h·kg)]	16.5	15.0	15.0	15.0
Muscle/unitless, the same below	0.18	0.18	0.18	0.18
Skin	0.07	0.08	0.06	0.06
Fat	0.03	0.06	0.05	0.05
Liver (arterial)	0.035	0.03	0.065	0.07
Gut (portal)	0.165	0.18	0.185	0.19
Other organs	0.52	0.47	0.46	0.45
Tissue volumes
Body weight/kg	0.02	0.3	4.0	80.0
Body water/unitless, the same below	0.65	0.65	0.65	0.65
Plasma	0.04	0.04	0.04	0.04
RBCs	0.03	0.03	0.03	0.03
Muscle	0.34	0.36	0.048	0.33
Skin	0.17	0.195	0.11	0.11
Fat	0.10	0.07	0.05	0.21
Liver	0.046	0.037	0.027	0.023
Gut tissue	0.031	0.033	0.045	0.045
Other organs	0.049	0.031	0.039	0.039

Extrapolation is one of the major applications of PBTK models, which can be performed by replacing or allometrically scaling physiological parameters across species^[1231], or by adjusting differential equations for the absorption site to predict dose-effect relationships under different routes of administration^[1232]. Additionally, it can also be used for extrapolation between different exposure scenarios, such as single exposure versus long-term exposure, high-concentration exposure versus low-concentration exposure^[1233]. Nowadays, PBTK models have been applied to study the effects of physiological specificity and age-dependent physiology on internal doses, extrapolation from in vitro to in vivo, integration of toxicity effects with models and their mechanisms, as well as evaluation of interactions between chemicals^[1234].

The rapid development of PBTK models has enabled their application in the study of new contaminants. For example, they have been used to simulate the toxicokinetics of PFOS or PFOA in rodents, monkeys, and humans. Some studies have even extended these models to simulate exposure during pregnancy and lactation^[1235,1236]. For instance, Loccisano et al.^{[1237⇓~1239]} conducted a series of PBTK model studies on PFOS and PFOA. They first developed PBTK models for PFOS and PFOA in monkeys and humans based on the models by Andersen et al.^[1240] and Tan et al.^[1241], describing the saturated reabsorption process of PFOS and PFOA through the kidneys, and simulating their concentrations in blood and urine after intravenous injection and oral exposure^[1237]. Subsequently, Loccisano et al.^[1238] considered gender differences in PFAS renal reabsorption processes in the model. Worley et al.^[1242] further used the model to explore the role of gender-related organic anion transporters in the renal reabsorption and excretion of PFOA, confirming that the gender difference in the serum half-life of PFOA is mainly influenced by the expression of transporters in the kidney. Later, Loccisano et al.^[1239] expanded the model to simulate human and rat exposure to PFAS during pregnancy and lactation by adding breast tissue, placenta, fetal chamber, and milk chamber. Some studies have also used PBTK models to elucidate the relationship between PFAS exposure and certain adverse outcomes. For example, Ngueta et al.^[1243] used an existing PBTK model to simulate plasma levels of PFOA and PFOS from birth to the participants' current age (18–44 years). Based on the simulation results, it was found that the relationship between PFAS levels and endometriosis could be attributed to the use of different contraceptive methods. Wu et al.^[1244] established a PBTK model based on the Monte Carlo method to simulate plasma PFAS levels in females aged 2–20 years, which can explain the relationship between PFAS and menarche age. Verner et al.^[1245] modified an existing PBTK model to simulate PFAS concentrations in maternal and umbilical cord plasma, finding that glomerular filtration rate can well explain the correlation between prenatal PFAS content and birth weight. Recently, Zhu et al.^[1246] accurately predicted the toxicokinetics of PFAS in human blood after long-term exposure through oral, nasal, and dermal routes by constructing a multi-pathway exposure PBTK model, finding that the nasal absorption process of PFAS occurs rapidly while the dermal absorption exhibits significant lag effects (Fig. 36). This will help identify the main exposure pathways of different PFAS-exposed populations from the perspective of internal exposure, assess the exposure risks of different pathways, and simultaneously aid in formulating reasonable measures to reduce PFAS exposure risks.

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图36 PFAS的动力学曲线与动力学参数。通过模型模拟的小鼠1 mg/kg单次暴露后的PFHxS(A)、PFOS(B)和PFOA(C)在小鼠血浆中的浓度-时间曲线,以及PFAS达到峰值的时间(T_max)(D)、最大浓度(C_max)(E)、消除半衰期(T_1/2)(F)和生物有效性(G)。相对C_max(经口或经皮或经鼻C_max与静脉注射C_max的比值)与生物有效性(H)之间的线性拟合,虚线表示95%置信区间^[1246]

Fig.36 Kinetic Curves and Parameters for PFAS. Concentration-time curves for PFHxS (A), PFOS (B), and PFOA (C) in mouse plasma following a single 1 mg/kg exposure, modeled to show the time to peak concentration (T_max) (D), maximum concentration (C_max) (E), elimination half-life (T_1/2) (F), and bioavailability (G). A linear fit between relative C_max (the ratio of oral, dermal, or nasal C_max to intravenous C_max) and bioavailability (H) is shown, with dashed lines representing the 95% confidence interval^[1246]

In the field of toxicology, the conversion problem of in vitro test results is particularly prominent. Given that animal experiments are gradually being phased out, more and more experiments will be replaced by alternative tests. However, the results obtained from in vitro experiments often cannot be directly used to predict biological responses in organisms after exposure to chemicals in vivo. Therefore, it is extremely important to establish a consistent and reliable extrapolation method from in vitro to in vivo.

Currently, two general solutions have been widely accepted: 1) increasing the complexity of in vitro systems to allow multiple cells to interact and mimic cell-cell interactions in tissues (e.g., "organs-on-chips" system)^[1247]; 2) using mathematical models for numerical simulations of complex system behaviors, where in vitro data provides parameter values for model construction^[1248]. These two approaches can be applied simultaneously, enabling in vitro systems to provide sufficient data for mathematical model development. To promote the development of alternative testing methods, increasingly challenging data are being obtained through more complex in vitro experiments, which can be integrated into mathematical models.

The concept of in vitro to in vivo extrapolation (IVIVE) involves using data obtained from in vitro experiments and reasonably extrapolating to predict the internal exposure concentration results. In toxicology research in the 21st century, emphasis has been placed on using in vitro assays to determine the potential biological targets of environmental and industrial chemicals. However, to make these in vitro findings relevant to human health risk assessment, the IVIVE approach is crucial. The process of converting in vitro biological activity concentrations into administered doses is typically referred to as "reverse dosimetry." For drug compounds, IVIVE has long been a standard practice. It involves parameterizing toxicokinetic (TK) models to associate blood and tissue concentrations with therapeutic doses from clinical studies. These methods usually include limited in vitro measurements such as hepatic clearance, plasma protein binding, and tissue distribution predictions derived from chemical structure. The model is parameterized and simulated using Simcyp software to predict in vivo kinetics based on in vitro data. Two main elimination pathways are considered in the model: metabolism and renal excretion. Simulations are conducted at a dose of 1 mg/(kg·d), and Monte Carlo analysis is used to simulate population variability in 100 healthy individuals aged 20–50 years (including both males and females). The output of the model is the median, upper, and lower percentile values of the steady-state concentration (C_ss), which are subsequently used as conversion factors to generate oral equivalents (Fig. 37).

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图37 基于人群的体外体内外推模型 (IVIVE)的建立流程

Fig. 37 Establishment process of a population-based in vitro to in vivo extrapolation (IVIVE) model

The IVIVE model predicts the steady-state blood concentration (C_ss) of compounds based on continuous daily oral dose intake and factors such as plasma protein binding, hepatic clearance, metabolism, non-metabolic renal clearance, and intestinal clearance (^{[1249⇓~1251]}) (Equation (4)).

(4)

C s s = k o (η G F R × F u b) + Q l i v e r × F u b × η C L, i n t, h Q l i v e r + F u b × η C L, i n t, h

wherein: k_o is the dose rate, mg/(kg·h); F_ub is the proportion of unbound compound in the blood; Q_liver is the hepatic portal vein blood flow per kg body mass, L/(kg·h); η_GFR is the glomerular filtration rate per kg body mass, L/(kg·h); η_CL,int,h is the total hepatic intrinsic clearance per kg body mass under first-order metabolism conditions, L/(kg·h). This model corresponds to the steady-state concentration in a three-compartment model (liver, intestine, and blood circulation in the body). Since this is a steady-state model (i.e., after sufficient time, the free chemical concentration in plasma will reach equilibrium with all tissues), tissue distribution is not included in the above formula. The model includes physiological parameters (GFR and Q_liver) and chemical-specific parameters (F_ub and η_CL,int,h). When chemical-specific estimates for F_ub and η_CL,int,h are available, the model can be parameterized for that chemical.

The predictions made using the IVIVE method are typically underestimated, with prediction values within a 3- to 10-fold error range compared to in vivo measured values; however, this level of error is considered acceptable in this field. A major challenge with HTS data is associating in vitro bioactivity concentrations with relevant in vivo dose values, particularly given the large number of chemicals that need to be assessed. There are several assumptions in the IVIVE process, which, although they may not perfectly reflect real-world conditions, are still considered reasonable. These assumptions include: pollutants can reach a steady state within the human body. This means that, despite intermittent environmental exposure, an average exposure level can be assumed and used as a basis for risk and impact assessment. Secondly, it is assumed that pollutant absorption is 100%. This implies that all exposed amounts of the chemical are completely absorbed into the body, without considering possible differences in absorption rates or the distribution, metabolism, and excretion of substances within the body. Although the IVIVE method was originally based on drug development, it has now been applied to high-throughput toxicokinetic (HTTK) models for chemicals. These models estimate the dose required to achieve steady-state plasma concentrations equivalent to those induced by in vitro assays. By comparing the lowest bioactive dose with the maximum expected exposure, IVIVE provides a risk-based evaluation for HTS data, serving as an indicator for prioritizing additional chemical testing.

In short, IVIVE is a key tool in the toxicologist's toolbox for high-throughput screening activities where large numbers of chemicals need to be evaluated and prioritized for traditional in vivo testing. It bridges the gap between in vitro data and potential in vivo effects, ensuring that the large amounts of data generated through HTS are put into the context of human health.

Since the concentration of exogenous contaminants in human blood is far lower than that of drugs and endogenous substances, and is often present at a nanomolar, picomolar or even lower level, it is difficult to analyze exogenous contaminants in human blood with high throughput. Therefore, screening and monitoring of high-risk substances existing in blood have become one of the major challenges in exposome research.

To achieve high-throughput prediction of blood concentrations from external exposure, Zhao et al.^[1252] developed the Human Exposure Concentration Prediction Model (HExpPredict), which can predict blood concentrations in humans using parameters such as biotransformation half-life, apparent volume of distribution, external exposure, exposure route, lg K_ow, and lg K_oa. This study constructed a blood concentration prediction model based on pollutant concentration data in human blood integrated from databases and literature searches as well as measured data, using three commonly used machine learning models (random forest, artificial neural network, support vector machine) (Fig. 38). The results showed that the random forest model had the best predictive performance (Fig. 39), with its predictive capability demonstrated in Table 22 and Table 23. This model has good predictive performance for various substances such as polychlorinated biphenyls, polycyclic aromatic hydrocarbons, phthalates, dioxins, perfluorinated compounds, OPE flame retardants, VOCs, polybrominated diphenyl ethers, and organophosphorus pesticides.

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图38 人体内暴露浓度预测模型(HExpPredict)的构建

Fig. 38 Construction of the human exposure prediction model (HExpPredict)

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图39 人体内暴露浓度预测模型(HExpPredict)的预测能力

Fig. 39 Predictive capacity of the human exposure prediction model (HExpPredict)

表22 机器学习模型的预测能力

Table 22 Predictive capacity of machine learning models

Model Types	Training Set (n = 172)				Test Set (n = 44)
Model Types	RMSE	MAE	MAPE	R²	RMSE	MAE	MAPE	R²
Random Forest	1.66	1.28	0.29	0.80	2.07	1.56	0.23	0.72
Artificial Neural Networks	2.83	2.13	0.39	0.41	3.07	2.56	0.35	0.39
Support Vector Machine	3.52	2.81	0.69	0.08	3.79	3.22	0.47	0.06

表23 随机森林模型对不同种污染物的预测能力

Table 23 Predictive capacity of the random forest model for different pollutants

Classtimes	N	RMSE	Mean RMSE
PCBs	86	0.64	0.91
PAHs	10	0.70
PAEs	5	0.71
Dioxins	11	0.73
PFCs	8	0.83
OPFRs	7	0.85
VOCs	15	0.86
PBDEs	31	0.94
PPCPs	29	1.18
OCPs	14	1.68

Finally, Zhao et al.^[1252] constructed the HExpPredict pollutant blood concentration prediction model using a random forest model. The model successfully predicted the concentrations of 7858 pollutants in human blood in high-throughput mode in ToxCast. Equivalent biological effects were calculated by combining the receptor binding activity test results in ToxCast. Ultimately, it was found that, apart from endogenous substances and drugs, food additives pose a higher exposure risk.

7.1.2 Toxicity Assessment

Toxicity assessment is a key step in evaluating the impact of pollutants on human health. It involves using various biological and chemical methods to test and analyze the potential hazards of pollutants. The results of toxicity assessment help determine which pollutants are harmful and the degree of risk they may pose to human health.

7.1.2.1 QSAR

A QSAR model is a computational tool widely used in chemistry, biology, and environmental science to predict the properties and behaviors of compounds. These models rely on the relationship between the molecular structure of compounds and their biological activity or other properties. In the field of environmental science, QSAR is used as a tool to estimate the potential toxicity of compounds, providing a basis for risk assessment of chemicals. Although the research methods of QSAR vary, they all follow similar core principles. The main research steps include: 1) establishing an accurate and reliable dataset; 2) selecting appropriate molecular structures and activity parameters from the dataset; 3) establishing a relationship model between structural parameters and activity parameters; 4) validating and optimizing the model, and determining its scope of application and error; 5) applying the model to predict and forecast the activity of organic pollutants.

The key points for the establishment of the QSAR model are as follows.

1) Acquisition of activity data. QSAR research aims to explore the relationship between the structure of compounds and their activities from numerous known data, and then predict the potential activities of unknown compounds. For this purpose, a large amount of accurate data is needed. These data usually come from three main channels: authoritative datasets, classic academic literature, and experimental data collected by researchers themselves.

2) Screening of structural parameters. In QSAR studies, selecting appropriate molecular structural parameters is crucial. The existing structural parameters are mainly divided into three categories: physicochemical properties, topological properties, and quantum chemical properties. To obtain a more robust model, researchers may take these parameters into account comprehensively. Although traditional QSAR studies rely on physicochemical properties, this method has certain limitations. Topological properties provide a new perspective for QSAR studies; they use graph theory techniques to describe the structural characteristics of molecules. Quantum chemical properties, on the other hand, can deeply reveal the internal structure and electronic properties of molecules.

3) Application of mathematical techniques. In QSAR research, constructing mathematical models based on experimental data is a key step. First, researchers obtain a series of property data of compounds through experiments, then select appropriate structural parameters and use mathematical tools to establish predictive models. Among them, regression analysis is the most common technique, especially multiple regression analysis. In addition, advanced techniques such as partial least squares method and artificial neural networks have also been widely applied in QSAR.

QSAR research initially developed as a subfield for drug design and gradually expanded with the growing demand for designing biologically active molecules. This methodology has played a crucial role in organic chemistry, pharmacology, and toxicology, particularly in the design, screening, and toxicity prediction of drug molecules. With the expansion of the chemical industry, large amounts of harmful organic substances have entered our ecological environment, posing threats to ecological balance and human health. In environmental science, QSAR is used as an assessment tool for hazardous chemicals, and QSAR models are often employed to predict toxicity endpoints such as acute toxicity LD₅₀, bioaccumulation factor (BCF), developmental toxicity, and teratogenicity. Zhu et al.^[1253] quantified the similarity of compounds using chemical descriptors and established QSAR models based on these descriptors to predict acute toxicity LD₅₀ in rats. This predictive method was applied by the U.S. EPA in the TEST predictive software. The BCFBAF (Bioconcentration Factor Bioaccumulation Factor) module (v. 3.00) in the EPA's EPI Suite software package achieved an R² value of 0.766 with an average absolute error (MAE) of 0.50 for the same chemicals that could be predicted by consensus methods^[1254]. The predictive accuracy of the EPA's TEST predictive software for developmental toxicity and teratogenicity using QSAR models was 0.85 and 0.79, respectively (https://clowder.edap-cluster.com/datasets/61147fefe4b0856fdc65639b#folderId=6352a8a6e4b04f6bb13cec84).

7.1.2.2 Machine Learning Prediction of Pollutant Toxicity

With the rapid development of machine learning technology, its application in the field of toxicity prediction has become an important branch of chemical and drug research. Machine learning, especially deep learning methods, has been widely used in predicting the toxicity of chemicals due to its efficiency in handling complex data and discovering potential patterns.

Machine learning (machine learning, ML) methods have been used to predict a variety of toxicity endpoints, including acute oral toxicity, hepatotoxicity, cardiotoxicity, endocrine disruption, and 12 Tox21 receptor activity endpoints. Clearly, depending on the type of endpoint data, when toxicity is considered as a yes/no activity problem, such as in the case of Tox21 endpoints, classification models are used; whereas for quantitative prediction, such as LD₅₀ prediction, regression models are used. Table 24 shows the different data sources used to build ML models for each toxicity endpoint in recent studies, as well as the best-performing machine learning models for public toxicology databases and toxicity endpoints. Due to the complexity of datasets, class distribution, or coverage of chemical space, ML methods exhibit varying performance across different datasets, making it difficult to compare algorithm performance across different toxicity endpoints. The comparison also becomes challenging due to different evaluation metrics, which are mainly determined by the ML method and the database used. The selection of evaluation metrics is also a critical step in ML model building and has been extensively studied.

表24 公共毒理学数据库及毒性终点的最佳表现机器学习模型

Table 24 Best performing machine learning models for public toxicology databases and toxicity endpoints

Toxicity Endpoints	Database Name	Machine Learning Model Ref
Cardiac Toxicity (hERG Binding)	PubChem bioassay, CHEMBL bioactivity	1255 ⇓⇓⇓~1259
Acute Oral Toxicity (LD₅₀)	Li et al. set, admetSAR, MDL Toxicity, EPA Toxicity Estimation Software Tool, SuperToxic	1260,1261
Hepatotoxicity (DILI)	Liver Toxicity Knowledge Base (LTKB), LiverTox, Hepatox	1262,1263
Nuclear Receptor and Stress Response	Tox21	1264 ⇓⇓~1267
Mutagenicity	Ames data collection	1268 ⇓~1270
Carcinogenicity	Carcinogenic Potency Database (CPDB)	1271
General Toxicity	TOXNET, Toxin and Toxin Target Database (T3DB), SIDER	1260, 1272,1273

7.1.3 High-Throughput Toxicity Screening of Emerging Contaminants: ToxCast In Vitro High-Throughput Screening and ToxPI Scores

ToxCast and Tox21 are high-throughput chemical screening programs designed in response to the 2007 National Research Council (NRC) report "Toxicity Testing in the 21st Century: A Vision and a Strategy." The report called for the use of in vitro assays to determine the effects of thousands of largely untested chemicals on "toxicity pathways." The ToxCast (Toxicity Forecaster) program is run by the Center for Computational Toxicology and Exposure (CCTE, formerly the National Center for Computational Toxicology) under the U.S. Environmental Protection Agency (EPA). According to the EPA's subsequent publication "In Vitro Screening of Environmental Chemicals for High-Throughput Toxicity Testing: The ToxCast Project," the overall goal of the program is to identify in vitro assays and responses related to in vivo toxicity, develop predictive models based on multiple assays and chemical property results, and screen environmental chemicals with little or no available toxicity data using these assays and models, prioritizing them for further testing. The term Tox21 is often used to describe the concept of 21st-century toxicity testing, but it is also the name of a formal collaboration established to address the development of new toxicity testing strategies.

ToxCast employs automated chemical screening technology known as high-throughput screening (HTS) assays. These assays mainly expose living cells, isolated proteins, or other biomolecules to chemicals and then detect changes in the biological activity of these cells or proteins to screen for compounds with potential toxic effects. These innovative approaches can limit the number of standard laboratory animal tests for basic toxicity while rapidly and efficiently screening the potential health impacts of thousands of chemicals.

The generation of ToxCast data involves chemicals, assays, and data processing and analysis. These data are publicly available for exploring the ToxCast data. ToxCast uses over 700 high-throughput assays covering a broad range of advanced cellular responses and approximately 300 signaling pathways. ToxCast assays include cell-based in vitro assays using intact cells to measure cellular changes following exposure to test substances such as human primary cells and cell lines as well as rat primary hepatocytes. They also include biochemical in vitro assays measuring the activity of biomolecules such as purified proteins or nucleic acids.

In the ToxCast program, chemical testing is coordinated by CCTE, and data are collected by external vendors before being analyzed and shared by CCTE. After ToxCast data are generated in the laboratory and processed by the US EPA, they can be downloaded from the website (https://epa.figshare.com/articles/dataset/ToxCast_Database_invitroDB_/6062623/11). The ToxCast program can be divided into three phases: Phase 1, Phase 2, and Phase 3 currently ongoing (Figure 40). Since the release of Phase 1 data in 2010, each phase has added new unique chemicals and experimental endpoint data to the database. By the completion of Phase 3 in 2018, ToxCast had screened over 4500 chemicals across more than 700 high-throughput assays, covering many advanced cellular responses and over 300 signaling pathways. The ToxCast chemical list includes both chemicals currently under processing and those that have been screened, and can be downloaded from an FTP site. The ToxCast dashboard summarizes information about the chemicals, including their structures and physicochemical properties. Users can use various filters to select chemicals of interest, including Chemical Abstracts Service registry numbers (CASRN), chemical names, chemical categories, usage categories, and physicochemical properties such as the octanol-water partition coefficient (lg K_ow).

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图40 ToxCast 3阶段化合物以及毒性终点数目发展情况

Fig. 40 Development of compounds and toxicity endpoint numbers in the three phases of ToxCast

In the evaluation of environmental pollutants' toxicity, multiple experimental methods and models are usually adopted. Toxicity calculation is an important step in assessing the toxic effects of chemical substances on organisms. When dealing with multi-toxicity endpoint data from high-throughput (HT) of ToxCast, the method of toxicity calculation is crucial. Reif et al.^[1274]developed a toxicity calculation method based on ToxPi (Toxicological Priority Index). ToxPi is a comprehensive indicator that combines multiple data sources, such as experimental data, model predictions, literature data, etc., to evaluate and rank the toxicity of chemical substances. By integrating and visualizing information from various data sources, this method comprehensively evaluates the potential toxicity of chemical substances, providing an intuitive way to compare and prioritize different chemicals, offering valuable information for environmental management and decision-making^[1252]. Specifically, ToxPi provides each chemical substance with a dimensionless index score, which is a weighted combination of all data sources, representing the formalized and rational integration of information from different fields. Visually, ToxPi is represented as slices of a unit circle, with each slice representing an information fragment (or relevant fragment). The distance of each ToxPi slice (from the origin or center) is proportional to the normalized value (e.g., assay potency or predicted bioavailability). As shown in Figure 41, 18 slices are assigned equal weights in the overall ToxPi calculation, so the graphical width of all slices equals the angle θ, obtained by dividing 2π radians into 18 parts, i.e., 2π/18 = 20°. For example, in this representation, bisphenol A's strong estrogen receptor activity is very significant. Additionally, ToxPi scores can be used to determine which chemicals may pose greater risks to ecosystems and human health, thereby providing directions for further research and management.

