Ecological Risks of Emerging Pollutants in Urbanizing Regions 9811996296, 9789811996290

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Yonglong Lu Pei Wang Jingjing Yuan   Editors

Ecological Risks of Emerging Pollutants in Urbanizing Regions

Ecological Risks of Emerging Pollutants in Urbanizing Regions

Yonglong Lu · Pei Wang · Jingjing Yuan Editors

Ecological Risks of Emerging Pollutants in Urbanizing Regions

Editors Yonglong Lu State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems, International Institute for Sustainability Science, College of the Environment and Ecology Xiamen University Fujian, China

Pei Wang Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems and Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies College of the Environment and Ecology Xiamen University Fujian, China

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing, China Jingjing Yuan Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems and Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies College of the Environment and Ecology Xiamen University Fujian, China

ISBN 978-981-19-9629-0 ISBN 978-981-19-9630-6 (eBook) https://doi.org/10.1007/978-981-19-9630-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Rapid urbanization contributes most of the global economic growth, but urban land expansion and industrial concentration also bring critical challenges for the delivery of global sustainable development goals (SDGs). It has generated many unexpected negative environmental impacts, such as emissions of emerging contaminants. Industrialization is usually but not necessarily coupled with the urbanizing process, making the emission and environmental fate of emerging pollutants change among regions, and require practical investigations. The high-emission sources are of particular concern for the ambient environment and residents. For non-point emission features, general evaluation approaches should be developed to protect the general population and the environment. In this book, we focus primarily on the emerging pollutants brought about by rapid urbanization and industrialization because they are not routinely monitored or regulated in the environment, and the knowledge of their adverse ecological and human health effects and risks is limited. Persistent organic pollutants (POPs), in particular, are of great concern due to their persistence, long-range transport, bioaccumulation, and toxic effects on humans and wildlife. Since 2004, the Stockholm Convention on POPs has been evaluating and listing the most hazardous POPs for global restriction actions, and these require solid scientific evidence and sophisticated laboratory experiments. What are the emerging contaminants as a result of rapid urbanization? How to identify the sources of emerging contaminants? How about their transportation and transformation of these pollutants along the multiple environmental media? What impacts may they have on natural ecosystems? What are their impacts on human health through different exposure pathways? How serious are the pollutants to both ecosystem health and human health? Moreover, what policy instruments are effective for preventing and reducing such contaminants? Traditional pollutants such as metals are still on the list of concern. The current knowledge requires a more integrated perspective, such as the mixture effects with emerging pollutants, multiple compartments, and comprehensive risk assessment. With the emergence of novel pollutants, identifying the priority list of chemicals is fundamental for policymakers to take practical control actions. In this regard, the

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mixed impacts of both emerging contaminants and traditional pollutants will also be explored for pollution control and environmental management. The primary purposes of this book are to characterize the severe pollution patterns of emerging contaminants, including sources, emission effluents, temporal and spatial distributions, multi-media transportation and transformation processes, exposure pathways to ecosystems and humans, and ecological risks; to establish an urbanizing region management concept and how urbanization and its regional ecology have evolved into a more integrated vision; and to decouple the relations between urbanization and emissions of emerging pollutants framed within a broad socio-ecological context considering institutions, policies, and governance. This book is intended to summarize our long-term research on emerging pollutants in urbanizing regions. It was begun with the environmental exposure of the emerging pollutants in Chap. 1; the potential emission sources and emission inventory were discussed in Chap. 2; after emission to the environment, the diffusion pathway of the pollutants also required systematic research, either based on the life-cycle view, which was discussed in Chap. 3, or using simulation modeling approach, which was discussed in Chap. 4; and then, the ecological risk assessment is required, which was discussed in Chap. 5; the manufacturers were identified in large-scale production of certain products and discharged the not fully treated sewage or waste to the environment directly. This brought more interest in a comprehensive investigation into the surrounding environment of the manufacturers, including (1) the soil, irrigation water, and agricultural ecosystem; (2) the sewage receiving rivers and the aquatic ecosystem; (3) the atmospheric environment via the monitoring of air, dust, and rain; (4) the exposure pathway to the residents, like drinking water and various kinds of food. These were discussed in Chap. 6; finally, environmental management and policy implications were presented from a global perspective. In this book, we try to present interesting methods, results, or topics. Emerging pollutants were identified with long-term field observation and monitoring, in-situ sampling, laboratory analysis, and spatial positioning. Emission estimation was conducted with sufficient historical and sampling data, new analytics, sophisticated equations, and high-performance computation. There are several exciting cities, regions, or watershed-scale practical examples of coupling relations among urbanization, industrialization, and emissions, integrating ecology, geography, environmental science, regional science, and governance to address multiple drivers and complex problems across China and even the globe. Emission pathways were explored through life-cycle analysis of chemicals and sampling analysis of pollutants taken from multiple media. Multi-media transportation model was developed to simulate the fates and risks of the emerging pollutants on a regional scale with increasing urbanization. Risk assessment, including identification of hot spot areas and hazards brought by a large set of pollutants in multi-compartments on a regional scale, was carried out through field surveys, risk ranking and screening, and risk characterization. Currently, there are some examples where we are taking an integrated approach to achieve risk management targets based on regional ecological functioning and ecosystem services. Our enhanced understanding of the ecological risks of emerging

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pollutants in urbanizing regions has also provided solid science-based support for the decision-making of rational urbanization in developing regions. All the theories, methods, and case studies presented in this book have been extracted from the publications by the authors that have gone through rigorous international peer review. Spatial distribution, pathways, and flow diagrams of the pollutants and interactions between urbanization and regional pollution are presented with figures and pictures of high visual effect. Data and synthesis are illustrated in tables and figures, which are easy for the readers to understand. Ecological risk characterization and expression are presented with maps using geographic information system (GIS), providing a general profile and spatial variation of risks. The readers are accessible to clear and systematic pictures of the ecological risks of emerging pollutants in urbanizing regions in China. This book will be valuable for senior university students, post-graduates, researchers, fellow academics, environmental NGOs, and regional managers such as urban planners, environmental agency staff, legal regulators, and decision-makers. They will be beneficial from an extensive and integrated synthesis of current knowledge and thinking in ecological risk management of urbanizing regions with the new evolving integrated approach to multi-scale risk identification, characterization, and management. This book also intends to bridge the gap between environmental science and policy and provide interdisciplinary theory, approach, and case studies that will facilitate applications of frontier science in ecology and environmental science to regional social and economic development. Xiamen, China October 2022

Yonglong Lu, Ph.D. Chair Professor of Xiamen University and Distinguished Professor of the Chinese Academy of Sciences (CAS) Fellow of TWAS, Member of Academia Europaea, and Foreign Member of Russian Academy of Sciences

Acknowledgements

The contents of this book represent a series of long-term research efforts over decades, with the engagement of many researchers, experts, and contributors. Most of the graduate students from the Regional Ecological Risk Assessment and Environmental Management Group at the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, and the College of the Environment and Ecology, Xiamen University, have participated in the field investigation and lab experiments, which provided important support for the method development and implementation of the research work. The authors are very grateful for all the financial support from the Ministry of Science and Technology of China, the National Natural Science Foundation of China, the Chinese Academy of Sciences, and Xiamen University. The writing of this book is supported by the National Key R & D Program of China “Coupling Relationship and Regulation Mechanism between Urbanization and Regional Ecology” under Grant No. of 2017YFC0505704, the National Natural Science Foundation of China and UNEP Cooperation Program “Ecological Effects and Sustainable Management of Coastal Mining and Mineral Resource Applications” under Grant No. of 71761147001, and the Key Program of National Natural Science Foundation of China “Impacts and Ecological Risks of Emerging Pollutants on Coastal Ecosystem Functioning” under Grant No. of 42030707. As the editor of the book, I would also like to thank all the co-authors for their dedicated efforts to make the book a success. Yonglong Lu, Ph.D. On behalf of all authors

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Contents

1 Environmental Exposure of Emerging Pollutants in Urbanizing Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pei Wang, Yonglong Lu, Tieyu Wang, Jing Meng, and Yueqing Zhang

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2 Source Identification and Emission Estimation of Emerging Pollutants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuangwei Xie and Yonglong Lu

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3 Environmental Pathways of Emerging Pollutants . . . . . . . . . . . . . . . . . . Zhaoyang Liu, Jing Meng, and Yonglong Lu

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4 Multimedia Modeling of the Fate for Emerging Pollutants . . . . . . . . . . Shuai Song, Shijie Liu, Chao Su, and Yonglong Lu

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5 Ecological Risk Assessment of Emerging Pollutants: Methodology and Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Yajuan Shi, Yonglong Lu, Meng Zhang, Chao Su, Yueqing Zhang, and Andrew C. Johnson 6 Evaluating the Comprehensive Effects of PFAAs Emited from the Fluorochemical Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Pei Wang, Zhaoyang Liu, Hongqiao Su, and Yonglong Lu 7 Environmental Health Policy Implications and Future Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Yonglong Lu, Jingjing Yuan, and Guizhen He

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Editors and Contributors

About the Editors Dr. Yonglong Lu is Chair Professor of Xiamen University and Distinguished Professor of the Chinese Academy of Sciences (CAS); Chair of Regional Ecological Risk Assessment and Environmental Management Group at the College of the Environment and Ecology, Xiamen University and CAS, China. Dr. Lu is a Fellow of TWAS (The World Academy of Sciences); a foreign member of the Academia Europaea (AE); a foreign member of the Russian Academy of Sciences (RAS); a member of 10-member Group of the United Nations Technology Facilitation Mechanism (UN/TFM) appointed by UN Secretary General; past President of Scientific Committee on Problems of the Environment (SCOPE); President of Pacific Science Association (PSA); Member of International Resource Panel, United Nations Environment Program (UNEP/IRP); and former member of Committee on Scientific Planning and Review, International Council for Sciences (ICSU/CSPR). He is the founding Editor-in-Chief of Ecosystem Health and Sustainability, an Associate Editor of Science Advances, and the founder and an Associate Editor of Environmental Development. Dr. Lu has a wide range of research interests, including ecological impacts and risk assessment of emerging pollutants, pollution and climate change interaction, sustainable coastal ecosystems management, urban ecological planning and assessment, and environmental management and policy. As an active sustainability and environmental ecologist, Dr. Lu has published extensively in peerreviewed journals such as Science, Nature, Science Advances, PNAS, Nature Comm., and is a highly cited scientist internationally. Dr. Pei Wang is an Associate Professor at the College of the Environment and Ecology, Xiamen University, China, since October 2020. Before that, he worked at Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. He has been working on the source and fate of Persistent Organic Pollutants (POPs) for years, especially on PFAS. His primary interest is to uncover the dominant emission sources with high concentrations of the pollutants; trace the relationship

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Editors and Contributors

with industry, production, and policy upward; and investigate the environmental distribution, bioaccumulation, and ecological and human health risks downward. Dr. Jingjing Yuan is a Senior Engineer at the College of the Environment and Ecology, Xiamen University, China, since November 2020. Before that, she worked at Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Dr. Yuan is the Managing Editor of Ecosystem Health and Sustainability. Her main research focus is on the sustainability ecology, ecological effects, and environmental management of regional development.

Contributors Guizhen He State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China Andrew C. Johnson Centre for Ecology and Hydrology, Crowmarsh Gifford Wallingford, Oxon, OX, UK Yonglong Lu State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems, International Institute for Sustainability Science, College of the Environment and Ecology, Xiamen University, Fujian, China; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China Shijie Liu State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China; Chinese Research Academy of Environmental Sciences, Beijing, China Zhaoyang Liu State Environmental Protection Key Laboratory of Soil Health and Green Remediation, College of Resources and Environment, Huazhong Agricultural University, Wuhan, China Jing Meng Key Laboratory of Environment Nanotechnology and Health Effects, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China Yajuan Shi State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China Shuai Song State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China Chao Su Institute of Loess Plateau, Shanxi University, Shanxi, China Hongqiao Su Journal of Management World, Beijing, China

Editors and Contributors

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Pei Wang Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems and Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Fujian, China Tieyu Wang Institute of Marine Sciences, Shantou University, Shantou, China Shuangwei Xie China National Offshore Oil Corporation, Beijing, China; State Key Laboratory of Urban and Regional Ecology, Research Center for EcoEnvironmental Sciences, Chinese Academy of Sciences, Beijing, China Jingjing Yuan Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems and Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Fujian, China Meng Zhang State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China Yueqing Zhang Ministry of Ecology and Environment of China, Nanjing Institute of Environmental Sciences, Nanjing, China

Abbreviations

ACFSMC AFDs AFFF AIS APFO AQSIQ ASCV ATL AWV BAF BaP BETR model BETR-UR model BPA CAA CAC CBZ CCC CCSA CIP CPEs CVs DAI DDE DDT DEHP DIN DIP DP EBM

All China Federation of Supply and Marketing Cooperatives Aqueous fluoropolymer dispersions Aqueous fire-fighting foams Accident Inquiry System Ammonium perfluorooctanoate General Administration of Quality Supervision, Inspection, and Quarantine Aquatic food consumption screening values Atenolol Avian wildlife values Bioaccumulation factor Benzo[α]pyrene Berkeley–Trent model BETR-Urban–Rural model Bisphenol A Civil Aviation Administration Chemical Accident Cases Carbamazepine Criteria continuous concentration China Chemical Safety Association Chemical industrial park Commercially produced eggs Coefficients of variation Daily Accidents Information Dichlorodiphenyldichloroethylene Dichlorodiphenyltrichloroethanes Diethylhexyl phthalate Dissolved inorganic nitrogen Dissolved inorganic phosphorus Dechlorane plus Ecosystem-based management xvii

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EC50 ECF EDCs EDI EPBs ERA ERL ERM ERY FCA FEQG Flu FOSAs FOSEs FP FT FTOH GAC HBCDs HCB HCH HCHs HFPO-DA HPEs IC50 IMO IP-PCBs JPCs LAS LC50 LCA LOEC MEANCS MEC MEP MET MIIT MoA MoC MoH MoT MPS MSW MTC MWR

Abbreviations

Median (half maximum) effect concentration Electrochemical fluorination Endocrine disrupting chemicals Estimated daily intake Environmental Protection Bureaus Ecological risk assessment Effect range low Effect range median Erythromycin Fluorocarbon alcohol Federal environmental quality guidelines Fluoranthene Perfluorooctane sulfonamides Perfluorooctane sulfonamidoethanols Fluoropolymer Fluorotelomer Fluorotelomer alcohol General Administration of Customs Hexabromocyclododecanes Hexachlorobenzene Hexachlorocyclohexanes Hexachlorocyclohexanes Hexafluoropropylene oxide-dimer acid Home produced eggs Median (half maximum) inhibitory concentration International Maritime Organization Intentionally produced PCBs Joint probability curves Alkylbenzene sulfonate Median (half maximum) lethal concentration Life-cycle assessment Lowest observed effects concentration Environmental Administration of New Chemical Substances Measured environmental concentration Ministry of Environmental Protection Metoprolol Ministry of Industry and Information Technology Ministry of Agriculture Ministry of Commerce Ministry of Health Ministry of Transport Ministry of Public Security Municipal solid waste Mass transfer coefficient Ministry of Water Resources

Abbreviations

MWWTPs Nap NDRC N-EtFOSAA N-EtFOSE NOEC NOR NP NPC NPX NRCC OCPs OFL PBDEs PCBs PEL PERA PFAAs PFAI PFAS PFBA PFBS PFBSF PFCAs PFDA PFDoDA PFHpA PFHxA PFHxS PFNA PFOA PFOA/PFO PFOS PFPeA PFPEs PFSAs PFUnDA Phen PNEC POPs POSF PPCPs PTFE REACH

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Municipal wastewater treatment plants Naphthalene National Development and Reform Commission 2-(N-ethyl perfluorooctane sulfonamido) acetic acid 2-(N-ethyl-perfluorooctane-sulfonamido) ethanol No observed effects concentration Norfloxacin Nonylphenol National People’s Congress Naproxen National Registration Center for Chemicals Organic chlorinated pesticides Ofloxacin Polybrominated diphenyl ethers Polychlorinated biphenyls Probable effect level Probabilistic ecological risk assessment Perfluoroalkyl acids Perfluoroalkyl iodide Per- and polyfluoroalkyl substances Perfluorobutanoic acid Perfluorobutane sulfonic acid Perfluorobutanesulfonyl fluoride Perfluoroalkyl carboxylic acids Perfluorodecanoic acid Perfluorododecanoic acid Perfluoroheptanoic acid Perfluorohexanoic acid Perfluorohexane sulfonic acid Perfluorononanoic acid Perfluorooctane acids PFOA and its salts Perfluorooctane sulfonic acid Perfluoropentanoic acid Perfluoropolyethers Perfluoroalkane sulfonic acids Perfluoroundecanoic acid Phenanthrene Predicted no-effect concentration Persistent organic pollutants Perfluorooctane sulfonyl fluoride Pharmaceuticals and personal care products Polytetrafluoroethylene Registration, Evaluation, Authorization, and Restriction of chemical substances

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RM RMPERA SAF SAG SAIC SAT SAWS SEPA SMX SSDs TDI TEL TF TFE TM TOC UGRM UP-PCBs WWTPs

Abbreviations

Removal efficiency Regional multi-compartment probabilistic ecological risk assessment State Administration of Forestry State Administration of Grain State Administration for Industry and Commerce State Administration of Taxation State Administration of Work Safety State Environmental Protection Administration Sulfamethoxazole Species sensitivity distributions Tolerable daily intake Threshold effect level Transfer factor Tetrafluoroethylene Telomerization Total organic carbon Urban–rural gradient model Unintentionally produced PCBs Wastewater treatment plants

List of Figures

Fig. 1.1 Fig. 1.2

Fig. 1.3

Fig. 1.4

Fig. 1.5

Fig. 1.6 Fig. 1.7

Location and sites for the studies along the Bohai and Yellow Seas in China and South Korea . . . . . . . . . . . . . . . . . Distributions of organochlorine pesticides (HCHs and DDTs) in sediments along the Bohai Sea and the Yellow Sea in China and South Korea (Note threshold effect level (TEL) and probable effect level (PEL) for HCHs from Feng et al. 2011; effect range low (ERL) and effect range median (ERM) for DDTs from Feng et al. 2011). For the details of the references, please refer to the original article (Meng et al. 2017)) . . . . . . . . . Distributions of PCBs in sediments along the Bohai Sea and the Yellow Sea in China and South Korea (Note ERL and ERM for PCBs from Zhao et al. 2010. For the details of the references, please refer to the original article (Meng et al. 2017)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distributions of a PBDEs and b HBCDs in sediments along the Bohai Sea and the Yellow Sea in China and South Korea (Note federal environmental quality guidelines (FEQG) for PBDEs and HBCDs from Environment Canada (2013) and (2016). For the details of the references, please refer to the original article (Meng et al. 2017)) . . . . . . . . . Distributions of PFOS and PFOA in surface water samples along the Bohai Sea and the Yellow Sea in China and South Korea (Note Avian wildlife values (AWV) and criteria continuous concentration (CCC) for PFOS and PFOA from Giesy et al. (2010). For the details of the references, please refer to the original article (Meng et al. 2017)) . . . . . . . . . Sampling sites (red plots) in the Daling River Basin, China . . . . Concentrations of PFAAs (ng/L) in surface water of the Daling River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Fig. 1.8

Fig. 1.9 Fig. 1.10 Fig. 1.11

Fig. 1.12

Fig. 1.13 Fig. 1.14 Fig. 1.15 Fig. 1.16

Fig. 1.17 Fig. 1.18 Fig. 1.19 Fig. 1.20

Fig. 1.21 Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4

List of Figures

log K d (a), log K OC (b) values for 11 PFAAs, and the trend of the values for PFBA, PFOA, and PFBS (c) from site 1 to site 14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling sites in the∑ Xiaoqing River Basin . . . . . . . . . . . . . . . . . a Concentrations of PFAAs and b relative contributions of individual PFAA in ∑water of the Xiaoqing River Basin . . . . . . a Concentrations of PFAAs and b relative contributions of individual PFAA in surface sediment of the Xiaoqing River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a log KOC values for PFAAs for sediments from X1 to X26 and b variation of log KOC for individual PFAA in sediments from X1 ∑ to X12 in the Xiaoqing River . . . . . . . . . . Concentrations of PFAAs in sediment cores of the Xiaoqing River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling sites around the HBCD producer . . . . . . . . . . . . . . . . . The spatial distribution of ∑HBCD in the soil . . . . . . . . . . . . . . . Transport of HBCD in the water and sediment. a Concentrations of HBCD diastereoisomers in the water and sediment. Columns in blue stand for the water concentration in the order of α-, β- and γ-HBCD from left to right. Columns in orange stand for the sediment concentration in the same order. b Trend of ∑HBCD and diastereoisomer concentration in water with distance from the plant. c Trend of ∑HBCD and diastereoisomer concentration in the sediment with the distance from the plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of HBCD diastereoisomer contributions in a sediment and b water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Temporal trends of main PFAAs in the Xihe and Daling River water from 2011 to 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . Levels of main PFAAs in the Xihe and Daling River water in four seasons of the year 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . a Construction status of fluorochemical facilities, b the main organo-fluorine products in the two parks, and c temporal trends based on the normalization of corresponding values divided by median . . . . . . . . . . . . . . . . . Main production processes and products in the parks correlated with the emission of the dominant PFAAs . . . . . . . . . . Production of PFOS from 2001 to 2011 in China . . . . . . . . . . . . . Three categories of PFOS-related chemicals and example structures (R = H or alkyl) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classification of industrial sources of PFOS in China . . . . . . . . . General methodology for estimating industrial emission of PFOS in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Figures

Fig. 2.5 Fig. 2.6 Fig. 2.7

Fig. 2.8 Fig. 2.9

Fig. 2.10 Fig. 2.11 Fig. 2.12

Fig. 2.13 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. 3.9 Fig. 3.10 Fig. 3.11 Fig. 3.12

Fig. 4.1

Share of major PFOS industrial sources in China (2010) and European Union (around 2000) . . . . . . . . . . . . . . . . . . . . . . . . Annual PFOS emissions and source patterns at the provincial level in China in 2010 . . . . . . . . . . . . . . . . . . . . . Spatial distribution of PFOS emission from industrial sources normalized to area (emission density) and GDP (emission intensity) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diagram for estimating domestic emission of PFOS equivalents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linear and log-linear scatter plots of domestic emission density of PFOS with population density in the areas served by municipal WWTPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of the domestic PFOS emissions derived from provincial level and county level estimations . . . . . . . . . . . . Spatial distribution of domestic emission densities and loads of PFOS in the eastern coastal region of China . . . . . . Means, 10th, 25th, 75th, and 90th percentiles of PFOS discharge from WWTP-1 (a) and domestic emission in Beijing (b) derived from the Monte Carlo simulation . . . . . . . Comparison of PFOS emission from domestic and industrial sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schematic diagram of sources and pollution pathways for PFOS/PFOA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environment release of PFOS and PFOA in different pollution pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contribution of PFOS a and PFOA b in different pathways . . . . Sources of PFOA/PFO during the life cycle . . . . . . . . . . . . . . . . . Life cycle analysis of PFOA/PFO in 2012. Flows between processes are reported in tons . . . . . . . . . . . . . . . . . . . . . Emission from the manufacture of PTFE to the hydrosphere (t) in 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flows of PFOA/PFO (t) during production and use by industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flows of PFOA/PFO (t) in production and use by stages . . . . . . . Flows of PFOA/PFO (t) in domestic use . . . . . . . . . . . . . . . . . . . . Flows of PFOA/PFO (t) in waste management by types . . . . . . . Flows of PFOA/PFO (t) in waste management by stages . . . . . . . Alternatives or mitigation techniques for PFOA/PFO. (Note The purple box represents techniques used during manufacture of PFOA/PFO; orange boxes represent alternatives or mitigation techniques during application of PFOA/PFO; green boxes represent mitigation techniques during waste treatment; in brackets is the representative enterprise) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Location of the study area and model segmentation . . . . . . . . . . .