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图41 双酚A的ToxPi毒性分数^[1252]

Fig. 41 Toxicity scores of bisphenol A according to ToxPi^[1252]

Based on the ToxCast data, Dong et al.¹²⁷⁵ used the toxicity equivalence factor (TEF) to assess the toxicity of 511 chemicals in indoor dust. The researchers identified 511 common pollutants in indoor dust through extensive literature review, and the concentrations of 355 chemicals have been recorded. The concentrations of these chemicals were estimated by calculating the weighted median of each study. To evaluate daily human exposure to chemicals in indoor dust, Dong et al.^[1275] mainly considered non-dietary dust ingestion and skin absorption, using equations from the Public Health Assessment Guidance Manual of the Agency for Toxic Substances and Disease Registry to calculate the daily dose of each chemical for humans (μg/(kg·d)) and estimate exposure to non-dietary dust ingestion and skin absorption (μg/d). The toxicity equivalent factor (TEF_i) of the chemicals mentioned in this study was calculated based on the relationship between their respective A_50,C and the most active positive control (Q_AC50,min) retrieved from the U.S. Environmental Protection Agency (EPA) iCSS ToxCast Dashboard (Equation (5)).

(5)

Q T E F i = Q A C 50, m i n ÷ Q A C 50, i

wherein:

Q T E F i

represents the equivalent factor; Q_{AC50, min} represents the most active positive control;

Q A C 50, i

represents the positive control corresponding to the toxicity equivalence factor.

7.1.4 High-Throughput Exposome Database (HExpMetDB)

In the field of environmental science, assessing the risk of human exposure to various chemicals is crucial. To better understand and quantify these risks, researchers can use the comprehensive database HExpMetDB (human exposome and metabolite database), which provides a full range of resources specifically for chemical risk assessment. The establishment of HExpMetDB has been achieved by integrating and curating existing chemical databases and literature. This process involves not only the original chemicals but also their potential metabolites within the human body. This integrated approach ensures the comprehensiveness and accuracy of the database, providing researchers with a valuable resource to better understand how chemicals are metabolized in the human body and what impacts these metabolites may have on human health. HExpMetDB integrates toxicological databases, human biomonitoring databases, government control lists, and pollutants monitored in various media from the literature.

To make this database more user-friendly, researchers also provided it with a graphical user interface (GUI)(Fig. 42). This interface allows users to search for chemicals in multiple ways, such as CASRN, molecular formula, or m/z, and retrieve related metadata. These metadata include identifiers, structures, predicted HLBs, exposure levels, and rat oral LD₅₀ information of the chemicals. In addition, users can further search for metabolites of the chemicals, which provides researchers with a convenient tool to better understand how chemicals are metabolized in the human body. During the integration process, researchers successfully mapped 20,756 compounds to the prioritized human exposome database. This means that not only does this database include a large number of chemicals, but also their metabolites. To further provide possibilities for the development and identification of exposure biomarkers, researchers also developed a metabolic metabolite database for predicting biotransformation. This database includes 95,976 metabolites derived from the initial 20,756 heterologous substances. Overall, HExpMetDB provides a valuable resource for researchers in the field of environmental science, helping them better understand and quantify the risks of chemicals. The establishment and application of this database will undoubtedly lay a solid foundation for future research, helping scientists better protect human health and environmental safety.

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图42 HExpMetDB的图形用户界面(GUI)

Fig. 42 Graphical user interface (GUI) of HExpMetDB

7.1.5 "New Contaminant Screening Strategies Based on Risk Assessment"

The identification of new contaminants is usually based on the non-target screening process of liquid chromatography (LC) or gas chromatography (GC) coupled with high-resolution mass spectrometry (HRMS). However, this method generates a large amount of data and complex data deconvolution, which poses great challenges to researchers in data processing. In order to quickly and effectively screen and identify high-priority compounds based on risk assessment from thousands of molecular mass spectral features, a series of priority screening strategies have gradually emerged. Below, four classic strategies are selected for introduction.

7.1.5.1 A Priority Framework for Rapid Automated Characterization of Compound Toxicity in Non-Targeted Analysis

With the advent of large-scale mass spectrometry databases, the identification of unknown compounds has become increasingly simple. For example, the US Environmental Protection Agency (EPA) ToxCast database, ChemSpider, PubChem, and other publicly available databases have been widely used in human biomonitoring programs^[1276]. Furthermore, the list of chemicals in the US EPA Distributed Structure-Searchable Toxicity Database (DSSTox) has also been utilized to establish computerized secondary spectral libraries for assisting compound identification^[1277]. Additionally, retention time prediction models based on quantitative structure-retention relationships (QSRR) have also been applied for preliminary compound identification^[1278,1279]. The combination of database searching and retention time prediction is currently a widely adopted chemical screening method; however, there remains room for improvement in its automation execution, especially in narrowing down large-scale mass spectrometry data to manageable high-priority substances.

Therefore, some research scholars have proposed an analytical strategy to determine priorities using toxicity prediction for the rapid identification of the most harmful pollutants. For example, using the toxicological priority index (ToxPi) in the ToxCast database can reduce a list of more than 300,000 suspect candidates to 818 candidates^[1280], and identifying specific MS/MS fragments representing common toxicity using deep learning methods can determine toxicity priority ranking, thereby obtaining a priority list of toxic compounds worthy of further identification^[1281]. However, most studies using this strategy still show certain deficiencies in handling different toxicity endpoints, resulting in priority bias for some compounds due to the assessment of a single toxicity endpoint. In view of this problem, Yang et al.^[1282] developed the R package application "NTAprioritization.R", establishing a high-throughput screening platform for prioritizing suspect candidates that combines mass spectrometry fragment information, chromatographic retention behavior, and toxicity evaluation, ensuring priority attention to compounds with high confidence and potentially high toxicity.

This rapid and automated framework for characterizing the toxicity of compounds includes five steps: 1) sample preparation and HRMS analysis; 2) data preprocessing; 3) retention time prediction and sorting by second-order mass spectrum matching; 4) toxicity prediction and ToxPi scoring; 5) final prioritization based on toxicity evaluation, retention time, and ranking of second-order mass spectra (Fig. 43). The platform has been validated using environmental samples with spiked substances, and it was found that it can prioritize high-risk compounds in sludge water samples containing pesticides, herbicides, pharmaceuticals, and personal care products, effectively improving the efficiency of researchers in identifying potential hazards.

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图43 基于非靶向分析的优先级划分工作流程概述

Fig. 43 Overview of the workflow for priority setting based on non-targeted analysis

7.1.5.2 Screening of Toxic Contaminants Based on Effect-Directed Analysis and Feature Indicator Fragments

To identify "unknown" compounds in complex matrices, mass spectrometry databases serve as a powerful analytical aid to help researchers quickly identify hundreds or thousands of mass spectral peaks. However, this approach will overlook "unknown unknowns" as well as compounds not previously recorded in the mass spectrometry database^[1283,1284].

EDA is a powerful tool for analyzing both "known" and "unknown" substances, capable of accurately "tagging" a small portion of suspicious bioactive substances in thousands of mass spectrometry peaks [^{1285⇓~1287]. This strategy mainly combines differential elution with bioassays and LC-MS with high mass accuracy. Each differential elution corresponds to a retention time window in chromatography, and the retention time windows containing compounds with detected effects are marked based on the biological activity measurement of the differential elution parts. Then, these retention time windows are linked to the raw data set of LC-HRMS with the same retention time, enabling the prior identification of toxic pollutants. Based on this, Loewenthal et al. [^[1288]] further proposed a dual labeling method, aiming to mark out certain types of potentially toxic substances from complex matrices by combining characteristic fragment ions of a specific compound class and unique biological activity reactions. They applied this method to screen organophosphate acetylcholinesterase inhibitors, successfully identifying multiple low-concentration neurotoxins and pesticides from soil and whole blood samples through the presence of organophosphate indicator ions in mass spectrometry fragments and the acetylcholinesterase inhibitory effect in differential elution fractions (see Fig. 44). This workflow can significantly reduce the number of interested mass spectral peaks, promoting precise recognition and rapid identification of unknown organophosphate cholinesterase inhibitors by researchers. At the same time, this dual labeling method can be easily extended to other different compound families and biologically active substances of interest.}

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图44 集成双重标记法的实验工作流程图

Fig. 44 Experimental workflow integrating dual labeling techniques

7.1.5.3 Molecular Descriptor Prediction Model Based on MS/MS Spectra

QSAR methods are based on the principle of similarity, closely linking the structure and properties of compounds. By using information about molecular features of compounds (such as chemical functional groups, molecular fingerprint descriptors, etc.), it predicts the physicochemical properties and biological effects of target compounds, which is a commonly used method for assessing compounds^[1289]. Especially with the development of ML technology, QSAR can handle more descriptors calculated from chemical structures, greatly expanding its application range and enhancing predictive performance^{[1290, 1291]}. However, whether traditional QSAR or ML-based QSAR methods require that the structure of the target compound is known and needs to be converted into molecular descriptors. These requirements limit the prediction of the properties and toxicity of chemicals in the environment distributed in the PubChem database, with only 14% of the compounds having available structural information^[1292]. Therefore, QSAR may face limitations in predicting the properties and toxicity of chemicals in the environment, especially the challenges in structural identification of most compounds screened by high-resolution mass spectrometry in environmental samples further hinder the hazard and risk assessment of these substances.

To address this challenge, some scholars proposed that if measurement data such as mass spectrometry are directly used for prediction without performing the structural identification process of compounds, it may be possible to expand the range of chemicals involved in hazard and risk assessment even without obtaining exact structural information. For example, Zushi et al.^[1293] developed a QSAR method based on XGBoost machine learning. This method predicts the properties and toxicity of compounds using analytical descriptors derived from mass spectra and retention indices obtained by gas chromatography-mass spectrometry, without requiring precise structural information. The predictive performance of this method has been validated by standard reference datasets and real samples, and the method has been made openly accessible, allowing any online user to execute the model through a Web application named Detective-QSAR (http://www.mixture-platform.net/Detective_QSAR_Med_Open/). The QSAR method that obtains analytical descriptors by combining mass spectrometry with machine learning can help evaluate the hazards and environmental risks of various unknown substances in the environment, thereby prioritizing them for subsequent detailed assessments.

7.1.5.4 "Application of Risk-Driven and Feature-Fragment-Prioritized Non-Targeted Screening Based on Machine Learning"

Non-targeted high-resolution mass spectrometry screening (NTS HRMS/MS) technology can detect thousands of organic compounds in environmental samples. These unidentified features are typically prioritized based on the intensity of the measured signals, with signal intensity regarded as a representative of abundance. Given that intensity does not necessarily reflect concentration, the traditional priority strategy fails to capture the environmental exposure of unknown compounds^[1294], and this approach also lacks toxicological elements, completely ignoring the fact that the environmental risks of emerging contaminants are jointly determined by exposure levels and hazard coefficients^[1295]. Therefore, new strategies are needed, focusing time-consuming identification work on feature fragments with the potential to cause adverse effects rather than the most abundant feature ions.

The EDA method introduced above can link the mass spectrometry data of HRMS with the toxic effects of complex mixtures by performing deductive identification of compounds in sample components that have specific toxic results^{[1285⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓~1296]}. Among them, the toxicity evaluation involved in EDA is usually assessed through in vitro bioassays. For example, hazard potential can be indirectly evaluated in cell cultures by focusing on single-cell mechanisms rather than overall toxic outcomes^[1297]. In order to reduce the manual workload of in vitro bioassays, high-throughput EDA (HT-EDA) has been developed in recent years^[1298]. However, not all bioassays can be used in HT-EDA mode. Additionally, since most unidentified HRMS/MS peaks have low toxicity and abundance, HT-EDA is not suitable for handling the thousands of NTS signals typically detected in complex matrices.

To address this challenge, Arturi and Hollender developed the MLinvitroTox framework based on xboost machine learning, which uses molecular fingerprints from secondary spectra for rapid classification, categorizing thousands of unknown HRMS/MS features into toxic/non-toxic groups according to over 400 specific targets from ToxCast/Tox21 and more than 100 cellular toxicity endpoints ^[1299]. Unlike traditional QSAR models that predict activity based on structural molecular fingerprints, MLinvitroTox is trained on the molecular structure from mass spectrometry, primarily used for predicting molecular fingerprints from experimentally measured MS2 spectra via CSI:FingerID/SIRIUS ^[299]. It also trains hundreds of supervised classification models using the invitroDB toxicity database to ensure broad toxicological coverage in its machine learning combination framework. The workflow for the development and validation of the model is shown in Figure 45. MLinvitroTox first models previously unexplored invitroDB endpoints through customized structural and toxicological data management, addressing issues with dirty data, sparse data, and imbalanced datasets. Development results show that over a quarter of toxic endpoints and most relevant mechanistic targets can be accurately predicted with sensitivity exceeding 0.95 using customized molecular fingerprints and models. Notably, the use of SIRIUS molecular fingerprints and xboost models, along with SMOTE (Synthetic Minority Over-sampling Technique) for handling data imbalance, proves successful and robust in modeling configurations. Validation using the MassBank spectral library indicates that toxicity can be predicted from molecular fingerprints of MS2 with an average balanced accuracy of 0.75. Further confirmation was achieved by applying MLinvitroTox to real environmental samples, validating targeted analysis experimental results ^[1300], narrowing the focus from tens of thousands of detected signals to 783 characteristic peaks related to potential toxicity, including 109 matched mass spectra confirming toxicity activity and 30 compounds. However, the MLinvitroTox framework has some limitations; currently, the predictions are meaningful only for toxic substances that exhibit adverse effects within the concentration range tested in invitroDB ^[1301], and the accuracy is affected by the quality of MS2 spectra in experimental data. It should be noted that MLinvitroTox was developed to prioritize the identification of unknown compounds, meaning it should serve as a foundation for further analysis rather than the basis for final decision-making.

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图45 MLinvitroTox模型开发和验证的工作流程图^[1299]

Fig. 45 Workflow for the development and validation of the MLinvitroTox model^[1299]

7.2 ADME Processes and Structure-Activity Relationships of Emerging Contaminants in Humans

7.2.1 New Contaminants' ADME Processes in Humans

Human exposure to new contaminants occurs through ingestion of contaminated drinking water and food, inhalation of indoor air, and contact with other contaminated media, where diet and drinking water are the main routes of human exposure^[1302]. It has been reported that seafood and meat account for 78.9% and 93.2% of total human intake of PFOS and PFOA, respectively^[1303]. Strong correlations between seafood consumption frequency and PFAS concentrations in human serum have been observed in various populations, such as seafood consumers, general residents, pregnant women, and fishery employees^{[1304, 1305]}. POPs may be released from food matrices during digestion, but only a portion can be absorbed into the systemic circulation. Studies show that the presence of lipids enhances the bioaccessibility of most POPs (e.g., PBDEs, PAHs, DDTs, etc.)^{[1306⇓⇓~1309]}. However, research on the bioavailability of new contaminants in different environmental media is still insufficient^[1310]. Zhu et al.^[1309] found that the solubilization effect of fatty acid-bile acid mixed micelles formed during high-fat food digestion and the carrying action of chylomicrons on PFAAs both increase their bioaccessibility. Typically, human exposure risk assessments of PFAS are based on total contaminant concentrations in food without considering their bioavailability. Future risk assessments should fully consider the bioavailability of pollutants in different media to avoid overestimating exposure risks.

In addition to the oral route, understanding the relative importance of other exposure pathways such as dermal and nasal exposure is crucial for interpreting levels of new contaminants in humans and predicting future exposure risks. The absorption processes of organisms vary under different exposure pathways, such as gastrointestinal digestion, skin barrier, and pulmonary gas exchange^[1311]. Route-specific toxicokinetic processes have been confirmed in some organic pollutants^[1312]. For example, Smith et al.^[1312] found that the metabolism of chlorpyrifos (CPF) was more extensive after oral exposure than through dermal exposure or intravenous injection in rats, which might be due to the first-pass metabolism in the intestine and the slow release of the contaminant at the subcutaneous injection site. After 5 days of gavage or intratracheal administration, more than 80% of PFAS were absorbed in mice, while only 62% of the dose was absorbed after skin application^[1313], indicating that the absorption of PFAS through the skin pathway is relatively slow.

New contaminants enter organisms through multiple pathways and subsequently distribute to various organs or tissues via the body's circulation. However, the primary tissues where different contaminants accumulate vary due to differences in their properties (Fig. 46). PFAS, because of their strong protein-binding ability, primarily accumulate in tissues with relatively high protein content such as the liver and kidneys ^[1314,1315]. Multiple animal experiments have shown that new PFAS, including those containing ether bonds (e.g., 6:2 Cl-PFESA and HFPO-TA) and fluorotelomer-based PFAS (e.g., diPAPs and PFPiAs), exhibit a stronger tendency to accumulate in the liver and kidneys ^[1314,1316]. OPEs, due to their strong hydrophobicity, typically accumulate in the gut and muscle tissues. Moreover, OPEs generally have a strong affinity for enzymes like cytochrome P450, resulting in relatively higher concentrations in the liver ^{[1317⇓~1319]}. Similarly, SCCPs are more distributed in muscle and adipose tissues ^[1320,1321]. However, their distribution is influenced by factors such as chain length, isomerism, and degree of chlorination, requiring further study.

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图46 PFAS在人体中的富集与分配^[1322]

Fig. 46 Accumulation and distribution of PFAS in the human body^[1322]

In terms of biotransformation, PFAS with their fully fluorinated alkyl chains possess excellent thermal and chemical stability due to the extremely high bond energy of the C-F bond. Therefore, traditional PFAAs hardly undergo biotransformation^[1323]. However, novel PFAS with sulfonamide-derived groups and fluoroalkylated groups can degrade into PFAAs with the same or shorter chain lengths, with a biotransformation rate ranging from 2% to 8%^{[1324⇓~1326]}. In contrast, OPEs are highly prone to phase I reactions such as hydrolysis, hydroxylation, and ester cleavage, as well as phase II reactions such as glucuronidation, sulfate conjugation, and methylation, resulting in over 10 transformation products with a typical biotransformation rate of around 50%^[1327,1328]. The biotransformation rates of different structural OPEs vary slightly, with overall reactivity following the order: OPEs containing aromatic rings > alkyl-chain OPEs > chlorinated OPEs^[1328,1329]. SCCPs exhibit stronger biotransformation potential, as more than 60% of the ingested SCCPs were found to undergo biotransformation in mass balance experiments^[1330]. Further studies have shown that SCCPs undergo various reactions such as oxidation to CO₂, chain scission, dechlorination and rearrangement, and hydroxylation in vivo. The conversion rate increases with decreasing chlorination degree, reaching up to 96%^[1321,1330].

PFAS, as typical POPs, have half-lives of several years in the human body (2.1 years for PFOA and 6.2 years for PFOS), and the half-life increases with the carbon chain length^[1331]. Some novel PFAS have even longer half-lives; for example, the half-life of 6:2 Cl-PFESA, a substitute for PFOS, is as long as 15.3 years^[1332]. The substitute for PFOA, HFPO-TA, has a half-life of 58.6 days in mice, which is significantly higher than that of PFOA (24.4 days)^[1333]. OPEs have faster clearance rates and biotransformation rates; the half-lives of alkylated and aromatic OPEs in the human body are less than 10 days, while the half-lives of chlorinated OPEs are relatively longer, reaching up to 53.8 days^[1334]. Currently, there are no actual measurement results for the half-lives of SCCPs, but a study based on the PBTK model found that SCCPs have a half-life of 5.1 years due to their high apparent volume of distribution, which is significantly higher than those of medium-chain (1.2 years) and long-chain chlorinated paraffins (0.6 years), indicating a relatively higher exposure risk for SCCPs^[1335].

7.2.2 The Impact of Chemical Structures of New Contaminants on ADME Processes

The chemical structure of new contaminants directly influences the physicochemical properties of the contaminants, which in turn affects their biological processes in living organisms. Therefore, investigating the relationship and regularity between the chemical structure of new contaminants and their biological processes through in vivo and in vitro experimental methods or computational toxicology approaches holds significant importance for the assessment of the biological processes of new contaminants and toxicity prediction. Quantitative structure-property relationship (QSPR) models can simulate the correlation between molecular structural parameters of new contaminants and various properties as well as biological activities, establish quantitative structure-activity relationships, and thus systematically predict their pharmacokinetic characteristics^[1336]. Additionally, machine learning, molecular docking simulations, and in vitro experiments based on the chemical structure of pollutants are widely used to gain a deeper understanding of the relationship between biological processes and molecular structures of pollutants^[1337], which plays an important role in exploring the negative impacts of new contaminants on human health^[1338].

Existing studies have demonstrated that the interaction between some new contaminants and proteins is an important factor affecting their accumulation in organisms^[1339]. For example, PFAS can bind to serum albumin, organic acid transporters, fatty acid-binding proteins, and other proteins to enter organisms and reach various tissues via the circulatory system^[1340,1341]. Investigating the binding affinity of PFAS with proteins can explain their distribution and ADME behavior in vivo and elucidate the mechanism of their toxic effects^[1341]. A large number of studies have shown that the hydrophobic perfluoroalkyl chain of PFAS occupies the binding cavity of target proteins, while the acidic groups of PFAS form hydrogen bonds with amino acid residues^[1341]. Liu et al.^[1342] found that 6:6 and 8:8 PFPiA could rapidly accumulate in carp, with higher bioaccumulation potential for 6:6 PFPiA than 8:8 PFPiA. In vitro equilibrium dialysis experiments and molecular docking results further showed that 6:6 PFPiA has a higher binding affinity with serum and liver proteins than 8:8 PFPiA, indicating that the tissue-specific distribution of PFPiAs highly depends on their binding affinity with specific proteins. Additionally, Chen et al.^[1343] utilized a 3D-QSAR model and found that the binding affinity of PFAS with transthyretin (ttr) is related to the nature of the terminal group. Ren et al.^[1344] used QSAR models to study the binding interactions of 16 structurally different PFAS with human ttr and found that fluorinated alkyl chains longer than 10 carbons and acidic terminal groups are the best choices for binding to ttr. For PFAS alternatives, various structural features such as chlorine atom substitution, oxygen atom insertion, and branched structures may alter the affinity of PFAS alternatives with endogenous proteins^[1341]. For instance, HFPO-DA and 6:2 Cl-PFESA have been proven to have similar or even higher affinities with endogenous proteins compared to traditional PFAS^[1345,1346]. Cao et al.^[1347] combined QSAR models with molecular dynamics simulations and found that the rigidity and cyclic structure of the novel PFOS substitute, perfluorodecane-2-sulfonic acid (PFDecS), increased its binding affinity with liver fatty acid-binding protein (LFABP) through more stable hydrophobic interactions. In contrast, N-diPFBS, due to other polar groups replacing the sulfonate group into the middle of the molecular structure, significantly reduced favorable electrostatic interactions, directly reducing its binding affinity with LFABP and thus decreasing its accumulation in the liver (Fig. 47).

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图47 PFOS及其替代品与LFABP的结合亲和力与其结构的关系^[1347]

Fig. 47 The relationship between the binding affinity of PFOS/its substitutes with LFABP and structure^[1347]

EDCs usually have strong binding affinities with receptor proteins in organisms and can cause adverse effects on the human body by directly or indirectly interfering with the hormone system^[1348]. For example, Tan et al.^[1349] proved that more than 7000 EDCs primarily disrupt the endocrine systems of organisms through 12 classic nuclear receptors (NRs), causing severe symptoms. The binding actions between EDCs and receptor proteins affect their accumulation and distribution in organisms to a certain extent. For instance, Verreault et al.^[1350] investigated the accumulation of chlorinated compounds (PCBs and chlordanes, CHLs), metabolic derivatives of polychlorinated biphenyls (methoxy sulfones MeO- and hydroxylated OH-PCBs), brominated flame retardants (PBDEs, PBBs, HBCD), PBDE metabolites, and natural compounds with similar structures (MeO-/OH-PBDEs) in organisms, finding that the binding ability of pollutants with proteins and their lipophilicity are important factors influencing their fate and toxicokinetics. The presence of chlorine, bromine, and other phenyl substituents (such as OH-) can directly affect the structure and properties of compounds, thereby influencing their distribution in organisms. Additionally, Yang et al.^[1351] discovered that the tissue-specific distribution of EHDPHP and its conversion product 5-OH-EHDPHP in zebrafish is related to their affinity with transport proteins, histones, and other factors, and that 5-OH-EHDPHP has higher bioaccumulation due to the enhanced affinity with proteins in vivo because of the presence of the hydroxyl group. Similarly, Cao et al.^[1352] found that the binding of PBDEs/OH-PBDEs with potential new target estrogen-related receptor gamma (Estrogen-related receptor γ, ERγ) is influenced by molecular size, the relative proportion of aromatic atoms, hydrogen bond donors, and acceptors, and that OH-PBDEs have stronger binding capabilities with ERγ than their parent compounds PBDEs (Fig. 48). Pollutants with stronger accumulation abilities in organisms pose greater potential health risks, which partly explains why OH-PBDEs often exhibit stronger toxicity than PBDEs.

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图48 (A)多溴联苯醚及其羟基化代谢物对雌激素相关受体γ(ERRγ)的结构依赖性活性研究,(B)分子对接得到的化合物与ERRγ结合口袋中配体的结合构象^[1352]

Fig. 48 (A) Structure-dependent activity of polybrominated diphenyl ethers and their hydroxylated metabolites on estrogen-related receptor γ (ERRγ), (B) Binding conformations of the compounds with ligands in the ERRγ binding pocket obtained by molecular docking^[1352]

Understanding the relationship between the chemical structures of new contaminants and their ADME processes in the human body is crucial for assessing their toxicity and formulating prevention and control strategies. Through in-depth research and analysis, our understanding of new contaminants can be enhanced, providing a scientific basis for protecting human health and environmental safety. Some achievements have been made in the study of new contaminants. However, due to the constant emergence of new environmental pollutants and the complex and variable structures of environmental pollutants, our comprehensive understanding of the toxic effects of some pollutants is still insufficient. Additionally, with the development and popularization of computer technology, using machine learning methods to replace traditional QSAR models for rapid high-throughput screening and prediction of biological activities and effects of new environmental pollutants will become a new direction of development.

7.3 New Contaminants' Environmental and Health Risks

7.3.1 The Impact on the Reproductive System

Many PFAS have estrogen-like effects and can competitively bind to corresponding receptors, causing hormonal secretion disorders and thus affecting the function of the hypothalamic-pituitary-ovarian axis, inducing reproductive toxicity. Rodent studies have shown that PFAS exposure can lead to a decrease in the number of corpora lutea during follicular atresia and follicular development, or affect oogenesis and oocyte development through mechanisms such as activating peroxisome proliferator-activated receptors or disrupting intercellular communication between granulosa cells via gap junctions^[1353]. PFAS exposure significantly promotes the production of reactive oxygen species in rats, interferes with the activity of complexes I, II, and III in the mitochondrial respiratory chain, leading to oocyte apoptosis^{[1354, 1355]}. Epidemiological studies have shown that contact with PFAS is associated with delayed menarche^[1356], menstrual irregularities^[1357], polycystic ovary syndrome^[1358], and increased risk of preterm birth^[1359]. The reproductive toxicity of PFAS on the male reproductive system mainly occurs through affecting the brain-pituitary-gonadal axis, leading to cell apoptosis of the male reproductive system^{[1360⇓~1362]}, increased permeability of the blood-testis barrier^{[1363, 1364]}, and decreased testosterone levels^[1365]. Epidemiological studies in males have shown that PFAS exposure is associated with reduced total and free testosterone levels^{[1366, 1367]}, high concentrations of PFAS exposure can lead to elevated estradiol levels^[1368], and higher PFAS exposure is negatively correlated with the proportion of morphologically normal sperm in adult males^{[1369, 1370]}.

Pregnant women and fetuses are susceptible populations to environmental pollutants. Exposure to OPEs during pregnancy may lead to decreased fertility, causing adverse pregnancy outcomes such as preterm birth, malformation, and miscarriage^{[1371⇓~1373]}. Exposure to OPEs during pregnancy can be transferred across the placenta to the fetus, causing metabolic abnormalities and affecting fetal reproductive development^{[1371⇓~1373]}. Epidemiological evidence shows that exposure to OPEs during pregnancy presents gender-specific risks of preterm birth, increasing the risk for female infants while reducing it for male infants^[1374]. A recent birth cohort study pointed out that prenatal exposure to OPEs promotes the feminization of offspring's reproductive development and reduces the anogenital distance in newborns^[1375]; among them, BBOEP contributes the most to the reduction of anogenital distance in female children, while BCIPP has the greatest contribution to male children^[1375]. Another study showed that women with higher levels of OPEs metabolites in their urine had significantly lower fertilization rates, implantation rates, successful pregnancy rates, and live birth rates after receiving assisted reproductive technology^[1376]. In addition, exposure to OPEs is also associated with reduced male sperm quality. For example, higher exposure to BBOEP can reduce sperm motility, while higher exposure to DPHP leads to a lower total sperm count in men^[1377]. Animal experiments have shown that long-term exposure to TMPP (≥200 mg/(kg·d)) causes histological abnormalities in the ovaries of adult Long-Evans rats, with increased numbers of follicles and corpora lutea^[1378]. Exposure to tris(o-cresyl) phosphate (TOCP) reduces implantation rates in mice, partly through inducing placental toxicity^[1379]. Long-term exposure to TOCP (4 weeks, ≥100 mg/(kg·d)) significantly reduces ovarian weight and follicle counts at all developmental stages, increases the proportion of atretic follicles, and severely damages ovarian tissue structure^[1380]. A zebrafish study found that TPHP and TDCPP can affect the hypothalamic-pituitary-gonadal (HPG) axis at environmentally relevant concentrations^[1381,1382]. OPEs can antagonize the binding of certain hormones to specific receptors, such as estrogen receptors, androgen receptors, and pregnane X receptors, further affecting the reproductive system; or play a role in endocrine axes or other metabolic pathways by regulating gene expression.

Toxicological studies have found that SCCPs may pose significant harm to male fertility. After 35 days of exposure to SCCPs, the sperm concentration and sperm motility in mice decreased, and sperm production dysfunction occurred. Meanwhile, oxidative stress in the testes of mice was aggravated, and the imbalance between reactive oxygen species production and antioxidant capacity might also lead to spermatogenic disorders, ultimately resulting in male infertility [^1383,1384].

Many epidemiological studies have found that exposure to phthalic acid esters (PAEs) has adverse effects on the development and reproduction of females (precocious puberty, primary ovarian insufficiency, endometriosis, preterm birth or in vitro fertilization) and males (sperm quality, anogenital distance, cryptorchidism, hypospadias and changes). Hashemipour et al. ^[1385] found that the plasma levels of DEHP metabolites (MEHP, 5OH-MEHP and 5oxo-MEHP) in 87 girls with precocious puberty were significantly higher than those in the control group. Cobellis et al. ^[1386] first discovered that the plasma DEHP concentration in 35 women with endometriosis was significantly higher than that in 24 normal female controls. Subsequent studies confirmed that PAEs (mainly DEHP) may be a risk factor for endometriosis ^{[1387⇓~1389]}. At the same time, many epidemiological studies have shown that exposure to PAEs may lead to a decline in sperm quality ^{[1390⇓~1392]}. Swan et al. ^[1393] found a negative correlation between the anogenital distance of 85 male infants (2–36 months) and the concentrations of four PAEs (MEP, MBP, MBzP and MiBP) in their mothers' urine. Their subsequent study again indicated a significant negative correlation between the concentration of PAEs metabolites (MEP, MnBP, MEHP, MEHHP and MEOHP) in maternal urine and the anogenital distance, and also found a negative correlation between penile width and testicular descent and one or more DEHP metabolites ^[1394]. A prospective cohort study pointed out that high molecular weight phthalate esters (DEHP and DNOP) exposure in utero was associated with increased testicular volume at age 10 and earlier genital and pubic hair development at age 13 in male fetuses; while exposure to high molecular weight phthalate esters (DEHP and DNOP) in utero in female fetuses was related to earlier pubic hair development at age 13 ^[1395]. In a Spanish study, prenatal exposure to DiBP, DnBP and DEHP was associated with precocious puberty in 7–10-year-old boys and girls, leading to earlier puberty in normal-weight boys and overweight/obese girls ^[1396]. The mechanisms by which PAEs induce reproductive toxicity mainly include: inducing dysfunction of the hypothalamic-pituitary-gonadal (HPG) axis ^[1397]; disrupting the abnormal release of gonadotropin-releasing hormone and gonadotropins involved in the HPG axis, affecting reproductive processes through sex hormone receptors and steroid hormone synthesis and secretion ^[1398]; or inducing oocyte apoptosis, increasing oxidative stress, reducing vtg protein and ER expression, affecting reproductive processes ^[1398].

7.3.2 The Effect on the Endocrine System

The endocrine system is a complex and sophisticated physiological system in the human body responsible for regulating and coordinating various bodily functions to maintain homeostasis. This system consists of multiple endocrine glands that produce and release hormones, which are transported through the bloodstream to different parts of the body, influencing cellular activities and physiological processes. The endocrine system is primarily controlled by three axes: the HPG axis, the hypothalamic-pituitary-adrenal (HPA) axis, and the hypothalamic-pituitary-thyroid (HPT) axis^[1399]. Both excessive and insufficient hormone secretion inevitably lead to diseases, with impacts that may extend to many different organs and functions and often cause debilitating conditions or even threaten life.

Laboratory studies have shown that long-term exposure to low doses of PFAS in female mice during puberty can lead to a decrease in gonadotropin levels and reduced estrogen secretion^[1400]. PFAS exhibits anti-androgenic effects at lower concentrations^[1401], reducing serum testosterone levels, inhibiting the proliferation and differentiation of Leydig cells, and leading to decreased androgen secretion in vivo^[1361,1402]. Acute and repeated dose toxicity studies indicate that PFAS exposure is associated with a decrease in serum triiodothyronine and thyroxine levels^[1403]. Epidemiological studies suggest that as the concentration of PFAS exposure increases, the risk of thyroid disease also increases^[1404]. In vitro study results show that exposure to PFAS reduces the cell viability and insulin release capacity of β-cells. At the same time, epidemiological studies found a significant association between high concentration PFAS exposure and increased risk of insulin resistance in elderly people^[1405].

OPEs have been proven to adversely affect the HPG, HPT, and HPA axes with endocrine-disrupting toxicity^[1406]. In vitro and in vivo studies have shown that OPEs can negatively impact these control axes by disrupting hormone synthesis and nuclear receptor binding. For example, TPHP increases the concentrations of E2 and T and the E2/T ratio by upregulating the expression of steroidogenic enzymes CYP11A1, CYP11B2, CYP19A1, and 3β-HSD2 in H295R cells^[1407]. After adult female mice were exposed to TCrP for 4 weeks, the serum E2 concentration and progesterone concentration increased, leading to a series of reproductive problems such as decreased ovarian weight, disordered ovarian structure, and delayed follicle development^[1380]. OPEs can interfere with cortisol and aldosterone production and disrupt activities such as corticosteroid receptor and glucocorticoid receptor, affecting normal adrenal function^[1408]. Additionally, animal experiments have observed that OPEs can disrupt thyroid hormone homeostasis^[1409]. Epidemiological studies have shown that OPEs metabolites DNBP, BBOEP, DoCP, and DpCP are associated with thyroid hormone level disorders^{[1410⇓⇓~1413]}. The levels of DoCP, DpCP, BBOEP, and DNBP in pregnant women's urine are positively correlated with neonatal thyroid-stimulating hormone, while DPHP is negatively correlated (Fig. 49)^[1371,1413]. At 26 weeks of gestation, the maternal urinary DNBP level is negatively correlated with total triiodothyronine (T3) and free T3 concentrations in umbilical cord blood^[1412]. Another prospective study pointed out that in American adolescents (12–19 years old), the level of BCEP in urine is negatively correlated with fasting insulin, indicating that BCEP may affect insulin homeostasis and resistance^[1414]. At the same time, the level of BCEP in women's urine (18–45 years old) is negatively correlated with serum cholesterol and total lipid content^[1415].

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图49 尿OPE代谢物三分位数与新生儿促甲状腺激素(TSH) 水平之间的关联^[1413]

Fig. 49 Associations between tertiles of urinary OPE metabolites and neonatal TSH levels^[1413]

BCEP was also reported to be negatively correlated with the free androgen index in male and female adolescents (12–19 years)^[1416]. Moreover, urinary BDCIPP levels were found to be closely related to various endocrine regulation and metabolic processes in humans, including thyroid hormones, sex hormone levels, and metabolic syndrome^[1371]. Since there is common communication and functional overlap among the axes of the endocrine system, for example, adrenal hormones can stimulate the production of gonadotropin-releasing hormone (GnRH) secreted by the hypothalamus, while thyroid hormones can inhibit GnRH production^[1406], this makes OPEs potentially affect the interactions between these axes.

Exposure to short-chain chlorinated paraffins (SCCPs) may lead to endocrine dysfunction. A study found a significant positive correlation between the mass fraction of maternal serum SCCPs and thyroid-stimulating hormone (TSH) concentrations in the Beijing population in China. Although no association with T3 was observed, there was a trend of decreasing T3 concentration with increasing SCCPs mass fraction, indicating that SCCPs might affect circulating thyroid hormone levels in humans^[1417]. Some toxicological studies have proven that SCCPs disrupt the endocrine system function of male animals. For example, embryonic exposure to SCCPs inhibits total thyroxine (T4) in male nestlings and rats and reduces T3, leading to male hypothyroidism^{[1418⇓~1420]}, as well as altering gene expression in the hypothalamic-pituitary-thyroid axis of zebrafish^[1421]. Research based on in vitro models shows that several SCCPs interfere with endocrine function through both nuclear receptor-mediated and non-receptor-mediated pathways. Three SCCPs, C10-CP (40.4% chlorine), C10-CP (66.1% chlorine), and C11-CP (43.2% chlorine), can exert potential estrogenic activity via ERα, among which C11-CP (43.2% chlorine) exhibits glucocorticoid receptor-mediated antagonistic activity^[1422], leading to diseases such as obesity, type 2 diabetes, and thyroid cancer.

Studies have shown that PAEs can interfere with the human endocrine system. Some PAE substances can act as ligands for transcription factors and interact with the endocrine molecular signaling system, leading to adverse health effects. Some PAEs have been identified as weak ER activators and AR antagonists, having varying effects on PPARs, which are closely related to the occurrence of breast cancer, insulin sensitivity, type 2 diabetes, and other adverse physiological processes mediated by PPARγ^[87,1423].In vitro studies also show that DBP, BBP, and DEP have weak estrogenic activity. For example, DBP and DEHP respectively induce 30% and 5% transcriptional activity of ERα, and DBP increases the activity of ERα in rat testes while decreasing the activity of ERβ, exhibiting ERβ antagonistic activity^[1424].Compared with humans, mouse PPARα or PPARβ are usually activated at lower concentrations of phthalate monoesters^[1425].A large number of epidemiological studies in the population also prove that PAEs exposure will affect the growth and development of thyroid in children and adults. For example, the negative correlation between T4 and T3 levels in adult male blood and the concentration of DEHP metabolites^[1426].The concentration of DBP metabolites in pregnant women's urine is negatively correlated with free T4 and total T4 levels in serum^[1427].Malene Boas found through a survey of 845 Danish children aged 4 to 9 that PAEs in urine were negatively correlated with thyroid hormones, insulin-like growth factor-1, and healthy growth of children^[1428].

7.3.3 The Effect on Growth and Development

Studies have shown that prenatal exposure to PFAS may harm fetal growth and development and produce long-term adverse effects on offspring health. Sufficient epidemiological evidence and convincing toxicological evidence indicate that prenatal PFOA exposure affects neonatal birth weight [1429], leading to intrauterine growth restriction, shortened birth length, reduced abdominal circumference, and decreased head circumference [1430, 1431]. Prenatal PFAS exposure is also closely related to childhood obesity; a prospective study conducted in Shanghai, China found that umbilical cord blood PFBS levels were positively correlated with the incidence of obesity in 5-year-old girls [1432]. Although research has revealed the harmful effects of PFAS on growth and development, there are still some controversies regarding the exact mechanisms of these effects. Some studies suggest that PFAS may influence growth and development through disrupting endocrine systems, interfering with lipid metabolism to increase fat storage, affecting thyroid function, or causing cellular oxidative stress. PFOA and PFOS exposure are associated with decreased DNA cytosine methylation levels, increased LINE-1 methylation levels, and changes in gene expression involved in cholesterol metabolism [1433 ⇓~1435]. PFOA and PFOS can bind to and activate peroxisome proliferator-activated receptors (PPAR-α/γ), thereby promoting adipocyte differentiation and increasing body fat [1436 ⇓~1438]. Additionally, PFOA, PFOS, and PFHxS inhibit 11β-hydroxysteroid dehydrogenase-2, which increases glucocorticoid concentrations and thus affects growth and brain development [1439].

OPEs have multifaceted effects on growth and development, including changes in body size (body weight and length), preterm birth, obesity, organ developmental abnormalities, and chromosomal abnormalities^{[1371,1374,1406]}.Prenatal exposure to OPEs may be closely related to the birth weight and length of newborns, and high-level exposure to OPEs may lead to reduced body weight, shorter length, and even trigger preterm birth^[1371].Taking BDCIPP, BBOEP, 4-HO-DPHP, and DPHP in the urine of pregnant women at different stages of pregnancy as examples, their concentration levels are negatively correlated with the birth weight of newborns, especially in late pregnancy, when fetuses are more susceptible to the developmental toxicity of BDCIPP and BBOEP^[1440].The probability of preterm birth for pregnant women with BDCIPP levels above the median in their urine is four times that of normal pregnant women, and an increase in BCEP and DPHP concentrations is associated with a longer gestation period and a reduced risk of preterm birth^[1440,1441].It has also been reported that the level of BCEP in maternal urine is positively correlated with the subscapular skinfold thickness of male infants and the thigh skinfold thickness of infants of different sexes^[1442].Some studies have also shown that an increase in the concentration of OPEs in the human body is related to an increase in the prevalence of childhood obesity and overweight^[1371,1443].DPHP is significantly associated with oxidative stress, endocrine disruption, and developmental toxicity, which can induce hyperglycemia and have negative effects on infant body size and brain development^[1371].

Prenatal exposure to SCCPs may also affect the birth weight of newborns. Research in Wuhan, China, has shown that the concentration of SCCPs in maternal serum is significantly negatively correlated with neonatal birth weight, which may be due to the fact that chlorinated paraffins with shorter carbon chains can be more effectively transported from mother to fetus^[1444]. However, the mechanisms by which SCCPs affect fetal growth and development require further study. Experimental evidence on the toxicity assessment of SCCPs during early life stages shows that exposure of zebrafish embryos to SCCPs leads to developmental toxicity, including decreased survival rates, growth inhibition, and teratogenicity^[1421]. SCCPs also induce deformities and growth inhibition in African clawed frog embryos^[1445].

Although inconsistent results have been reported in different epidemiological studies regarding the association between prenatal exposure to phthalic acid esters (PAEs) and birth weight, most studies suggest a negative correlation between prenatal PAEs exposure and newborn birth weight^[1446,1447]. A study conducted by the Salinas Mother-Child Health Assessment Center showed that maternal exposure levels of DEP, DnBP, BBzP, and DEHP during pregnancy were closely related to body mass index, waist circumference, and percentage of body fat in offspring at different ages (5–12 years). Notably, in 12-year-old children, prenatal concentrations of DEP, DnBP, and DEHP metabolites were positively correlated with overweight or obesity^[1448]. A cross-sectional study by Wang et al.^[1449] found that urinary PAEs metabolites (MEHP and MEP) in 259 Chinese schoolchildren were positively correlated with body mass index or waist circumference. Xia et al.^[1450] found that urinary MnBP concentration in 149 children was positively correlated with overweight/obesity, while MMP, MEP, MEHP, MEOHP, and MEHHP levels showed no correlation with obesity. In a study of 242 Iranian children aged 6–18 years, urinary PAEs metabolites (MBzP, MBP, MMP, MEHP, and MEHHP) concentrations were significantly positively correlated with childhood obesity^[1451].

7.3.4 Impact on the Nervous System

In vitro studies have shown that there are two potential mechanisms for PFAS to enter the brain: one is to disrupt the tight junctions of brain endothelial cells and increase the permeability of the blood-brain barrier; the other is to cross the plasma membrane by binding to transport proteins^[1452]. In 2018, Liew et al.^[1453] reviewed 21 epidemiological studies and found that the effects of PFAS exposure on neurodevelopment were inconsistent. A prospective study involving 218 mother-infant pairs showed that higher prenatal PFOS exposure was associated with poorer executive function in offspring^[1454]. Another prospective study also indicated that increased prenatal PFOS and PFOA exposure levels respectively led to a 70% and 110% increased risk of cerebral palsy^[1455]. Meanwhile, children's serum PFAS concentrations were associated with an increased rate of attention deficit hyperactivity disorder (ADHD) or drug use for controlling ADHD^{[1456, 1457]}. However, some studies found that prenatal PFAS exposure was positively correlated or not correlated with children's neurobehavior^{[1458⇓⇓⇓~1462]}. Older adults' PFAS exposure was related to better language learning ability and cognitive flexibility, and PFAS might have a protective effect on memory disorders^[1463]. PFAS exposure may disrupt calcium homeostasis and cause neurotransmitter dysfunction^[1452], while calcium ions participate in mediating processes such as neuronal proliferation, synaptogenesis, apoptosis, and neurotransmitter secretion. Several studies reported the effects of PFAS on neurotransmitters such as dopamine, glutamate, acetylcholine, and cholinergic system^{[1464⇓⇓⇓⇓~1469]}. Given the complexity of the nervous system and the heterogeneity of assessment tools and methods, more research is needed to explore the effects of PFAS exposure on the nervous system.