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64 67 72 78 80 82 84 84 85 86 87 88 89

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Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7

Fig. 4.8

Fig. 4.9 Fig. 4.10 Fig. 4.11 Fig. 4.12 Fig. 4.13 Fig. 4.14

Fig. 4.15 Fig. 4.16 Fig. 4.17 Fig. 4.18

Fig. 4.19

Fig. 4.20 Fig. 4.21

List of Figures

Contaminant fate processes in region i of the model . . . . . . . . . . Selected rivers and hydrometric stations in Bohai coastal region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emission of BaP in Bohai coastal region in 2008 . . . . . . . . . . . . . Modeled concentration of BaP in the air (a), soil (b), and sediment (c) in Bohai coastal region . . . . . . . . . . . . . . . . . . . . Comparison between measured and modeled concentration in different compartments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeled concentrations of BaP in vegetation (a), freshwater biota (b), and marine biota (c) in Bohai coastal region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentages of BaP amount in compartments of each sub-region of Bohai (a), and correlation between percentages of BaP amount and surface area of sediment (b); correlation between percentages of BaP amount and surface area of coastal water (c) . . . . . . . . . . . . . . . . Inter-media transport processes of BaP in Bohai coastal region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The flux of BaP in each sub-region entering the sea in the Bohai coastal region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . BaP spatial transferring fluxes into (a) and out from (b) each sub-region in the Bohai region . . . . . . . . . . . . . . . . . . . . . . . . Percentage of BaP removed by advection and degradation processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage of BaP removed by various degradation processes in each sub-region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimate distribution of BaP emission rate (a), concentration in air (b), and concentration in soil (c) in segment 37 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Integrated emissions of PFOS and compartment distribution for different scenarios . . . . . . . . . . . . . . . . . . . . . . . . . Emissions of PFOS in six compartments for different scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distribution of emissions of PFOS in the air (a), soil (b), and freshwater (c) for ES2 in the Bohai coastal region . . . . . Concentrations of PFOS in freshwater (a), fresh water sediment (b), and soil (c) for different emission scenarios in the Bohai coastal region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measured and modeled concentrations of PFOS in fresh water, freshwater sediment, and soil in Bohai coastal area (Note For the details of the references, please refer to the original article (Liu et al. 2015)) . . . . . . . . . . . . . . . . . . . . . Compartment distribution of PFOS in the Bohai coastal region in ES2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sources of PFOS in the soil in the Bohai coastal region . . . . . . .

100 102 105 106 107

107

109 110 110 111 112 112

116 119 120 121

122

123 124 124

List of Figures

xxv

Fig. 4.22 Fig. 4.23

125

Fig. 4.24 Fig. 4.25 Fig. 4.26 Fig. 4.27

Fig. 4.28

Fig. 4.29 Fig. 4.30

Sources of PFOS in coastal water in the Bohai coastal region . . . Spatial inflow (a) and outflow (b) of PFOS in the Bohai coastal region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The seasonal variance of concentration of PFOS in fresh water in the Bohai coastal region . . . . . . . . . . . . . . . . . . . . . . . . . . The seasonal variance of the flux of PFOS entering the sea transported by runoff water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contaminant fate processes in the region i linked with j in the BETR-UR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Representative examples of distance-concentration functions of air and soil. a Total load of PAHs in the top 10 cm soil, as a function of distance from the plant, Netherlands (Brummelen et al. 1996a, b); b PAH concentrations in air distribution using a model extrapolated from linear regression parameters. 0 km is Toronto’s central business district (Csiszar et al. 2013). The average value is the simulated value . . . . . . . . . . . . . . . . . . . . Air flow rate from i to j in the BETR-UR model Note G(i, j ) = G(a1, d) + G(a1, c) + G(b, c) + G(a2, c) + G(a2, d) + G(b, d) G(i, j ) = G(a, c) + G(a, d) + G(b, c) + G(b, d) = Ga(i, j ) + Gb(i, j ) Ea(i, j ) + Eb(i, j ) = Ga(i, j ) × Z (a, j ) × f (a, j ) + Gb(i, j ) × Z (b, i ) × f (b, i ) Da(i, j) × f (a, i ) = Ea(i, j ) Db(i, j ) × f (b, i ) = Eb (i, j) Da(i, j) = Ga(i, j ) × Z (a, i ) + Gb(i, j) × Z (b, i) × f (b, i )/ f (a, i) Db(i, j ) = Ga(i, j ) × Z (a, i) × f (a, i )/ f (b, i) + Gb(i, j) × Z (b, i )Z (x, y)' , fugacity capacity of compartment x in segmentation y, mol/Pa/m3 ; G(x, y), flow rate from x to y (m3 /s) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulated and measured BaP and Phen concentrations in air, water, urban soil, rural soil, and sediment . . . . . . . . . . . . . . Emission and simulated concentrations of BaP and Phen in air and soil in Bohai coastal region . . . . . . . . . . . . . . . . . . . . . .

125 127 127 129

130

135 140 141

xxvi

Fig. 4.31

Fig. 4.32 Fig. 4.33 Fig. 4.34

Fig. 4.35

Fig. 4.36 Fig. 4.37 Fig. 4.38

Fig. 4.39

Fig. 4.40

Fig. 4.41

List of Figures

Intermedia transport flux of BaP and Phen in the study area. (Note others for BaP fluxes include the transport processes from vegetation to urban soil, fresh water to rural air, fresh water to urban air, rural air to fresh water, vegetation to rural air, vegetation to urban air, fresh water to coastal water, coastal water to rural air, sediment to fresh water, rural soil to rural air, rural soil to vegetation, urban soil to vegetation, and urban soil to urban air. Others for Phen fluxes include the transport processes from rural air to fresh water, fresh water to urban air, urban air to fresh water, vegetation to urban air, fresh water to rural air, fresh water to coastal water, rural soil to vegetation, urban soil to vegetation, rural soil to rural air, urban soil to urban air, and sediment to fresh water.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inflows and outflows of BaP in Bohai coastal region . . . . . . . . . . Inflows and outflows of Phen in Bohai coastal region . . . . . . . . . Modeled PFOS concentration trends in fresh water and urban soil of the daling river basin (grid 46) and measured PFOS concentrations in fresh water from 2011 to 2014. (Note the units for fresh water and urban soil are ng/L and ng/g, respectively.) . . . . . . . . . . . . . . Spatial distributions of peak concentrations for compartments. a Fresh water, b Urban soil, c Rural soil, d Coastal water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeled temporal trends of PFOS concentrations in fresh water in selected regions of Bohai rim . . . . . . . . . . . . . . . . . . . . . Compartmental distribution of PFOS in selected regions . . . . . . . Projected changes in concentrations of PFOS in fresh water, rural soil, urban soil, and coastal water under specific climate change scenarios for emission scenario 1 . . . . . . . . . . . . . Total annual mass fluxes (kg) to the Bohai Sea, sources, and removals for a 2010. Ratios of total annual mass fluxes: b 2035/2010, c 2065/2010, and d 2100/2010 under scenario A1B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Model structure and transfer flux of PFOS for each process in grid 26 during 2010 (g/yr); and a summary of mass balance of PFOS for grid 26 b baseline 2010, c 2016–2035, d 2046–2065, e 2081–2100 under emission scenario 1, moderate climate change scenario A1B. (Note the pie size indicates the total intermedia transport flux amount.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Compartmental distribution of PFOA/PFO releases (a) and spatial distribution of PFOA/PFO releases to compartments in the Bohai Rim, China: b fresh water, c rural air, d urban air, e rural soil, and f urban soil . . . . . . . . . . . .

143 144 145

153

155 156 157

165

167

168

172

List of Figures

Fig. 4.42 Fig. 4.43 Fig. 4.44 Fig. 4.45 Fig. 4.46

Fig. 4.47 Fig. 4.48

Fig. 4.49

Fig. 4.50

Fig. 4.51

Fig. 4.52 Fig. 4.53

Spatial distribution of PFOA/PFO concentrations in fresh water and coastal water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The fate distribution of PFOA/PFO in the Bohai Rim . . . . . . . . . Percentage of source pathways to fresh water in the Bohai Rim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial inflow (a) and outflow (b) of PFOA/PFO in the Bohai Rim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intermedia transport pathways of PFOA/PFO in the Bohai Rim. (Note Others included the transport pathways from urban soil to urban air, rural soil to rural air, coastal water to rural air, fresh water to urban air, fresh water to rural air, vegetation to urban soil, and vegetation to rural air because the flux for each process was too small. The size of the pies did not indicate the total transport flux of PFOA/PFO.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling sites and land cover information of the study area along the Bohai and Yellow Seas . . . . . . . . . . . . . . . . . . . . . . Spatial distribution of IP-PCB (a), UP-PCB (b), and total PCB (c) emissions in 2013 at a resolution of 10 km × 10 km, and emissions of seven PCB congeners in different cities (d) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of PCBs and PCB congeners. Each chart represents the mean composition of PCB congeners in each city . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban-to-rural gradient of PCBs; r is the mean distance from urban areas, which is divided into bins of 2–5 km, 5–7 km, 7–10 km, 10–15 km, 15–20 km, 20–30 km and 30–50 km. The mean soil concentration at the distance of a given radius r is given by Crs , and Cus is soil concentration in urban areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of latitudinal and longitudinal trends of total PCB emissions with PCB concentrations in soils in 2013. Emissions of PCBs and soil concentrations represent mean values within a 0.5º zone. (Note Rizhao (RZ), Cangzhou (CZ), Dalian (DL), Weifang (WF), Zibo (ZB), Anshan (AS), Tianjin (TJ), and Tangshan (TS).) . . . . . . . . . . . . . . . . . . . . Soil PCB emissions over time and their sources . . . . . . . . . . . . . . Distribution of sampling sites and PAH concentrations along the Bohai Sea and the Yellow Sea . . . . . . . . . . . . . . . . . . . .

xxvii

173 175 176 177

178 179

186

187

188

189 191 193

xxviii

Fig. 4.54

Fig. 4.55

Fig. 5.1 Fig. 5.2

Fig. 5.3

Fig. 5.4

Fig. 5.5 Fig. 5.6

Fig. 5.7 Fig. 5.8 Fig. 5.9 Fig. 5.10

List of Figures

A logarithmic relationship exists between distance r from the edge of urban areas and the ratio of rural and urban soil concentrations (a). The relationship between measured and simulated values in the studied region (b). Measured total load of 10 PAHs in the top 10 cm of soil (in the Netherlands) and simulated data using the URGM model (c). Measured total load of 16 PAHs for the top 2 cm of soil in northern Oslo (in Norway) and simulated data using the URGM model (d). The relationships of PAH amounts in soils and population in different cities (e), the relationships between the artificial surface area in the 10 km buffer area and concentration of PAHs (f), and the relationships between total organic carbon and PAH concentrations (g) . . . . . . . . . . . . . . . . . . . . . . . . Predicted distribution of PAHs. a Simulated results using the urban–rural model at a regional scale. b Simulated results calibrated by population combined with land cover data at a city scale and with an artificial surface area at a 10-km scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Framework for regional multi-media probabilistic ecological risk assessment (RMPERA) . . . . . . . . . . . . . . . . . . . . . Study sub-regions (a) and spatial distribution of Cadmium risks in soils, river, coastal water (b), river sediment, and coastal sediment (c) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Box-plot of cadmium measurement in environmental media (μg/L in water, mg/kg in soil and sediment; ° refers to mild outliers, * refers to extreme outliers) . . . . . . . . . . . . . . . . SSDs for cadmium in soil, river water, river sediment, coastal water, and coastal sediment (EC50/LC50/IC50 values were divided by a safety assessing factor of 5) . . . . . . . . . Risk characterization of cadmium in the specified environmental medium in Northern Bohai Rim . . . . . . . . . . . . . . Cadmium concentrations in local benthic organisms, benthic community health status and cadmium risk to coastal sediment organisms in Northern Bohai Rim. Note Marine biotic index is a qualitative index, we define “1 = Good-moderate, 2 = Good, 3 = Excellent-Good” . . . . . . . Framework for a regional integrated ecological risk assessment for multiple pollutants . . . . . . . . . . . . . . . . . . . . . . . . . Case study region, sub-regions, and sampling locations . . . . . . . Box-plot of As, Cd, Cr, Hg, and Pb measurement in the soil (mg/kg); ° refers to mild outliers, * refers to extreme outliers . . . SSDs models based on native species toxicity data in literatures and the ECO-TOX database (level II) . . . . . . . . . . .

194

197 211

215

216

217 219

221 222 225 226 226

List of Figures

Fig. 5.11 Fig. 5.12 Fig. 5.13 Fig. 5.14 Fig. 5.15 Fig. 5.16 Fig. 5.17 Fig. 5.18 Fig. 5.19

Fig. 5.20

Fig. 5.21

Fig. 5.22

SSDs models based on native species toxicity data in literatures and the ECO-TOX database (level I) . . . . . . . . . . . . Spatial distribution of As, Cd, Cr, Hg, and Pb risks (level II) to the terrestrial ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distribution of As, Cd, Cr, Hg, and Pb risks (level I) to the terrestrial ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total risk characterization of heavy metals in cities around the Bohai and the Yellow Seas . . . . . . . . . . . . . . . . . . . . . . . . . . . . The overall risk of each heavy metal in the region around the Bohai Sea and the Yellow Sea . . . . . . . . . . . . . . . . . . . . . . . . . Sampling locations for chemicals monitored in the Bohai Region Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling locations for chemicals monitored in the Yangtze river network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling locations in the Pearl River basin . . . . . . . . . . . . . . . . . Risk ranking of the metals and norfloxacin, gamma-HCH in freshwater in the Bohai Region, and their comparison with rivers in the UK. Note Solid filled circle: reported effect concentrations reported. Solid filled square: freshwater concentrations in the Bohai Region. Solid filled diamond: water concentrations in the UK rivers (the third and final dataset). Hollow black circle: median values . . . . . . . . . Risk ratio ranking of the chemicals found in the freshwater of the Bohai Region compared with that for UK rivers based on median data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Temporal changes of metals risks based on median values only for the Haihe River Basin. Note For each metal going from left to right the solid filled circle: effect concentrations reported, the solid filled square: freshwater measurements since 2010. Finally, the horizontal line: freshwater concentrations from 2000–2009. Hollow black circle: median points. There was a lack of Fe concentrations from 2000–2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . Risk ranking of PPCPs, EDCs, and metals using all wildlife ecotoxicity data. a Comparison of effect concentrations (circles, left-hand column of each pair) with measured Bohai coastal freshwater concentrations (diamonds, right-hand column of each pair) for PPCPs, EDCs, and metals. The median values are plotted as open black circles. The numbers next to the open circles represent the median values for each data set. b Here the risk ranking for the chemicals is shown by plotting the ratios of the median environmental concentration and median effect concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxix

227 227 228 230 230 231 232 232

236

236

239

240

xxx

Fig. 5.23 Fig. 5.24

Fig. 5.25

Fig. 5.26

Fig. 5.27 Fig. 5.28

Fig. 5.29

Fig. 5.30

Fig. 5.31

Fig. 5.32 Fig. 5.33

List of Figures

Risk ranking of PPCPs and EDCs based on their risks to algae, fish, or invertebrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Toxicity information for different organism groups of each PPCPs and EDCs. Note A represents surfactants, including LAS. B represents EDCs, including DEHP, NP, and BPA. C represents antibiotics, including NOR, SMX, OFL, and ERY. D represents anti-inflammatory drugs, including NPX. E represents β-blockers, including ATL and MET. F represents antiepileptics, including CBZ. The median values are plotted as yellow circles with black borders . . . . . . . . Rivers in which the highest PPCPs and EDCs relative risk value. a The spatial distribution of rivers with high risks; b The highest relative risk value of each chemicals shown by histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial difference and monthly variation of linear LAS in Bohai Rim in 2013. a Spatial difference of annual average relative risk in every site; b monthly variation of space average relative risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Spatial distribution of erythromycin (ERY) relative risk in Liaohe River Basin in 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Risk ranking of 14 POPs in rivers in Bohai Region with decreasing concern. Effect concentrations data (left) and predicted water concentrations (right) are shown in pairs, with median values marked in black circles . . . . . . . . . . Risk ranking of 14 POPs based on the ratio comparing the median ecotoxicity concentration and the median river concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect concentrations of different species groups (left) and environmental level (right) of γ-HCH in rivers in Bohai Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Risk ranking of chemicals for different groups of species. a algae, b fish, c insects/spiders, d molluscs/crustaceans/invertebrates. X means not enough ecotoxicological data available for this chemical . . . . . . . . . . . . . Distribution of hot-spots areas with relative higher ecological risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ranking of POPs based on the median value of bioconcentration factor (BCF) . . . . . . . . . . . . . . . . . . . . . . . . . .

242

242

243

244 245

246

247

247

248 249 250

List of Figures

Fig. 5.34

Fig. 5.35

Fig. 5.36

Fig. 5.37

Fig. 5.38

Fig. 6.1 Fig. 6.2

Paired data of all the collected ecotoxicity effect and measured river concentrations for each chemical studied in Bohai Region rivers with the UK rivers as a comparison. Note The highest risk chemicals are on the left and the lowest risk on the right. The colors refer to the chemical groups, showing the ecotoxicology data on the left, followed by the monitoring data in the middle, and for comparison, the UK monitoring data last (in grey) . . . . . Paired data of all the collected ecotoxicity effects and measured river concentrations for 29 chemicals in the Yangtze River network. Note For each chemical, three rows of data are plotted side by side with the ecotoxicity values on the left, Chinese environmental data in the middle, and, for comparison (in gray), measurements for England and Wales on the right. The ecotoxicity data set shows all values used as colored dots with the median for a particular species as a black horizontal line. Open circles denote the medians (of the species medians for the toxicology data and all measurements for environmental data). The highest-risk chemicals for Chinese rivers are on the left and the lowest risk on the right. The colors refer to the chemical groups . . . . . . . . . . Paired data of all the collected ecotoxicity effects and measured river concentrations for each chemical studied in the Pearl River with the UK rivers as a comparison). Note The highest risk chemicals are on the left and the lowest risk on the right. The colors refer to the chemical groups, showing the ecotoxicology data on the left, followed by the monitoring data in the middle, and for comparison, the UK monitoring data last (in grey) . . . . . Risk ratios from the median ecotoxicity value compared to the median environmental value for each river basin. The larger the value, the higher the risk (ordered by risk ratio in the Yangtze River) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The number of monitoring values as a percentage that exceeds the 10th percentile (most sensitive) ecotoxicity value for a the Yangtze River basin, b the Bohai region rivers, c the Pearl River basin, and d the United Kingdom (chemicals with no overlap are ranked by medians) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research framework on the influence of PFAAs emitted from fluorochemical industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling locations for surface water and groundwater around the FIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxxi

251

251

252

252

253 260 261

xxxii

Fig. 6.3 Fig. 6.4

Fig. 6.5

Fig. 6.6 Fig. 6.7 Fig. 6.8 Fig. 6.9

Fig. 6.10

Fig. 6.11

Fig. 6.12

Fig. 6.13

Fig. 6.14 Fig. 6.15 Fig. 6.16 Fig. 6.17

List of Figures

Spatial distribution of PFAAs in surface and ground water adjacent to the Dongzhulong River . . . . . . . . . . . . . . . . . . . . . . . . Attenuation dynamic of PFAAs with the increase in distance from the polluted ∑ river (a) and (b); concentration change of PFAAs (c) and relative abundance of individual PFAA (d) with the increase in distance . . . ∑ ......................................... Distribution of PFAAs in the groundwater with ∑increasing distance from the FIP (a); Change of PFAAs levels (b) and relative abundance of individual PFAA (c) with the increase in distance . . . . . . . . . . . . . . . . . . . . . Risks to health from drinking water of PFAAs in surface and groundwater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampling locations for crop grain (wheat and maize) and agricultural soil collected around the FIP . . . . . . . . . . . . . . . . The research diagram of the influence of the emission from the FIP on the agricultural ∑ environment . . . . . . . . . . . . . . . . The spatial distribution of PFAAs and relative contribution of individual PFAA in agricultural soil with the increase in distance from the FIP (a) and (c); ∑ the decline curve of PFAAs in agricultural soil and groundwater with distance from the polluted river (b) . . . . . The relationship (a,b,c) between PFAAs, PFCAs, and PFOA found in agricultural soil and corresponding irrigation water and the different profiles (d) of PFAAs in agricultural soil and irrigation water . . . . . . . . . . . . . . . . . . . . . Spatial distribution (a,b), decline process (c), and profiles (d) of PFAAs in wheat and maize grain with the increasing distance from the FIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . BAFs for several major PFCAs (a), correlations between log BAF and carbon chain length (b,c), and bioaccumulation equations of PFAAs (d–i) . . . . . . . . . . . . . . Estimated daily intakes (EDI) of PFOA via consumption of wheat and maize (ng/kg bw/day) for various age groups. (Note For the details of the references, please refer to the original article (Liu et al. 2017)) . . . . . . . . . . . . . . . . . . . . . The two selected fields for crop sampling around the FIP . . . . . . Concentrations and compositions of PFASs in soil and edible parts of multiple crops . . . . . . . . . . . . . . . . . . . . . . . . . The profiles of individual PFAA components in agricultural soils and∑ corresponding crops . . . . . . . . . . . . . . . . . . . . . . . . . . . . BAFs of PFAAs for the edible parts of multiple crops (a) and for specific organs of vegetables (b) and grain crops (c) . . . .