OPEs may have adverse effects on neurodevelopment, including negative impacts on the infant nervous system and cognitive function. Hall et al.^[1470] pointed out through a nested case-control investigation in the Norwegian Mother, Father, and Child Cohort Study that prenatal exposure to specific OPEs, such as DNBP and BDCIPP, is associated with an increased risk of ADHD in preschool children. A Chinese study showed that when the average concentration of BDCIPP in pregnant women's urine increased by two times, the psychomotor developmental index (PDI) scores and mental development index (MDI) scores of their offspring (≥2 years old) decreased, indicating that BDCIPP may impair children's cognition, language, and social skills^[962]. An American study found that the concentration of OPE metabolites in maternal urine during pregnancy was related to cognitive and behavioral development in children at 2–3 years old; for example, higher concentrations of BDCIPP were associated with more withdrawal and attention problems in children, while higher concentrations of DPHP were associated with greater hyperactivity and attention problems^[1471]. It is worth noting that the effects of OPEs on neurodevelopment are gender-specific. For instance, as the exposure to BBOEP increases, the probability of girls developing ADHD decreases, while the probability for boys increases slightly^[1470]; another study showed that maternal exposure to BDCIPP was only negatively correlated with neurodevelopmental scores in boys but had no significant effect on girls^[962].

Toxicity tests proved that SCCPs may affect the function of the nervous system. Behavior tests on zebrafish (including movement, path angle, and social interaction between two fish) found that SCCPs could cause neurotoxicity in zebrafish larvae. When the chlorination degree of CP increased, the inhibitory effect on larval behavior was significantly increased, while an increase in carbon chain length weakened the toxicity. The influence of chlorine content was greater than that of carbon chain length^[1472]. In addition, SCCPs can induce the proliferation of astrocytes in male mice. The activation of astrocyte function may lead to neuroinflammation, which ultimately results in cognitive impairment^[1473].

Research has shown that PAEs can penetrate the blood-brain barrier and widely bind to neural receptors, blocking the transmission of nerve signals through competitive binding and ligand-mediated regulation, thus affecting intelligence and neural function, such as influencing attention deficit/hyperactivity disorder (ADHD) and intelligence performance^[1474]. A cross-sectional study including 1493 children from NHANES during 2001-2004 found a positive correlation between childhood ADHD and the concentration of PAEs (DEHP, MBzP, MCPP, MEHP, MEHHP, and MEOHP) in their urine^[1475]. Engel et al.^[1476] based on case-control studies demonstrated that maternal urinary DEHP concentrations were associated with an increased risk of hyperactivity disorders in Norwegian children. Additionally, cohort studies have shown that the risk of hyperactivity disorder symptoms in children is positively correlated with the concentration of DEHP and BBzP metabolites in their urine^[1477]. Kobrosly et al.^[1478] conducted a cohort study showing that higher prenatal urinary MBzP concentrations were positively correlated with higher scores for oppositional/defiant problems and conduct problems in boys. Li et al.^[1479] found that MBP and MiBP concentrations were positively correlated with lower IQs in children; however, more studies suggest that PAE concentrations are negatively correlated with children's IQ performance^[1479,1480]. For example, a cross-sectional study by Choi et al.^[1481] on 667 Korean children showed that IQ scores and children's vocabulary scores were negatively correlated with MEOHP, MEHP, and MBP.

7.3.5 On the Influence of Chronic Disease Occurrence and Development

The study shows that PFAS exposure may be associated with hypertension, diabetes, and hyperuricemia. Based on the data of 6509 adults in the NHANES database from the United States between 2003 and 2012, it was found that compared with the lowest quartile concentration, the highest quartile serum PFOA concentration and PFOS concentration could significantly increase the risk of hypertension occurrence^[1482]. Bao et al.^[1483] conducted a cross-sectional survey of 1612 adults and found that serum PFAS concentrations (especially in women) were significantly related to the risk of hypertension, and also found that branched PFAS isomers showed stronger effects than linear PFAS isomers, suggesting that PFAS exposure is an important risk factor for the occurrence of hypertension. PFAS exposure may also be associated with abnormal glucose tolerance and diabetes. Su et al.^[1484] found in a case-control study in Taiwan, China, that PFOS exposure was associated with increased prevalence of abnormal glucose tolerance and diabetes, and PFOA, PFNA, and PFUA had potential protective effects against abnormal glucose tolerance and diabetes. In addition, a case-control study results showed that increased PFAS exposure raised the risk of hyperuricemia, and the mediation analysis results showed that creatinine, creatine, and phospholipids mediated 25%-68% of the relationship between exposure and disease risk^[1485].

A potential association exists between OPEs mixed exposure and increased risk of cardiovascular disease. Guo et al.^[1486] found in the study data of 5067 participants from the NHANES database between 2011 and 2018 that the highest percentile of urinary OPE metabolites was positively correlated with the risk of cardiovascular disease (CVD), but this relationship did not reach statistical significance. Another study investigated the relationship between OPEs exposure and chronic kidney disease (CKD) in the general adult population in the United States using NHANES data from 2013 to 2014 and found that BCEP, BDCIPP, and DnBP were all associated with CKD^[1487]. OPEs exposure has also been reported to be associated with elevated blood pressure in children and adolescents, leading to diseases such as hypertension^[1486]. In addition, epidemiological evidence suggests that exposure to OPEs may be associated with an increased risk of type 2 diabetes in adolescents or elderly people^[921,1411]. The triggering factors for molecular initiating events (such as insulin receptor and glucose transporter type 4) and subsequent key events, including disruption of signaling pathways (such as phosphatidylinositol 3-kinase/protein kinase B and insulin secretion signaling) and biological functions (glucose uptake and insulin secretion), may trigger the diabetogenic effects of OPEs^[921].

Exposure to short-chain chlorinated paraffins (SCCPs) may increase the risk of health problems such as diabetes, liver injury, and chronic kidney disease in humans. A nested case-control study in East China showed that C10-CP and C11-CP were associated with an increased risk of type 2 diabetes, and the risk for males exposed to SCCPs was four times higher than for females, indicating significant gender differences^[1488]. A study in Jinan also indicated that SCCPs interfere with liver biomarkers in a gender-dependent manner^[1489]. This specific association might be attributed to different toxicity mechanisms of SCCPs between males and females, such as disrupting estrogen or androgen receptor signaling pathways, which alter metabolic regulation mechanisms^[1490]. Toxicological studies have shown that SCCPs may promote the development of type 2 diabetes by disturbing lipid homeostasis in male rats, causing a significant increase in inflammatory factor concentrations^{[1491, 1492]}, leading to chronic diseases such as liver damage. The interaction of SCCPs with peroxisome proliferator-activated receptor alpha (PPARα) may disrupt fatty acid metabolism in male rats^{[1493, 1494]}. Overexpression of PPARα in the hearts of diabetic patients may lead to more severe cardiomyopathy^[1495]. In men in Jinan, exposure to SCCPs was associated with an increased risk of glomerular hyperfiltration, suggesting possible early kidney damage^[1496]. Elevated SCCP concentrations were related to leukopenia, indicating potential destruction of male immune function^[1497]. Long-term exposure of male rats to SCCPs leads to increased kidney weight and chronic nephritis. Under high-dose exposure, phenomena such as glomerular proliferation and increased oxidative stress occur, indicating that SCCPs may cause kidney damage by inducing the specific expression of α2u globulin in male rats^{[1498⇓~1500]}(Fig. 50).

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图50 氯化石蜡 (SCCP) 诱导的潜在靶器官和潜在毒性机制^{[1498⇓~1500]}

Fig. 50 A scheme of the potential target organs and underlying mechanisms of toxicity induced by short-chain chlorinated paraffins (SCCPs)^{[1498⇓~1500]}

A cross-sectional study of NHANES in the United States from 2003 to 2008 and from 2009 to 2012 found that the concentration of phthalates (PAEs) in cosmetics and personal care products was negatively correlated with blood pressure. Similarly, a study conducted in Iran on 242 children aged 6 to 18 years found that mono-n-butyl phthalate (MBP) concentration was significantly associated with elevated blood pressure^[1451]. Some studies^[1501] have shown that exposure to PAEs can cause oxidative stress and insulin resistance, increasing the risk of type 2 diabetes. Women with higher concentrations of MnBP, MiBP, MBzP, MCPP, and ΣDEHP had a greater risk of diabetes than those with lower concentrations^[1502]. Phthalate metabolites are bioactive, including activating peroxisome proliferator-activated receptors and anti-androgenic effects, thereby increasing the risk of obesity^[1503]. Xia et al.^[1504] found that compared with normal-weight children, overweight and obese children had higher concentrations of monobutyl phthalate (MnBP) in their urine samples^[1450]. A cross-sectional study showed an association between adult urinary BBzP metabolite (MBzP) concentration and asthma. Meanwhile, PAEs (DnBP, BBzP, DEHP, MEHP) may also be positively correlated with childhood asthma, rhinoconjunctivitis, or atopic dermatitis^{[1505⇓~1507]}.

7.3.6 The Impact on Cancer Occurrence and Development

Exposure to PFAS may be associated with an increased risk of kidney cancer, testicular cancer, prostate cancer, and breast cancer, but there is still no definitive conclusion. A study conducted by Vieira et al.^[1508] in Ohio and West Virginia, USA, showed that elevated serum PFOA levels may be related to an increased risk of testicular cancer, kidney cancer, prostate cancer, ovarian cancer, and non-Hodgkin lymphoma. Girardi et al.^[1509] evaluated the association between PFAS exposure and mortality and found that workers with occupational exposure to PFOA had an increased risk of death from liver cancer and malignant tumors of lymphoid and hematopoietic tissues compared to workers without occupational exposure to PFOA. A Danish case-control study (77 cases of breast cancer and 84 control subjects) found that PFCAs, PFSAs, PFNA, PFDA, PFOA, and PFOS were positively correlated with the risk of breast cancer within a certain concentration range^[1510]. However, another case-control study (902 cases of breast cancer and 858 control subjects) did not find an association between female exposure to PFOA and PFOS and the incidence of breast cancer^[1511]. It is worth noting that the impact of PFAS exposure on breast cancer may occur early in life. The results of a case-control study indicated that maternal exposure to PFOS was associated with an increased risk of breast cancer in female offspring. Further research is needed to investigate the impact of PFAS exposure on cancer in the population^[1512].

Exposure to OPEs may be associated with an increased risk of thyroid cancer, gastrointestinal cancer, breast cancer, cervical cancer, and so on^{[939,1513,1514]}. Liu et al. ^[1514] analyzed the levels of OPEs in the plasma of patients with four female-specific tumors and found that the concentration of EHDPP was related to the incidence risk of breast cancer, while the concentrations of TnBP, TMPP, TPHP, and EHDPP were related to the risk of cervical cancer. A study from Wuhan, China, showed that the concentrations of TEP, TCIPP, TPHP, TMPP, TEHP, and EHDPP in the blood of gastrointestinal cancer patients were significantly associated with the occurrence of gastric cancer, and the concentrations of TEP, TCIPP, TPHP, TMPP, and TEHP were significantly associated with the occurrence of colorectal cancer^[1513]. Moreover, elderly male gastric cancer patients were more sensitive to EHDPP exposure, while relatively younger gastrointestinal cancer patients were more sensitive to TEP exposure^[1513]. These findings suggest that OPEs may play a role in the occurrence of gastrointestinal cancers. Another case-control study from Shandong Province, China, found a significant high-risk association between OPEs (such as TPrP, TCPP, TDCPP, TBEP) exposure and thyroid cancer in both males and females^[939]. Exposure to OPEs may lead to changes in thyroid function, thereby increasing the risk of thyroid cancer.

At present, only one epidemiological study has shown that SCCPs exposure may increase the risk of cholangiocarcinoma. This study focused on automobile workers exposed to metalworking fluids and found that SCCPs exposure might increase the risk of cholangiocarcinoma. However, due to the small number of cases, the confidence interval was relatively wide^[1515]. Some in vivo/in vitro and computer simulation studies have explained the mechanisms of SCCPs causing liver, renal tubule, thyroid, and breast cancer^[1516]. The 2-year gavage experiment conducted by the National Toxicology Program on rats and mice showed that long-term exposure to SCCPs increased the incidence of hepatocellular carcinoma, renal tubular carcinoma, and thyroid adenocarcinoma^[1517]. In vitro studies have proven that exposure to SCCPs at environmental dose levels reduces the viability of human hepatocarcinoma HepG2 cells and disrupts the metabolic profile, leading to liver toxicity^[1518], and SCCPs also have the potential to disrupt the thyroid by interfering with the binding of T4 and ttr^[1519]. Molecular docking technology based on computer simulations indicates that SCCPs have strong binding ability with ERα, which helps the spread of breast cancer^[1520]. Currently, there is limited epidemiological research on the carcinogenic risks of SCCPs, but SCCPs (especially C12-CP (60% chlorine)) have been listed as Group 2B carcinogens by the International Agency for Research on Cancer^[1521], and their carcinogenic risks should not be ignored.

The results of animal and human studies indicate that PAEs may induce breast cancer and liver cancer. For example, a life-long exposure study in rats revealed the carcinogenicity of PAEs^[1474]. A case-control study by LÓpez-Carrillo et al.^[1522] first showed that the concentrations of some PAEs metabolites in urine were significantly related to the occurrence of breast cancer. A predictive study based on data statistics and model fitting showed that as the exposure concentration of PAEs increases, the cancer risk would significantly increase^[1523]. The main mechanisms of PAEs-induced carcinogenesis include: promoting and stimulating the differentiation and proliferation of cancer cells through the AhR regulatory system, inducing cell apoptosis through mitochondria and caspase-3, inducing proliferative effects through the PI3K/AKT signaling pathway, and promoting tumor growth in ER-negative breast cancer cells.

8 New Contaminants Treatment

8.1 Challenges in the Control of New Pollutants

8.1.1 New Contaminants Migration Transformation, Difficulty in Research on Toxicity Mechanism

New contaminants, such as POPs, antibiotics, endocrine disruptors, and microplastics, are typically semi-volatile substances that can volatilize into the atmosphere or adsorb onto atmospheric particles. Under specific circulation conditions, they can be transported over long distances through the atmosphere and enter different environmental media such as soil, groundwater, air, and biota. Some new contaminants have bioaccumulation properties, carcinogenicity, and mutagenicity, which may adversely affect ecosystems and human health. So far, certain achievements have been made in the study of the migration, transformation, and toxicity mechanisms of some new contaminants such as POPs in the environment. However, the complexity and concealment of new contaminants make their migration, transformation, and toxicity mechanism research quite challenging, and there is still a need for extensive basic research and exploration. Due to their different characteristics from traditional pollutants, the environmental behavior and toxicity mechanisms of new contaminants may involve multiple complex physical, chemical, and biological processes.

The adsorption and desorption behaviors of new contaminants in air, water, and soil play a decisive role in their migration and transformation. Therefore, it is crucial to deeply understand the physicochemical parameters of organic compounds for elucidating their migration and transformation mechanisms. Research on predicting organic carbon partitioning in natural and engineered systems has mainly focused on developing methods that rely on training individual organic carbon molecules using existing partitioning data, including formulas, functional groups, sizes, conformations, polarity, and other properties predicted by quantum chemical calculations. However, cross-validation and benchmarking results of existing methods indicate that for sparingly soluble compounds with sparse functionality, there can be up to one order of magnitude difference between the predicted and experimental results for water-air partitioning and related properties (such as saturated vapor pressure). Moreover, for compounds with higher solubility and higher functionality (common characteristics of many new contaminants), the deviation among different prediction methods can reach up to ten orders of magnitude, introducing significant uncertainty into the prediction of the migration and transformation of new contaminants. Consequently, the current research methods for physicochemical parameters of new contaminants are not yet mature enough, posing great difficulties for studying the migration, transformation, and toxicological mechanisms of new contaminants^[1524].

Comprehensive identification of new contaminants and assessment of their exposure levels are prerequisites for accurately evaluating their health hazards. Taking PAH derivatives as an example, they mainly exist in the air and are a class of new contaminants derived from the parent PAH structures. Although the environmental concentrations of these PAH derivatives may be lower than those of their corresponding parent PAHs, the substituents greatly increase their carcinogenic, teratogenic, and mutagenic potentials. For instance, the mutagenicity of 1,8-dinitropyrene in nitro-PAHs is three times that of benzo[a]pyrene (B[a]P), which is one of the most toxic PAHs. 7,12-dimethylbenzo[a]anthracene (7,12-DMBA), an alkyl derivative of PAHs, has a toxicity equivalent factor 20 times higher than its parent compound and twice that of benzo[a]pyrene, indicating that low concentrations of 7,12-DMBA might significantly increase the toxic effects after exposure. Moreover, oxy-PAHs, as major components of environmental fine particulate matter, can induce inflammatory effects. Some polar substituents (such as oxy- and nitro-) may enhance the long-range migration characteristics of PAH derivatives in environmental media such as soil and sediments. However, compared to the high attention given to typical PAHs, research on PAH derivatives like NPAHs, oxy-PAHs (OPAHs), halogenated PAHs (XPAHs), and alkylated PAHs (APAHs) is quite scarce [sup][1525][/sup]. Furthermore, the composite toxicity of new contaminants also needs further study; the comprehensive potential toxicity of multiple pollutants is much greater than the simple sum of individual toxicities. EDA is a powerful tool that can be used to assess the bioavailable toxicity of integrated chemicals, relying simultaneously on in vivo/in vitro biological assays and target/non-target instrumental analysis. However, currently we still face significant challenges in effective fraction acquisition and unknown contaminant identification [sup][1526][/sup]. Additionally, new contaminants such as flame retardants, widely distributed in living environments, have high exposure levels and frequencies for humans. Their odorless or non-irritating properties reduce people's awareness of their existence, leading to chronic and long-term exposures. Dust and fine particulate matter can also serve as important reservoirs for new contaminants [sup][1527][/sup], and the composite toxicity of particulates with new contaminants requires further study. Therefore, the next step should comprehensively apply interdisciplinary research methods and technical means, including chemical analysis, environmental monitoring, biological testing, and model simulation, to gain a deep understanding of the physicochemical parameters, environmental distribution, transformation pathways, and biological effects of new contaminants. This is crucial for elucidating the migration and transformation patterns and toxicity mechanisms of new contaminants.

8.1.2 New pollutants have the characteristics of diverse types, large quantities, and wide distribution. It is difficult to clarify their production, use, and environmental pollution status.

Chemical production and human activities have led to ubiquitous pollution, with the presence of emerging contaminants in daily products such as pesticides, surfactants, flame retardants, antibacterial consumer products, and waterproof materials^[1528], posing significant threats to human health, global ecosystems, and biodiversity. Studies have found a large number of emerging contaminants in wastewater, surface water, drinking water, and groundwater, and these contaminants are diverse and complex, with environmental and health risks that remain to be further studied.

A recent survey found more than 500 new contaminants in biosolids from multiple regions in the United States. These new contaminants usually accumulate in biosolids and produce ecological toxicological effects during land application. They are lipophilic, hydrophobic, non-ionic, environmentally persistent, and bioaccumulative, potentially posing a risk to human liver, blood, and reproductive system health [1529]. Antimicrobials are a class of new contaminants found in biosolids that can inactivate microorganisms. The most common antimicrobials are triclosan, triclocarban, and quaternary ammonium compounds, which are cationic surfactants. Triclosan and triclocarban have the largest usage amounts, with concentrations as high as one part per million (ppm, 1 ppm = 1 × 10⁻⁶) in biosludge. Since there are many types and large quantities of organic pollutants in biosolids, their potential ecological toxicological effects require methods with higher costs and high throughput to determine, which increases the difficulty of regulating and studying the toxicity of new contaminants [1529].

Liquid crystal devices have wide applications in our daily life. Liquid crystal monomers are the main components of liquid crystal device displays. LCMs have very similar backbone structures with some POPs, such as PCBs, PBDEs, etc., which lead to their persistence, bioaccumulation, and toxicity. Studies have found that the transcriptional activities of some genes in chicken embryo hepatocytes are altered after exposure to liquid crystal monomers. Currently, the environmental status of liquid crystal monomers has received extensive attention and research. A database containing 1173 liquid crystal monomers was established in 2022, and the potential POPs characteristics of these 1173 liquid crystal monomers were estimated. The results identified LCMs as priority chemicals^[1530]. However, the environmental status and behavior of these liquid crystal monomers with POPs long-distance migration characteristics in remote areas (such as mountains, polar regions) are still in a relatively blank stage of research.

New pollutants (and old ones that bring new environmental problems) appear almost every day. In the United States alone, oil and gas companies use tens of thousands of unknown and unregulated chemicals in tens of thousands of hydraulic fracturing operations for shale gas each year^[1531]. Faulty well casings may allow these chemicals to contaminate aquifers directly. At the same time, new compounds such as biotoxins, novel pharmaceuticals, and nanomaterials are being produced commercially. New pollutants causing human concerns in the next decade may include biological toxins, antibiotic resistance genes, and new waterborne pathogens^[1532]. The numerous types and large quantities of new pollutants, with their widespread distribution, make it difficult to understand their production, usage, and environmental pollution baseline. Further research on their environmental behavior is necessary.

8.1.3 New contaminant control should not only focus on large-scale regional collaborative prevention and control but also precise management.

In the mid-20th century, organic chlorine pesticides were first detected in Antarctic wildlife. Since then, several studies have shown that due to long-distance migration, polar regions contain POPs^[1533]such as hexachlorocyclohexane, dichlorodiphenyltrichloroethane, and PCBs^[1534]. In addition to traditional POPs, new contaminants are increasingly being detected in human bodies in the Arctic region, including brominated flame retardants (polybrominated diphenyl ethers, etc.), perfluoroalkyl and polyfluoroalkyl substances (perfluorooctane sulfonate (PFOS), perfluorooctane, etc.). Polybrominated diphenyl ethers, perfluorooctane sulfonate, and perfluorooctane sulfonyl fluoride have been found in Arctic wildlife^[1531,1535]. According to the 2009 Arctic Monitoring and Assessment Programme report, in remote areas with fewer consumer goods, food chains are the main source of exposure to polybrominated diphenyl ethers^[1536]. However, there is a lack of global research on the migration of new contaminants to the poles, and there is insufficient understanding of their changes and behaviors in pristine environments. Assessing the fate of new contaminants in the global ecosystem is crucial for evaluating human health or ecological risks. Over the past few decades, new contaminant emissions caused by commercial and industrial activities (marine transportation, coal mining, etc.) are usually localized, but the long-range transport of new contaminants provides a pathway for the redistribution of air pollutants on a global scale. Therefore, it is necessary to focus on pollutant dynamics over a broad spatial scale to assess the coverage of monitoring programs and achieve coordinated prevention and control within large-scale regions. The high cost of continuous spatial monitoring, the feasibility of analyzing new contaminants in the Arctic environment, and the lack of "in-situ" basic data make regional collaborative control a challenge. Despite these challenges, we still need to make efforts to evaluate the global fate of new contaminants to better predict their impacts on the environment and human health. In this regard, advanced models and computational methods can be considered to simulate the migration and transformation processes of pollutants. At the same time, strengthening cooperation with international organizations and research institutions to jointly promote global pollutant monitoring and control work will help solve this problem.

Traditionally, monitoring pollutants in the environment has mainly relied on directed analysis methods to analyze potential pollutants in different matrices (water, sediments, soil, etc.). The progress of analytical instruments has improved the sensitivity of targeted methods, with routine measurements of compounds in water reaching values as low as parts per trillion. Moreover, the widespread use of high-resolution instruments has enabled the implementation of non-targeted analysis methods, which can detect hundreds to thousands of compounds in certain cases and preliminarily identify tens to hundreds of compounds. Despite the continuous improvement in the ability to detect environmental pollutants, the understanding of their biological impacts has not kept pace. Therefore, although chemical substances in the environment can be identified and quantified, the knowledge about the potential biological effects of hundreds to thousands of individual pollutants is still insufficient^[1537]. Since different types of pollutants exhibit varied behaviors in ecosystems, precise management is needed for different kinds of new pollutants. Fluoroaromatics are structurally similar to chloroaromatics but are less well-known or widely used compared to chloroaromatics. It is estimated that the global production capacity of the most important fluoroaromatics reached 35,000 tons annually in 2000, and past trends indicate that the production and use of fluoroaromatics have further increased since then. Fluoroaromatics are used as intermediates in the fluorine chemical industry and may enter the environment through industrial leaks or wastewater discharge. High concentrations of fluoro-benzene up to 700 μg/L have been detected in groundwater near an abandoned industrial site, and fluorinated pharmaceuticals are frequently detected in both influent and effluent at wastewater treatment plants. Due to the high stability of C–F bonds, they persist longer in the environment and are difficult to degrade. Thus, existing traditional POPs control schemes cannot be directly applied, and a more suitable control mechanism tailored to the structural characteristics of new pollutants urgently needs to be studied^[1538].

8.1.4 Substitutes and Substitute Technologies Are Not Easy to Develop

In 2023, the Ministry of Ecology and Environment, the Ministry of Industry and Information Technology, the Ministry of Agriculture and Rural Affairs, and other six departments jointly released the "List of Priority Controlled New Pollutants (2023 Edition)", which clearly defined the key control measures for 14 new pollutants, including prohibition, restriction, and emission reduction environmental risk control measures. The development of substitutes and alternative technologies targeted at the uses of the controlled new pollutants has become an important way to control the production and use of new pollutants. However, the development of green substitutes often falls into a vicious circle of "new pollutant causing environmental pollution - substitution - re-pollution". For example, PFAS are widely used in industrial production due to their high thermal stability, chemical inertness, and electrical insulation properties. However, after the use of long-chain PFAS (with 8 or more carbons), such as PFOS and PFOA, is restricted, short-chain PFAS substitutes like PFBS, PFHxS, and PFHxA begin to be extensively used^[1539,1540]. Currently, short-chain PFAS substitutes with lower persistence have become an important component of PFAS in various environmental samples. However, studies show that short-chain PFAS substitutes also exhibit bioaccumulation and similar biological toxicity mechanisms to long-chain PFAS^[1541]. Studies have shown that PFBS may cause various toxicities, such as cytotoxicity^[1542], developmental toxicity^[1543], endocrine disruption^[1544], immunotoxicity^[1545], reproductive toxicity^[1546], hepatotoxicity^[1547], and neurotoxicity^[1548]; exposure to PFHxS may cause neurotoxicity and oxidative stress^[1549], and affect embryo development^[1550]; the half-life of some PFHxA in serum is even longer than that of long-chain PFASs, its biological toxicity is 3 to 5 times that of PFOA, and it may cause varying degrees of tissue damage by disrupting the diversity of gut microbiota^[1539,1551]. Since the 1970s, F-53B and sodium perfluorononanoate (OBS) have been widely used as novel PFOS substitutes in the Chinese market. The former is considered to have moderate toxicity, while the latter has been proven to have acute toxicity similar to PFOS^[1552,1553]. Since 2009, hexafluoropropylene oxide dimer acid (GenX) has become a typical substitute for PFOA. However, recent studies have shown that GenX's toxicity and bioaccumulation are comparable to or even higher than those of PFOA, and it is more difficult to be degraded by traditional purification facilities^[1554,1555].

Dechlorane Plus (DP), an unregulated highly chlorinated flame retardant, was first introduced to the market in the 1960s as a substitute for the flame retardant BDE-209 and heptachloride^[1556]. Due to its lower cost, lower density, and superior thermal and photochemical stability compared to other brominated flame retardants, it has been preferentially used in industrial polymers, plastics, resins, wires, cables, and other fields^[1557]. DP has been widely used over the past 40 years but was not reported to exist in the environment until 2006 and was officially listed under the Stockholm Convention in 2023. Studies have shown that DP has oxidative damage potential and may induce neurobehavioral defects^[1558]. DP is also considered a potential endocrine disruptor, interfering with the hypothalamic-pituitary-thyroid axis at the molecular level, posing significant health risks^[1558]. DBDPE, another alternative to BDE-209, is widely applied in industrial production. China is a major producer and user of brominated flame retardants, and since 2012, China's annual production of DBDPE (25,000 tons) has already exceeded that of BDE-209^[1559]. Concentrations of DBDPE in urban dust have been found to exceed the total of all polybrominated diphenyl ethers, and its widespread use can lead to rapid increases in human accumulation levels^[1560]. The half-life and toxicity mechanisms of DBDPE in zebrafish are similar to those of BDE-209, potentially associated with endocrine disruption, neurodevelopment and behavioral effects, liver toxicity, changes in gene expression, and cancer-related health risks^{[1561, 1562]}. Research shows that DBDPE has stronger ultraviolet radiation resistance than BDE-209. After 224 days of natural light exposure, DBDPE in thermoplastic polymers showed no obvious changes, while the photodegradation half-life of BDE-209 was 51 days^[1563].

Since the discovery and application of penicillin in the 1940s, antibiotics have played an unparalleled role in the prevention, control, and treatment of infectious diseases in humans and animals. However, the extensive use of antibiotics in animal husbandry has led to the emergence of antibiotic-resistant bacteria and resistance factors, posing a significant risk to human and animal health. To ensure safety for humans, animals, and the environment, many countries prohibit the use of antibiotics, hormones, herbicides, and pesticides in organic agriculture and adopt various alternatives to reduce the occurrence and spread of bacterial diseases, including antimicrobial vaccines, immunomodulators, and antimicrobial peptides, among others^[1564]. Traditional vaccines are generally divided into attenuated live vaccines and inactivated vaccines. The main drawback of attenuated live vaccines is that they remain in the animal's body for a longer period and carry the risk of reverting to full virulence^[1565]. Inactivated vaccines are relatively safe and cost less but lack protective antigens and broad-spectrum protection. DNA vaccines can induce a wider range of immune responses, but their vaccination efficiency is low, and the immune response is weak^[1566]. Developing a vaccine that is both practical and affordable, enabling it to be widely used in poor countries, remains a key issue. Although vaccines can reduce the reliance of animal husbandry on antibiotics, they cannot fully replace antibiotics. Immunomodulators can nonspecifically enhance the innate immune function of the host and improve the host's resistance to disease^[1567]. Immunomodulators exhibit different effects in different animals, and the timing of their use is crucial. Administering immunomodulators to animals with immature immune systems may adversely affect the normal development of the immune response^[1568]. Currently, there are no unified standards for evaluating the efficacy and safety of immunostimulants, so immunomodulators can only serve as auxiliary means for antibacterial treatment^[1569]. Antimicrobial peptides exhibit broad-spectrum and highly efficient bactericidal activity and are expected to become a new class of antibiotics. However, the high production costs limit their large-scale use. Currently, traditional antimicrobial peptides are produced by culturing wild strains, with low yields and complex purification processes, while the synthetic antimicrobial peptides produced by emerging genetic engineering technologies fail to reach the activity of natural antimicrobial peptides^[1570]. Besides the aforementioned alternatives and alternative technologies, products such as bacteriophages, intracellular lytic enzymes, synbiotics, and plant extracts are also used for the prevention and treatment of livestock diseases but have not been widely adopted^[1564].

Apart from the above-mentioned emerging pollutants, there are other organic substances such as short-chain chlorinated paraffins, HCBD, dichloromethane, chloroform, nonylphenol (NP), etc. As important raw materials and intermediates in fine chemical industry, it is currently difficult to find suitable substitutes for them. An ideal substitute for emerging pollutants should have several characteristics: 1) harmless to animals and humans, without side effects; 2) capable of rapid degradation in biological organisms, not easily accumulated in biological organisms; 3) with a short half-life, reducing the harm to the environment; 4) cost-effective and easy to promote. However, so far, no substitute can fully meet these requirements. The existing substitutes and alternative technologies often lack comprehensive safety assessment, which increases the uncertainty of environmental and health risks.

Strict implementation of chemical management, and strengthening the research, development, and promotion of green substitutes and alternative technologies for new pollutants are crucial. At the same time, it is necessary to enhance safety assessments before the use of substitutes, reduce the usage of toxic chemicals, and reinforce their removal to minimize their entry into the environment as much as possible. This requires joint efforts from governments, enterprises, and research institutions to adopt comprehensive measures to promote the development and application of substitutes, thereby alleviating the burden on the environment and human health. Only in this way can the goal of sustainable development be achieved, and a cleaner and healthier ecological environment be built.

8.1.5 Part of new contaminants are unintentionally generated substances or metabolites, and the research on their formation mechanisms and emission reduction technologies is difficult.

The main new pollutants are four major categories: first, POPs; second, endocrine disruptors; third, antibiotics; and fourth, microplastics. The "Stockholm Convention on Persistent Organic Pollutants" lists unintentionally produced and emitted POPs of anthropogenic origin in Annex C, including polychlorinated dibenzo-p-dioxins/polychlorinated dibenzofurans (PCDD/Fs), PCBs, PCNs, PeCBz, hexachlorobenzene (HxCBz), and HCBD. Among them, PCDD/Fs are the only POPs that have never been intentionally produced for any purpose or application in Annex C, while all other listed POPs were once intentionally produced^[1571]. Unintentional emissions of POPs from typical industrial thermal processes, such as waste incineration, metal smelting, and electronic waste recycling, have drawn significant attention. However, the byproducts of organic synthesis industries receive little attention. Studies show that not only do POPs byproducts with the same precursors as the final products exist in commercial chemical production^[1572,1573], but also unintentional emissions of POPs may occur throughout the entire process of chemical production (from raw materials to final products)^[1574]. Therefore, it is necessary to track the formation of byproducts during the production process to control environmental risks.

The cost of developing routine monitoring and analysis methods for POPs is quite high, and monitoring the emissions of all POPs would impose a huge financial burden^[1575,1576]. To reduce the cost of monitoring, quantitative relationships or predictive models for overall emission risks of multiple POPs can be established based on data from individual POPs. Currently, research reports have documented quantitative correlations of POPs generated in specific industrial processes, including the correlation between PCDD/Fs and HxCBz, as well as the correlation between PCDD/Fs and PCN^[1577,1578]. However, due to the complexity of organic pollutants in industrial thermal processes, there is still a lack of studies explaining the quantitative correlations among multiple POPs. Therefore, it is necessary to conduct extensive field investigations and statistical analyses on all industrial sources of POPs to identify indicators for key-controlled POPs. Currently, research has shown that 2,3,4,7,8-PeCDF is an indicator of PCDD/Fs TEQ^[1579], CB-118 is an indicator of PCBs TEQ^[1580], CN50, CN27/30, CN52/60, CN66/67 can serve as indicators of PCN^[1581]. However, the existing indicators cannot meet the needs of monitoring multiple POPs. Therefore, extensive research is needed on various industrial sources to determine potential indicators for the overall toxicity equivalent quantity of multiple POPs and clarify the quantitative correlation between these indicators and total emissions.

Endocrine disruptors are produced by specific compounds through metabolic processes in the body. Nonylphenol ethoxylates (NPEOs), a major class of nonionic surfactants widely used in detergents, cleaning agents, plastics, paper, and agricultural chemicals, have NP as their final degradation product, which has been confirmed as an endocrine disruptor capable of interfering with reproductive hormone functions^[1582]. The main persistent metabolite of DDT, p,p'-DDE (1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene), is an androgen receptor antagonist. p,p'-DDE can inhibit androgen binding to androgen receptors and androgen-induced transcriptional activity in developing, pubertal, and adult male rats, leading to male reproductive abnormalities^[1583]. Ethylene thiourea (ETU), one of the major metabolites of ethylene bisdithiocarbamate fungicides, has higher exposure levels in rubber and fungicide production workers. ETU is recognized as an endocrine disruptor, and its exposure can lead to endocrine disruption, hypothyroidism, teratogenicity, carcinogenicity, and genetic damage^[1584]. Long-term exposure to low concentrations of ETU may cause renal function and structural damage and increase the formation of renal cysts^[1585,1586]. Understanding the formation mechanisms and biological transformation pathways of specific toxic metabolites and revealing critical points in the metabolic process are key methods for reducing exposure to toxic metabolites. Identifying potential risk sources related to specific toxic metabolites, including industrial emissions, pesticides, drugs, personal care products, and food, along with their associations with specific metabolites, establishing monitoring systems to monitor and investigate precursors of toxic metabolites, and formulating emission reduction strategies based on research and monitoring results, such as improving industrial production processes, standardizing drug and pesticide use, promoting green chemicals, and strengthening waste treatment and disposal measures, can effectively reduce exposure to precursors of toxic metabolites and ensure public health and environmental safety.

8.2 New Contaminant Treatment Technology

On May 24, 2022, the General Office of the State Council issued the "Action Plan for the Governance of New Pollutants," which proposed an overall working idea of "screening, evaluation, and control" and "prohibition, reduction, and treatment." This article lists the existing corresponding technologies for "screening, evaluation, and control" respectively.

8.2.1 Non-targeted Screening Technology

Non-targeted screening technology is a popular technique in environmental pollutant analysis. It detects samples by using gas or liquid phase high-resolution mass spectrometry instruments, nuclear magnetic resonance and other analytical instruments, and interprets data through auxiliary analysis technologies, obtaining compound information by using spectrum databases or fragment prediction. Non-targeted screening technology has the advantage of high throughput and extends the breadth of traditional targeted analysis. Currently, the main analytical techniques applied to non-targeted screening are GC-HRMS and LC-HRMS^[1587].

GC-HRMS is one of the most commonly used techniques in non-targeted screening, featuring fast detection speed, high analytical sensitivity, high separation efficiency, and good reproducibility. However, its samples must be vaporizable, showing excellent analytical performance for small molecules, volatile substances, semi-volatile thermally stable substances, etc., while strongly polar and non-volatile substances need to be derivatized into corresponding volatile derivatives before being analyzed by gas chromatography-mass spectrometry. The commonly used instruments in non-targeted screening research are GC-TOF-MS and GC×GC-MS. TOF-MS is a mass spectrometry method that analyzes compound structures by utilizing ions with the same kinetic energy but different mass-to-charge ratios moving in a constant electric field, with the difference in time required to traverse a fixed distance used for analysis. In recent years, the resolution of TOF-MS can reach 55,000 (full width at half maximum, FWHM)^[1588], featuring high throughput, high sensitivity, and strong selectivity, which has great advantages in detecting organic compounds in complex matrices. GC-TOF-MS can perform full scans on samples in Scan mode, screening hundreds of compounds at once. In recent years, GC-TOF-MS has been widely applied in the non-targeted screening of environmental media for non-polar and medium-polar compounds such as PAHs, halogenated compounds, organophosphorus compounds, and pesticides^[1589]. Lee et al.^[1590] constructed a database containing 215 POPs using the GC-TOF-MS platform and analyzed environmental samples from the Arctic region using this database, finding that the main pollutants included silicones, PCBs, PAHs, OPFRs, phthalates, and synthetic musks. Comprehensive two-dimensional gas chromatography-mass spectrometry is also a powerful tool in non-targeted screening. When the components of complex samples exceed hundreds, traditional one-dimensional gas chromatography cannot separate them well, whereas GC×GC has strong separation capabilities, very high resolution, and peak capacity, better meeting the analytical requirements of complex samples^[1591]. It connects two chromatographic columns with different but independent separation mechanisms in series, where the sample is separated by the first column and then enters the second column through a modulator in a pulse manner for further separation, achieving orthogonalization of gas chromatographic separation characteristics via differences in polarity and temperature. Comprehensive two-dimensional chromatography has greater peak capacity and separation ability, combined with the high resolution and sensitivity of mass spectrometry, providing stronger advantages for substances that cannot be separated by one-dimensional chromatography. Hoh et al.^[1592] combined comprehensive two-dimensional GC×GC-MS with a direct automatic sampling system to analyze organic pollutants in fish oil. Skoczyńska et al.^[1593] extracted and purified sediments from the Elbe River (Czech Republic), and using GC×GC-TOF-MS polarity analysis, they preliminarily screened over 400 compounds from three fractions, including potentially toxic chlorinated PAHs, alkylated PAHs, quinones, dihydroxynaphthalenes, and sulfur aromatic hydrocarbons.

LC-HRMS combines the high efficiency and rapid separation of LC with the high sensitivity and accuracy of MS or MSn, and has the advantages of good resolution, fast analysis speed, and high detection sensitivity for non-volatile or thermally unstable compounds. It is one of the most widely used non-target screening technologies at present^[1594].Meng et al.^[1595] used LC-Orbitrap-MS to screen pharmaceuticals and personal care products in Dianshan Lake surface water, matching 95 compounds from four categories: pesticides, drugs, plasticizers, and surfactants through database matching. They further confirmed and quantified 19 substances that were detected at higher levels and had greater risks. Hernández et al. used LC-TOF-MS to analyze pesticides and their metabolites^[1596], including 377 insecticides, 40 metabolites, 47 antibiotics, 20 other products, and 7 emerging contaminants, providing a reference for the screening of pesticides and their metabolites in food and water. Martínez et al. used ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) to screen for residual drugs in river water^[1597], confirming drug components such as ketorolac, trazodone, fluconazole, metformin, and venlafaxine, and determining the mass concentration using ultra-high performance liquid chromatography-triple quadrupole tandem mass spectrometry (UPLC-QQQ-MS), ranging from 14 to 677 ng/L. The study shows that the combination of LC-TOF-MS and LC-QQQ-MS and other quantitative analysis instruments is an effective method for qualitative and quantitative analysis of unknown pollutants in environmental samples.

When performing non-target compound analysis, if the characteristic information of the mass spectrometry fragment ions obtained by full scan is not sufficient, there may be a situation of misidentification of compound structures. In order to analyze the elemental composition, molecular formula, and structure of unknown compounds more comprehensively and deeply, other auxiliary analytical methods may also be needed. For example, instruments such as nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) have also developed the technology of coupling nuclear magnetic resonance with liquid chromatography tandem mass spectrometry (NMR-LC-MS/MS)^[1598]. Alves Filho et al.^[1599] used high-performance liquid chromatography-solid phase extraction-nuclear magnetic resonance (HPLC-(UV/MS)-SPE-NMR) to analyze unknown compounds in Latin American wastewater treatment plants, and screened and analyzed pollutants such as linear alkyl surfactants.

8.2.2 Risk Assessment Techniques

8.2.2.1 Toxico-genomics Technologies

Toxicogenomics integrates the theories and technologies of genomics, bioinformatics, and toxicology. By testing the dynamic responses of life genomes to pollutants under different exposure doses and time conditions, it has become an important tool for studying the impact of environmental pollutants on human health. Toxicogenomics has advantages such as whole-genome coverage, high throughput, and support for big data analysis, providing a historic opportunity for developing innovative methods for pollutant toxicity testing and screening. The currently widely applied toxicogenomic technologies in the environmental field mainly involve genomics, transcriptomics, proteomics, and metabolomics, which can identify response changes at multiple biological levels from molecules to communities under toxic pollutant exposure, providing critical genomic features for pollutant risk assessment^[1600].Research has shown that the predictive accuracy of endocrine disruptor models constructed using toxicogenomics data is greater than 90%, higher than that of predictive models based on in vitro high-throughput test data (69%-85%)^[1601]. In summary, toxicogenomic technology contributes to the development of screening and risk assessment methods for toxic pollutants, demonstrating significant advantages.

Obtaining high-throughput biological pathway mechanism information. Omics-based bioassays have greatly increased the number of target molecules tested, and by batch detecting changes in genes, proteins, and metabolites in organisms exposed to different doses of pollutants, the potential toxicity of pollutants can be studied. The number of genes, proteins, or metabolites involved in the test can include almost all genes, proteins, and metabolites in the organism. Wang et al.^[1602] used transcriptomics, proteomics, and metabolomics to analyze the hepatoxicity mechanism induced by TPhP, revealing the effects of TPhP hepatoxicity on different biological functions. Dose-dependent simplified transcriptome testing enables high-throughput quantitative analysis of pollutant interference with biological pathways. The impact of pollutants on gene expression often manifests as biological pathways being disrupted, and the disruption of biological pathways can be detected by examining a small number of key genes that represent the entire biological pathway^[1603]. Based on this principle, simplified transcriptome testing can obtain the dose-effect relationship of transcriptome expression for large numbers of samples. Zhang et al.^[1604] developed a simplified transcriptome profiling method to characterize genes related to zebrafish neurogenesis and early neuronal development, thereby determining the potential neurodevelopmental toxicity of environmental chemicals.

Systemic toxicology assessment at multiple biological levels. Multi-omics technologies can identify biological pathways associated with adverse outcomes from different biological levels. Establishing links between key events in adverse outcome pathways and omics test results is crucial for deciphering the toxicity effects of harmful pollutants. The combination of multi-omics can achieve comprehensive biological network analysis at multiple scales. Lee et al.^[1605] found through a combined proteomics and metabolomics analysis that the effect of perfluorooctane sulfonate exposure on the nervous system of zebrafish larvae was mainly due to axonal deformation, stimulation of neuroinflammation, and dysregulation of calcium ion signaling, while single-omics could only show disturbances caused by perfluorooctane sulfonate exposure in neural function, oxidative stress, and energy metabolism pathways. The adverse outcome pathway network connects adverse outcomes triggered by toxic pollutants from molecular to individual and higher biological levels, which helps systematically identify the biological effect mechanisms of toxic pollutants and conduct ecological and human health risk assessments based on this.

Precision toxicology for individual susceptibility. Due to the differences in susceptibility among populations, susceptible individuals face a greater health risk from exposure to toxic pollutants. Conducting precise toxicological research on individual susceptibility is currently a bottleneck issue. Genome-wide association studies are methods that use high-density molecular markers screened across the whole genome to scan the studied population and analyze the correlation between the obtained molecular marker data and phenotypic traits. A study of maternal and fetal genetics by Traglia et al.^[1606] elucidated the genetic molecular mechanisms underlying PBDEs and PCBs exposure affecting maternal and fetal metabolic dysfunction. Genome-wide association analysis identified genes CYP 2B 6, PRKCDBP, SUMF 1, and NDUFS 4 associated with xenobiotic and lipid metabolism as genetic determinants of maternal and fetal metabolic diseases under organic halogen exposure. Dai et al.^[1607] utilized genome-wide association studies to investigate gene variations in the blood of 1557 PAHs-exposed individuals (coke oven workers), elucidating that SNPs in the DNA damage repair-related gene ANRIL may be key events in PAHs-promoted lung cancer development.