263

264

266 267 269 269

270

271

272

274

275 277 278 279 282

List of Figures

Fig. 6.18

Fig. 6.19

Fig. 6.20

Fig. 6.21 Fig. 6.22 Fig. 6.23 Fig. 6.24 Fig. 6.25

Fig. 6.26 Fig. 6.27 Fig. 6.28

Fig. 6.29

Fig. 6.30

Fig. 6.31

Fig. 6.32 Fig. 6.33

The relationship between BAFs of individual PFAAs and their carbon chain lengths for edible parts of multiple crops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimated daily intakes (EDI) of PFAAs via consumption of contaminated crops (ng/kg bw/day) for local urban and rural residents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Detailed information on the locations of the fluorochemical industry parks and WWTPs, and sampling in the river and artificial wetland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The main processes of the three WWTPs, the orange triangles indicated sampling points . . . . . . . . . . . . . . . . . . . . . . . . Concentrations and percentages of PFAAs in the main processes of the WWTPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFAAs levels and removal efficiency from the processes of the wetland . . . . . . . . . . . . . . . . . . . . . . . .∑ ................. Percentages of the 4 dominant PFAAs and PFAAs (mean values for each species) in the aquatic plants . . . . . . . . . . . Concentrations of the dominant PFAAs in sequential emerged plants in the wetland (The two species of submerged plants were distributed all over the wetland) . . . . . Sequences of the bioaccumulation efficiency for all the aquatic plant species in this study . . . . . . . . . . . . . . . . . . . . . . Sampling of aquatic organisms in the Xiaoqing River and Laizhou Bay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFOA concentrations (ng/g, ww) in the freshwater species at site XQ4 (the value for the river crab (E. sinensis) was obtained from muscle tissue of males) . . . . . . . . . . . . . . . . . . PFOA concentrations (ng/g, ww) in marine species collected between the shore and site XQ-S (values for the two sea crabs (P. trituberculatus and C. japonica) were obtained from muscle tissue of males) . . . . . . . . . . . . . . . . . a PFOA concentrations in the three aquatic species (ng/g, ww) of the river-estuary-sea environment and water (ng/L); b The change of trophic levels along with carbon sources of the three species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage (%) of PFOA concentrations (ng/g, ww) in the aquatic organisms measured in this study that fall into the corresponding ACSV ranges from the four scenarios . . . Sampling sites for home and commercially produced eggs around the FIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PFAAs in egg yolks, egg whites, and whole eggs of HPEs and CPEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxxiii

284

286

288 289 290 292 294

295 297 298

301

302

303

309 311 312

xxxiv

Fig. 6.34

Fig. 6.35

Fig. 6.36 Fig. 6.37 Fig. 6.38

Fig. 6.39

Fig. 6.40 Fig. 6.41

Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4 Fig. 7.5 Fig. 7.6 Fig. 7.7

List of Figures

a Map of the study area and sampling sites; b Spatial distribution of PFAAs in indoor dusts; c Relative abundance of individual PFAA in indoor dusts; d Spatial distribution of PFAAs in outdoor dusts; e Relative abundance of individual PFAA in outdoor dusts; f Comparison of PFAAs concentration in indoor dust and outdoor dust. The lower and upper ends of the box are the 25th and 75th percentiles of the data. The horizontal solid line within the box is the median value, and the symbol ▲ represents the arithmetic mean value . . . . . . . Decline in C4–C8 PFCAs and ∑PFAAs concentrations in indoor dust samples with the distance from the FIP. (Note The decline curve was based on the arithmetic mean concentration. The lower and upper ends of the box are the 25th and 75th percentiles of the data. The horizontal solid line within the box is the median value, and the symbol ▲ represents the arithmetic mean value.) . . . . . . Schematic diagram of sources of PFAAs in the dust around the FIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EDI (ng/kg bw/day) of PFOA via indoor dust for residents around the FIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geographical information on air sampling (this study), outdoor dust sampling (Su et al. 2016), and rain sampling (Liu et al. 2017) around the FIP . . . . . . . . . . . . . . . . . . . . . . . . . . . Trends ∑ on the percentages of dominant PFAAs to 12PFAAs with increasing concentrations in air, dust, and rain (P < 0.05 indicates that the trend is significant) ....... ∑ Comparison of the atmospheric diffusion of 12PFAAs (in log10 values) in air, dust, and rain . . . . . . . . . . . . . . . . . . . . . . . Comparison of the EDI of PFOA (ng/kg bw/day) via air inhalation and dust ingestion among five age groups in the same study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urbanization and industrialization level in China from 1978 to 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Five urban agglomerations in China . . . . . . . . . . . . . . . . . . . . . . . Changes in total arable land area in China from 1978 to 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arable land loss from 1996 to 2008 and grain-producing areas in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mortality rate of infectious diseases in rural and urban China from 1990 to 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prevalence of chronic diseases in rural and urban China from 1993 to 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Perceived roles of the chemical industrial parks by the respondents (n = 418) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

315

316 317 319

320

324 325

327 337 338 341 341 343 343 348

List of Figures

Fig. 7.8 Fig. 7.9 Fig. 7.10 Fig. 7.11 Fig. 7.12

Fig. 7.13

Fig. 7.14

Fig. 7.15 Fig. 7.16

Public awareness of the chemical industrial parks in Dalian (n = 418) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The chemical accident reporting system in China . . . . . . . . . . . . Major dangerous chemical accidents in China from 1970 to 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Average of deaths per major dangerous chemical accident in China from1970 to 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The number of accidents, number of deaths caused by accidents, number of factories, gross industrial product, and number of employees of the petro-chemical industry, per province in China from 1978 to 2008 . . . . . . . . . . . . . . . . . . . Developments in the number of major chemical accidents, number of death, number of petro-chemical plant, and the gross petro-chemical industry product in China from 1978 to 2008 (1 Yuan is about 0.155 USD) . . . . . . . . . . . . . Developments in the number of injury, gross petro-chemical industry product, and the number of petro-chemical plant in China from 1978 to 2008 (1 Yuan is about 0.155 USD) . . . . . Phases of emergency management . . . . . . . . . . . . . . . . . . . . . . . . . Global distributions of contaminants along the coast. (Note Different shapes represent different contaminants. For each chemical, the concentration was logarithmically normalized and then divided into five groups at equal intervals. Different colors from green to red indicate different ranges, with red indicating the top 20% range. The cyclone map indicates the worst areas that suffered from storms. The thermometer shows that coastal waters are warmer than other areas.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxxv

349 353 354 355

356

357

357 358

368

List of Tables

Table 1.1 Table 1.2 Table 1.3 Table 1.4 Table 2.1 Table 2.2 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 4.7 Table 4.8 Table 4.9 Table 4.10 Table 4.11

Summary of studies of POPs in environmental media along the Bohai and Yellow Seas in China and South Korea . . . Summary of PFAAs concentrations (ng/L) in annual monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of PFAAs concentrations (ng/L) in seasonal monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concentration ratios of PFBS/PFBA and PFBA/PFOA in the sites 3 to 8X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Provincial emissions of PFOS equivalents from major industrial sources in China (t/a) . . . . . . . . . . . . . . . . . . . . . . . . . . PFOS concentrations in selected municipal WWTPs and their characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mass balance equations of the model . . . . . . . . . . . . . . . . . . . . . Sensitivity, coefficient of variance, mean, standard deviation, and source of the key parameters . . . . . . . . . . . . . . . . Comparison of forecasts and fitted distributions of BaP emission rate, concentration in air, and soil in region 37 . . . . . . Measured K OC values of PFOS in sediment . . . . . . . . . . . . . . . . Z value and D value formulations specific to the BETR-UR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mass balance matrix Algebra format for the improved regional model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Air and freshwater flow balances for region i among connected regions in the BETR-UR model . . . . . . . . . . Physical–chemical properties of PAHs . . . . . . . . . . . . . . . . . . . . Measured, modeled, and Liu’s simulation values (a) in different compartments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitivity, mean coefficient, standard deviation, and variance of BaP for segment 46 . . . . . . . . . . . . . . . . . . . . . . Sensitivity, coefficient of mean, standard deviation, and variance of Phen for segment 46 . . . . . . . . . . . . . . . . . . . . . .

4 32 34 35 49 57 101 114 116 117 131 133 136 137 138 146 148 xxxvii

xxxviii

Table 4.12 Table 4.13 Table 4.14 Table 4.15 Table 4.16

Table 4.17 Table 4.18 Table 4.19 Table 4.20 Table 4.21 Table 4.22 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 6.1 Table 6.2

Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7

Table 7.1

List of Tables

Distribution of predictions of model uncertainty . . . . . . . . . . . . Logistic curve parameters used to define the dynamic emission input in the BETR-UR model . . . . . . . . . . . . . . . . . . . Values of changing rates for environmental parameters . . . . . . . Regional dynamic mass balance of PFOS by the BETR-UR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The estimated emissions of PFOS and their compartmental distribution in the study area under four scenarios (kg/a) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The increase rate of parameters considered in the climate change scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Physical–chemical properties of PFAAs at 20 °C . . . . . . . . . . . The physical–chemical properties of PCBs . . . . . . . . . . . . . . . . Key values of PCB emissions and model output . . . . . . . . . . . . Annual activities, emission factors, and annual emissions . . . . Descriptive statistics of PAHs in soils . . . . . . . . . . . . . . . . . . . . . The regression parameters and 95% percentile value of cadmium exposure in the northern Bohai rim . . . . . . . . . . . . The regression parameters and 95% percentile value of SSDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . AHP weight matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of the surface waters examined in China and the UK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The 29 different chemicals examined and their different classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of PFAAs concentrations (ng/g, dw) in the aquatic plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of PFAA concentrations (ng/g, ww) in aquatic organisms (n = 43, freshwater species; and n = 42, marine species) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Calculation of the PFOA/PFOS ratio based on the published health guideline values . . . . . . . . . . . . . . . . . . . ASCV (ng/g, ww) and EDI (ng/kg bw/d) adjusted for PFOA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EDI (ng/kg bw/day) of PFAA via HPEs and CPEs consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of PFAAs concentrations in air (pg/m3), outdoor dust (ng/g) and rain (ng/L) around the FIP . . . . . . . . . . The intake (ng/day) and EDI (ng/kg bw/day) of PFOA via inhalation of outdoor air (O) and indoor air (I) for different age groups under scenario 1 (S1) and scenario 2 (S2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Major providers of data and information on chemical accidents in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

149 151 152 158

160 161 170 182 183 184 192 216 218 220 233 233 293

300 307 308 313 322

326 351

List of Tables

Table 7.2 Table 7.3 Table 7.4 Table 7.5

xxxix

Categories of the work safety accidents (1 Yuan is appr. 0.155 USD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pesticide-relevant laws and regulations at the national level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . National governmental ministries and commissions and their functions in chemicals management in China . . . . . . . Laws and regulations related to POPs in China . . . . . . . . . . . . .

352 361 362 366

Chapter 1

Environmental Exposure of Emerging Pollutants in Urbanizing Regions Pei Wang, Yonglong Lu , Tieyu Wang, Jing Meng, and Yueqing Zhang

1.1 Overview Emerging pollutants encompass a wide range of synthetic or naturally occurring chemicals, are of unique features and are used in numerous applications, making them widely distributed. Usually, these pollutants are related to urbanization in the form of consumer products, industrialization in the form of production and usage, and globalization in more complex forms, especially with a shift in production, which would lead to a shift in emission (Wang and Lu 2021). The levels of urbanization and industrialization can be evaluated with multiple indicators, and they were positively correlated with pollutant emission (Wang et al. 2012a, b; Han et al. 2021). This P. Wang Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems and Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Fujian 361102, China Y. Lu (B) State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems, International Institute for Sustainability Science, College of the Environment and Ecology, Xiamen University, Fujian 361102, China e-mail: [email protected]; [email protected] State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China T. Wang Institute of Marine Sciences, Shantou University, Shantou 515063, China J. Meng Key Laboratory of Environment Nanotechnology and Health Effects, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China Y. Zhang Ministry of Ecology and Environment of China, Nanjing Institute of Environmental Sciences, Nanjing 210042, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Lu et al. (eds.), Ecological Risks of Emerging Pollutants in Urbanizing Regions, https://doi.org/10.1007/978-981-19-9630-6_1

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requires monitoring efforts on the concentrations of emerging pollutants in different media to provide direct evidence for the existence of these pollutants in the living environment. Monitoring requires proper research design, intensive field survey and sampling, and experimental approaches to obtain the concentrations data. So, the spatial and temporal distributions of the pollutants can be analyzed. The choice of the study region is fundamental, and usually, the regions with high levels of urbanization have the potential for notable emissions of emerging pollutants. Meanwhile, industrialization may be an indication for high emission sources. The research design in the study region requires familiarity with the natural and social conditions, such as hydrology, land cover, and potential emission points. The number and locations of sampling sites need to be sufficient. Sampling in environmental media (including water, sediment, biota, soil, etc.) must avoid potential interferences. The samples need to be stored and transported in stable condition, and then the pollutants can be identified and quantified by extraction and instrumental analysis following standard operation procedures. Accordingly, this chapter summarizes our long-term monitoring efforts of emerging pollutants. Our research on urbanization, environmental consequences, and management was begun in 1995 (Lu 1995), followed by a series of city-scale monitoring work in Beijing City, China (Zhang et al. 2004; Wang et al. 2005a, 2011a, 2012a, b; Shi et al. 2005a, 2009; Luo et al. 2008, 2010a, b; Hu et al. 2009), when the average urbanization rate was about 30% in China. Since then, the urbanizing process in China has accelerated, and our monitoring work has expanded to the regional scale since 2008, which is the coastal region of the Bohai Sea and the Yellow Sea, China (Hu et al. 2010; Luo et al. 2010a, b; Wang et al. 2011b; Jiao et al. 2012). The region has been under rapid urbanization and industrialization, leading to a tremendous increase in energy consumption and pollutant emissions, resulting in a severe deterioration of the regional environment. When the Stockholm Convention on persistent organic pollutants (POPs) was adopted in 2001 and put into implementation in 2004, we devoted more research efforts to POPs pollution in China (Lu and Giesy 2005; Wang et al. 2005b; Zhang et al. 2005; Shi et al. 2005b). Thus, the discussion in this chapter focused on a few typical POPs. With our monitoring efforts and other studies in the same region, we aimed to draw a clear picture of the spatial and temporal trends of the emerging pollutants in different environmental media (Meng et al. 2017). Furthermore, we also took a closer look at a few emission hot spots (Wang et al. 2015, 2016a, 2016b; Zhang et al. 2018). The production activities of the manufacturers were analyzed for a better understanding of the emission patterns. All the findings formed a solid basis for further analysis in the following chapters.

1.2 Spatiotemporal Distribution of Emerging Pollutants In order to investigate the spatiotemporal distribution of typical POPs in different environmental media, the study regions were set at the coastal areas along the

1 Environmental Exposure of Emerging Pollutants in Urbanizing Regions

3

Bohai Sea and the north Yellow Sea in China, and the West Sea of South Korea was taken for comparison (Fig. 1.1). The POPs were divided into three groups: (a) chlorinated POPs, including organic chlorinated pesticides (OCPs) and polychlorinated biphenyls (PCBs); (b) brominated POPs, including polybrominated diphenyl ethers (PBDEs) and hexabromocyclododecanes (HBCDs); and (c) fluorinated POPs, mainly per- and polyfluoroalkyl substances (PFAS) (Table 1.1). Dichlorodiphenyltrichloroethanes (DDTs) of the OCPs group and PCBs were included in the 12 initial POPs (the dirty dozen) under the Stockholm Convention. In 2009, hexachlorocyclohexanes (HCHs) of the OCPs group, PBDEs, and perfluorooctane sulfonic acid (PFOS) of the PFAS group were listed in the convention. Later in 2013 and 2019, HBCDs, and perfluorooctane acids (PFOA) of the PFAS group were listed, respectively. Thus, this review covered a series of traditional and new POPs from 101 previous studies (Meng et al. 2017). The research on each chemical group was comparable except for new POPs such as PBDEs and HBCDs. Among the environmental media, POPs in sediments have been widely studied, followed by soil and water. In particular, there were few studies on

Fig. 1.1 Location and sites for the studies along the Bohai and Yellow Seas in China and South Korea

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Table 1.1 Summary of studies of POPs in environmental media along the Bohai and Yellow Seas in China and South Korea Countries and regions

Types

Survey year

Chlorinated POPs

Brominated POPs

Fluorinated POPs

OCPs

PCBs

PBDEs

PFAS

HBCDs

Chinese Coasts C1. Dalian Bay

B

1996

✓

C2. Liaohe River

E, R

2004–2005, 2009–2010, 2012, 2015

✓

✓

✓

C3. Daliao River

B, E

1996, 2001–2007

✓

✓

✓

C4. Shuangtaizi River

E

2013

✓

✓

C5. Daling River

R

2009, 2011

✓

C6. Liaodong Bay

E

2008, 2013

✓

C7. Haihe River

E, R

2004, 2006, 2007–2010

C8. Tianjin

E

2009, 2010

✓

✓

✓

2004, 2006–2013

✓

C10. Yellow River

E

2012

✓

C11. Xiaoqing R River

2014

C12. Laizhou Bay

B, E, R

2007, 2009–2010, 2014

C13. Qingdao

C

2006, 2007

C14. Jiaozhou B, C Bay C15. Haizhou Bay

1996, 2006–2007, 2009, 2014

C

2001

B

1996, 2001–2003, 2005–2007

✓ ✓

C9. Bohai Bay B, C, E

✓

✓ ✓

✓

✓

✓ ✓

✓

✓ ✓

✓

✓

✓

Korean Coasts K1. Incheon Harbor

✓

✓

✓

✓

(continued)

1 Environmental Exposure of Emerging Pollutants in Urbanizing Regions

5

Table 1.1 (continued) Countries and regions

Types

Survey year

Chlorinated POPs

Brominated POPs

Fluorinated POPs

OCPs

PCBs

PBDEs

HBCDs

PFAS

K2. Gyeonggi B, R Bay

1996, 2000–2005, 2008–2009, 2012

✓

✓

✓

✓

✓

K3. Lake Sihwa

I, L

1996, 1998, 2000, 2005, 2008–2012

✓

✓

✓

✓

✓

K4. Namyang Bay

B

1996

✓

K5. Asan Bay B

2001–2003, 2005, 2008, 2010–2012

✓

✓

✓

K6. Taean Coast

C

2001–2003, 2008, 2010–2012

✓

✓

✓

K7. Geum River

E, R

2001–2004, 2008–2012

✓

✓

✓

K8. Saemangeum Coast

C, R

2001–2003, 2006–2007, 2009

✓

✓

✓

2001–2003, 2006–2012

✓

✓

✓

K9. Youngsan B, C, E, River I, R

Note B: bay; C: coast; E: estuary; I: inland; L: lake; R: river

POPs except for PFAS in water due to their low solubility in water and hydrophobic characteristics. Therefore, distributions of POPs focused on OCPs, PCBs, PBDEs, and HBCDs in sediments and PFAS in water collected from the study region. Initially, most studies were limited to reporting traditional POPs such as OCPs and PCBs. Later, more studies reported new POPs, such as PBDEs, HBCDs and PFAS.

1.2.1 Chlorinated POPs Since the 1950s, HCH and DDT had been commercially applied in China until they were formally banned in 1983. Research on OCPs in the Bohai and Yellow Seas has been extensively conducted since the 1990s, and discovered their wide distribution in different environmental media, especially in river estuaries (Fig. 1.2). Generally, concentrations of OCPs in the river sediments were higher than in marine ones. Moreover, those rivers flowing into Bohai Bay and Jiaozhou Bay had relatively higher levels of OCPs, with concentrations of HCHs and DDTs exceeding

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Fig. 1.2 Distributions of organochlorine pesticides (HCHs and DDTs) in sediments along the Bohai Sea and the Yellow Sea in China and South Korea (Note threshold effect level (TEL) and probable effect level (PEL) for HCHs from Feng et al. 2011; effect range low (ERL) and effect range median (ERM) for DDTs from Feng et al. 2011). For the details of the references, please refer to the original article (Meng et al. 2017))

100 ng g−1 dw. From 2004 to 2010, HCHs showed an upward trend, and DDTs showed a downward trend. While in Bohai Bay, HCHs and DDTs concentrations decreased, below 50 ng g−1 dw. From 2004 to 2012, HCHs and DDTs showed an overall downward trend. Concentrations of HCHs and DDTs in Jiaozhou Bay and its coastal area were less than 100 ng g−1 dw. Concentrations of HCHs and DDTs varied greatly in different areas. Concentrations in sediments from the Haihe River were relatively higher. The highest concentration of HCHs was detected downstream of the Haihe River, which was 11,806 ng g−1 dw. Concentrations of DDTs were generally less than that of HCHs, with the highest 1,417.08 ng g−1 dw in the coastal area of Bohai Bay. Compared with quality standards, HCHs from most study areas exceeded the probable effect level (PEL, 0.99 ng g−1 dw) (Feng et al. 2011), possibly causing some harm to the environment and wildlife. Most DDTs were below the effect range median (ERM, 46.1 ng g−1 dw) (Feng et al. 2011), but these sites above ERM would often suffer from adverse biological effects in the Haihe River and Bohai Bay.