Composite pollution effect evaluation. In the real environment, we are exposed to chemical mixtures, but only a small part of them have been identified and a large number of chemicals remain unknown. Through effect-directed analysis, the separated components can be fractionated by chromatography or physical methods according to physicochemical properties such as molecular weight and hydrophobicity, and then subjected to biological tests including omics. Each bioactive component is further fractionated until one or more bioactive subcomponents are identified, from which key toxic substances are recognized. Xiao et al.^[1608] Based on effect-directed analysis, using the method of fluorescent protein, the activity of deethylase in RTL-W1 cells under environmental pollutant stress was detected, and it was identified that the key toxic components activating AhR in the sediments of the Three Gorges Dam were PAHs with 4-5 rings and benzothiazole, etc. Qi et al.^[1609] Based on effect-directed analysis, using methods such as fluorescent protein and ammonium molybdate, the effects of five enzymes in Chironomus during 10 days were detected, and the toxicity of Pearl River sediments was comprehensively evaluated. It was found that the oxidative stress effects of cypermethrin, dimethomorph, metarhizium anisopliae, and phenyl chloride were important reasons for the death of Chironomus caused by sediments.

8.2.2.2 Computational Toxicology Techniques

Environmental pollutants disrupt the normal functions of cells by interfering with the interactions between biomacromolecules and natural small molecules, thus leading to harmful effects at various biological levels. However, existing experimental approaches are limited by methodological sensitivity and are difficult to use for obtaining relevant molecular toxicological information. Biological tests also struggle to cover all toxicity effects of pollutants. Computational toxicology, integrating bioinformatics, cheminformatics, structure-activity relationships, and even systems biology methods, provides a scientific and convenient approach for exploring and rapidly predicting mechanisms of environmental biological effects of pollutants. Computational toxicology tools are gradually becoming an important way to obtain data required for hazard screening of chemical substances. Typical computational toxicology methods include molecular docking, molecular dynamics simulation, machine learning modeling, etc.

Molecular docking is a mature molecular simulation method that explores the interaction between biomacromolecules and small molecules to predict their binding mode and binding affinity. Molecular docking can provide diverse information about molecular binding modes based on the structural characteristics of biomacromolecules and small molecule ligands, and it can also use this binding information to give mechanistic information about the interaction between ligands and biomacromolecules^[1610]. Currently, due to its advantages of high throughput and the ability to simulate static ligand-receptor binding processes, molecular docking has played an important role in the virtual screening studies of environmental emerging pollutants. At the same time, molecular docking also faces technical problems such as receptor rigidity, use of approximate scoring functions, and insufficient sampling^[1611], which limits its capability in studying the structure-activity relationships of environmental pollutants and biomacromolecules. Liang et al.^[1612] used molecular docking to study the binding process of parabens with TRα/β, finding that all parabens entered the binding pocket of TRα/β in the correct binding mode, with binding affinities ranging from -6.36 to -6.01 kcal/mol (1 kcal = 4.186 kJ), proving that there was significant binding activity between parabens and TRα/β. Li et al.^[1613] used molecular docking to study the binding process of OPEs with membrane thyroid hormone receptors (mTR) and TRβ, finding that the binding sites of target OPEs were consistent with those of reference ligands; they ranked the toxicity potency of OPEs based on binding affinity, and the simulated results showed strong positive correlation with in vitro experimental results (R²=0.94).

Molecular dynamics can simulate the flexible binding process of environmental pollutants-biomacromolecules and the dynamic allostery process of complexes, providing more comprehensive information about interaction mechanisms^[1614]. Therefore, molecular dynamics simulation represents an effective molecular simulation tool for identifying and evaluating the structural stability of environmental pollutant-biomacromolecule complexes. Although molecular dynamics simulation can explore the dynamic process of ligand-receptor binding as much as possible and provide toxic mechanism information for environmental pollutants, there are still two problems. 1) Low throughput. As molecular simulation technology gradually explores the dynamic interaction process between chemicals and proteins at the molecular level, the improvement in mechanism information mining is accompanied by a gradual decrease in throughput and a gradual increase in time consumption. How to balance the in-depth mining of toxic mechanisms and maintain a high throughput is one of the key challenges currently faced by molecular dynamics simulations. 2) Toxicity prediction based on pathways. Molecular dynamics simulates and studies a certain key event. According to the theoretical framework of adverse outcome pathways, environmental pollutants cause harmful effects at the individual level by affecting the activity of pathways regulated by biomacromolecules. However, molecular dynamics cannot predict the toxic effects of environmental pollutants based on pathways.

Machine learning modeling mainly utilizes publicly available structural information and chemical, in vitro, and in vivo biological activity data to construct QSAR models based on machine learning algorithms. It also rapidly improves the virtual screening efficiency of target molecular target environmental pollutants and further deepens the elucidation of the interaction mechanisms between pollutants and biomacromolecules under complex biological backgrounds^[1615,1616]. Therefore, machine learning modeling is a novel computational toxicology technology completely different from molecular simulation. Therefore, machine learning modeling is a novel computational toxicology technology different from molecular simulation. Machine learning has traditionally shown excellent predictive capabilities for linear data, but it can no longer cope with the explosively developing biological big data. The massive amount of data and complex biological processes have led to more advanced nonlinear deep learning algorithms. Highly nonlinear decision boundaries and hyperparameter-dependent prediction problems give machine learning modeling both high predictive accuracy and low interpretability. Machine learning/deep learning cannot explain the biological significance of each node in the network or the importance of each feature to the model's predictive performance. Currently, in the study of interactions between environmental pollutants and biomacromolecules, researchers are attempting to apply many methods (such as tree-based random forest feature importance for random forest algorithms and feature interaction network analysis frameworks for deep neural network algorithms) to compensate for the "black box" defects of machine learning prediction models^[1617,1618]. Lomana et al.^[1619] first collected eight key molecular targets related to thyroid hormone homeostasis and constructed qualitative models for each target based on machine learning, achieving comprehensive virtual screening of whether environmental pollutants may cause potential thyroid hormone disorders by interfering with a specific molecular target. Cheng et al.^[1620] used 26 types of activity data combined with five supervised algorithms (logistic regression, random forest, artificial neural networks, and two graph neural networks) to first perform QSAR modeling on 1012 perfluoroalkyl and polyfluoroalkyl substances. Then, based on the highly accurate prediction models, they found that most bioactive PFASs had a perfluoroalkyl chain length less than 12.

8.2.3 New Contaminant Control Technologies

8.2.3.1 Physical removal techniques

Physical removal methods include filtration, adsorption, desorption, and other methods. Physical removal technology reduces the concentration of pollutants in environmental media through physical transfer, thereby decreasing the harm of new pollutants to the environment and human beings. However, it does not completely destroy or eliminate the pollutants. Different physical removal technologies can be adopted for different new pollutants in various environmental media.

The filtration method can be used for the removal of new pollutants with particle size, such as microplastics in water bodies. Ultrafiltration uses asymmetric ultrafiltration membranes with pore sizes ranging from 1 to 100 nm to remove proteins, bacteria, suspended solids, and other particles and macromolecules in water. Ma et al.^[1621] used iron-based coagulants and treated polyethylene microplastics in drinking water through ultrafiltration and coagulation processes. The removal rate of microplastics by a single coagulation process was less than 15%, while after adding polyacrylamide to improve coagulation performance, the removal rate of small polyethylene particles (d < 0.5 mm) increased from 13% to 91%. Although ultrafiltration combined with other technologies can effectively remove microplastics in water, it is necessary to consider the impact of plastic particles on the formation of filter cakes and subsequent scaling. Membrane bioreactors (MBRs), which are systems formed by coupling biological catalysts with membrane separation systems, play a role in reducing the complexity of solutions during the treatment of microplastics through biological degradation, making it easier to purify and further treat microplastics. After the pre-treated water flows into the biological reactor, biological degradation and decomposition of organic matter occur here, and then the mixed liquid produced is separated under the action of the membrane. Due to the filtering effect of the membrane, microplastics are concentrated in the sludge. Compared with traditional tertiary treatment processes, MBR processes have obvious advantages, with a microplastic removal rate as high as 99.9%. It was also found that compared with conventional activated sludge treatment processes (CAS), the microplastic content in the effluent of the MBR process was only 0.4/L, lower than the 1.0/L in the CAS process. Therefore, it can be seen that the MBR process can effectively remove microplastic pollution in water (Fig. 51).

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图51 一体式膜生物反应器示意图^[1622]

Fig. 51 Schematic diagram of integrated membrane bioreactor^[1622]

The adsorption method utilizes porous solid materials (adsorbents) to adsorb certain or several pollutants from environmental media for the purpose of recovering or removing these pollutants, thereby purifying the environmental medium. The adsorption method has advantages such as low cost, high efficiency, and simple operation, and can be widely applied in the removal of various pollutants in different environmental media. Some researchers proposed the application of porous materials to adsorb microplastics in water. The electrostatic interaction, hydrogen bond interaction, and π-π interaction between the adsorbent and microplastics can effectively remove microplastics. Chen et al.^[1623] utilized magnesium/zinc-modified magnetic biochar to remove microplastics from water bodies with removal rates of 98.75% and 99.46%, respectively, while maintaining adsorption capacity through thermal regeneration. Deng et al.^[1624] revealed the adsorption mechanism of perfluorinated compounds on carbon nanotubes by using carbon nanotubes to adsorb perfluorinated compounds. Currently, domestic and international adsorption materials are shifting from conventional materials such as peat and zeolites to new materials like resin particles and polymer composites (Fig. 52). Liu et al.^[1625] constructed a magnetic bifunctional β-cyclodextrin nanocomposite to adsorb bisphenol A. Murray et al.^[1626] tested the removal rates of four endocrine disruptors by submicron-sized resin particles (SMR), which reached 98%, 80%, 87%, and 97%, respectively. Song et al.^[1627] prepared magnetic mesoporous melamine-formaldehyde composites (Fe₃O₄-mPMF), and the adsorption ability for bisphenol A, NP, and 4-tert-octylphenol is mainly attributed to π-stacking, hydrogen bonds, and hydrophobic interactions.

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图52 碳纳米管吸附全氟化合物机理图^[1624]

Fig. 52 Mechanism diagram of carbon nanotubes adsorbing perfluorinated compounds^[1624]

Thermal desorption is a physical separation technology that destroys the structure of pollutants, mainly used for the remediation of pollutants in soil. By heating, moisture and organic pollutants are separated from the soil and transferred to the tail gas treatment system by carrier gas or vacuum system. When dealing with difficult sites such as heterogeneous pollution, deep pollution, high excavation difficulty, low permeability clay, etc., in-situ thermal desorption technology has great advantages^[1628].Based on different heating temperatures, thermal desorption can be divided into low-temperature (100~300 ℃) thermal desorption and high-temperature (300~500 ℃) thermal desorption. Low-temperature in-situ thermal desorption technology also has good remediation effects on semi-volatile organic compounds such as chlorobenzene and hexachlorocyclohexane^[1629].Chen Keyu et al.^[1630] used low-temperature in-situ electrothermal desorption technology in a pilot test for the remediation of a retired reagent factory site in northern China, where the soil temperature was heated to around 100 ℃, achieving a removal rate of 99.3%. Zhao Tao et al.^[1631] used electrically heated rotary kiln thermal desorption technology to treat contaminated soil, finding that under processing conditions of 350 ℃ and 500 ℃ for 10 minutes, the removal rates of total PAHs were 98.83% and 98.94%, respectively, allowing soil PAHs to reach the target value for remediation. The sole soil thermal desorption technology only transfers pollutants from the solid phase to the gaseous phase without eliminating the pollutants; thus, it must be combined with a wastewater and exhaust gas treatment system to achieve complete degradation of pollutants^[1632].

8.2.3.2 Chemical Removal Technology

Advanced oxidation processes (AOPs) mainly generate large amounts of ·OH radicals, which are second only to fluorine in terms of oxidizability in water systems. These radicals oxidize and break down the structures of large organic compounds to remove target pollutants from water. AOPs have high removal efficiency, short treatment time, and mild reaction conditions, but their disadvantage is that they require a large amount of chemicals and have high processing costs. AOPs can be divided into chemical, electrochemical, and photochemical advanced oxidation processes based on the methods of free radical generation^[1633,1634].The most common free radical generation/catalytic systems include Fenton reaction systems (Fe³⁺, Fe²⁺), hydrogen peroxide, ozone, ultraviolet light/solar radiation, photocatalysts, and combinations of these systems^[1635]. Le et al.^[1636] degraded 99% of hexabromocyclododecane (15 mmol/L) in water within 9 hours using 1 g/L palladium/iron nanoparticles and proposed that the addition of humic acid could enhance the degradation effect of hexabromocyclododecane by palladium/iron nanoparticles. Monteoliva-García et al.^[1637] used a photochemical advanced oxidation process to remove carbamazepine, ciprofloxacin, and IBU from urban wastewater and found that ciprofloxacin could be completely degraded within 20 minutes under neutral pH, 20°C, and different concentrations of hydrogen peroxide. The degradation rates for IBU and carbamazepine were 90%-100% and 80%-100%, respectively. Sani et al.^[1638] studied the catalytic oxidation of ciprofloxacin in wastewater under ozone conditions with γ-Al₂O₃ nanoparticles as catalysts. When the pH value was 9.5, the removal rate of ciprofloxacin in synthetic wastewater reached 93%, and the addition of γ-Al₂O₃ nanoparticles promoted the degradation rate of ciprofloxacin. Wei et al.^[1639] first reduced the de-bromination of 2,2',4,4'-tetrabromodiphenyl ether (BDE47) through surface-modified zero-valent zinc, then oxidized the de-brominated product low-brominated BDEs into short-chain carboxylic acids via ·OH radicals generated from the Fenton reaction (Fig. 53). Advanced oxidation processes are still at the laboratory research stage for microplastic degradation. Using a thermally activated persulfate system to treat polystyrene and polyethylene microplastics in deionized water, more than 80% of the microplastics had a particle size reduction of over 50% after treatment^[1640]. However, for actual halogen-containing sewage, the strong oxidative active species produced by the persulfate system tend to react with halide ions to form toxic halogenated by-products, causing secondary pollution. In recent years, electrochemical advanced oxidation technology has developed rapidly and has become one of the most promising technologies in advanced oxidation processes. Related studies have shown that electrochemical advanced oxidation processes can efficiently remove various refractory organic pollutants including endocrine-disrupting substances in water, and even completely mineralize organic pollutants^[1641].

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图53 四溴二苯醚还原脱溴并通过芬顿反应降解的机理^[1639]

Fig. 53 Mechanism of tetrabromodiphenyl ether reduction and debromination through fenton reaction^[1639]

Photocatalytic degradation is an effective process for treating new pollutants, featuring strong oxidation ability and mild reaction conditions, which can mineralize various organic pollutants into H₂O and CO₂ (Fig. 54). Photocatalytic degradation is an oxidation-reduction process in which semiconductor photocatalysts absorb photons of appropriate wavelengths and electrons in the valence band, exciting them to the conduction band, leaving positive holes. The electrons and positive holes react with adsorbed water and oxygen to produce radicals such as superoxide radicals and hydroxyl radicals. These active substances further react with organic polymers to decompose them, leading to chain breaks in the polymers or even complete mineralization. Chinnaiyan et al.^[1642] studied the photocatalytic degradation of amoxicillin and metformin hydrochloride in synthetic hospital wastewater using TiO₂ as a catalyst and a 125 W low-pressure mercury vapor lamp as a light source. At an initial concentration of 10 mg/L, an initial pH value of 7.6, a TiO₂ dosage of 563 mg/L, and a treatment time of 150 min, they achieved removal rates of 90% for amoxicillin and 98% for metformin hydrochloride. They also found that the mineralization rates of amoxicillin and metformin hydrochloride reached over 60%. Wang et al.^[1643] synthesized polyphenylene microspheres (PPMPs) by copolymerizing pyrene with chloromethyl methyl ether emulsion and then used them as catalysts for photocatalytic degradation of rhodamine B, achieving a degradation efficiency of 63.2% to 98.43%. As one of the most widely used photocatalysts, TiO₂ has been applied to remove various new contaminants from water. However, TiO₂ can only absorb ultraviolet light with a wavelength of 387 nm and has no photocatalytic activity under visible light irradiation. Visible light photocatalysis is a promising environmentally friendly, low-cost, and efficient process. Gao Shengwang^[1644] prepared magnetic nano Fe₃O₄/BiOI composite materials via an in-situ chemical precipitation method at room temperature. Under visible light irradiation, these materials catalyzed the degradation of BPA, BPS, and TBBPA. The results showed that under a catalytic dose of 1000 mg/L and pH = 9.0, the removal rates of BPA, BPS, and TBBPA reached up to 92.0%, 90.6%, and 98.5%, respectively. Ong et al.^[1645] prepared ZnO/rGO nanocomposites (NCs) using a low-temperature sol-gel method. Under visible light irradiation, the NCs achieved a mineralization rate of 90.91% for PFOA in aqueous solutions. Uheida et al.^[1646] used glass fibers as carriers and performed photocatalytic degradation of polypropylene spherical particles suspended in water under visible light irradiation in a flow system. After two weeks of irradiation, the average particle volume of polypropylene decreased by 65%. The above studies demonstrate that photocatalysis can effectively degrade new contaminants. Besides the photocatalyst, no additional chemicals are required, and natural sunlight can be used for the mineralization process. However, issues such as photocorrosion and difficult catalyst regeneration exist. For microplastics, photocatalysis also faces problems such as low degradation efficiency, limiting its widespread application.

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图54 可见光催化降解卤代有机污染物的机理图^[1647]

Fig. 54 Mechanism diagram of visible light-induced photocatalytic degradation of halogenated organic pollutants^[1647]

Thermal degradation technology is currently mainly used for the removal of POPs in gas or solid phases. Thermal degradation technology does not simply transfer pollutants but completely destroys and decomposes them into non-toxic substances discharged into the environment, with the advantage of high efficiency. For example, organic pollutants can be treated by cement kiln co-processing and waste incineration co-processing technologies. Taking cement kiln co-processing technology as an example, this technology has the advantages of high temperature, long residence time, and alkaline atmosphere, which can control the generation of POPs and ensure their complete degradation. Yan et al. [1648] found that when using a cement kiln to treat DDT reagents and contaminated soil containing DDT, the destruction rate of DDT reached over 99.9%. However, when treating chlorine- and bromine-containing pollutants, PCDD/Fs or brominated dioxins (PBDD/Fs) may be secondarily generated due to incomplete combustion. Ebert et al. [1649] found that thermal treatment of materials containing brominated flame retardants, especially PBDEs plastics, often generates PBDD/Fs, and water, as well as some metals and metal oxides, can act as catalysts for PBDD/Fs formation. Yang et al. [1650] found that using cement kiln co-processing to treat PBDEs-contaminated soils achieved a removal rate of over 99.999% for PBDEs, and the emission of PBDD/Fs was significantly higher than that of PCDD/Fs. It can be seen that secondary generation of POPs easily occurs during thermal treatment. Therefore, it is necessary to remove secondary pollutants from flue gas in subsequent processes. In the flue gas purification process, low-temperature catalytic degradation of POPs is an effective method, but the development of efficient low-temperature catalysts remains a challenge. Commonly used catalysts include noble metal catalysts and non-noble metal catalysts. Noble metal catalysts can efficiently catalyze the degradation of chlorinated aromatic organic compounds at low temperatures but have poor stability, are expensive, and prone to chlorine poisoning. Transition metal catalysts have high thermal stability, low cost, and are easy to form porous structures with high specific surface areas. Everaert et al. [1651] used SCR catalyst V₂O₅-WO₃/TiO₂ to catalytically degrade dioxins, showing that under actual municipal waste incineration conditions, the removal efficiency of dioxins exceeded 95% at 210–230 °C. Yu et al. [1652] studied the performance of V₂O₅/CeO₂-TiO₂ catalysts in catalytically degrading dioxins at 180 °C. The results showed that under conditions of 180 °C and 11% oxygen content, the catalytic degradation efficiency of dioxins reached 67.6% (Fig. 55). However, research on applying thermal degradation technology to other emerging pollutants is relatively lacking. Considering the differences in environmental media where different pollutants exist, not all pollutants in all media are suitable for thermal treatment.

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图55 钒钛催化剂催化降解二口恶英的催化循环^[1653]

Fig. 55 Catalytic cycle of vanadium-titanium catalyst for the degradation of dioxins^[1653]

The current application of chemical retardation technology is mainly in the industrial heat process, where retardants are sprayed in the pre-mixing zone before combustion and in the tail area after combustion to destroy the formation conditions of POPs such as dioxins, thereby achieving the purpose of source suppression. The commonly used retardants mainly include alkaline retardants, sulfur-based retardants, nitrogen-based retardants, etc.^[1654,1655] They mainly inhibit the generation of POPs by absorbing halogen elements or reducing the catalytic activity of transition metal catalysts. Lin et al.^[1656] inhibited the generation of POPs in a system with PAHs as precursors and CuCl₂ as catalysts by using CaO as a retardant, with inhibition rates for PCDD/Fs, PCBs, and PCNs reaching 97%, 93%, and 97%, respectively. At the same time, the inhibition rate for environmental persistent radicals generated during the reaction was over 85%, achieving synergistic inhibition of POPs and environmental persistent radicals (Fig. 56). Ma et al.^[1657] added diammonium hydrogen phosphate ((NH₄)₂HPO₄) and thiourea (CH₄N₂S) as retardants in a waste incineration plant, achieving an inhibition efficiency of 45.7% to 58.5% for dioxins. However, the use of sulfur-based and nitrogen-based retardants may increase the purification pressure for subsequent desulfurization and denitrification of flue gas^[1658,1659]. If the dosage and temperature are not well controlled, they may even promote the generation of UPOPs^[1660,1661].

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图56 氧化钙对POPs与环境持久性自由基的协同抑制^[1656]

Fig. 56 Synergetic inhibition of POPs and environmentally persistent free radicals by calcium oxide^[1656]

8.2.3.3 Biological Removal Technology

Biological methods utilize microorganisms (mainly bacteria and fungi), plants, and animals to degrade or convert various pollutants into carbon dioxide, water, inorganic salts, and other by-products. This technology has the advantages of large treatment capacity, low operating costs, good purification effects, and low energy consumption, and can be used for the degradation of many new pollutants. However, it has a longer treatment time and lower efficiency. The biological degradation of new pollutants mainly includes microbial, plant, and animal remediation technologies. Currently, research on animal and plant remediation of new pollutants is scarce and cannot achieve complete degradation of new pollutants, thus having significant limitations.

Microbial remediation technologies include aerobic methods using activated sludge, anaerobic methods, and bioenzyme methods, etc.^[1662]. Activated sludge method is a typical aerobic biological treatment method, which mainly utilizes the biological coagulation, adsorption, and oxidation of sludge to remove pollutants from the medium. The treatment effect of this method is influenced by factors such as treatment temperature and pollutant type. Gani et al.^[1663] studied the removal efficiency of activated sludge for diethyl phthalate, achieving a total removal rate of 23.9 μg/(g·d) (92% ± 6%) when the mixed liquor suspended solids (MLSS) concentration was between 3461 and 4972 mg/L. Kasonga et al.^[1664] attached five South African fungi to the sequential batch reactor process and found that the composite system with South African fungi achieved removal rates of 89.8%, 95.8%, and 91.4% for carbamazepine, diclofenac, and IBU, respectively, after one day. Becker et al.^[1665] discovered that low concentrations of enzymes can degrade hormones and endocrine disruptors. In wastewater treatment, the optimal removal rate for estrogens was 82%, while the optimal removal rate for androgen activity was 99%. Xiong et al.^[1666] studied the biodegradation of sulfapyridine in water using microalgae and plant microalgae, showing that using microalgae alone could remove 25.8% of sulfapyridine, whereas plant microalgae could achieve a removal rate of up to 74%. Peng et al.^[1667] identified a newly isolated Rhodococcus strain P52, which could completely remove dibenzofuran at 500 mg/L within 48 hours when it was the sole carbon source and energy source. Strain P52 could also remove 70% of 2-chlorodibenzofuran at 100 mg/L within 96 hours and could metabolize various aromatic compounds.

The biological degradation of microplastics includes several stages, and the primary stage involves microorganisms colonizing on the surface of microplastics. However, this process is strongly influenced by the plastic polymer and the hydrophobicity of the microbial surface. After colonization, microorganisms on the surface secrete extracellular enzymes that bind to the plastic surface. These extracellular enzymes contain redox enzymes, which can oxidize the chemical bonds of polymers under the influence of oxygen, metals, and ultraviolet radiation, resulting in the production of monomers or other oligomers. Subsequently, microorganisms utilize these monomers as energy and carbon sources, further breaking them down into CO₂, H₂O, and inorganic molecules^[1668]. Paço et al.^[1669] developed the marine fungus Zalerion maritimum for the degradation of polyethylene particles, achieving the highest degradation rate (56.7% mass reduction) after 7–14 days of cultivation. Park et al.^[1670] studied the degradation of polyethylene microplastics by Bacillus using a mesophilic mixed culture method. After 60 days of incubation, the molecular weight of the polymer decreased by 14.7%, and the average particle size reduced by 23%. Therefore, it can be seen that the efficiency of microbial methods for removing microplastics is much lower than that for other emerging contaminants such as endocrine disruptors and POPs.

8.3 Policy on Emerging Contaminants Environmental Management in China

With the in-depth development of the battle against pollution prevention and control, remarkable achievements have been made in the treatment of sensory indicators such as "haze" and "black and odorous". It is inevitable to attach importance to the treatment of new pollutants and develop towards the stage of treating new pollutants with more long-term and hidden hazards. Carrying out the treatment of new pollutants is not only an inevitable result of the in-depth advancement of the battle against pollution prevention and control, but also an intrinsic requirement in the process of continuous improvement of ecological and environmental quality.

The Action Plan adopts the overall working ideas of "screening, evaluation, control" and "prohibition, reduction, treatment", carries out environmental risk screening and assessment, dynamically releases the key controlled new pollutant list, takes environmental risk control measures such as prohibition, restriction and emission limitation, and implements source control, process control and end-of-pipe comprehensive management for new pollutants.

Strengthen the construction of laws and regulations system by legislation, establish and improve the technical standard system, and establish and improve the management mechanism for new pollutant governance. Establish the environmental information survey system for chemical substances and the environmental investigation and monitoring system for new pollutants, and carry out screening work.

Establish a chemical substance environmental risk assessment system and dynamically release the list of key controlled new pollutants. In terms of control, implement source prohibition and limitation, process emission reduction, and end-of-pipe treatment. In terms of safeguard measures, increase scientific and technological support, broaden funding channels; strengthen basic capacity building, reinforce supervision and law enforcement; strengthen organizational leadership, and enhance publicity guidance. The environmental management of new pollutants in each province and city needs to form policy characteristics based on their own situations, focusing on specific monitoring fields, types of new pollutants, and specific industries, and propose industries that need close attention according to the characteristics of industrial structure.

8.4 International Experience in New Contaminants Environmental Management

The control of new pollutants focuses on the source, with the core being the risk management and prevention of chemical substances^[1671]. Since the 1990s, many countries and international organizations have begun to launch investigation and research work on the status quo and hazards of new pollutants. Through continuous exploration and practice, they have established a new pollutant risk prevention and governance system based on the concept of "risk" full life cycle management and optimization grading, and formed a series of successful experiences and practices, which have good reference significance for China's new pollutant governance.

8.4.1 Establishment of new pollutant control laws, regulations, standards and system

Formulate laws and regulations. In the field of chemical substance management, the EU is currently the most improved region in terms of chemical substance control and management system in the world. By the basic law of chemical substance management, "Regulation on Registration, Evaluation, Authorization and Restriction of Chemicals", it has fully implemented the registration, evaluation, licensing and restriction system within the EU. Products that are not applicable to the regulation will be managed according to special decrees respectively^[154].

Revisions to relevant standards. Japan revised its drinking water quality standards in 2015, adding five new endocrine disruptors as water quality indicators and setting stricter limits. The EU revised its standard for biological pesticide endocrine disruptors in 2018, imposing more stringent requirements for the determination and use of endocrine disruptors; In March 2023, the newly revised "Classification, Labeling and Packaging Regulation for Substances and Mixtures in the European Union" was officially released, introducing new hazard categories such as endocrine-disrupting properties and persistent accumulative and migratory substances, applicable to a variety of chemical products including industrial chemicals, raw materials of daily chemicals, pesticides, etc., to ensure higher levels of protection for human health and ecological environment.

8.4.2 Building a Multi-party Coordination Mechanism

Establishment of cooperation and coordination mechanisms at the national level and among international organizations. In 1996, the United States Environmental Protection Agency established the Endocrine Disruptor Screening and Testing Advisory Committee, whose members came from various levels of government and society to coordinate endocrine disruptor screening and testing activities. In the same year, OECD established the Working Group on National Coordinators for the Test Guidelines Program and the Advisory Group on the Testing and Assessment of Endocrine Disrupters to coordinate member states in conducting risk prevention work related to endocrine disruptors. In 1997, Japan established the Endocrine Disruptor Committee to coordinate domestic research on endocrine disruptors. In 2014, UNEP set up the Consultative Group on Environmental Exposure and Effects of Endocrine Disruptors, jointly participated by government and non-governmental organizations, responsible for strategic and policy research on transnational prevention and control of endocrine disruptors. In terms of microplastic governance, in 2010, Argentina and Uruguay jointly established the Maritime Frontline Technical Committee with the aim of strengthening cross-border control over microplastic sources. These cooperation and coordination mechanisms at the national level and among international organizations help ensure systematic monitoring and response to new pollutants, safeguarding public health and environmental safety.

8.4.3 Conducting Multi-level Risk Assessment and Monitoring of New Contaminants

Establish a screening, evaluation and monitoring framework. Since the 1990s, OECD, the European Union, the United States, Japan and others have successively established basic frameworks for screening and monitoring of endocrine disruptors and constructed two-level assessment frameworks. Since 2002, OECD has built a five-level endocrine disruptor assessment framework including "collection of existing information - in vitro experiments - simple in vivo experiments - information verification - complex in vivo experiments" to guide member states in assessing the risks of endocrine disruptors. In May 2001, the international community adopted the Convention, taking global actions against toxic and harmful chemicals such as POPs. The existing mechanism for assessing POPs under the Convention, including the review criteria for POPs substances, the review process, the Review Committee, etc., provides references for establishing screening and review standards and procedures for new pollutants, thereby enabling the rapid establishment of a feasible and effective assessment mechanism for new pollutants^[1672].

Publish test guidelines and lists. The United States Environmental Protection Agency published 14 test guidelines for implementing tiered screening in 2008, and then published chemical test lists in 2009 and 2013 respectively.

A monitoring programme should be set up and implemented. Among the 43 member States of the European Convention on Long-range Transboundary Air Pollution (LRTAP), which was formed under the framework of the European Monitoring and Evaluation Programme (EMEP) and came into effect in 1988, 24 member States have established a total of 100 new POPs monitoring sites to implement monitoring activities for understanding the development trends of new POPs in Europe. The European Commission adopted the Endocrine Disruptors Strategy in 1999, which included establishing a monitoring programme to estimate the exposure and effects of endocrine disruptors on the priority list.

8.4.4 Attention to Scientific Research on New Pollutant Control

For a long time, developed countries and regions such as the United States and Europe have carried out many research works on endocrine disruptors, involving the identification, screening, hazard testing and other aspects of endocrine disruptors. In recent years, international organizations and developed countries have shifted their research focus to persistent toxic substances (PTS, including POPs, toxic organic metal compounds and typical heavy metals, etc.), conducting research on their ecological toxicity, health hazards, environmental risks, formation mechanisms, migration transformation and emission reduction, control, disposal and replacement technologies, etc.

8.5 Problems and Suggestions of New Contaminants Environmental Management in China

In November 2020, the Central Committee of the Communist Party of China and the State Council proposed in "The Proposal of the Central Committee of the Communist Party of China on Formulating the 14th Five-Year Plan for National Economic and Social Development and the Long-Term Goals for 2035" that we should continue to improve environmental quality and attach importance to the governance of new pollutants. In recent years, our country has put forward clear requirements and made a series of major decisions and deployments regarding the governance of new pollutants, comprehensively carrying out a series of governance measures such as screening and monitoring, precise identification, scientific assessment, and environmental risk control of new pollutants. The governance of new pollutants has become the focus of environmental protection work at present and for a period of time in the future. However, overall, the governance of new pollutants in our country started relatively late and is still in the development stage, with many shortcomings. In the future, we should actively draw on international experience, address practical problems encountered during the governance of new pollutants in our country, and promote the process of new pollutant environmental management by combining pollution levels and governance levels. Further improve a series of regulatory measures in policies, technologies, and scientific research related to new pollutant governance, and comprehensively deploy work on the governance of new pollutants.

8.5.1 Strengthen top-level design, improve the construction of laws, regulations, and standards

The analysis of the 2022 Global Environmental Performance Index (EPI) report shows that among the 180 countries and regions participating in the evaluation, China scored 28.4 points (out of a maximum of 100) and ranked 160th (https://epi.yale.edu/). The EPI is a quantitative measure of environmental performance in national policies, and China's low ranking to some extent highlights the weakness of current environmental management and the severity of implementing environmental governance for pollutants. Additionally, there is a strong positive correlation between EPI scores and per capita GDP, indicating that countries do not have to sacrifice environmental sustainability for economic prosperity. Indicators of good governance, including sound laws and fair enforcement of regulations, are closely related to high EPI scores. However, legislative gaps remain in the governance of new pollutants such as persistent pollutants, endocrine disruptors, antibiotics, and microplastics, and there is still a lack of industry emission standards and comprehensive laws and regulations. Therefore, it is recommended to first carry out an environmental information survey on new pollutants. A national strategic plan should be implemented as soon as possible to investigate the current status, production, use, and emission inventory of new pollutants, understand their distribution, accelerate the formulation and supplementation of management lists for new pollutants, and encourage local areas to formulate supplementary lists and management plans suitable for their region to address new pollutants. Second, specialized laws and regulations on new pollutant management should be issued as soon as possible. For example, relevant clauses for the prevention and control of new pollutants should be added to laws concerning air, water, and soil pollution prevention and control, and legal authorization should be granted for mandatory elimination and restricted use measures for new pollutants, enabling the implementation of new pollutant prevention and control through legal means, providing priority order for subsequent governance and supervision of new pollutants. Third, the discharge permit system and clean production audit system should be improved, clearly defining emission limit standards and total emissions. Exploratory work should be carried out to establish environmental monitoring and analysis methods and monitoring networks for new pollutants. Pilot projects for environmental investigation and monitoring of new pollutants should be established in key industries and regions involving chemicals, pesticides, breeding, and plastic products, emphasizing the importance of preventing new pollutant emissions from the source. National and local policy standards should be improved, and the formulation of industry standards for production, use, and emissions should be promoted.

8.5.2 Clarify responsibilities and establish a cross-departmental and cross-regional collaboration mechanism

In recent years, the ecological environment department has been actively engaged in related work on new pollutant management, such as systematic planning for new pollutant governance, statistics on dioxins and chlorinated paraffins as persistent organic pollutants (POPs), control of mercury or mercury compounds under international environmental treaties on chemicals, and antibiotic investigations in key areas like drinking water sources and marine aquaculture zones. However, new pollutant environmental management is a systemic project involving coordination among multiple departments. It has a long industrial chain with overlapping functions. Currently, there still lacks overall coordination ability among different departments, with unclear responsibilities and inadequate supervision. Therefore, cross-regional collaborative governance of new pollutants should first strengthen the overall leadership by governments at all levels, establishing a "New Pollutants Governance Coordination Group." A cross-departmental coordination mechanism should be established with the Ministry of Ecology and Environment taking the lead, and relevant departments including the National Development and Reform Commission, Ministry of Agriculture and Rural Affairs, Ministry of Science and Technology, Ministry of Industry and Information Technology, Ministry of Finance, National Health Commission, State Administration for Market Regulation, General Administration of Customs, etc., jointly participating. Responsibilities should be clearly defined to promote integrated governance of new pollutants and industrial development, building a social management mechanism and model for new pollutant environmental risk prevention from institutional, technical, and societal aspects. This will form a unified vertical and horizontal governance system for new pollutant management that combines internal and external efforts and involves multi-party participation. Secondly, the advantages of each province, city, or region should be fully utilized following the principle of national coordination, provincial responsibility, and municipal and county implementation. A cross-regional new pollutant governance and control mechanism should be established, adhering to the principle of "jointly protecting the environment without large-scale development." Strengthening joint prevention and control of new pollutants should be emphasized. For example, in June 2023, the Sichuan Provincial Department of Ecology and Environment, Chongqing Municipal Bureau of Ecology and Environment, and the Technical Center for Solid Waste and Chemicals Management under the Ministry of Ecology and Environment jointly signed the first inter-provincial new pollutant governance joint prevention and control mechanism in China—the "Agreement on Establishing Joint Prevention and Control Mechanism for New Pollutants Environmental Risk in Sichuan and Chongqing." This effectively leverages the technical resource advantages of all parties to jointly advance joint investigations into new pollutant governance, continuously sharing and optimizing cross-regional control outcomes, enhancing the systematic and precise level of new pollutant environmental management systems. Thirdly, cities and counties, as the monitoring entities for new pollutants, have significant gaps in technical capabilities. Cooperation between environmental monitoring departments at the city and county levels and research institutes and universities should be strengthened to fully utilize the advantages of scientific research platforms, improving the monitoring technology capabilities of cities and counties.

8.5.3 Strengthen monitoring and evaluation to grasp the baseline of new pollutant pollution

In recent years, neither national nor comprehensive pollution source censuses have involved new pollutants. Currently, the low content and dispersed distribution of new pollutants are observed, and their alternatives keep emerging. The types of monitoring need continuous dynamic expansion, and different compounds within the same category exhibit significant differences in physicochemical properties. More advanced detection technologies are required, and the high cost of monitoring has resulted in a lack of clarity regarding the current status, production volume, and emission situation of new pollutants in China. Consequently, it is difficult to fully grasp the basic situation of new pollutants in China, posing risks that remain undefined. Therefore, the following suggestions are proposed. First, a comprehensive survey on new pollutants should be conducted to understand the pollution sources across various watersheds, regions, and industries. Basic information such as the types, quantities, and uses of typical new pollutants in industrial production, agricultural activities, and daily life should be clarified. Relevant departments must identify key areas and compile a priority assessment chemical substance list based on the national priority assessment plan for new pollutants. Further detailed investigations should be carried out on production, processing use, environmental discharge quantity and pathways, hazard characteristics, and sensitive populations. Second, relevant departments should combine the data from the survey to create a spatial distribution map of pollution loads. The pollution and diffusion characteristics of new pollutants should be sorted and analyzed. By relying on the existing ecological environment monitoring network, a specialized spatial database serving pollution source investigation should be established. This will enable full-chain monitoring of pollution source information from front-end input, network transmission, data storage, and back-end management. A key controlled new pollutant list should be dynamically released. Third, research and development of related technologies for new pollutant treatment should be promoted. Typical new pollutant monitoring equipment should be reasonably configured to expand the monitoring coverage range. The monitoring level of new pollutants should be effectively improved. Key laboratories for new pollutant treatment should be built rapidly, and professional training for technical personnel in new pollutant monitoring should be conducted to address the "bottleneck" issues in key technologies for new pollutant monitoring and treatment.

8.5.4 Focus on Basic Research to Provide Technical Support for Accurate Pollution Control

Our country has relatively weak research foundations for new pollutant treatment, particularly with regard to the environmental health risks and mechanisms of harm, non-targeted environmental monitoring technologies, local toxicity biological parameters, and green alternatives. These deficiencies severely restrict the progress of our country's work in new pollutant treatment. Therefore, we should focus on the following four aspects. 1) We must adhere to the concept of full life-cycle environmental risk management, accelerate in-depth research on the migration and transformation, pollution characteristics and ecological toxicity, as well as human exposure and health risks of new pollutants such as POPs, endocrine disruptors, antibiotics, and microplastics. Strengthen basic scientific research in relevant fields, comprehensively enhance understanding of the hazards of new pollutants, and achieve positive interaction and integrated development between basic research and applied research. 2) Conduct research on the methods for studying the environmental emission characteristics and source emission amounts of new pollutants, carry out studies on exposure scenario construction and risk assessment technologies, build a new pollutant exposure model based on the characteristics of our population (including parameters such as exposure scenarios, contact concentrations, and exposure pathways), and clarify the health risk thresholds and environmental health benchmarks of typical new pollutants. 3) Accelerate the research and development of treatment and green alternative technologies. With the removal of seriously harmful levels of new pollutants as the orientation, conduct research on the environmental degradation processes and mechanisms of typical new pollutants, and promote the development and application of collaborative removal and green alternative technologies for key controlled new pollutants. 4) Make full use of technological means, such as constructing high-throughput virtual screening technology based on computational toxicology, through multi-source collaborative analysis of big data, to conduct real-time tracking, tracing, and dynamic monitoring of new pollutants in various regions, achieving precise control and scientific governance of new pollutants.

8.5.5 Strengthening publicity and education to stimulate the public's enthusiasm for participating in environmental governance

At present, the treatment of new pollutants in our country is still in its infancy. Both the state and local governments have insufficient understanding and attention to the treatment of new pollutants, which leads to inadequate public participation. Therefore, the first priority is to strengthen publicity work on new pollutants. Especially, we should carry out multi-faceted and three-dimensional publicity regarding the types, hazards, sources, and prevention and control measures of new pollutants, making full use of traditional media and new media. We should also do a good job in policy and regulation training, actively conduct popular science publicity and education on the governance of new pollutants, give full play to the role of social opinion supervision, and guide the public to scientifically understand the environmental risks of new pollutants. Secondly, organize activities related to the governance of new pollutants in schools and communities, using holidays such as Arbor Day and Environment Day as opportunities to carry out a series of publicity campaigns with the theme of "governance of new pollutants." Through methods such as shooting promotional videos, producing display boards on the theme of new pollutant governance, and distributing promotional materials, we can create an atmosphere where everyone participates in environmental governance and supports it, effectively promoting the work of new pollutant management. Thirdly, establish and improve incentive systems for participating in the governance of new pollutants. For example, the government can cooperate with some enterprises and social welfare organizations to set up environmental protection funds related to the governance of new pollutants, adopt economic subsidies and other measures to stimulate public enthusiasm for participation, and effectively promote and standardize the public's involvement in new pollutant management work.

In summary, the governance of new pollutants is a task with long-term, dynamic, and complex characteristics. It is one of the main challenges in building a beautiful China where humanity and nature coexist harmoniously, but it is also a driver for high-level protection and high-quality development. In the face of new situations and challenges, we must adhere to addressing prominent issues in new pollutant governance as a powerful tool to measure work effectiveness. We must always protect the ecological environment with the strictest systems and the most rigorous rule of law, strengthen complementary system construction and scientific and technological innovation, accelerate the improvement of relevant legal and regulatory systems, effectively promote new pollutant governance, and fully embark on a new journey to build a beautiful China.

9 Key Scientific Issues and Outlook

9.1 New Contaminants Are Not Fully Understood

New pollutants have a wide range of sources and involve many fields such as industrial production and life consumption, as well as numerous industries including medicine, chemicals, agricultural planting, aquaculture, textiles, construction, plastic processing, automobiles, aerospace, electronics, firefighting foam, waste incineration, etc. In addition, new pollutants may also originate from unintentional production and environmental transformation/degradation processes^[1673]. New pollutants involve extensive industries, long industrial chains, and various types, with low content and wide range when released into the environment, and they are also stealthy and difficult to detect^[1674,1675], which is an important reason for the unclear bottom line of new pollutants.

No national-scale investigation on the pollution status and source emissions of new pollutants has been carried out in China yet^[1676], which makes it difficult to fully understand the environmental distribution characteristics of new pollutants in China and provide data support for the governance of new pollutants, especially the support from big data. This poses a challenge to the investigation and research of China's new pollutant inventory^[1676,1677]. On one hand, new pollutants often exist in the environment at trace levels, requiring high sensitivity in detection techniques and methods. The emergence and entry of new pollutants and their substitutes into the environment necessitate continuous updates and upgrades of methods and technologies to meet the practical needs of dynamically expanding the monitoring categories of new pollutants. These reasons make the detection of new pollutants difficult and costly. On the other hand, the standard system for identifying and detecting new pollutants has not yet been established. Only some new pollutants have local or national standards, while most new pollutants' monitoring methods are self-established in laboratories. There are significant differences in method parameters, and they lack strong universality^{[1678⇓~1680]}, making it impossible to carry out large-scale and systematic monitoring work^[1679,1680].

At present, there is an urgent need to carry out environmental information surveys of chemicals, improve the monitoring system for new pollutants, build information sharing platforms, strengthen monitoring cooperation and division of labor, and help solve the problem of "unclear bottom line and unclear situation" of new pollutants to support precise governance of new pollutants^[1681]. First, we should organize the screening of new pollutant types that are currently in production and use, have higher concentrations and detection rates in environmental media, and pose significant potential environmental and health risks. Focus on high-concern, high-production (usage), and high-hazard new pollutants at home and abroad, and investigate various pollution sources^[1682]. Second, develop detection technologies and upgrade certain technologies based on China's current status of new pollutants, establish unified detection methods and standards, cultivate professional technical talents and allocate them reasonably, include typical new pollutants in routine environmental monitoring, conduct regular monitoring, establish a system for testing capability inspection and certification, strengthen certification of new pollutant testing institutions, enhance monitoring capabilities, and conduct research on high-throughput, high-coverage non-targeted analysis technology^[1681]. Third, carry out investigation work on new pollutants. To address the issue of unclear emissions and sources of new pollutants, select key areas and main river basins to conduct pilot investigations of new pollutants. Focus on clarifying the production, sales, and usage of products containing new pollutants in industrial and agricultural production and residents' lives. On this basis, summarize and sort out pilot experiences and gradually carry out nationwide investigations, investigate the basic situation of pollution sources by region, river basin, or industry, determine the amount of typical new pollutant emissions and pollution control situations, draw spatial distribution maps of pollution loads, establish files and information databases of key pollution sources, build a digital and intelligent full-process traceability and supervision system, and create a specialized service platform for new pollutant governance to provide support for comprehensive cross-industry and cross-regional chain governance^[1683]. Fourth, establish a full-chain inspection system for products containing new pollutants in key industries, formulate and improve relevant product quality testing standards, establish a risk assessment plan for new pollutants, strengthen environmental risk assessments of key new pollutants, and dynamically update the list of key controlled new pollutants^[1684].