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Along the west coast of South Korea, HCHs and DDTs extensively existed in sediments. High concentrations of HCHs occurred in Lake Sihwa (0.55–10.7 ng g−1 dw). Concentrations of HCHs were relatively higher in sediments from industrialized and urbanized areas, with some exceeding the PEL. Although the production and use had been banned since the 1970s, DDTs were still detected in various marine samples and extensively existed along the coasts. In South Korea, DDT was forbidden for agricultural use from 1971. From 1949 to 1971, approximately 758 tons and 1,320 tons of DDT were manufactured and imported. This study showed that coastal sediments from the West Sea were extensively contaminated with DDTs, the main organochlorine pesticide. Incheon Harbor was an area of concern due to DDTs contamination, but concentrations did not exceed the ERM. In general, concentrations of OCPs in sediments from the West Sea of South Korea were usually lower than those in some areas of the Bohai and Yellow Seas in China. In soils, OCPs were investigated only in Haihe River Basin, Daling River Basin, Yellow River Delta, and Qingdao. The highest concentration was detected in the Haihe River Basin, with HCHs at 12,549 ng g−1 dw and DDTs at 2,033 ng g−1 dw, mainly because of the samples from the surrounding chemical industrial plant. Another study also found higher OCPs in Haihe River Basin, indicating that the current residues of OCPs were ten times lower than those in the 1980s. According to China’s National Environmental Quality Standards for Soils, the soil is divided into three levels due to residual concentrations of HCHs and DDTs: Grade I (≤50 ng g−1 dw), Grade II (50–500 ng g−1 dw), and Grade III (500–1000 ng g−1 dw). Concentrations of HCHs and DDTs in some soils from the Haihe River Basin exceeded the Grade II, meaning a risk to plants and the environment of farmland, orchard, and other agricultural lands. Concentrations in some soils even exceeded the Grade III and could not be applied as land for agricultural and forestry production. HCHs and DDTs in soils from the Daling and Yellow River Basins, the coastal area of the Bohai Sea, and Qingdao were all lower than 100 ng g−1 dw. Meanwhile, although OCPs in soil samples from the west coast of South Korea were rarely studied, they were generally lower than those around the Bohai Sea. A few studies on OCPs in rivers or seawater mainly concentrated in estuaries and bays. Overall, concentrations of OCPs in estuaries were higher than that from bays. In the Daling, Yellow, and Haihe River Estuaries, concentrations of OCPs were the highest in the Haihe River Estuary, with HCHs of 1,290–5,900 ng L−1 and DDTs of 520–4,840 ng L−1 , followed by those from the Daling River Estuary. In Jinzhou, Bohai, Liaozhou, and Jiaozhou Bays, concentrations of OCPs in seawater from Bohai Bay were the highest. In South Korea, only one study reported water contamination with OCPs, focusing on the Saemanfeum Coast. The results showed that contamination was lower than in China but similar to Jiaozhou Bay at the same latitude. Over the past 30 years, PCBs have been found in various environmental media worldwide. In China, approximately 10,000 tons of PCBs were manufactured from 1965 to 1974, after which their production was forbidden. The total output of trichlorobiphenyl and pentachlorobiphenyl were 9,000 tonnes and 1,000 tonnes, respectively (Xing et al. 2005). Studies were carried out across the whole area

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covered by this review. In general, concentrations of PCBs in sediments were ten times lower than that of OCPs (Fig. 1.3). Concentrations of PCBs varied greatly along coastal areas of Liaodong Bay. The highest concentration was in the Liaohe River (1,075.61 ng g−1 dw) flowing into Liaodong Bay. Other studies reported that PCBs concentrations in Liaodong Bay were lower than 50 ng g−1 dw. From 2004 to 2012, PCBs in this area showed a downward trend. In Bohai Bay and its coastal areas, PCBs detected in the Haihe River were higher, up to 253 ng g−1 dw. Concentrations of PCBs in Jiaozhou Bay and its coastal areas were similar to those in Bohai Bay and its coastal areas. The minor concentrations of PCBs in Haizhou Bay were equivalent to that in the other three bays. In the broader ocean, sediments were less contaminated with PCBs. PCBs in the coastal areas of the Bohai and Yellow Seas were reported at the beginning of industrial development. PCB Concentrations in more developed cities, including Dalian, Tianjin, Qingdao, and Lianyungang, were generally lower than the ERL (22.7 ng g−1 dw), with the most negligible adverse biological effects (Zhao et al. 2010). PCBs from some sites in the Liaohe and Haihe Rives and Bohai, Qingdao, and Jiaozhou Bays were detected with higher concentrations than the ERL, and even some from Liaohe River were significantly higher than the ERM (180 ng g−1 dw), which was expected to lead to adverse biological effects. Although PCBs as dielectric fluid in capacitors and transformers have been prohibited in South Korea since the 1970s, PCBs were frequently detected in sediments. The highest concentrations of PCBs often occurred in the highly industrialized regions, such as Incheon Harbor (1.0–580 ng g−1 dw) on the west coast of South Korea, with the similar highest concentration in China. Concentrations of PCBs in sediments at some sites exceeded the ERM. Overall, concentrations of PCBs in sediments from the west coast in South Korea and the Bohai Sea regions in China were comparable. PCBs in soils were reported in relatively developed areas of China, such as Tianjin, Qingdao, Dalian, and the Yellow River Delta. Among the four areas, the highest concentrations were found in Tianjin, up to 373 ng g−1 dw, followed by those in the Yellow River Delta. Dagu Chemical Co., Ltd was recognized as the primary source in Tianjin. In Qingdao and Dalian, PCB concentrations were below 15 ng g−1 dw, but no significant primary source was identified. According to a study on PCBs in soils across China in 2005, concentrations were between 0.14 and 1.84 ng g−1 dw. Concentrations of PCBs in soils from coastal Bohai and Yellow Seas were moderate, dominated by tri-PCB homologue and di-PCB homologue. According to Environmental Quality Standards for Soils (revised draft), the second-level criteria for agricultural and industrial lands are 100 and 1,500 ng g−1 dw, respectively, indicating that adverse effects may occur at concentrations above this level. According to the guideline, most sites could be used for agricultural purposes, while some could be used for other land uses with no need for remediation. One study reported concentrations of PCBs in soils from South Korea, indicating that soil contamination was lower than that observed in China. PCBs in rivers and seawater were investigated only in the northern Bohai Sea, such as the Daliao and Haihe Rivers and Jinzhou and Bohai Bays. Overall, total concentrations of PCBs in water from Jinzhou Bay and the Haihe River Estuary were higher, with values of 215.4–3,161 ng L−1 and 310–3,110 ng L−1 , respectively.

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Fig. 1.3 Distributions of PCBs in sediments along the Bohai Sea and the Yellow Sea in China and South Korea (Note ERL and ERM for PCBs from Zhao et al. 2010. For the details of the references, please refer to the original article (Meng et al. 2017))

Concentrations of PCBs in water from Bohai Bay were ten times lower than those from Jinzhou Bay and the Haihe River Estuary. A study on the downstream of the Haihe River to Bohai Bay showed that the concentrations of PCBs in the Haihe River were considerably higher than those from Bohai Bay. The concentrations of PCBs were highest in the estuary. The monitoring revealed an increasing trend in PCBs from

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2004 to 2006 and identified a new source. Concentrations of PCBs in the Daliao River Estuary were the lowest. Concentrations of PCBs in water were generally higher than those of OCPs (mainly HCHs and DDTs) in these areas, probably due to the phaseout of HCHs and DDTs.

1.2.2 Brominated POPs PBDEs in sediments surrounding the Bohai Sea, from the north Liaohe River Basin to south Laizhou Bay, have been studied (Fig. 1.4a). The most polluted area was Laizhou Bay and Bohai Bay because of the production of PBDEs and the use and disposal of electronic products. The highest concentration of PBDEs was found in the Bailong River, Laizhou Bay, up to 1,800 ng g−1 dw. Rivers flowing into the Bohai Sea near Bohai Bay showed the heaviest contamination, with the highest concentration of 42.79 ng g−1 dw. Three studies were carried out to investigate PBDEs in the Liaohe River Basin. Similar concentrations were detected among the studies at concentrations below 15 ng g−1 dw. Furthermore, lower concentrations of PBDEs were found in marine sediments from coastal areas of the Bohai Sea. Bohai and Laizhou Bays showed more serious pollution than other areas of the Bohai Sea. Only one study investigated PBDEs in marine sediments from the Yellow Sea, and similar concentrations were found in those from the Bohai Sea. Principal component analysis (PCA) indicated that PBDEs from the Yellow Sea mainly came from surface runoff (69%) and atmospheric deposition (31%). Sediment contamination by PBDEs was ubiquitous along the Korean coast. Previous studies indicated that the highest concentrations of PBDEs were detected in creeks flowing into Lake Sihwa (8.5–18,700 ng g−1 dw). In particular, concentrations of PBDEs detected in Lake Sihwa near industrial parks exceeded the federal environmental quality guidelines (FEQG) (Environment Canada 2013) and were among the highest compared with worldwide values. Significant reductions in concentrations of PBDEs from sediments have been observed in recent years. This decrease appears to be the result of South Korea’s recent prohibition on commercial PBDEs’ use and production. More investigation on PBDEs and new brominated flame retardants or other non-brominated alternatives in coastal environments are essential. Concentrations of PBDEs in soils from coastal areas of the Bohai Sea varied greatly. The most polluted areas were located near places where PBDEs were produced or used for making electronic products or dismantling electronic equipment. Several studies investigating PBDEs in soils around manufacturing sites of Laizhou Bay showed that BDE-209 was the dominant congener. The highest PBDEs concentration in these studies was 226,906 ng g−1 dw. Concentrations of PBDEs ranged from 1.34 to 343.88 ng g−1 dw in Tianjin, where electronics were dismantled. A series of studies investigated PBDEs in soils from various provinces around the Bohai Sea. Concentrations of PBDEs were between 0.01 and 948.84 ng g−1 dw in soils from north China, including Beijing, Tianjin, Shandong, Hebei, and Shanxi.

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Fig. 1.4 Distributions of a PBDEs and b HBCDs in sediments along the Bohai Sea and the Yellow Sea in China and South Korea (Note federal environmental quality guidelines (FEQG) for PBDEs and HBCDs from Environment Canada (2013) and (2016). For the details of the references, please refer to the original article (Meng et al. 2017))

There was an upward trend from inland to coast, and the most polluted areas were located in Shandong, especially Laizhou Bay. Concentrations of PBDEs in soils along the west coast of South Korea were lower than that near the Bohai and Yellow Seas of China. According to regulatory restrictions on PBDE formulations, the use of HBCD has increased over the past decade, and concentrations of HBCDs in environmental media. However, there was little information on environmental concentrations of HBCDs in the Bohai and Yellow Sea areas in both China and South Korea. Several studies have investigated HBCDs in sediment and soils (Fig. 1.4b). Overall, concentrations of HBCDs were not high, except in samples from manufacturing sites near

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Laizhou Bay. Laizhou Bay area is the center of the brominated chemical industry in China, and many BFR production plants are located in Weifang industrial park. HBCDs in soils and sediment on and off-site of HBCDs production and processing plants were investigated. Concentrations of HBCDs in on-site and off-site soils were 96.1–560.4 and 0.11–31.1 ng g−1 dw, respectively. Two sediment samples were collected around plants at 20.4 and 24.2 ng g−1 dw, respectively. Another study reported relatively higher concentrations of HBCDs in the environment near manufacturing plants in the Laizhou Bay area. Concentrations in soils and sediment were 0.88–6,901 and 2.93–1,029 ng g−1 dw, respectively. Concentrations of HBCDs detected in river sediments and Tianjin’s harbor were relatively higher, ranging from 1.35 to 634 ng g−1 dw. In these areas, γ-HBCD was dominant, consistent with the distribution of diastereomer in industrial products. HBCDs in soils from coastal cities along the Bohai and Yellow Seas ranged from 0.12 to 363 ng g−1 dw. Overall, soil pollution by HBCDs from coastal cities along the Bohai Sea was more severe than those along the Yellow Sea. Among 21 coastal cities, the highest average concentrations of HBCDs were 34.6 ng g−1 dw in Weifang, 12.3 ng g−1 dw in Cangzhou, and 11.1 ng g−1 dw in Tianjin. Average concentrations in the other 18 cities were lower than ten ng/g dw. Furthermore, some studies were carried out in the Yellow River Delta, rivers flowing into Laihou Bay and Jiaozhou Bay area, and the detection of HBCDs was lower. The highest concentration of HBCDs did not exceed the FEQG for sediment (1,600 ng g−1 dw) but was close (Environment Canada 2016). Therefore, continuous production and accumulation of HBCDs in sediment may cause harm near these production sites. There were few studies on occurrences of HBCDs worldwide, including in South Korea. Results revealed that higher concentrations of HBCDs were found in some sites near industrial plants, ports, and aquaculture farms. HBCDs concentrations detected in sediments did not exceed the FEQG. In order to strengthen assessment and management, additional studies are needed on sources, distributions, and potential toxic effects of emerging contaminants such as HBCDs on marine organisms.

1.2.3 Fluorinated POPs PFAS were widely distributed in the rivers and marine environments of the Bohai and Yellow Seas due to the solubility in water and negligible vapor pressures. Studies on PFAS in water and sediment of rivers of the Bohai Rim were abundant (Fig. 1.5). Concentrations of PFOS and PFOA in water from the northern Bohai coastal areas were n.d. −30.9 and n.d. −81.7 ng L−1 , respectively, while those in south Bohai coastal area were 0.40–12.78 and 0.96–4,534.41 ng L−1 , respectively. Concentrations of PFOS and PFOA in the corresponding sediment from the northern Bohai coastal areas were n.d. −1.97 and n.d. −0.54 ng g−1 dw, respectively, while those in southern Bohai coastal areas were 0.03–0.44 and 0.005–29.02 ng g−1 dw, respectively. Concentration was the highest in the Xiaoqing River, possibly due to local

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fluorine chemical industries. PFAS in the water and sediment of Liaodong Bay basin was investigated, and results revealed that PFAS in the Daling River was relatively high. Occurrences and fates of PFAS in marine sediments from the Bohai and Yellow Seas were reported. Concentrations of PFOS and PFOA in the Bohai Sea were n.d. − 0.15 and 0.06–2.70 ng g−1 dw, among which relatively high concentrations occurred near Liaodong Bay. Concentrations of PFOS and PFOA in the Yellow Sea were n.d. −0.40 and nd. −1.52 ng g−1 dw. Concentrations of PFASs were reported in the surroundings of fluorine chemical plants in the Daling River basin. Concentrations of PFOS and PFOA in water from Daling River were 0.28–0.54 and 27.2–668 ng L−1 , respectively, while those in sediment were n.d. and 0.18–18 ng g−1 dw, respectively. Sediment cores and overlying water were also collected to trace the pollution history of the Daling River basin. Concentrations of PFOS and PFOA in water were 0.80–8.7 and 0.63–284 ng L−1 , respectively. Concentrations of PFOA in water from the Xiaoqing River basin were between 38.6 and 1,707,290 ng L−1 . The highest concentrations of PFAS in sediment were detected in the Xiaoqing River, with the dominance of PFOA at concentrations of 3.86–456.20 ng g−1 dw. Furthermore, since there was no apparent production of PFAS and related products, PFOS and PFOA in water were lower than 100 ng L−1 , and those in sediment were lower than 10 ng g−1 dw in Liaohe, Haihe, and Yellow Rivers. It was noticed that concentrations of PFOA in China were generally higher than that of PFOS. Concentrations of both PFOS and PFOA in water did not exceed water quality guidelines, avian wildlife values (AWV), or criteria continuous concentration (CCC), indicating that no harm to wildlife or aquatic organisms is expected (Giesy et al. 2010). In recent years, concentrations of PFAS have generally declined on the west coast of South Korea. Distributions of concentrations of PFAS showed that they were higher in freshwater than in marine water, suggesting that PFAS came from point sources of surrounding inland areas rather than non-point sources. The highest concentrations of PFOS and PFOA in water were observed in Gyeonggi Bay, followed by Lake Sihwa and Lake Asan (Fig. 1.5). Concentrations of PFOA in some water samples at these sites exceeded the AWV, which was expected to have some adverse effects. Concentrations of PFAS in water samples indicated decreasing trend since 2008. The relative contributions of PFOS have decreased, while those of shorter-chain PFAS, such as PFBA, have increased in recent years, suggesting that recent global restrictions on using some PFAS have resulted in reduced production and emission. In 2010, the government of South Korea designated PFAS as “restricted chemicals to restrict their commercial use and reduce emission to the coastal ecosystem”. Overall, concentrations of PFOS and PFOA in coastal areas of China were higher than those in South Korea. Sediment samples were collected from the West Sea, Han River and Yeongsan River coastal areas. Overall, concentrations of PFOS and PFOA from these rivers were small. Concentrations of PFOA and PFOS from coastal areas of the West Sea were similar to those in China, except for higher PFOA near some manufacturing sites, such as the Xiaoqing River basin in China. Few studies have been done on PFAS in soils, but they almost cover entire coastal areas of the Bohai Sea of China and the West Sea of South Korea. Overall, concentrations of PFOS and PFOA in soils were small, mostly 90%. For other sites located on tributaries (X13, X14, X16–X21, X24-X26) of the Xiaoqing River and the reference rivers (R1–R4), no obvious sources of PFAAs were observed. ∑ PFAAs in surface sediment and sediment cores. Concentrations of PFAAs ranged from 2.0 ng/g, dw (dry weight) to 10.7 μg/g, dw in surface sediment from X1 to X26. Spatial distributions of PFAAs in surface sediment were consistent with those observed in water (Fig. 1.11a), especially the greatest concentration. However, the profiles of individual PFAA varied (Fig. 1.11b). PFOA was still dominant at most sampling sites. Those sites with the direct impact of F1 showed average contributions of PFOA >90%. However, in other sites, contributions of PFOS and longer chain PFCAs were more remarkable in surface sediment than in water. Even the greatest concentration of PFOS was 10.6 ng/g, dw in sediment found at X22, and the

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∑ Fig. 1.11 a Concentrations of PFAAs and b relative contributions of individual PFAA in surface sediment of the Xiaoqing River Basin

greatest contribution of PFOS was 54% in sediment found at X6. Among the longer chain PFCAs, C10 perfluorodecanoic acid (PFDA) was dominant (12.5, 0.14–38.9), followed by C12 perfluorododecanoic acid (PFDoDA) (6.37, 0.14–25.3%), C11 perfluoroundecanoic acid (PFUnDA) (3.71, 0.17–14.9%) and C9 perfluorononanoic acid (PFNA) (2.91, 0.14–8.74%). The difference in the profiles of PFAAs between water and surface sediment indicated their different partitioning behaviors. Considering that PFAAs have limited vapor pressure, partitioning between water and sediment is necessary to evaluate the fate of PFAAs during transport along the river. Methods used to calculate partitioning coefficients, including log K d and log K OC were the same as the Eqs. (1.1) and (1.2). Both log Kd and log K OC were directly proportional to carbon chain length from C5 PFPeA to C12 PFDoDA. The introduction of additional one carbon made log K OC 1.78 log unit higher than log K d for all PFAAs (Fig. 1.12a). Interestingly, in this study, with high concentrations of PFOA and short-chain PFCAs in water from X8-X12, values of log K OC were less than those from X1–X7 with low concentrations. These trends were not observed for

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Fig. 1.12 a log KOC values for PFAAs for sediments from X1 to X26 and b variation of log KOC for individual PFAA in sediments from X1 to X12 in the Xiaoqing River

longer-chain PFCAs or PFOS (Fig. 1.12b). This finding indicated that the log K OC values would decrease with increasing emission of C4–C8 PFCAs to water. Profiles of PFAAs in sediment cores were similar to those in surface sediments, but vertical trends showed notable differences (Fig. 1.13). In Core X3, PFOS was at lower levels within the depth from 0 to 6 cm but higher and consistent in deeper sections. PFOA concentrations fluctuated while PFDA concentrations were more consistent. Other PFCAs were less dominant. Concentrations of PFAAs in core X6 generally decreased with depth from 16 to 8 cm and then increased from 8 to 0 cm. In core X9, due to the staggering emission from F1, concentrations of PFOA ranged from 43.4 to 333 ng/g, dw. The concentrations became even greater below a depth of 10 cm. Correlations among all the 12 PFAAs in core X9 were significant (p < 0.01 or 0.05). This indicated that all PFAAs in core X9 were associated with the emission of F1. Core X12 showed decreasing concentrations of PFAAs from the surface to 10 cm depth, and then the concentrations increased slightly. Correlations among all PFAAs were not as significant as those observed in X9, indicating the influence of estuary water exchange. The sorption of PFAAs from water to sediment is mainly influenced by the fraction of organic carbon (f OC ) in sediment. In this study, core X9 had the greatest f OC with a mean value of 3.05%, followed by core X3 (2.10%), X6 (1.18%), and X12 (0.56%). The trend of f OC was similar to that of PFAAs concentrations in core X9, with significant correlations among f OC and all PFAAs. This indicated that f OC could also impact the concentrations in the aqueous environment in addition to the emission sources. Besides, when evaluating the temporal trend of PFAAs concentrations using sediment cores, changes in concentrations should not be used solely to explain the changes in emission. Unlike lakes and seas, river sedimentation can be influenced by factors such as flow rate, flood events, people fishing on foot

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Fig. 1.13 Concentrations of

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∑ PFAAs in sediment cores of the Xiaoqing River Basin

in the river, and dredging. Thus, it is difficult to date sediment cores from rivers, but they can be used as a valuable indicator for pollution on a larger scale. Mass loads of PFAAs from various sources. When assessing the input and output of PFAAs from various sources, concentrations are insufficient. Multiple water inputs, such as tributaries and drain outlets, might dilute concentrations. So mass loads (g/d) of PFAAs were calculated using water discharge (m3 /s) multiplied by concentrations of individual PFAA (ng/L) in water from each sampling site. Even if PFAAs concentrations only increased 1.5-fold from X1 to X2, mass loads of PFAAs in X1 and X2 indicated that the emission from Jinan City contributed 40.5 g/d of PFAAs to the river. For manufacturer F2, a relatively low mass load of 83.9 g/d was due to the slight discharge. However, emissions of PFAAs from manufacturer F1 to water were calculated to be 174 kg/d. PFOA was dominant (159 kg/d), followed by PFBA (5.6 kg/d), PFHxA (4.2 kg/d), PFPeA (3.3 kg/d) and PFHpA (2.0 kg/d). After the confluence with the Xiaoqing River, mass loads of PFAAs decreased to 127 kg/d at X8. Although X23 took away 6.3 kg/d, there was still 41 kg/d missing. In our field survey, many small irrigation canals and large farmland areas were found nearby. Previous studies also found that PFAAs, especially PFOA, can migrate downward into the underlying aquifer with river discharge (Davis et al. 2007). Thus, the missing masses of PFAAs could be lost via horizontal flow into irrigation canals and vertical migration to the underlying aquifer. The above two factors could also explain the fluctuation of mass loads from X8 to X12. The mass loads of PFAAs from the Xiaoqing River to Laizhou Bay were estimated to be 118 kg/d, including 111 kg/d of PFOA. The value was from X11, as salinity in water at X11 was consistent with more upstream sites, rather than X12, located in the estuary with frequent exchange

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of freshwater and seawater. The mass loads of PFAAs in the Xiaoqing River in 2014 increased almost tenfold compared with our calculation in 2011 (Wang et al. 2014). Mass loads demonstrated a greater pattern of increasing emissions than did concentrations. Production activities in F1 under global PFOA emission reduction operations. PFOA is of next greatest global concern after PFOS. Since 2006, some official organizations had been conducting hazard assessments of PFOA, which resulted in restraints on the production and import of PFOA and related chemicals in the market. However, with no regulation on PFOA by the government in China, the production and use of PFOA could be immoderate. Meanwhile, developed countries in the EU and North America will still import fluoro-polymer/-telomer-based products, which PFOA was used as a processing aid during production processes. Thus, residual levels of PFOA were expected to increase, especially in China, where it is currently produced. Of the facilities studied, F1 started mass production of fluoropolymer in 2001, with an annual capacity of 3,000 tons for PTFE. The capacity was expanded to over 49,000 tons by the end of 2013, with an average annual growth of approximately 25%. Meanwhile, as a self-sufficient manufacturer, F1 can directly produce PFOA and other PFAAs, apply them in producing fluoropolymers and numerous intermediates or surfactants and sell various products to other manufacturers. PFOA is the predominant PFAA used in these processes, leading to the greatest emission to the environment. This is consistent with the results of this study. A large portion of F1’s products is exported to other countries, including several manufacturers that joined the USEPA 2010/2015 PFOA Stewardship Program. The annual progress reports of the Program told that the emissions of PFOA, precursors, and greater homologues to all media from fluoro-polymer/-telomer manufacturing, including all operations of the major manufacturers, reduced from 50 tons in 2006 to approximately 4 tons in 2013. However, mass loads at X22 indicated that the emission of PFOA from F1 was 159 kg/d, equivalent to approximately 58 tons for 2013. This was due to the following three facts: (1) Market demand for fluoropolymers is still solid; (2) No suitable substitutes can replace PFOA in producing most fluoropolymers. Heydebreck et al. (2015) investigated an alternative named hexafluoropropylene oxide-dimer acid (HFPO-DA), but the concentration of HFPO-DA was only 0.4% of that of PFOA in the downstream of F1; (3) Existing treatment technologies cannot efficiently remove PFOA from wastewater. Thus, it is strongly recommended that the shifts in emissions be considered when evaluating the actual reductions in emissions from the major manufacturers.

1.3.3 Emission and Transport of HBCD from a Large Producer Unlike PFAS, which are mainly emitted into the water, some emerging pollutants are emitted in the solid phase, such as HBCD. As a result, the emission source identification of HBCD requires monitoring their distribution in the soil environment,

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and the influence range of HBCD is limited compared to PFAS. Thus, a regional investigation into HBCD in surface soils from coastal cities in Northern China directly resulted in the finding of a large producer (Zhang et al. 2016). The sampling campaign was conducted in July 2015. The distance between the producer and the coastline was about 8 km. The waterbody could be divided into two sections: the wastewater discharge trench and the natural riverbed of the Mihe River, receiving the water from other trenches (Fig. 1.14). Transport of HBCD into the soil. HBCD was detected in soil, and its concentration ranged from 4.20 to 11,700 ng/g. All the concentrations were presented in dry-weight of soil. The concentrations of individual diastereoisomers were 0.57–1,620 ng/g for α-HBCD, 0.19–876 ng/g for β-HBCD, and 2.90–9,180 for γ-HBCD. Among the 24 sampling sites, the greatest concentration of HBCD occurred at the HBCD production facility, as expected. This value of 11,700 ng/g was higher than other investigations. Among the three diastereoisomers, γ-HBCD was predominant. The

Fig. 1.14 Sampling sites around the HBCD producer

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proportions to the total HBCD were 16 ± 8, 8 ± 6, and 76 ± 10% for α-, β- and γ-HBCD respectively (average value ± standard deviation). Since the production facility was the primary emission source of HBCD, this diastereoisomer profile was similar to the commercial, technical products composed of 70–89% γ-HBCD and 11–30% α- and β-HBCD. The relations between the diastereoisomer profile in the soil and in the industrial activities were discussed in our previous study (Zhang et al. 2016). In order to investigate the declining trend of HBCD in soil with distance from the point source, soils were collected at distances of 2, 4, and 6 km in eight directions. Without disturbance from human activities, air deposition is likely to be the significant input of HBCD into the bare land. As for the agricultural land, irrigation here usually depends on rainfall, and HBCD is not used in any agricultural chemicals, so air deposition can be considered the major input of HBCD in the farmland. With increasing distance, a dramatic declining trend of HBCD concentration was observed from 11,700 to 100 ng/g (median value) from the center to 2.0 km, and then the decrease slowed down and reached 28.6 ng/g (median value) at 4.0 km and 24.8 ng/g (median value) at 6.1 km. The decreasing rates of HBCD concentration were 0.85, 0.24, and 0.21% at a distance of 2.0, 4.0, and 6.1 km as in site S01, respectively. HBCD concentration in the soil decreased dramatically in the first 2 km. The spatial distribution of HBCD in the soil varied in different directions (Fig. 1.15). A prominent peak was noticed in the south, suggesting another HBCD point source. The highest concentration was in the southwest, possibly driven by the wind. The prevailing wind direction was SSE, SE, and S, with a total frequency of 38%. The strong wind direction was NE and NNE, with a frequency of 2% for wind force >7. Considering the HBCD spatial distribution and the wind observations, the strong wind was one of the drivers affecting the fate of HBCD in the soil. HBCD transport in water. Individual HBCD diastereoisomers were present in river water near the point source. α-HBCD ranged from 1.23 to 1,800 ng/L, β-HBCD ranged from 0.85 to 1,120 ng/L, and γ-HBCD ranged from 1.10 to 2,150 ng/L. The HBCD concentrations ranged from 3.28 to 5,080 ng/L. It was the highest concentration in published studies, which was not surprising because of the proximity to the production capacity. In the water, the average proportions of the three diastereoisomers were 39 ± 5, 24 ± 4, and 37 ± 5% (average value ± standard deviation) for α-, β- and γ-HBCD respectively. The diastereomer pattern can be changed by thermal processes, environmental factors, and physical property variation among the three diastereomers. Natural light exposure was found to change the diastereomer pattern of HBCD; after five-week light exposure in dust, the proportion of γ-HBCD decreased from 62 to 43% while α-HBCD increased from 25 to 43% (Harrad et al. 2009). Similarly, in our study, the intense sunlight in summer on shallow water could induce a diastereomer change and result in a drop of γ-HBCD proportion. In addition, the higher log Kow of γ-HBCD (5.07 for α-HBCD, 5.12 for β-HBCD and 5.47 for γ-HBCD) might lead to preferential sorption in sediments and loss from the water column relative to the other diastereomers.

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Fig. 1.15 The spatial distribution of ∑HBCD in the soil

The river was divided into two sections to explore the transport of HBCD in the water: the trench and the natural river. In the trench, 5 km upstream of the production site was chosen as a background point, and the concentration was 4.29 ng/L. After receiving the effluent, the concentration reached 5,080 ng/L but soon decreased to 75.3 ng/L after 2 km and then 27.9 ng/L after 4 km. The reduction occurred in the first 4 km, and the decreasing trend became less pronounced. Meanwhile, in the natural river, the trend of HBCD concentration variation was smaller and ranged from 4.98 to 41.3 ng/L. When the two sections mixed at the estuary, the concentration was 31.5 ng/L, which was not a negligible value and suggested a considerable amount of HBCD entering the sea. HBCD in the sediment. In the river and trench sediment, α-HBCD ranged from 0.49 to 1,020 ng/g, β-HBCD ranged from 0.46 to 383 ng/g, and γ-HBCD ranged from 0.57 to 5,340 ng/g. The total HBCD ranged from 1.52 to 6,740 ng/g in the sediments. HBCD in sediments had been widely reported worldwide. Concerning research on contamination from known point sources, the levels of HBCD in this study were close to the highest concentration in the world, which was found at a production site in Japan (7,800 ng/g) (Oh et al. 2014). The level of HBCD in sediment was about two orders of magnitude higher than those without a point source. The transport trend of HBCD in the sediment differed from that in the water (Fig. 1.16). The highest concentration was observed near the effluent, with a concentration of 6,740 ng/g. The concentration soon decreased to 610 ng/g after 2 km,

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68.3 ng/g after 4 km, and 1.52 ng/g after 7 km at the estuary site. The correlation between the logarithm of HBCD concentrations and the distance from the point source fitted a negative linear curve. Despite the impact from the river branches, the trend of HBCD concentrations in the major part of the river decreased by order of magnitude with distance. In the sediment, the average proportions of the three diastereoisomers were 19 ± 8, 13 ± 8, and 68 ± 15% for α-, β- and γ-HBCD, respectively. The predominant diastereoisomer was γ-HBCD. The proportion of γ-HBCD did not correlate with sediment TOC or water conductivity (p >> 0.05) in this case study. Even in such a small area, the proportion of γ-HBCD varied from 37.7 to 83.5%, which was inconsistent during transport. Environmental factors were assumed to affect the diastereomer pattern in the sediment. In published studies, γ-HBCD was usually the predominant diastereoisomer in sediments, but its proportion varied from 16.1 to 100% in different case studies. The lower proportion of γ-HBCD than the original formula has been explained by thermal isomerization from γ-HBCD to α-HBCD during thermal processing and a faster degradation rate of γ-HBCD. The proportion of HBCD diastereoisomers in the sediment and water was compared (Fig. 1.17). First, the diastereoisomer profile differed in the sediment and the water. In the sediment, γ-HBCD was the predominant diastereoisomer. The proportion for γ-HBCD was 38–84%, only 11–32% for α-HBCD, and 5–30% for βHBCD. However, there was no predominant diastereoisomer in the water, as γ-HBCD

Fig. 1.16 Transport of HBCD in the water and sediment. a Concentrations of HBCD diastereoisomers in the water and sediment. Columns in blue stand for the water concentration in the order of α-, β- and γ-HBCD from left to right. Columns in orange stand for the sediment concentration in the same order. b Trend of ∑HBCD and diastereoisomer concentration in water with distance from the plant. c Trend of ∑HBCD and diastereoisomer concentration in the sediment with the distance from the plant

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and α-HBCD had similar proportions. The proportion was 32–49% for α-HBCD and 30–45% for γ-HBCD. Second, in both the sediment and the water, the trends of diastereoisomer proportions fluctuated among the sampling sites, but α-HBCD and γ-HBCD were in opposition, with one increased while the other decreased. Environmental burden of HBCD. In order to assess the environmental burden of HBCD in this area, the mass inventories were calculated by Eq. (1.3). Ii = ∑Ci j Ai j dρ

(1.3)

C ij (ng/g) is the estimated concentration of diastereoisomer i in the soil cell j after the area was converted into a 50 × 50 matrix. The logarithms of concentrations were gridded by the Kriging Correlation in OriginPro 9.0. Aij is the soil cell area (m2 ). d is the thickness of the soil (m), which was assigned as 0.2 m. ρ is the bulk density of the dry soils (kg/m3 ), which was assigned as 1,500 kg/m3 . The mass inventories were 685 kg for α-HBCD, 415 kg for β-HBCD, and 3,906 kg for γ-HBCD, which meant a total burden of 5,006 kg of HBCD in the soil of this area. Moreover, the environmental burdens in the areas with radiuses of 2, 4, and 6 km were calculated by cutting the 50 × 50 matrix into three concentric circles. For the three areas with radiuses of 2, 4, and 6 km, the environmental burdens were 3,210, 3,770, and 4,590 kg, respectively. This suggested that 64% of HBCD occurred in the 2-km-radius circle, 75% of HBCD occurred in the 4-km-radius circle, and 92% of HBCD occurred in the 6-km-radius circle. To estimate the mass inventories of HBCD in the river sediment, the river was treated as two sections and divided into 50 cells. The concentration of each cell was interpolated from the logarithms of the measured concentrations. Under the setting of a sediment depth of 0.1 m and a sediment bulk density of 1,500 kg/m3 , the mass inventories were 3.6 kg for α-HBCD, 1.5 kg for β-HBCD and 20.4 kg for γ-HBCD, which meant a total burden of 25.5 kg of HBCD in the sediment of the trench. Meanwhile, in the natural river, the mass inventories

Fig. 1.17 Comparison of HBCD diastereoisomer contributions in a sediment and b water

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were 0.8 kg for α-HBCD, 0.5 kg for β-HBCD, and 3.5 kg for γ-HBCD with a total of 4.8 kg of HBCD in the sediment. Comparing the mass inventories of HBCD in the soil (5,006 kg) and the sediment (30.3 kg), it could be inferred that soil was the main sink of HBCD in this region around a large producer.

1.4 Temporal Variation of PFAAs Emitted from Fluorochemical Industry The production activities of the manufacturers generated continuous emissions of emerging pollutants to the environment. Moreover, the environmental concentrations of the pollutants depend on the emission and natural conditions, such as hydrological changes. Continuous monitoring is necessary to understand how the emission source influences the environment. For this purpose, we chose the Daling River basin with high emission of PFAAs as a case study area and carried out annual and seasonal monitoring of PFAAs in water (Wang et al. 2016b). The sampling sites were the same as shown in Fig. 1.6. Annual sampling campaigns were conducted in October of the years from 2011 to 2014, while seasonal samplings were conducted in January (winter), April (spring), July (summer) and October (autumn) in the year 2013.

1.4.1 Annual Trend The annual trend showed that PFBS and PFBA have always been the dominant PFAAs, followed by PFOA and C5–C7 PFCAs (Table 1.2, Fig. 1.18). One-way ANOVA analysis for PFAAs concentrations at sites downstream of the industrial parks in the Xihe River (sites 3–8X) indicated that PFBA levels showed a significant increase from 2011 to 2014, PFBS levels showed a significant increase from 2012 to 2014, and PFOA levels showed no clear trend. Among the C5–C7 PFCAs, C6 PFHxA was dominant, with the highest concentration up to 795 ng/L in 2013. C6 PFAAs were also considered essential substitutes for long-chain PFAAs. However, even in the site with the highest concentration, PFHxA level was a factor of 5 less than PFBA, indicating lower importance than C4 PFBA as a substitute. Regarding the spatial distribution, the highest PFBS and PFBA were associated with Park 1 (sites 3 and 4) throughout the four years (Fig. 1.18). For PFOA, the spike in concentration from site 3 was less evident than those of PFBS and PFBA, except in 2013. Further emissions from Park 2 maintained the elevated levels of the three dominant PFAAs from site 5 to site 8X, while the dilution from the tributaries might cause decreased levels in site 6. Along the Daling River from site 8X to site 12, concentrations were generally reduced by over 50% due to dilution. The fluctuation of concentrations in site 13 and site 14 at the estuary could be affected by factors such

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Table 1.2 Summary of PFAAs concentrations (ng/L) in annual monitoring PFBA

Time

PFPeA

PFHxA

PFHpA

PFOA

PFBS

PFOS

Annual 2011

2012

2013

2014

Min

0.31

0.04

0.03

0.04

0.09

0.04

Max

1346

82.2

59.2

15.9

348

2896

Mean

374

20.4

18.3

4.24

132

856

0.05 12.6 3.97

Median

223

15.9

14.3

3.34

102

386

2.50

Min

156

0.25

0.09

0.08

0.58

0.47

0.16

Max

1566

81.2

180

45.5

675

2341

2.27

Mean

276

19.6

49.4

12.7

200

516

0.89

Median

156

12.1

29.5

7.80

113

253

0.77

Min

1.62

0.05

0.08

0.10

0.61

0.74

0.06

Max

3 698

198

795

103

3948

2714

Mean

1051

41.8

100

22.3

533

830

11.4 3.59

Median

945

34.0

56.4

20.1

344

712

2.43

Min

1.42

ND

0.11

0.10

2.17

ND

0.47

Max

2575

56.1

196

55.0

772

3780

6.95

Mean

830

23.6

60.7

19.3

239

1090

2.60

Median

536

21.1

42,2

15.5

155

668

1.52

as the frequent mixture of fresh and saline water, aquaculture, construction activities, etc.

1.4.2 Seasonal Trend Differences in PFAA levels could be observed over the different seasons of 2013 (Table 1.3, Fig. 1.19). For PFBS, the overall concentrations from sites 1 to 14 were highest in summer (max. 3.87 mg/L) and autumn (max. 2.71 mg/L) with lower levels in spring (max. 1.98 mg/L) and winter (max. 690 ng/L), while the trend was autumn (3.70 mg/L) > summer (2.44 mg/L) > spring (1.97 mg/L) > winter (628 ng/L) for PFBA, and summer (2.28 mg/ L) > autumn (1.95 mg/L) > winter (753 ng/L) > spring (749 ng/L) for PFOA, respectively. PFOS was found at low concentrations, except notable 483 ng/L found in site 5, downstream of Park 2. This indicated that PFOS could still be produced and emitted in some cases. Relatively high C6 PFHxA has been emitted since the summer of 2013. It may be due to the unstable status of construction and production in the parks, which will be discussed later. The highest levels of the important compounds in river water were found in either summer or autumn. Given that rainfall and hence river flow (and dilution) tend to be highest in summer and autumn in China, this may appear as a surprise.

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Fig. 1.18 Temporal trends of main PFAAs in the Xihe and Daling River water from 2011 to 2014

Considering the occasional high emission of PFOS in spring, the seasonal pattern of PFAA emissions might depend on fluctuating demand from the market. Besides, constructing existing and new facilities would also influence emission patterns. All the factors could form general industrial cycles, which were also partially reflected in the trend of annual monitoring.

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Table 1.3 Summary of PFAAs concentrations (ng/L) in seasonal monitoring PFBA

Time

PFPeA

PFHxA

PFHpA

PFOA

PFBS

PFOS

Seasonal Winter

Spring

Summer

Autumn

Min

0.64

0.06

0.33

0.12

0.27

0.04

0.87

Max

628

139

183

29.4

753

690

39.9

Mean

231

38.7

45.0

8.20

202

209

7.94

Median

171

12.9

17.0

5.69

102

130

4.08

Min

1.36

0.05

0.02

0.08

0.47

0.04

0.59

Max

1 973

79.6

168

36.8

749

1980

483

Mean

351

15.7

32.8

8.53

150

409

54.0

Median

125

5.06

9.65

2.84

47.9

149

6.54

Min

5.78

0.21

0.15

0.38

2.51

0.26

1.02

Max

2 435

273

603

119

2 279

3870

21.2

Mean

643

60.1

122

26.8

495

969

4.27

Median

372

20.7

43.0

10.1

179

581

2.90

Min

1.62

0.05

0.08

0.10

0.61

0.52

0.06

Max

3 698

198

795

103

3 948

2714

11.4

Mean

1 051

41.8

100

22.3

533

830

3.49

Median

945

34.0

56.4

20.1

344

712

2.43

1.4.3 PFAAs Emissions Related to Construction and Production Activities The dominant PFAAs levels in terms of the annual and seasonal trends can be used to identify potential changes to emission sources and the dynamic of market demand and production capacity. Furthermore, concentration ratios can provide comparisons of the temporal variations of the dominant PFAAs. The mean values of the PFBS/PFBA ratio from site 3 to site 8X were 2.7 and 3.0 in 2011 and 2012, respectively. However, the ratios became 0.8 in 2013 and 1.4 in 2014 (Table 1.4). The mean values for PFBA/PFOA ratios showed a general increasing trend from 2012 to 2014. These might imply that the emission of PFBA increased more than PFBS and PFOA, and PFBA was more critical in the fluorochemical applications. This was consistent with the two parks’ planning and construction of fluoropolymer facilities (Fig. 1.20). In our previous study in 2011, limited fluoropolymer facilities existed in the study area, and production processes were also limited (Wang et al. 2015). However, due to continual development, this task has become more complex. From 2011 to 2014, dozens of facilities were planned to construct, produce, or under production (Fig. 1.20a), especially the number of facilities under production showed a steadily increasing trend. This brought an increasing capacity of fluorochemical products related to the emission of PFAAs (Fig. 1.20b). Especially, the production of fluorocarbon alcohol (FCA) and PTFE would directly generate or use PFAAs (Fig. 1.21),

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Fig. 1.19 Levels of main PFAAs in the Xihe and Daling River water in four seasons of the year 2013 Table 1.4 Concentration ratios of PFBS/PFBA and PFBA/PFOA in the sites 3 to 8X Site

PFBS/PFBA 2011

2012

PFBA/PFOA 2013

2014

2011

2012

2013

2014

3

1.9

2.3

0.7

1.4

9.4

0.3

0.9

4.6

4

2.2

1.4

0.7

1.5

3.9

3.5

3.1

10.6

5

5.3

1.6

0.9

1.3

1.3

2.1

2.3

3.3

6

3.0

7.0

1.0

1.3

2.7

0.4

2.2

3.3

7

2.1

2.9

0.8

1.3

3.5

0.9

2.4

2.4

8X

2.0

2.7

0.8

1.3

3.4

0.9

2.5

2.5

Mean

2.7

3.0

0.8

1.4

4.0

1.3

2.2

4.5

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Fig. 1.20 a Construction status of fluorochemical facilities, b the main organo-fluorine products in the two parks, and c temporal trends based on the normalization of corresponding values divided by median

but the trends were quite different (Fig. 1.20c). FCA, including PFAIs, FTOHs, and related products, are featured in the study area, and their production capacity showed an increasing trend. However, the production of PTFE showed a decreasing trend. This might be related to the production activities of the manufacturer in the Xiaoqing River catchment, where the PTFE capacity has been expanding rapidly and resulted in staggeringly high emissions of PFOA (Wang et al. 2016a). In addition, there is a large and increasing capacity for producing various intermediates for pesticides and medicines using organofluorine. Developing a central wastewater treatment plant (WWTP) in the parks could also affect the emission of PFAAs to the river. Some processes were explicitly designed to remove fluorine in wastewater from individual fluorochemical facilities in Park 2, which needs a more detailed study.