9.2 Environmental and Ecological Toxicological Effects of Low-Dose Long-Term Exposure

The distribution concentrations of new contaminants in the environment are generally low, and most new contaminants have environmental persistence and bioaccumulation, making them difficult to degrade in the environment and prone to long-term accumulation in the environment and organisms^[1682,1685]. They may cause significant harm to biological reproductive systems, nervous systems, endocrine systems, immune systems, and multiple organ systems. However, the health risks caused by low-dose, long-term exposure to new contaminants are still unclear, and the toxicity thresholds have yet to be determined. For example, PFAS can persistently remain in the environment and cause biological toxic effects, posing health risks. The highest concentration of HFPO-TA in the downstream water body of the Xiaoqing River in Shandong was 68.5 ng/mL, with concentrations in fish clearly higher than in other rivers. As HFPO-TA accumulates gradually in aquatic organisms, it can produce various toxic effects on organisms^[1686]. A study on the exposure risk of typical PFAS around a fluorine chemical production plant in Shandong showed that the average daily exposure levels (per bw) through drinking water, indoor dust, and food for C4-7 perfluorocarboxylic acids, PFOA, HFPO-DA, and HFPO-TA were 1605, 457, 52.1, and 231 ng/(kg·d), respectively^[1687]. Environmental fluoride exposure can cause biological toxic effects, and PFOS has reproductive, developmental, and neurotoxic effects on mammals and amphibians, with potential carcinogenicity for high-exposure populations. Low-dose, long-term exposure to PFOA can induce energy metabolism disorders in rodents, over-proliferation of peroxisomes, renal toxicity, and also suppress the immune system, interfere with mitochondrial metabolism, and cause liver cell damage^{[1688⇓~1690]}. In addition, exposure to perfluorinated compounds may be associated with testicular cancer, breast cancer, liver enlargement, and pancreatic tumors^{[1674,1691,1692]}. Given the characteristics of new contaminants being present at low concentrations and persisting in the environment, future research should further clarify the biological toxic effects of new contaminants under conditions of low-dose, long-term exposure to accurately understand and assess the human health risks of environmental exposure to new contaminants.

The current research on the ecological and health impacts of new contaminants is also increasing. For instance, ARGs (antibiotic resistance genes), which are DNA fragments containing genetic information, can be vertically transferred through bacterial parental chromosomes' self-replication, ultimately achieving vertical gene transfer. Besides, the ecological and health risks of ARGs can also result from gene horizontal migration across species. Different organisms can use mobile genetic elements such as plasmids, transposons, and integrons as carriers to spread ARGs among different species via transformation, conjugation, and transduction, leading to the widespread dissemination of ARGs through horizontal gene transfer [sup][1693][/sup].For another example, although low concentrations of environmental estrogens can promote algae growth, high concentrations will reduce the content of photosynthetic pigments in algal cells, disrupt intracellular oxidative balance, induce cytotoxicity and genotoxicity, and ultimately interfere with the community structure and productivity of algae. In addition, the release of organic metabolites by algae and the increase in microcystin levels will affect the safety of the entire aquatic ecosystem [sup][1694][/sup].

9.3 "COMPOSITE EFFECTS OF NEW CONTAMINANTS AND OMICS STUDIES ON HUMAN EXPOSURE"

In most cases, pollutants in the environment do not exist alone but coexist with multiple pollutants and produce combined effects^[1695,1696]. For example, microplastics are not only themselves environmental pollutants but also carriers of other pollutants. The pollutants they carry can come from chemical additives added during plastic manufacturing, as well as from adsorbed and accumulated environmental pollutants^[1697]. As tiny particles, microplastics can "merge" with other chemical pollutants. When ingested by living organisms, both the microplastics themselves and the various pollutants they carry may be released into the organism, producing a combined effect^{[1698⇓⇓~1701]}. Studies have shown that microplastics have negative effects at individual, cellular, and molecular levels. Due to the synergistic effects of microplastics themselves, their additives, and adsorbed substances, they can induce oxidative stress, decreased predation and behavioral responses, reduced energy metabolism, and various adverse reactions such as growth and development, neuroimmune responses, and intestinal damage^[1686]. The combined effects between new pollutants and traditional pollutants should not be overlooked. Studies have shown that the toxicity of tetracycline increases when combined with Fe(III) and Cu(II), decreases when combined with Al(III), while the toxicity of oxytetracycline increases when combined with the three metal ions^[1695]. Additionally, under co-exposure to environmental pollutants bisphenol A and dibutyl phthalate, cell death in HepG2 cells accelerates, accompanied by increased oxidative damage and DNA damage^[1702].

Concerns over the composite effects of new pollutants have gradually drawn attention. However, there are still many problems: 1) New pollutants are diverse in type, and different types or even different isomers of the same type may produce completely different composite effects when interacting with other pollutants or even non-pollutants; 2) Complex external conditions may significantly influence the composite effects of pollutants. For example, natural organic matter, temperature, light conditions, pH values, etc., can affect the adsorption, hydrolysis, photolysis, and redox reactions of pollutants, thereby influencing various composite effects; 3) Biological factors can also interfere with composite effects. For instance, certain microorganisms in the soil may participate in the degradation of some pollutants, thus reducing or altering the composite effects. Taking microplastics as an example, they have three possible toxicological actions: the properties of plastic particles themselves (size effect), the release of pollutants adsorbed on plastics, and the leaching of plastic additives. Studies have shown that microplastic particles may have potential toxicological effects on the intestines of organisms [sup][1682][/sup], but the research on their potential nanomaterial generation and composite effects with other pollutants is still very limited. In the future, based on the toxicological effects of individual pollutants, starting from actual environmental pollutant situations, considering pollutant concentration levels, mixed exposures, complex environmental factors, etc., the mechanisms of composite toxicological effects under new pollutant exposure can be deeply studied by constructing quantitative effect relationship models and other methods.

Human exposure to new contaminants is complex and diverse in terms of ways and pathways, and the impact of new contaminants on health has not been systematically recognized, which brings many challenges to accurately assess human exposure and health risks. The main difficulties currently faced by omics research on human exposure to new contaminants are summarized as follows^[1703]: 1) Insufficient or inaccurate data, for new contaminants, there is still a lack of systematic toxicological data or environmental monitoring data, which cannot provide sufficient data support for subsequent exposome research; 2) Unclear sources and types of new contaminants, humans are simultaneously exposed to multiple pollutants from various environmental media such as food, water, air, and soil, and the toxicological effects of different pollutants vary, and they may interact with each other, increasing the complexity of exposome research; 3) Unclear exposure pathways and doses, there are various ways for humans to be exposed to pollutants, mainly including ingestion, respiration, skin contact, etc., and the exposure doses and bioavailability through different pathways vary greatly, which also increases the difficulty of exposome research on human exposure to new contaminants; 4) Individual differences exist in the exposed population, factors such as gender, age, lifestyle, and health status will affect their exposure and absorption of pollutants, and individual differences also bring a lot of uncertainties to exposome research on human exposure; 5) The integration of internal and external exposures is not tight enough, lacking critical information for mutual validation, etc.

Through the study of 24 per- and polyfluoroalkyl substances in paired blood and urine samples, as well as their corresponding 7-day repeat diet, drinking water, and dust samples from healthy volunteers (n = 20), it was found ^[1704] that PFOA was the major component, followed by 6:2 chlorinated polyfluoroalkyl ether sulfonate (6:2 Cl-PFESA). Isomer analysis and toxicokinetic modeling indicated that approximately 19% of PFOS in humans is derived from its precursors. By comparing and measuring blood concentrations, the bioavailability coefficient of 6:2 Cl-PFESA in food matrices was determined to be 0.186, which suggests that human exposure may have been overestimated compared with commonly used empirical coefficients.

In response to the challenges of omics research on human exposure, future efforts can be made in the following aspects to deepen the research in this field: focusing on basic research, studying the physicochemical properties of new pollutants in depth, clarifying toxic effects and dose-response relationships; based on this theoretical foundation, continue to improve the omics research methods for human exposure to pollutants, develop more precise monitoring and analysis methods, and accurately quantify the dose and rate of human exposure to pollutants; taking into account individual differences and environmental factors to improve the accuracy of assessment; combining multidisciplinary forces including but not limited to environmental science, toxicology, epidemiology, and big data combined with machine learning simulation, revealing the comprehensive impact of pollutants on health through multiple means, and providing a comprehensive perspective for the omics of human exposure to pollutants.

9.4 High-risk Chemicals Control and Green Development Strategies

New pollutants are mostly artificial chemicals. It is extremely important to strengthen the control from the source. The dynamic process of chemical circulation should be paid attention to during the period before and after entering the market, and the responsibility division of production and use enterprises should be clarified while improving the government supervision and public participation mechanism^[1705,1706]. In addition, the international trade control of high-risk chemicals needs to be strengthened to prevent pollution transfer caused by cross-border trade^[1707].

In addition to strict dynamic control of chemicals already produced, it is more necessary to encourage the optimization of production processes to reduce the use or generation of toxic chemicals associated with new pollutants at the source. The development of alternative materials that can reduce the discharge of new pollutants from the source, and the promotion and application of various pollution control technologies and alternative materials in key industries, key industrial parks, and enterprises should be carried out to cut pollution discharge from the source.

Green development strategies can support the development of high-performance and low-risk alternative chemicals by fully considering the important intrinsic properties of molecules from the initial design stage to determine whether the production process and the produced chemicals are renewable, toxic, or persistent^[1708,1709]. The alternatives developed using this method are often more environmentally friendly. However, some alternatives may retain similar toxic effects due to their molecular structure and physicochemical properties being similar to those of the substances they replace. For example, some PFOS alternatives still retain the toxic effect characteristics similar to PFOS^{[1682,1710,1711]}. Therefore, it is necessary to continuously strengthen independent development capabilities, advocate green production, encourage technological innovation, pool resources from multiple parties to develop efficient, low-toxicity, environmentally friendly, and economically reasonable alternatives and alternative technologies, promote and apply various pollution control technologies and alternative materials in key industries, key industrial parks, and enterprises, reduce pollution emissions at the source, and accelerate the restriction and elimination of high-risk chemicals^[1712,1713].

9.5 "Construction of an Environment Sample and Human Exposure Database Based on Machine Learning"

Establishing a complete and intelligent database for emerging pollutants' environmental safety is the foundation for carrying out environmental health risk assessment and control. Since the 1980s, developed countries and organizations such as the United States, the European Union, and Japan have successively developed a series of chemical management and health risk-related databases, including the ECOTOX ecological toxicological database, the RAIS Risk Assessment Information System, the HHBP pesticide human health benchmark database, the OPPALB pesticide aquatic life benchmark database, the CHRIP Chemical Risk Information Platform, the eChemPortal database, and the QSAR database, covering information on more than tens of thousands or even hundreds of thousands of chemicals. These databases include data on their physicochemical properties, ecological toxicity, environmental fate, toxicokinetics, and exposure risks, providing important basic data support for the construction of our country's database^{[1714⇓⇓⇓⇓⇓~1720]}. However, considering that foreign databases are based on local environmental samples and human exposure characteristics in those regions, they do not fully conform to China's actual conditions. Direct borrowing may lead to significant errors and has issues of limited data and delayed updates, which could hinder the development of new pollutant risk control work in China. Therefore, there is an urgent need to obtain and collect samples and health risk toxicity data of native research objects, improve and integrate relevant parameters, and establish a national-level coordinated database platform suitable for China's national conditions^{[1721⇓~1723]}. Among them, machine learning methods combined with big data analysis can provide important technical support. By using machine learning algorithms to build and optimize models with large amounts of data from existing databases and actual monitoring, it will enable more precise predictions and judgments, significantly improving the dynamic and efficient levels of new pollutant risk warnings and pollution prevention and control^[1724,1725].

9.6 "Construction of Technological Support Capacity for New Contaminants Control Actions in China"

The high difficulty in environmental risk control of new pollutants puts forward higher requirements for the scientific and technological support capacity. The types and emission sources of new pollutants are diverse, their toxic mechanisms are complex, the migration transformation and fate processes in the environment are diversified, the pollution control is difficult, and there is a weak research foundation for the replacement, treatment, and emission reduction technologies of related chemicals. If the scientific and technological support is insufficient, we will be more passive in international negotiations and domestic industrial and agricultural alternative technology development, easily being "strangled" by developed countries, which seriously restricts the scientific assessment and precise control of new pollutant environmental risks in China^[1684,1706]. Therefore, it is necessary to further increase financial input, strengthen the construction of research platforms and bases for new pollutants, and enhance the scientific and technological support capacity. Conduct fundamental research on ecological risk prevention and control of new pollutants that fits the national conditions and industrial characteristics of China, especially focusing on tracking and tracing, environmental fate, hazard identification, exposure prediction, health risk assessment, green alternatives, pollution control, and monitoring detection technology research, so as to improve the scientific research level of new pollutants in China. This is not only the basis and driving force for promoting the protection of China's ecological environment and green technological development, but also a major demand for achieving green and sustainable social and economic development in China^[1714,1724].

9.7 Coordination and development of ecological environment monitoring capabilities, fine-grained support for new pollutant treatment, and construction of targeted risk prevention and pollution control systems for new pollutants

At present, the national environmental monitoring system still focuses on the monitoring of conventional pollutant indicators, while the monitoring of new pollutants is just beginning. Although targets and requirements for the monitoring of new pollutants have been proposed, compared with conventional pollutants, there is still a lack of effective deployment in terms of specific implementation methods such as monitoring indicators, technical approaches, and evaluation details. Moreover, due to significant differences in ecological and environmental monitoring levels and personnel forces across different regions, the monitoring capabilities for new pollutants vary unevenly and need further balanced development. Based on the existing environmental monitoring sites, it is urgent to accelerate the construction of a batch of typical facilities and equipment for monitoring new pollutants. Key areas, important river-lake sections, and drinking water sources should be selected as priority pilot projects, gradually conducting a comprehensive survey of new pollutants to establish a national-level monitoring system for pollution levels of new pollutants, providing technical support for comprehensively understanding the emissions and pollution status of new pollutants^[1724,1726].

The situation of environmental and health risks of new pollutants in our country is complicated. It not only faces common problems of international new pollutants but also has special problems of Chinese new pollutants. At present, the refinement support for the governance of new pollutants is insufficient. Therefore, it is urgent to build a risk prevention and pollution control system for new pollutants with Chinese characteristics. First of all, improve laws, regulations, and standards system. Gradually increase clauses on the governance of new pollutants in relevant environmental protection laws and regulations, involving their production and use regulations, emission standards, and monitoring specifications. Secondly, strengthen strategic planning for the prevention and control of new pollutants, formulate and implement action plans for the prevention and control of new pollutants, and clarify emission reduction targets and control measures. At the same time, improve relevant lists. Dynamically update the "List of Priority Control Chemicals", the "List of Toxic Chemicals Strictly Restricted in China", the "List of Toxic and Hazardous Air Pollutants", the "List of Toxic and Hazardous Water Pollutants", and the "List of Key Controlled Soil Toxic and Hazardous Substances" based on different risk assessment results of new pollutants. Finally, establish a warning system for new pollutants. When the discharge of new pollutants exceeds a certain threshold, activate the warning, order related enterprises to reduce or suspend production, and promote the construction of the monitoring and early warning system for environmental and health risks of new pollutants¹⁷²⁴^[1713].

With the enhancement of comprehensive national strength and the increasing desire of the people for a better life, China has entered a new stage of focusing on the governance of new pollutants. After the General Office of the State Council released the "Action Plan for the Governance of New Pollutants" in May 2022, the governance of new pollutants was rapidly promoted nationwide. However, it cannot be denied that China's governance of new pollutants is still in its infancy, facing difficulties and challenges such as high difficulty in governance, complex technology, and insufficient scientific cognition. There is an urgent need to establish a relatively complete mechanism for scientific and technological innovation to promote the research and application of new pollutant governance technologies, improve the effectiveness of new pollutant governance, and provide effective guarantees for building a healthy China and a beautiful China.

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1 Concept, Types, and Characteristics of New Contaminants

1.1 Definition and Basic Characteristics of New Contaminants

表1 《2023年重点管控新污染物清单》中列出的14种化学品[10]

1.2 Typical New Contaminants

1.2.1 Perfluorinated Compounds

1.2.2 Brominated Flame Retardants

表2 各类环境介质和生物样品中 BFRs 的浓度[55]

1.2.3 Chlorinated Paraffins

图1 2019 年中国 SCCPs 和 MCCPs 从生产、使用到排放的质量平衡分析[60]

图2 世界各地区(A)与中国(B)CPs工业产品中S/M/LCCPs的分布情况[81]

1.2.4 Environmental Endocrine Disruptors

1.2.5 Pharmaceuticals and Personal Care Products

图3 PPCPs的源和汇[98]

1.2.5.1 Pollution Characteristics and Levels of PPCPs

1.2.5.2 Ecological and Health Risks of PPCPs

图4 再生水浇灌的土壤具有一定的健康风险[129]

1.2.5.3 Analysis and Removal of PPCPs

1.2.6 Microplastics

1.2.6.1 Definition and Classification of Microplastics

图5 基于聚合物单体来源和生物可降解性分类的常见微塑料类型[141]

1.2.6.2 Environmental Distribution and Abundance of Microplastics

1.2.6.3 Environmental Microplastic Characteristics and Effects

1.2.7 Antibiotics and Antimicrobial Resistance Genes

1.3 New Scientific Issues in the Study of Emerging Contaminants

2 New Contaminants: Production, Usage, and Emissions

2.1 Production, Use, and Emissions of POPs

2.1.1 Organic Chlorine-based POPs

图6 1930—2020年全球商品级 CPs 年生产量、使用量和5个行业的应用比例(单位:百万t/a)[179]

图7 2019年中国SCCPs和MCCPs从生产、使用到排放的质量平衡(单位:kt)[180]

2.1.2 Organic Brominated POPs

图8 1970—2020年全球多溴二苯醚生产和使用趋势:(a)多溴二苯醚产量;(b)十溴二苯醚使用量;(c)八溴二苯醚混合物使用量;(d)五溴二苯醚使用量,改绘自Abbasi 等[187]

图9 基于动态物质流分析与多介质环境归趋模拟的中国HBCD的全生命周期排放估算:(a)生产、加工、使用和废物过程的排放;(b)向大气、水和土壤的排放[192]

2.1.3 Organic Fluorinated POPs

图10 中国各地区电镀、消防和农药行业PFOS环境排放量分布[204]

图11 2004—2012年间中国PFOA生产和排放状况[207]

2.1.4 Flame Retardant-free POPs

2.2 Production, Use, and Discharge of Antibiotics

2.2.1 Production and Use of Antibiotics

图12 中国抗生素生产和使用量(2013—2020年)

图13 2018年全球人用抗生素使用量,注:DDD为限定日剂量

图14 2020年全球动物用抗生素使用量,PCU(population correction units)是一个标准化指标,表示一个国家的动物总数(活的或屠宰的),乘以治疗时动物的平均质量

2.2.2 Antibiotic Discharge and Pollution Concentration

图15 全球主要国家水环境中抗生素质量浓度水平

表3 全球水环境中各类别抗生素质量浓度水平

2.3 Production, Use, and Emission of Endocrine Disruptors

2.3.1 Use of EDCs

图16 EDCs类化学品在各类别产品中的使用[237,238]

图17 EDCs类化学品在产品中的功用(同一类化学品用同一颜色展示)[238]

2.3.1.1 Monomers and Intermediates

2.3.1.2 Processing Aids

图18 产品中EDCs类化学品的含量范围[241]

2.3.1.3 Additive

2.3.2 Production Situation of EDCs

图19 EDCs类化学品年产量以及经济合作与发展组织高产量化学品清单中涵盖的物质(图上数字代表生产年份;为方便展示分成4个子图)[238,242]

2.3.3 Release of EDCs

2.3.3.1 Release Source

2.3.3.2 Release Amount

图20 EDCs类化学品的环境释放量[239,240]

3 Identification and Characterization of New Contaminants

3.1 Non-targeted Analysis Techniques for the Identification and Characterization of New Contaminants

3.1.1 Preprocessing Techniques

3.1.2 Instrumental Analysis Techniques

3.1.2.1 High-Resolution Mass Spectrometry

3.1.2.2 Scanning Method

3.1.2.3 Ion Source: Types and Basic Principles of Parameter Setting

3.2 New Contaminants Identification and Characterization: Data Analysis Techniques

3.2.1 Data Processing of Mass Spectrometry

3.2.1.1 Screening Strategy for the List

表4 常见的数据库清单信息

表5 常见转化产物清单生成平台

3.2.1.2 "Structure Feature-Oriented Analysis Strategy"

3.2.1.3 Knowledge-Oriented Analysis Strategy

3.2.2 Structural Identification of Mass Spectrometry Data

3.2.2.1 Confidence Level of Structural Identification

3.2.2.2 Secondary Spectrum Library Retrieval

3.2.2.3 Structure Prediction Software

3.3 Application of New Contaminant Identification and Detection Technologies

表1 《2023年重点管控新污染物清单》中列出的14种化学品^[10]

表2 各类环境介质和生物样品中 BFRs 的浓度^[55]

图1 2019 年中国 SCCPs 和 MCCPs 从生产、使用到排放的质量平衡分析^[60]

图2 世界各地区(A)与中国(B)CPs工业产品中S/M/LCCPs的分布情况^[81]

图3 PPCPs的源和汇^[98]

图4 再生水浇灌的土壤具有一定的健康风险^[129]

图5 基于聚合物单体来源和生物可降解性分类的常见微塑料类型^[141]

图6 1930—2020年全球商品级 CPs 年生产量、使用量和5个行业的应用比例(单位:百万t/a)^[179]

图7 2019年中国SCCPs和MCCPs从生产、使用到排放的质量平衡(单位:kt)^[180]

图8 1970—2020年全球多溴二苯醚生产和使用趋势:(a)多溴二苯醚产量;(b)十溴二苯醚使用量;(c)八溴二苯醚混合物使用量;(d)五溴二苯醚使用量,改绘自Abbasi 等^[187]

图9 基于动态物质流分析与多介质环境归趋模拟的中国HBCD的全生命周期排放估算:(a)生产、加工、使用和废物过程的排放;(b)向大气、水和土壤的排放^[192]

图10 中国各地区电镀、消防和农药行业PFOS环境排放量分布^[204]

图11 2004—2012年间中国PFOA生产和排放状况^[207]

图16 EDCs类化学品在各类别产品中的使用^[237,238]

图17 EDCs类化学品在产品中的功用(同一类化学品用同一颜色展示)^[238]

图18 产品中EDCs类化学品的含量范围^[241]

图19 EDCs类化学品年产量以及经济合作与发展组织高产量化学品清单中涵盖的物质(图上数字代表生产年份;为方便展示分成4个子图)^[238,242]

图20 EDCs类化学品的环境释放量^[239,240]

图22 文献报道的PFCs中的log BAF与碳链长度的关系^{[445⇓⇓⇓⇓⇓⇓⇓⇓⇓⇓~456]}

图23 文献报道的SCCPs中的log BAF与碳链长度的关系^[473⇓~475]

图25 不同营养级生物体内检出的新型PFAS及最大检出浓度^{[799,803,807⇓⇓⇓⇓⇓ ~813]}