1.5 Executive Summary The environmental monitoring of multiple pollutants has got important findings not only on their wide distribution but also on their unique features. In general, due to poor solubility in water, OCPs and PCBs were mainly detected in sediments,

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Fig. 1.21 Main production processes and products in the parks correlated with the emission of the dominant PFAAs

PBDEs and HBCDs were mainly detected in sediments and soils. PFASs, which have greater solubility, were mainly detected in the hydrosphere. The conventional POPs, such as OCPs and PCBs, have a more extended global restriction period; they were identified with non-point source pollution and were accumulated in large rivers, lakes, and bays. While for emerging POPs, such as PBDEs, HBCDs, and PFAS, due to the rapid urbanization and industrialization process, emerging manufacturers have been expanding rapidly in recent years, and led to staggeringly high emissions in more minor scales, such as those found in the Daling River basin and Xiaoqing River basin for PFAAs, as well as the producer for HBCD. Even for the chemicals in the same group, their production and emission could be very different. As emission hot spots for PFAAs, the Daling River basin featured the emission of short-chain PFBS and PFBA, while the Xiaoqing River basin featured the emission of long-chain PFOA. The monitoring work was conducted after the Stockholm Convention listing PFOS, and related compounds in 2009, so limited emission of PFOS was observed from the fluorochemical manufacturers. After the monitoring work, PFOA and related compounds were also listed in 2019. Thus, it is predictable that the point-emission of PFOA will reduce in the future due to the restrictions. However, as POPs, their residues in the environment can last for an extended period. Meanwhile, the fluorochemical industry is developing fast because PFAS enables critical products. It is a similar case for many other emerging pollutants. Continuous baseline data monitoring is still necessary, and special attention should be paid to the alternatives, together with their analytical methods, and their adverse effects.

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Chapter 2

Source Identification and Emission Estimation of Emerging Pollutants Shuangwei Xie and Yonglong Lu

Overview Monitoring emerging pollutants in the environment is valuable to locate the notable high-emission sources, usually the manufacturers with direct production or massive usage of the chemicals. However, due to the numerous applications of the chemicals, many more industries with considerable emissions challenge the analysis of emission characteristics and quantification of emission loads. Meanwhile, the residues of the chemicals in consumer products can be released to the environment as domestic emission sources, especially in urban areas with limited industries. The source identification and emission estimation of emerging pollutants are essential to understanding which industries make the main contribution and the differences between industrial and domestic emissions. Furthermore, the results could provide a solid scientific basis for effective emission control. In this study, we took PFOS as an example. PFOS and related substances are synthetic chemicals manufactured for their desirable properties of chemical stability, high surface activity, and water and oil repellence (Giesy and Kannan 2001, 2002). As surfactants and performance chemicals, they have been widely used in various consumer products and industrial processes, such as fire-fighting foams, carpets, leather, fabric, paper, cleaning products, pesticides, hydraulic fluids, semiconductors, S. Xie China National Offshore Oil Corporation, Beijing 100010, China Y. Lu (B) State Key Laboratory of Marine Environmental Science, Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems, International Institute for Sustainability Science, College of the Environment and Ecology, Xiamen University, Fujian 361102, China e-mail: [email protected]; [email protected] S. Xie · Y. Lu State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Lu et al. (eds.), Ecological Risks of Emerging Pollutants in Urbanizing Regions, https://doi.org/10.1007/978-981-19-9630-6_2

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photolithography, and metal plating. Although the major global producer voluntarily halted the manufacture of PFOS-based products in the 2000s, and the production and use of PFOS had been restricted or eliminated in most developed countries, PFOS and related substances were still manufactured and used in relatively large quantities in the early 2010s in China because of the lack of cost-efficient alternatives. Consequently, while in developed countries, most emissions of PFOS arise from consumer products during use and disposal (Paul et al. 2009), industrial processes in which PFOS-related compounds were produced or applied were still significant sources of PFOS releases to the environment in China. The industrial emissions of PFOS equivalents from five major industries in China were estimated based on the production/use and emission factors of PFOS-related chemicals at the provincial level in 2010 (Xie et al. 2013a). The contributions of various industrial emissions to the total emissions were extracted and compared with those in the European Union. The domestic emissions of PFOS equivalents were conducted based on the assumption that diffusive releases of PFOS from various consumer products or residues would be collected by municipal wastewater treatment plants (WWTPs) before entering the environment, and PFOS-precursors would break down to PFOS within the wastewater treatment processes (Sinclair and Kannan 2006). Thus, the mass flows of PFOS should reflect domestic emissions in the service areas of the municipal WWTPs, and further show correlations with catchment population or other geographic indicators. If a significant relationship was found valid, this enabled the predictions of domestic emissions for other areas without monitoring data. This methodology was used to estimate the domestic emissions of PFOS equivalents in the eastern region of China (Xie et al. 2013b). The comparison of the provincial emissions of PFOS between domestic and industrial sources was also conducted. Three major urbanized regions, including Beijing-Tianjin Region, the Yangtze River Delta, and the Pearl River Delta, were remarkable in both industrial and domestic emissions.

2.1 Industrial Emission Estimation of PFOS 2.1.1 Identification of Industrial Sources Generally, industrial sources of PFOS consist of manufacture and industrial uses of PFOS-related chemicals. China began to produce PFOS-based products much later than developed countries. The earliest reported production was merely 30 t in 2001, and commercial manufacturing of PFOS started in 2003 with a total output of less than 50 t. Because of the rapid growth of domestic demands and increasing overseas applications in metal plating, fire-fighting foams, photographic, semiconductor and aviation industries, the annual yield of PFOS-related chemicals increased sharply from 2003 to 2006. According to surveys by the China Association of Fluorine and

2 Source Identification and Emission Estimation of Emerging Pollutants

43

Fig. 2.1 Production of PFOS from 2001 to 2011 in China

Silicone Industry, currently, there are about 15 enterprises producing PFOS and its derivatives, most of which are situated in Hubei and Fujian provinces, with 2–3 in Shanghai and Guangdong provinces. In recent years, the national output was 220– 240 t/a for export and domestic sales, nearly half of which were exported to Brazil, the EU, and Japan. The trend for the production of PFOS-related compounds in China from 2001 to 2011 was presented in Fig. 2.1, and the cumulative historical production of PFOS-related chemicals was estimated to be 1800 t in China. In China, different PFOS-related chemicals have been widely used in various industries, including textiles, pesticides, fire-fighting foams, semiconductors, metal plating, petroleum, cleaning products, leather, photography, aircraft hydraulic fluids, and paper treatment. For estimating emission, PFOS-related chemicals have been divided into three groups, PFOS-salts, PFOS-substances, and PFOS-polymers, according to the relative difficulty of production (Fig. 2.2). PFOS-salts, effectively PFOS itself, attributing direct emission of PFOS to the environment, are widely applied in metal plating, firefighting foams, aviation, and pesticides production. PFOS-substances, derived from perfluorooctane sulfonyl fluoride (POSF) and their simple derivatives, include N-alkyl substituted perfluorooctane sulfonamides (FOSAs) and N-alkyl substituted perfluorooctane sulfonamidoethanol (FOSEs)-type substances. They are considered potential sources of PFOS through degradation, and this type of PFOS-related chemicals are used in paper treatment and the semiconductor industry. PFOS polymers, including the higher molecular weight polymers derived largely from FOSEs, are assumed to break down to PFOS in the environment and may contain residual PFOS-substances, leading to PFOS releases. PFOS-polymers are used in textile and leather treatment. Both PFOS-salts and PFOSpolymers may be used in the photography industry (Brook et al. 2004). The industrial sources of PFOS, based on the types of PFOS-related chemicals produced or used, can be identified as direct sources and indirect sources (Fig. 2.3). Direct sources were

44

S. Xie and Y. Lu

Fig. 2.2 Three categories of PFOS-related chemicals and example structures (R = H or alkyl)

defined as releases during the manufacture and application of PFOS. In contrast, indirect sources were defined as releases of PFOS as impurities formed during manufacture of POSF-derivatives, or by the breakdown in the environment from PFOS-precursors (Paul et al. 2009).

2.1.2 Estimation of Industrial Emission: Methodology PFOS-related industries, including PFOS production, textile treatment, metal plating, fire-fighting, and semiconductors, were identified as the major sources of PFOS emissions (Liu et al. 2008). Thus, the five industries were selected for analysis of PFOS releases. The estimation was carried out with the production/use quantity of PFOSrelated substances and the emission factors for a specific industry (Fig. 2.4). The data on industry-specific production and consumption in China was obtained from officially published sources. However, there were no available emission factors in China; thus, the emission factor data were extracted from relevant emission scenario documents or research reports by other countries or organizations (Brook et al. 2004;

2 Source Identification and Emission Estimation of Emerging Pollutants

45

Fig. 2.3 Classification of industrial sources of PFOS in China

Fig. 2.4 General methodology for estimating industrial emission of PFOS in China

European Commission 2003; Organisation for Economic Co-operation and Development 2004). For comparison between different industries and regions, the environmental releases of PFOS-related chemicals were uniformly converted to emissions of PFOS equivalents based on the respective formation factors, which presented the degradation yields of PFOS by weight from PFOS-substances and PFOS-polymers, respectively (Brook et al. 2004). PFOS manufacture. Previously used by global producers, ECF is the standard production process in Chinese PFOS manufacturers. The PFOS emission scenario of production processes overseas could be applied in China since there was no significant difference between them. The total historical global production of PFOS equivalents was about 96,000 t, while the estimated releases into water and air during

46

S. Xie and Y. Lu

production were 230–1450 and 435–575 t, respectively (Paul et al. 2009). Applying the same emission factors, the estimated historical releases of PFOS from Chinese producers were 4.3–27 t to water and 8.2–11 t to air, and the recent annual national emissions into water and air were 0.55–3.5 and 1.0–1.4 t/a, respectively. Considering that about 15 producers existed in China, the average amount released on the local scale was estimated to be 38–231 kg/a to water and 69–92 kg/a to air. Environmental releases of PFOS during the production process in Hubei, Fujian, Shanghai, and Guangdong provinces were calculated based on the market share of PFOS manufacture in different provinces. Textile treatment. PFOS-related chemicals have been applied to manufacture textile finishing agents for the treatment of high-quality cloth to impart water, oil, and soil resistance properties, which are considered as PFOS-polymers, containing residual PFOS substances at a level of 1% (C PFOS-substance ). The average content of PFOS polymers in the formulations for textiles (C PFOS-polymer, finishing agent ) was ~27% (Brook et al. 2004). Based on the Emission Scenario Document on textile processing, losses are from the back coating of textiles, with an emission factor (EF water, textile ) of 1.4% (OECD 2004). The emissions of PFOS-polymers and PFOSsubstances would be converted to PFOS emissions with respective formation factors. The formation factor of PFOS substances (FF substance ) is 94% based on the relative molecular weights, and the formation factor of PFOS-polymers is used as 30% (FF polymer ) because the PFOS moiety makes up, on average, about 30% for the polymers by weight (Brook et al. 2004). Equation (2.1) is used to estimate the emissions of PFOS equivalents from textile treatment to water (E textile, water ) on a regional scale. Qfinishing agent denotes the amount of finishing agents consumed per year in a region. Etextile, water = Qfinishing agent · CPFOS−polymer, finishing agent · EFwater, textile ) ( · FRPFOS−polymer + CPFOS−substance · FRPFOS−substance

(2.1)

Metal plating. In order to prevent the formation of mists containing potentially harmful components, PFOS-related chemicals, which are of the PFOS-salts type, are mainly used as mist suppressants during the metal plating process. According to the Emission Scenario Document on Metal Finishing, releases of PFOS from a relatively large-scale processor to local water and air compartments were derived to be 0.36 and 0.66·10−3 g/day, respectively (Liu 2008). The content in the plating bath would be discarded after use for a long time; therefore, on a larger scale, it is assumed that all of the PFOS-salts used in this area in a year are released into the environment (Brook et al. 2004). Hence, the emission factors on a regional scale are 0.998 to water (EF water, plating ) and 0.002 to air (EF air, plating ). Equations (2.2) and (2.3) could calculate regional emissions of PFOS to water (E plating, water ) and to air (E plating, air ), where Qmist suppressant is the national use level of mist suppressants, M plate, region and M plate, nation is the production of coated plate in a region and in China, respectively. Eplating, water = Qmist suppressent · EFwater, plating · Mplate, region /Mplate, nation

(2.2)

2 Source Identification and Emission Estimation of Emerging Pollutants

Eplating, air = Qmist suppressant · EFair, plating · Mplate, region /Mplate, nation

47

(2.3)

Fire-fighting. Aqueous fire-fighting foams (AFFF), which are synthesized by PFOS-related chemicals and considered to be of the PFOS-salts type, are mainly applied for fire protection in petrochemicals, fire brigades, military facilities, etc. There are more than 50 enterprises producing AFFF in China, consuming approximately 100 t of PFOS-related chemicals per year. Because of a lack of specific data regarding emissions from AFFF production, emission factors of the formulation process from the Technical Guidance Document (TGD) are used, which are 0.02 to water and 0.001 to air (European Commission 2003). The resulting releases of PFOS from a typical AFFF producer to local water and air compartment were 40 and 2.0 kg/a, respectively. Larger scale emissions are not considered for this use pattern. Annually, the fraction of the AFFF stock used by fire-fighting services is 10% (F use ) (Liu 2008). The concentration of PFOS-salt in the foams is 0.4–0.6% by weight (Lim et al. 2011), the average of which, i.d. 0.5% (C PFOS, AFFF ), is used in the estimation. Based on the assumption that there is no containment of the foam and water, which is the case in China, 50% (EF water, AFFF ) of the release goes to surface water without treatment and 50% (EF soil, AFFF ) to soil (Brook et al. 2004). Equations (2.4) and (2.5) can be used to calculate the regional emissions of PFOS to water (E AFFF, water ) and soil (E AFFF, soil ), where QAFFF, stock is the amount of AFFF stockpiled in China, N fire, region and N fire, nation are the number of fire accidents happened per year in a region and China, respectively. EAFFF, water = QAFFF, stock · Fuse · CPFOS, AFFF · EFwater, AFFF · Nfire, region /Nfire, nation EAFFF, soil = QAFFF, stock · Fuse · CPFOS, AFFF · EFsoil, AFFF · Nfire, region /Nfire, nation

(2.4)

(2.5)

Semiconductors. In the semiconductor industry, PFOS-related chemicals are mainly used in photoresists. The types of PFOS-related chemicals used here are PFOS-substances, the content of which in photoresists is up to 0.1% (C PFOS-substance, photoresist ). According to the mass balance estimation made by ESIA (European Semi-Conductor Industry Association) and SEMI (Semiconductors Equipment and Materials International), the emission factor to water (EF water, semiconductor ) is 53.3% (Brook et al. 2004; Liu 2008), and using Eq. (2.6), the regional emissions of PFOS equivalents (E semiconductor, water ) can be estimated, where Qphotoresist denotes the quantity of photoresists consumed per year in China, M IC, region and M IC, nation are the annual output of the integrated circuit in a region and China, respectively. Esemiconductor, water = Qphotoresist · CPFOS - substance, photoresist · EFwater, semiconductor · FRPFOS - substance · MIC, region /MIC, nation

(2.6)

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2.1.3 Summary of the Emissions of PFOS Equivalents The emissions of PFOS equivalents from various industries mentioned above, including PFOS manufacture, textile treatment, metal plating, fire-fighting, and semiconductor, in 31 provinces of China could be estimated (Table 2.1), with the industryspecific statistical data of production and consumption in 2010. Different provinces have diverse and numerous enterprises of these PFOS-related industries. More than thousands of metal plating enterprises are densely located in Hebei, Shanghai, Jiangsu, Hubei, and Liaoning provinces, where steel and mechanical treatment industries are relatively developed in China. As for southeastern coastal provinces, such as Jiangsu, Guangdong, Zhejiang, and Fujian, there is mainly the distribution of textile and apparel enterprises. Semiconductor enterprises are scattered in coastal regions and some western provinces like Gansu and Sichuan. Besides, AFFFs are widely used in different regions.

2.1.4 Contributions of Various Industrial Sources In China, the total release amount of PFOS equivalents in 2010 from major industrial sources was estimated at 70 t. The PFOS manufacturers, mainly dispersed in Hubei and Fujian Provinces, lead to the estimated release of PFOS into the environment between 1.6 and 4.8 t/a. On the national scale, consumer of PFOS in the metal plating industry was considered the largest source of PFOS pollution, followed by textile treatment, fire-fighting, and semiconductor industry, with environmental releases of 35, 22, 9.1, and 0.25 t/a, respectively. The relative contributions of these sources to PFOS emissions in China are illustrated in Fig. 2.5, compared with those in the European Union (Brook et al. 2004). Metal plating, the largest source of PFOS release, contributed approximately 50% of the total emission in China and the EU, while the semiconductor industry only accounted for a tiny portion in China (0.36%) and the EU (1.2%). Apart from these, PFOS emission profiles were remarkably different between China and the EU. As China is the world’s largest textile and apparel producer and exporter, the textile treatment contributed 32% of the total PFOS release, which is much higher than 5.2% in the EU. Likewise, fire-fighting foams industries contributed 13% of the total in China, while in the EU, probably due to their smaller population and special fire protection measures, it was not a significant contributor to the emission, only accounting for 2.9% of the total PFOS emission. As the remaining PFOSproducing country, another critical PFOS source in China was PFOS manufacture, which contributed 4.6% of the total and was a unique feature in the emission profile compared to the EU. Although the paper treatment industry in China lacked specific information on using PFOS for evaluation, the EU was an important contributor

1.0–1.4

0

0

0.083–0.11

0

0

0

0

0

0

0.55–3.47

0

0.011–0.069

0.33–2.1

0

0.044–0.28

0

0

0.16–1.0

0

0

0

0

0

0

0

0

0

0

0

China

Jiangsu

Guangdong

Hubei

Zhejiang

Shanghai

Hebei

Shandong

Fujian

Liaoning

Tianjin

Anhui

Beijing

Sichuan

Inner Mongolia

Henan

Hunan

Jilin

Xinjiang

Jiangxi

0

0

0

0

0

0.31–0.41

0

0

0.62–0.83

0.021–0.028

Water

Water

0.16

0.038

0.022

0.13

0.41

0.13

0.20

0.022

0.15

0.026

0.12

2.4

2.2

0.30

0.67

3.8

0.30

5.3

5.6

22

Textile treatment

Air

PFOS production

Region

0

0.12

0

0.36

0.36

0.47

0.58

1.1

1.5

2.2

3.3

0.82

2.3

4.8

4.7

2.1

3.4

2.1

4.5

35

Water

Metal plating

0

2.3E-04

0

7.3E-04

7.1E-04

9.4E-04

1.2E-03

2.2E-03

3.0E-03

4.4E-03

6.6E-03

1.6E-03

4.6E-03

9.7E-03

9.5E-03

4.2E-03

6.8E-03

4.2E-03

9.0E-03

0.070

Air

Table 2.1 Provincial emissions of PFOS equivalents from major industrial sources in China (t/a)

0.16

0.18

0.27

0.10

0.12

0.30

0.21

0.19

0.18

0.040

0.19

0.14

0.25

0.16

0.20

0.13

0.32

0.21

0.18

4.6

Water

Fire-fighting

0.16

0.18

0.27

0.10

0.12

0.30

0.21

0.19

0.18

0.040

0.19

0.14

0.25

0.16

0.20

0.13

0.32

0.21

0.18

4.6

Soil

0

0

0

0

0

0

0.010

9.7E-03

1.9E-04

3.4E-03

3.9E-04

4.6E-05

7.7E-04

5.0E-05

0.044

0.012

3.8E-06

0.062

0.086

0.25

Water

(continued)

Semiconductors

2 Source Identification and Emission Estimation of Emerging Pollutants 49

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Chongqing

Shaanxi

Shanxi

Ningxia

Heilongjiang

Gansu

Yunnan

Guangxi

Qinghai

Guizhou

Hainan

Tibet

0

0

0

0

0

0

0

0

Air

PFOS production

Water

Region

Table 2.1 (continued)

2.7E-04

1.1E-03

1.6E-03

6.6E-03

0.038

4.0E-03

4.7E-03

0.021

0.028

9.5E-03

0.039

0.052

Water

Textile treatment

0

0

0

0

0

8.6E-03

0.065

0

0

0

0

4.9E-03

Water

Metal plating

0

0

0

0

0

1.7E-05

1.3E-04

0

0

0

0

9.8E-06

Air

7.5E-03

0.036

0.057

0.056

0.044

0.071

0.039

0.10

0.12

0.15

0.16

0.17

Water

Fire-fighting

7.5E-03

0.036

0.057

0.056

0.044

0.071

0.039

0.10

0.12

0.15

0.16

0.17

Soil

0

0

8.0E-05

0

1.4E-03

0

0.021

0

0

0

0

0

Water

Semiconductors

50 S. Xie and Y. Lu

2 Source Identification and Emission Estimation of Emerging Pollutants

51

Fig. 2.5 Share of major PFOS industrial sources in China (2010) and European Union (around 2000)

to PFOS emissions, with a 38% portion supported by previous reports (Brook et al. 2004). Therefore, further investigation should be done to determine the use of PFOSrelated chemicals in this area in China.

2.1.5 Spatial Distribution on the Industrial Emission Sources Emission patterns of PFOS had significant differences among various provinces in China, which was caused by various degrees of economic development and industrialization. In Fig. 2.6, ArcGIS mapping presented the 2010 annual emission rates and source patterns in individual provinces, autonomous regions, and municipalities directly under the central government jurisdiction. The annual PFOS emissions at the provincial level, with widely varied source patterns, ranged from 10 t in Jiangsu to 0.015 t in Tibet. Jiangsu province, a typical industrial province where the GDP ranks second in China, contributed the largest portion of PFOS emission, with a source pattern including 53% for textile treatment and 43% for metal plating. A similar pattern could be seen in Guangdong, Zhejiang, Shanghai, Shandong, and Fujian provinces, all heavily industrialized regions located in the southeast coastal areas of China. For instance, Guangdong, the most developed province in China specializing in manufacturing, ranked second in the emission, of which textile treatment and metal plating accounted for 93%. In the provinces and municipalities located in the coastal areas of the North Bohai Sea, such as Hebei, Liaoning, Tianjin, and Beijing, metal plating was the most significant PFOS source and responsible for 73%–95% of the total PFOS emission. Most provinces in the west of China and several northeastern provinces, like Jilin and Heilongjiang, had a source pattern dominated by fire-fighting services and substantially lower emission rates than China’s eastern region. Moreover, it is worth emphasizing that PFOS

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Fig. 2.6 Annual PFOS emissions and source patterns at the provincial level in China in 2010

manufacture in Hubei and Fujian contributed considerably to PFOS emissions, which deserves more attention. The industrial emission of an individual province depends on its area, location, and status of socioeconomic development. As shown in Fig. 2.7, the annual PFOS emissions of various provinces were normalized to the area (emission density) and GDP (emission intensity). Overall, the PFOS emission density decreased from eastern to western China. The eastern coastal provinces, the most intensively industrialized regions in China, showed significantly higher emission density of PFOS at an average of 14 g/km2 ·a. Except for the cities directly under the central government’s jurisdiction, which are small in size (e.g., Shanghai), Jiangsu, Zhejiang, Guangdong, Fujian, Hubei, and Shandong had the highest emission densities, ranking in the top six. They are either major PFOS producers (Hubei and Fujian) or among China’s most economically developed provinces (Guangdong, Jiangsu, Shandong, and Zhejiang). The GDP-normalized map (Fig. 2.7, panel below) is similar to the emission density, except for Hubei and Fujian provinces, with extremely high emission intensity. Interestingly, they both show the exceptional contribution to PFOS emission made by PFOS manufacturers, compared to other provinces, which implied that the environmental releases of PFOS during manufacture were quite intense.

2 Source Identification and Emission Estimation of Emerging Pollutants

53

Fig. 2.7 Spatial distribution of PFOS emission from industrial sources normalized to area (emission density) and GDP (emission intensity)

2.1.6 Uncertainty Analysis There may exist some uncertainties and limitations in the estimation of PFOS emission. As new POPs, the study of PFOS emission is still at an initial stage in China. Getting access to the emission factors suitable for China’s situation is resource and time-consuming and has not been accomplished yet. However, due to the urgent necessity for controlling PFOS emissions and effectively implementing the “Stockholm Convention” for China, it is significant to have a systematic understanding

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of PFOS emission status. In this case, the estimation applied the emission factors for the EU or other developed countries. Consequently, the estimates may be relatively conservative compared to the actual values since the developed countries have enforced more strict emission regulations. This research only discussed the main PFOS-related industries because of a lack of detailed information about PFOS consumption in China. Some industries using minimal quantities of PFOS-related chemicals, including paper making, oil exploitation, cleaning products, photo-electricity industry, etc., may also be potential industrial sources of PFOS released to the environment. As a result, the industrial emissions of PFOS in China might be underestimated. Moreover, because the use amounts of PFOS-related chemicals were unavailable in most provinces, the provincial emissions were calculated with production value or volume of each industry, which may be a another area of uncertainty. Furthermore, considering the lack of accurate data on the degradation rates of polymers and the extent to which PFOS may be produced, the estimated emissions of PFOS-polymers in this study could merely reflect the worst case in which all polymers would convert to PFOS. Hence, further investigations should be conducted to improve the estimation and explore the future environmental behaviors and ecological risks of PFOS-related chemicals.

2.2 Domestic Emission Estimation of PFOS 2.2.1 Theoretical Assumption It’s estimated that the global historical releases of PFOS equivalents from consumer use and disposal were 42,000 and 235 t to water and air, respectively (Paul et al. 2009), which can be further inferred that the majority of domestic emissions from various PFOS-containing consumer products are released to the aqueous environment, while a relatively small amount of precursor residuals will volatilize into air and degradate (Armitage et al. 2009). It is assumed that both PFOS and precursor chemicals released to the atmosphere, dust, and water would enter the municipal wastewater collection system by cleaning, wiping, and washing the products and indoor environment. However, the municipal wastewater system is not likely to effectively collect all the PFOS-related chemicals from the air, especially outdoor air, which may lead to a slight underestimation of the actual PFOS emissions. As the character of fully fluorinated, the aerobic decomposition of PFOS is limited (Lange 2001), which indicates that losses in the wastewater treatment processes can be neglected. Transformation of PFOS precursors is assumed to occur during the treatment processes (Loganathan et al. 2007). For instance, there is evidence that 2-(N-ethyl-perfluorooctane-sulfonamido) ethanol (N-EtFOSE alcohol) and 2-(Nethyl perfluorooctane sulfonamido) acetic acid (N-EtFOSAA) are bio-transformed

2 Source Identification and Emission Estimation of Emerging Pollutants

55

to PFOS in the activated sludge treatment process (Boulanger et al. 2005; Lange 2000). Nevertheless, some PFOS precursors have also been detected in the effluent (Motegi et al. 2012), indicating that part of them could not be transformed during the WWTPs and would be degraded into PFOS in the environment. In addition, the occurrence of PFOS in sludge from several WWTPs suggests some partitioning from the waste stream to solids during the treatment processes (Higgins et al. 2005). Water-to-air transport of PFOS and its precursors is also believed to exist within the WWTPs, especially during the aeration process (Vierke et al. 2011). With shortage of related monitoring data in China, not all these emission pathways can be fully quantified. Previous studies on the occurrence of PFOS and precursor compounds in effluent, sludge, and air from WWTPs in China and other countries indicate that PFOS mass flows in effluents and sludge are assumed to be the predominant emission pathways, which can approximately reflect the domestic emissions in the areas served by municipal WWTPs. According to the above assumptions, by measuring the PFOS concentrations in the effluent and sludge, the domestic emission density (ED, mg/km2 ·day) of PFOS equivalents occurring in the service area of a municipal WWTP can be estimated as follows: ( ) ED = Qeffluent × CPFOS, effluent × 10−3 + Msludge × CPFOS, sludge /AM W W TP (2.7) where Qeffluent is the effluent flow (m3 /day), i.e., treatment capacity of the municipal WWTP, M sludge is the production of dry sludge (t/day), AMWWTP denotes the area served by municipal WWTP (km2 ), C PFOS, effluent and C PFOS, sludge are PFOS concentrations in the effluent (ng/L) and sludge (ng/g dry weight), respectively.

2.2.2 Study Area and Data Collection Previous studies monitoring PFOS concentrations in WWTP effluent and sludge were limited in China, most of which took the eastern coastal region of China as the research area. The region, consisting of 7 provinces and three municipalities directly under the central government, only occupies 10% of the land area in China, contains 40% of the population, but contributed 62% of the GDP in 2010. The most important economic arteries of China, the Yangtze River Delta, Pearl River Delta, and Bohai Rim, are all located in this region, with significant urbanization and rapid economic development that have further induced increasing amounts of municipal wastewater. The sewage discharge in this region was nearly 22 billion cubic meters in 2010, accounting for 58% of the national total. Most WWTP effluents in the coastal region are directly released into the marine environment, which acts as a sink for PFOS and may eventually impact the global environment (Ahrens et al. 2009). At the same time, humans may be exposed to PFOS in the marine environment when they feed on aquatic products such as fish and shellfish. Furthermore, compared to

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other regions of China, industrial emissions of PFOS equivalents in this region have been significantly higher (Xie et al. 2013a). Thus, the eastern coastal region of China has been set as the study area. From all the WWTPs providing monitoring data, 37 municipal WWTPs in the eastern coastal provinces mainly receiving domestic influents were selected. Table 2.2 presented the mean concentrations of PFOS in effluent and sludge and detailed information on these selected WWTPs, such as location, treatment capacity, main treatment processes, sludge production, service area, and population. Calculated with Eq. (2.7), the domestic emission densities of PFOS equivalents in the areas served by these municipal WWTPs are listed in Table 2.2.

2.2.3 Modeling the Domestic Emission Aiming to explore the relationship with pressure elements present in the service areas of municipal WWTPs, the calculated emission densities were analyzed, further suggesting potential domestic sources of PFOS. It has been shown that domestic emissions of PFOS caused by wide-dispersive use of related compounds can be correlated with population (Murakami et al. 2008; Pistocchi and Loos 2009). Thus, emission densities are presumably related to population density (PD), which could be derived from the service population divided by each municipal WWTP service area. Besides, since PFOS-related chemicals are usually applied to high-quality products to improve material performance, residents’ living standards and consumption capacity are likely to affect the PFOS releases from domestic activities. Therefore, per capita disposable income (PCDI) is identified as the most important determinant of consumption and is often used to measure the regional living standard, used as an additional parameter here. Data from the areas served by the selected municipal WWTPs were examined for their quantitative relationships with domestic emission densities of PFOS equivalents. Both linear and nonlinear regression models were tested for the quantitative relationships by using SPSS software. Based on the fitting equation ED = f (PD, PCDI) and corresponding statistical data on population density and per capita disposable income, the regional domestic emission density of PFOS equivalents could be estimated, further multiplied by the area of the region, finally obtaining the domestic emission loads. The general methods and processes for estimating domestic emission density and load of PFOS equivalents are illustrated in Fig. 2.8. The methodology is valuable in analyzing the similar cases of other regions in China based on sufficient and available data on PFOS concentrations in municipal WWTPs in the future.

3.9

1.7

3.3

WWTP-16

WWTP-17

WWTP-18

0.60

n.d

2.9

n.d

9.2

2.9

WWTP-14

2.6

169

7.0

WWTP-13

WWTP-15

93

52

8.9

7.2

48

42

148

0.79

WWTP-11

Liaoning

5.6

1.4

WWTP-12

2.1

WWTP-10

60

6.5

WWTP-8

Tianjin

3.6

WWTP-7

WWTP-9

1.1

5.3

WWTP-5

WWTP-6

2.0

WWTP-4

3.1

8.6

2.4

WWTP-3

1.8

9.7

2.8

Sludge (ng/g dry weight)a

3.4

Beijing

WWTP-1

Effluent (ng/L)

PFOS concentration

WWTP-2

Province

Name

5.1

0.53

11

34

18

194

22

110

53

5.0

94

104

15

10

12

8.7

11

32

Domestic emission density (mg/km2 /day)

60,000

20,000

200,000

400,000

400,000

260,000

80,000

400,000

450,000

13,000

50,000

40,000

900,000

80,000

100,000

400,000

200,000

1,000,000

Treatment capacity (m3 /day)

Table 2.2 PFOS concentrations in selected municipal WWTPs and their characteristics

CAS

CAS

CAS

BAF

CAS

CAS

A/O

SB

A/O

SB

A/O

A2O

CAS

7.5

2.5

25

50

50

33

10

50

56

1.6

6.3

5.0

113

10

13

A2O + MBR CAS

50

25

125

Sludge production (t/day)

A2O

OD

CAS

Main processesb

22

9.3

189

80

100

108

20

111

86

11

49

10

242

18

40

81

48

240

Service population (10 × 4)

40

64

74

44

64

38

50

74

68

31

42

1.5

224

15

21

159

86

97

Service area (km2 )

(continued)

20,094

19,128

20,069

19,936

20,173

27,266

24,259

24,293

24,429

18,706

30,029

27,081

30,134

27,081

30,134

33,351

30,134

30,156

Per capita disposable income (RMB)

2 Source Identification and Emission Estimation of Emerging Pollutants 57

Jiangsu

Zhejiang

2.7

3.3

9.5

9.0

WWTP-35

WWTP-36

1.5

6.5

2.4

6.9

5.9

WWTP-33

67

WWTP-32

2.8

13

6.7

5.5

6.7

2.5

9.4

11

9.8

21

7.7

23

0.52

Sludge (ng/g dry weight)a

WWTP-34

8.7

3.6

WWTP-30

21

WWTP-29

WWTP-31

11

3.3

WWTP-27

WWTP-28

1.0

1.8

WWTP-26

15

WWTP-24

WWTP-25

Guangdong

75

34

4.9

WWTP-22

Shanghai

WWTP-23

WWTP-21

2.5

20

Effluent (ng/L)

PFOS concentration

WWTP-20

Province

WWTP-19

Name

Table 2.2 (continued)

7.6

12

33

20

462

24

17

97

10

43

3.7

32

271

263

1000

54

79

5.4

Domestic emission density (mg/km2 /day)

160,000

150,000

300,000

300,000

640,000

200,000

150,000

640,000

300,000

200,000

30,000

110,000

1,700,000

2,000,000

75,000

60,000

70,000

80,000

Treatment capacity (m3 /day) 10

A2O

A2O

MBBR

CAS

A/O

A2O

A2O

A2O

A2O

A2O

CAS

A2O

CF

A2O

CAS

PASF

20

19

38

38

80

25

19

80

55

24

12

32

255

276

54

7.5

8.8

CAS + BF A2O

Sludge production (t/day)

Main processesb

65

35

75

76

156

39

143

213

135

59

6.0

13

235

356

40

20

22

50

Service population (10 × 4)

65

127

70

38

94

33

90

142

125

55

16

16

107

272

6.7

6.5

20

38

Service area (km2 )

(continued)

30,166

28,930

27,750

27,131

25,739

28,362

30,251

30,658

28,592

29,349

31,362

29,725

31,838

30,521

31,838

31,838

21,653

20,953

Per capita disposable income (RMB)

58 S. Xie and Y. Lu

Province

2.9

Effluent (ng/L)

5.9

Sludge (ng/g dry weight)a

PFOS concentration

5.8

Domestic emission density (mg/km2 /day) 60,000

Treatment capacity (m3 /day) OD

Main processesb

7.5

Sludge production (t/day) 30

Service population (10 × 4) 38

Service area (km2 ) 30,166

Per capita disposable income (RMB)

b

n.d.: not detected CAS: Conventional activated sludge process; OD: Oxidation ditch; A2O: Anaerobic/anoxic/oxic process; MBR: Membrane bio-reactor; SB: Secondary biochemical process; A/O: Anaerobic/oxic process; BAF: Biological aerated filter; BF: Biofilm process; PASF: Removing phosphorus and nitrogen combined activated sludge and filter technology; CF: Chemical flocculation; MBBR: Moving bed biofilm react

a

WWTP-37

Name

Table 2.2 (continued)

2 Source Identification and Emission Estimation of Emerging Pollutants 59

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Fig. 2.8 Diagram for estimating domestic emission of PFOS equivalents

2.2.4 Prediction of Domestic Emission Density First, the univariate regression analysis indicated an evident positive correlation (p < 0.001) between the domestic emission density of PFOS equivalents and population density in the service areas of municipal WWTPs. However, the result of a linear regression model fitted to the data is insufficient for prediction because of large residuals with R2 = 0.34. The linear model was forced to a zero intercept; removing this constraint only slightly improved the correlation coefficient (R2 = 0.36). An exercise using log-transformed variables was repeated, and the scatter plots with regression lines are shown in Fig. 2.9. Compared with the above model, the loglinear model has a significantly higher correlation coefficient (R2 = 0.53). However, after an examination of the residuals of the single variable log-linear model, which were calculated by subtracting the recorded emission densities from the predicted ones, it was found that the domestic emission densities in the areas with relatively high per capita disposable income (PCDI) were generally underestimated, with the highest relative residual (residual/estimated value) up to −10. Moreover, the logtransformed emission density positively correlated (p < 0.05) with PCDI. It appears that the domestic emission density of PFOS is not only affected by population density but also influenced by PCDI, which varies from 18,706 RMB to 33,351 RMB in the service areas of selected municipal WWTPs.

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Fig. 2.9 Linear and log-linear scatter plots of domestic emission density of PFOS with population density in the areas served by municipal WWTPs

Taking the influence of PCDI into account, bivariate regression models of various forms were tested and compared, and the following equation could achieve a reasonable degree of fit: ED = f (PD, PCDI ) = 1.254 × 10−11 × PD1.317 × PCDI 1.664

(2.8)

where the units of ED, PD, and PCDI were mg/km2 ·day, km−2 , and RMB, respectively, the R2 value was 0.68 after removing one outlier (WWTP-7) from consideration. The service area of WWTP-7 was a residential block in the downtown area of Beijing with an extremely high population density, resulting in an overestimation of the domestic emission density of PFOS. In order to examine its reliability and applicability, the residuals of this multivariate model were further analyzed. It was found that the vast majority of relative residuals were distributed between −1 and 1 with only one exception, which was acceptable. Hence, Eq. (2.8) was determined as the appropriate model to predict the domestic emission density of PFOS equivalents in the study area.

2.2.5 Estimation of Domestic Emission Based on the regression model for domestic emission density and the population, land area, and per capita disposable income data in 2010, emissions of PFOS equivalents from domestic sources were estimated for all counties in the eastern coastal region of China. The overall prediction was further evaluated by plotting the estimations directly at the provincial level against those calculated at the county level and summed for each province. All data points fell around the 1:1 line on a log–log plot without systematic error in the results (Fig. 2.10). The total domestic emission of PFOS equivalents calculated for all counties was 381 kg in 2010.

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Fig. 2.10 Comparison of the domestic PFOS emissions derived from provincial level and county level estimations

The study area’s emissions were normalized to compare with other countries. The average per capita emission load of PFOS equivalents from domestic activities in the eastern coastal region of China was 1.91 µg/day per capita, consistent with a previous estimate of 1.68 µg/day per capita in Korea (Kim 2012). However, the value was several factors lower than those from domestic WWTPs of developed countries: 57 µg/day per capita in Switzerland (Huset et al. 2008), 42 µg/day per capita in the U.S. (Schultz et al. 2006), 40 µg/day per capita in Germany (Becker et al. 2008), and 24 µg/day per capita in Singapore (Yu et al. 2009), respectively, and also lower than European river water concentration-derived value of 27 µg/day per capita. The above comparison suggests that China and other developing countries have a much lower rate of per capita PFOS equivalents released by domestic sources, in contrast to developed countries. Significant differences in local social-economic conditions led to the considerable distinctions in domestic emissions of PFOS equivalents between regions. In Fig. 2.11, PFOS emission densities and emission rates in the eastern coastal region of China were presented at county resolution using ArcGIS mapping software. The estimated value of the average emission density of PFOS equivalents from domestic sources in the eastern coastal region of China was 0.37 g/km2 ·a in 2010. Generally, the Beijing–Tianjin area, the Pearl River Delta, and the Yangtze River Delta, as the most economically developed areas in China, showed significantly higher emission densities at an average of 1.6, 1.3, and 0.66 g/km2 ·a, respectively. Besides, the emission densities in Xiamen, Shantou, and the municipal districts of Shijiazhuang, Fuzhou, Handan, Qingdao, Quanzhou, Baoding, Anshan, and Jinan were ranked among the top. It is noted that there existed geographical variations within individual provinces. For example, the average emission density in Jiangsu Province was 0.54 g/km2 ·a, similar to the average for all provinces, while the emission density of the five southern cities near Shanghai has a higher value (1.2 g/km2 ·a). In Fujian Province, emission density averaged 0.38 g/km2 ·a for the six coastal cities but

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Fig. 2.11 Spatial distribution of domestic emission densities and loads of PFOS in the eastern coastal region of China

only 0.028 g/km2 ·a for the three inland cities. Zhejiang and Guangdong Provinces also presented noticeable intra-province variations. For all provinces, major cities stood out as a source of the highest emissions. The three municipalities (Beijing, Tianjin, and Shanghai) and capital cities of each province, including Shenyang, Shijiazhuang, Jinan, Nanjing, Hangzhou, Fuzhou, and Guangzhou, as well as several large cities such as Dalian in Liaoning; Tangshan and Handan in Hebei; Yantai, Qingdao, Zibo, and Weifang in Shandong; Suzhou, Wuxi, and Nantong in Jiangsu; Ningbo and Wenzhou in Zhejiang; Quanzhou and Xiamen in Fujian; Shenzhen, Dongguan, and Foshan in Guangdong can be clearly pinpointed with notably higher domestic emissions than those of surrounding areas. For the largest 142 counties or municipal districts with a population greater than 1 million, the average emission density was 1.0 g/km2 ·a in 2010, more than two times higher than the mean for the entire study area, which occupied only 26% area of the total territory but contributed to 74% of the total emission.

2.2.6 Uncertainty Analysis and Study Limitation The uncertainty analysis results on domestic emission estimations are presented in Fig. 2.12. The probability distribution of PFOS discharge from a municipal WWTP, taking WWTP-1, for example, was complied with lognormal distribution with a

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Fig. 2.12 Means, 10th, 25th, 75th, and 90th percentiles of PFOS discharge from WWTP-1 (a) and domestic emission in Beijing (b) derived from the Monte Carlo simulation

standard deviation of 2.23. Semi-interquartile range (SR, the difference between the 75th and the 25th percentiles) and coefficient of variability (CV) were applied for the uncertainty analysis. It was found that the SR value was 88.3% of the median, while the CV value was 0.72 for the PFOS discharge from WWTP-1. According to the sensitivity analysis, the PFOS concentration in effluent, contributing 99.5% of the variance, had a dominant effect on the uncertainty for the estimation. Taking Beijing as an example, the probability distribution of the predicted regional domestic emission also fitted well with the lognormal distribution with a standard deviation of 2.72. For the estimated PFOS emission from domestic sources in Beijing, the SR value was 11.7% of the median, and the CV value was 0.087. Per capita, disposable income accounted for approximately 62% of the variance in forecast value, which caused a more significant effect on the prediction than population and area.

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The domestic emissions of PFOS equivalents were estimated based on measurements of PFOS concentrations in effluents and sludge from municipal WWTPs. However, other emission pathways are mentioned in the Assumption section, which may lead to underestimating the PFOS emission. The actual regional emissions of PFOS equivalents from domestic sources in the eastern coastal region of China should be calculated by the following equation: ED = f (PD, PCDI ) + δ1 + δ2

(2.9)

where δ 1 denotes the releases of PFOS and precursor compounds to the air during the wastewater treatment processes, δ 2 denotes the emissions from PFOS precursors that pass the WWTPs without transformation. Up to now, there is still a lack of available data on the air concentrations of PFOS-related chemicals within the WWTPs in China. A study in Canada has shown that the per capita emissions of PFOS and its precursors (∑FOSA & FOSE, i.e., N-alkyl substituted perfluorooctane sulfonamides and N-alkyl substituted perfluorooctane sulfonamidoethanol) from WWTPs to air were estimated to be 45 µg/a per capita and 31 µg/a per capita, respectively (Ahrens et al. 2011). Multiplying by a formation factor of 94%, the emission of precursor compounds could be converted to the release of PFOS equivalents, which meant the degradation yield of PFOS based on the relative molecular weights (Brook et al. 2004). Thus, the per capita emission of PFOS equivalents to air was 74 µg/a in total, ~250 times lower than those of PFOS to water in effluents from WWTPs in other western countries (Becker et al. 2008; Huset et al. 2008; Schultz et al. 2006). The approximate value of δ 1 was estimated to be 0.0032f (PD, PCDI) due to the PFOS discharge in effluents accounted for ~80% on average of the total effluents and sludge from the 37 selected municipal WWTPs in the eastern coastal region of China. It is extremely limited in China that monitor data on the presence of PFOS precursors in effluents and sludge. As reported, concentrations of precursor compounds (∑FOSA) in effluents from 28 municipal WWTPs in 11 cities in China were n.d. −3.9 ng/L, ~18 times lower than those of PFOS on average, while these precursors were not detected in sludge (Zhang et al. 2013). Accordingly, the value of δ 2 was about 0.042f (PD, PCDI). Not all precursors are likely to be discharged into the environment by volatilizing or with the effluents measured in past studies, which may also impact the estimations. Because the water and atmosphere are the most important media for receiving and dispersion in the environment, this study took the release of PFOS-related chemicals to those media into account (Paul et al. 2009). The estimation of emissions from solid wastes has not yet been done in this paper. However, it must be assumed that solid landfilled wastes also contribute to the environmental burden by the slow discharge of PFOS. The only effective way to destroy PFOS is to apply high-temperature incineration to the destruction of hazardous waste. Therefore, further investigations on this issue are expected to improve the estimation and reduce the uncertainty.

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2.3 Comparison Between Industrial and Domestic Emission To compare with the industrial emission of PFOS equivalents, the domestic emissions calculated at a county level were summed for the individual province. The industrial emissions were estimated on a provincial scale based on the availability of industryspecific production and consumption data. The total emission of PFOS equivalents released from primary industrial sources in the eastern coastal region, the most intensively industrialized area in China, was approximately 53 t in 2010 (Xie et al. 2013a), which was much larger than that from domestic sources. Although the worst case of transformation in a short period from all precursors to PFOS was reflected by the estimated industrial emissions, while actual discharge loads of PFOS were calculated according to domestic sources, it could be concluded that PFOS-related industry processes were predominant contributors to PFOS emission in the eastern coastal region of China, as the type of PFOS-related chemicals accounted for nearly 59% of PFOS equivalents released from industrial sources were of PFOS-salts, which was effectively PFOS itself. Meanwhile, the rest were PFOS precursors with the fate of degradation to PFOS in the environment. The geographical distributions of domestic and industrial emissions were similar (Fig. 2.13), which was reasonable since most large industrial complexes are situated alongside dense population centers. Except for the three municipalities directly under the central government, which have a small area but high population density, Jiangsu, Guangdong, Zhejiang, and Shandong Provinces presented the highest emission density. It contributed to approximately 60% of the total emissions from both domestic and industrial sources. Among the three municipalities, Shanghai contributed the most considerable emission with exceptionally high emission density. Beijing had greater domestic emissions than Tianjin, owing to the larger population and higher consumption level. In contrast, the industrial emission was relatively small, which may be caused by the fact that in recent years high-polluting enterprises have been moved out of Beijing to improve the environmental quality of the capital city.

2.4 Executive Summary The identification of emission sources is fundamental for pollution control. Environmental monitoring provides direct evidence to trace the notable emission sources. However, many pollutants are generated from widely distributed emission sources, and not all of them can be identified with apparent environmental concentrations. Although PFOS and PFOA both belong to the PFAS group and are produced from the fluorochemical industry, the manufacturers seem to be highly selective in the production of individual chemicals, like the findings in the Daling River basin and the Xiaoqing River basin. This chapter provided a different perspective to identify the

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Fig. 2.13 Comparison of PFOS emission from domestic and industrial sources

emission sources of emerging pollutants, followed by quantification of the emission loads. It was estimated that the total emission of PFOS equivalents from major industrial sources in China was 70 t in 2010. Industrial use of PFOS in metal plating was identified as the largest source of PFOS pollution at the national level, followed by textile treatment, fire-fighting, PFOS manufacture, and the semiconductor industry. At the regional level, greater contributions were made by metal plating and textile treatment in most provinces of eastern China, while in the western part of China and several northeastern provinces, fire-fighting was the predominant source. The contribution by PFOS manufacturers was considerable in Hubei and Fujian provinces. Total emission, emission density, and emission intensity showed geographical variations. In general, the eastern coastal provinces, the most intensively industrialized regions of China, were characterized by significantly higher emission rates, density, and intensity than those in western and northern China. The total emission load of PFOS equivalents from domestic sources in the eastern coastal region of China was 381 kg in 2010, and large cities were prominent as the emission centers. The domestic emission density averaged 0.37 g/km2 ·a for the study area. Generally, the Beijing-Tianjin area, Pearl River Delta, and Yangtze River Delta,

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the most populous and economically developed areas in China, showed significantly higher emission density. Geographical variations within individual provinces were noteworthy. The average per capita discharge load of PFOS equivalents arising from domestic activities was 1.91 µg/day per capita in the eastern coastal region of China, which is consistent with previous estimates in Korea but lower than those calculated for developed countries. These estimation results were consistent with those findings from environmental monitoring data. The spatial distributions of provincial PFOS emissions from industrial and domestic sources were similar. However, the industrial emission loads were much more prominent for all the provinces.

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Lange C (2001) The 18-day aerobic biodegradation study of perfluorooctanesulfonyl-based chemistries. U.S. Environmental Protection Agency, Washington, DC. (Docket AR-226-E010415) Lim TC, Wang B, Huang J, Deng SB, Yu G (2011) Emission inventory for PFOS in China: review of past methodologies and suggestions. Sci World J 11:1963–1980 Liu C (2008) Preliminary evaluation of PFOS present situation in China and environmental risk assessment of PFOS in Jiangsu Province. Peking University, Beijing Liu C, Hu JX, Liu JG (2008) Preliminary risk assessment of semiconductor manufacturing PFOS emissions and near-site environmental concentrations. Sci Technol Eng 8(11):2898–2902 Loganathan BG, Sajwan KS, Sinclair E, Kumar KS, Kannan K (2007) Perfluoroalkyl sulfonates and perfluorocarboxylates in two wastewater treatment facilities in Kentucky and Georgia. Water Res 41(20):4611–4620 Motegi M, Nojiri K, Horii Y (2012) Occurrence of perfluorinated compounds in effluent from large and small scale wastewater treatment plants in Saitama. Jpn Organohalogen Compd 74:235–238 Murakami M, Imamura E, Shinohara H, Kiri K, Muramatsu Y, Harada A, Takada H (2008) Occurrence and source of perfluorinated surfactants in rivers in Japan. Environ Sci Technol 42:6566–6572 Organisation for Economic Co-operation and Development (2004) OECD environmental health and safety publications series on emission scenario documents number 7: emission scenario document on textile finishing industry. ENV/JM/MONO(2004)12 Paul AG, Jones KC, Sweetman AJ (2009) A first global production, emission, and environmental inventory for perfluorooctane sulfonate. Environ Sci Technol 43(2):386–392 Pistocchi A, Loos R (2009) A map of European emissions and concentrations of PFOS and PFOA. Environ Sci Technol 43:9237–9244 Schultz MM, Barofsky DF, Field JA (2006) Quantitative determination of fluorinated alkyl substances by large-volume-injection liquid chromatography tandem mass spectrometry—characterization of municipal wastewaters. Environ Sci Technol 40:289–295 Sinclair E, Kannan K (2006) Mass loading and fate of perfluoroalkyl surfactants in wastewater treatment plants. Environ Sci Technol 40(5):1408–1414 Vierke L, Ahrens L, Shoeib M, Reiner EJ, Guo R, Palm WU, Ebinghaus R, Harner T (2011) Air concentrations and particle-gas partitioning of polyfluoroalkyl compounds at a wastewater treatment plant. Environ Chem 8(4):363–371 Xie S, Lu Y, Wang T, Liu S, Jones K, Sweetman A (2013a) Estimation of PFOS emission from domestic sources in the eastern coastal region of China. Environ Int 59:336–343 Xie SW, Wang TY, Liu SJ, Jones KC, Sweetman AJ, Lu YL (2013b) Industrial source identification and emission estimation of perfluorooctane sulfonate in China. Environ Int 52:1–8 Yu J, Hu J, Tanaka S, Fujii S (2009) Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) in sewage treatment plants. Water Res 43(9):2399–2408 Zhang W, Zhang YT, Taniyasu S, Yeung LWY, Lam PKS, Wang JS, Li XH, Yamashita N, Dai JY (2013) Distribution and fate of perfluoroalkyl substances in municipal wastewater treatment plants in economically developed areas of China. Environ Pollut 176:10–17

Chapter 3

Environmental Pathways of Emerging Pollutants Zhaoyang Liu, Jing Meng, and Yonglong Lu

3.1 Overview The emission estimation of PFOS provides a clear profile of the industrial and domestic sources. After emission from the sources, how do the pollutants enter the environment? In this chapter, a comprehensive estimation of pollution pathways of emerging pollutants released into the environment was provided (Liu et al. 2017), followed by a life cycle assessment (LCA) perspective (Meng et al. 2017). Except for PFOS, PFOA was also selected as an example of emerging pollutants. After the Stockholm Convention restricted PFOS, there has been increasing concerns over the adverse effects of PFOA. Eventually, PFOA, its salts, and PFOA-related compounds were listed in Annex A of the Stockholm Convention (decision SC−9/12) as of May 2019. The estimation of PFOS/PFOA environment releases includes emission and transport processes such as atmospheric deposition, runoff, soil leaching, and surface water seepage. This allows a further calculation of the load of the pollutant in Z. Liu State Environmental Protection Key Laboratory of Soil Health and Green Remediation, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China J. Meng Key Laboratory of Environment Nanotechnology and Health Effects, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China Y. Lu (B) State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems, International Institute for Sustainability Science, College of the Environment and Ecology, Xiamen University, Fujian 361102, China e-mail: [email protected]; [email protected] Z. Liu · J. Meng · Y. Lu State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Lu et al. (eds.), Ecological Risks of Emerging Pollutants in Urbanizing Regions, https://doi.org/10.1007/978-981-19-9630-6_3

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different environmental compartments. The LCA for PFOA/PFO provides more detailed processes from production and use to waste management and environmental transport. This approach was used to estimate the emissions of PFOA/PFO at multiple life cycle stages. The results presented not only current but also future flows of PFOA/PFO and could further help policymakers with more accurate risk assessments and implement feasible policies for control over the emission of PFOA/PFO at an important stage of their life cycle.

3.2 Pollution Pathways of PFOS and PFOA 3.2.1 Identification of Sources and Pollution Pathways The primary sources and pollution pathways for PFOS/PFOA were involved with various processes as presented in Fig. 3.1. There were two main pollution pathways for industrial emission of PFOS/PFOA: discharge of PFOS/PFOA from industrial wastewater into surface waters and air emission of PFOS/PFOA followed by wet and dry deposition, and further deposited into surface water through runoff or infiltrate deeper into groundwater. PFOS/PFOA in consumer products can be released into surface water through municipal WWTPs, as discussed in Sect. 2.2. Previous studies suggested that the concentrations of PFOA increased from influent to effluent of WWTPs, due to the potential biodegradation of precursors. A certain amount of PFOS/PFOA absorb into sludge or enter landfill with waste disposal, which could result in further leaching into soils and surface/groundwater. Other sources include

Fig. 3.1 Schematic diagram of sources and pollution pathways for PFOS/PFOA

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the dispersion of AFFF and the natural breakdown of the pesticides in agricultural applications. The main pathways for PFOS/PFOA entering groundwater were surface water seepage and soil leaching.

3.2.2 Estimation Methods of Environmental Releases The production process commonly used by Chinese PFOS and PFOA manufacturers is ECF, which was used byglobal producers in the past. In the case of PFOS and PFOA synthesis, the ratio of linear to branched perfluorinated C chains is roughly 70 to 80% linear, and 20 to 30% branched (Buck et al. 2011). Considering that a large part of PFOS/PFOA produced in China meets the domestic demands, the release estimation took into account both the linear and branched isomers of PFOS/PFOA. The production and use of PFOS and PFOA in China experienced a decade of substantial growth and entered a plateau between 2010 and 2013. The vast majority of PFOS and PFOA were emitted in China’s central and eastern regions. The first step was collecting and further processing emission data for PFOS and PFOA from China’s central and eastern regions from 2010−2013. Industrial emission data of PFOS are derived from primary sources such as PFOS manufacturing, metal plating, textile treatment, and the semiconductor industry. At the same time, those of PFOA come from primary sources, including PFOA production, FP manufacturing and processing, use of aqueous fluoropolymer dispersions (AFDs), and industrial processes of POSF- and fluorotelomer (FT)-based products. Domestic emission data of PFOS/PFOA were mainly collected on domestic wastewater, landfill leachate, and application of AFFF, pesticide, and biosolid. The next step was to estimate environmental release for transport processes such as atmospheric deposition, runoff, soil leaching, and surface water seepage. The presence of PFOS/PFOA in different environmental compartments following the release from different routes was then calculated. During the collection and processing of PFOS and PFOA emission data, PFOS precursors included FOSAs and FOSEs, while PFOA precursors primarily referred to FTOHs, FOSAs, and FOSEs (Buck et al. 2011). Some precursors such as FTOHs, FOSAs, and FOSEs are industrial products that may be released into the environment during manufacture and use. FOSAs and FOSEs are produced based on POSF, while FTOHs are manufactured based on FT (Prevedouros et al. 2006). In addition, these precursors were also released as impurities or by-products when manufacturing other FT-based or POSF-based products, including PFOS and PFOA (Wang et al. 2014a). Environmental release data of these precursors were collected or calculated according to the production or use amount and corresponding emission factors and/or content as impurities in a particular industry or use process (Xie et al. 2013; Li et al. 2015). Then, these precursors were assumed to be converted to PFOS/PFOA equivalents. Wastewater and sewage sludge discharge. The release estimation of PFOS/PFOA for industrial wastewater was based on the quantity of PFOS/PFOArelated chemicals produced or used and the emission factors to water for a relevant

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industry. According to the above method, the estimated PFOS and PFOA releases from industrial wastewater discharge were 58 and 27.3 t/yr, respectively. The starting point for estimating PFOS/PFOA releases through domestic wastewater discharge was the assumption that PFOS, PFOA, and precursors would enter the drain/sewer system and further MWWTPs following cleaning, wiping, and washing of the products in the domestic indoor environment. Regional emissions of PFOS/PFOA from local MWWTPs can be estimated from corresponding statistics of population density (PD) and per capita disposable income (PCDI). Based on the estimation equations and regional statistical data, the release of PFOS/PFOA via MWWTP emission (E M W W T P ) was calculated in this study. According to monitoring data from 28 MWWTPs in 11 cities of central and eastern China, the total discharge amount of FOSA and EtFOSAA, which passed through the MWWTPs without degradation, were ~5% of those for PFOS and ~2% of those for PFOA on average (Zhang et al. 2013). The predicted releases of these precursors from MWWTPs were then further converted to PFOS and PFOA equivalents through respective transformation factors. The mass flow of PFOS/PFOA in wastewater and sewage sludge from 43 MWWTPs in these regions was also collected to calculate emission proportion through domestic wastewater (PM W W T P water ) and through sewage sludge (PM W W T P sludge ). Equation 3.1 was used to estimate the release of PFOS/PFOA (W M W W T P ) from domestic wastewater to surface water. W M W W T P = E M W W T P × PM W W T P water

(3.1)

In China, approximately 8.5 million tons of dry-weight municipal sewage sludge (biosolid) is generated annually. It is calculated that the biosolids would contain average concentrations of 18 ng/g for PFOS and 8.3 ng/g for PFOA. About 48% of these biosolids were applied to land via agriculture and urban greening (PBiosolid application ) as a soil amendment, while 35% of them were disposed of as landfill (S Biosolid land f ill ) (Chen et al. 2012). No further treatment occurs for these PFOS/PFOA in biosolids in landfill or applied to the local soil compartment. Equations 3.2 and 3.3 were used to estimate the release and leaching of PFOS/PFOA into the soil from biosolid application to the land (S Biosolid land f ill ) and the landfill (S Biosolid land f ill ). S Biosolid application = E M W W T P × PM W W T P biosolid × PBiosolid application

(3.2)

S Biosolid land f ill = E M W W T P × PM W W T P biosolid × PBiosolid land f ill

(3.3)

AFFF runoff and infiltration. POSF-based AFFF, mainly produced by consuming the PFOS-salts, is the predominant type of foam in China and is also known to contain PFOA impurities. During the application of AFFF, PFOS is released directly into the environment as the main ingredient, while PFOA is released in the form of impurities. However, due to a lack of information on the contents of PFOS/PFOA precursors, these substances were not considered in the calculation.

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The concentration ratio of PFOS: PFOA in POSF-based AFFF, produced by a similar process to those in China, has been estimated to be 10:1 or even lower (Andreas and Leo 2009). The emission of PFOS during AFFF application to the environment was estimated to be 7 t/yr (Xie et al. 2013). Based on the PFOS: PFOA ratio (10:1) in POSF-based AFFF, a more modest emission of PFOA was estimated at 0.7 t/yr. This study considered fire training and fire intervention when estimating the environmental release of PFOS/PFOA in AFFF. The environmental release of PFOS/PFOA during AFFF application was estimated based on a commonly used scenario (Brooke et al. 2004), that there is no containment of this fire-fighting foam after discharge, which is the case in China, and 50% of the release goes to surface water without treatment and 50% to soil which could ultimately leach to groundwater. Therefore, the release of PFOS in AFFF application to surface water and soil was 3.5t/yr, while those of PFOA were 0.35t/yr, respectively. Atmospheric deposition, runoff, and infiltration. The air emission of PFOS from production and metal plating was estimated to be 1.3 t/yr. Based on previous calculations of total environmental emission (40t), PFOA emissions to the atmosphere were estimated at 7.3 t/yr. Except for small amounts of PFOS/PFOA precursors, most airborne PFOS/PFOA was combined with particulate matter deposited to land and ocean. In the Bohai Rim, located in the eastern coastal region of China, Liu et al. (2015a) estimated that ~ 65% of PFOS generated from air emissions could deposit to the local land, and the remaining will transport to a longer distance. Although a higher proportion of airborne PFOS/PFOA would be deposited to land in some inland regions, the proportion (65%) was used conservatively due to the lack of detailed information about atmospheric transport. Air emission of PFOS/PFOA was assumed to be evenly deposited to a large surrounding expanse of terrestrial surface and surface water. The maximum mass fractions of PFOA and PFOS in the air have been observed in particulate matter of < 0.3 and < 10 µm, respectively, most of which would ultimately be deposited by wet precipitation. Removal by precipitation was also expected to take out both the smallest size particles (20 km × 20 km) with a relatively small number of grids (e.g., V. natans > C. demersum > Ulothrix (Fig. 6.24). The highest PFAAs were in P. crispus of site F3 (664 ng/g, dw). However, the profiles of PFAAs varied much among different species. For P. crispus, PFBA accounted for 72.7%, followed by PFBS (23.8%), while PFOA and PFOS accounted for small portions (1.56% and 0.59%, respectively). V. natans and C. demersum had similar PFAAs levels and profiles, with PFBA and PFBS dominant. However, Ulothrix presented more accumulation of PFOA and PFOS (7.51% and 12.39%, respectively), and even PFBA and PFBS were still dominant (39.8% and 24.5%, respectively). In addition, the natural spatial distribution of the submerged plants was also different. P. crispus Table 6.1 Summary of PFAAs concentrations (ng/g, dw) in the aquatic plants Type

Location

Submerged plants

River (n = 18)

Wetland (n = 6)

Emerged plants

River (n = 3)

Wetland (n = 12)

Stat

PFBA

Min

4.63

Max

477

PFOA 0.64 10.0

PFBS

PFOS

∑ PFAAs

nd

0.31

11.7

182

5.45

664

Mean

107

3.16

53.8

2.21

170

Median

73.9

3.03

61.1

2.03

151

Min

20.4

3.17

2.61

0.80

35.6

Max

124

11.9

3.55

142

Mean

75.6

5.91

7.94

1.88

95.1

Median

79.1

4.68

7.79

1.49

98.8

Min

12.2

Max

268

Mean

106

12.0

0.98 12.6 5.25

1.44

nd

17.4

48.6

1.07

336

17.4

0.71

133

Median

37.0

2.13

2.15

1.07

44.1

Min

14.7

0.46

6.50

nd

28.5

Max

969

331

2.35

1143

12.2

Mean

198

2.64

59.9

0.72

265

Median

73.5

1.33

13.6

0.60

101

Note nd indicated not detected. Other PFAAs were in much lower levels and detection ratios, so they were not listed

294

Fig. 6.24 Percentages of the 4 dominant PFAAs and the aquatic plants

P. Wang et al.

∑ PFAAs (mean values for each species) in

was the most widespread in the river and the wetland, followed by C. demersum, Ulothrix and V. natans. Therefore, P. crispus was the most distributed and richest accumulated for PFAAs, especially for PFBA. This implied that P. crispus could be used for bioremediation of PFAAs polluted water. However, as a primary producer, it could also lead to the biological transformation of PFAAs in the aquatic environment. Emerged plants. The emerged plants were cultivated in bunches in the wetlands so that water would flow through them in sequence (Fig. 6.25). TPFAAs levels were relatively low for the six species∑ at AW1. However, at AW2, the same two species showed increasing levels, with PFAAs of 319 ng/g dw in T. angustifolia and 984 ng/g dw in N. nucifera, respectively. At AW3, the latter emerged species showed higher PFAAs levels, with the highest detection in J. serotinus (1,143 ng/g dw). J. serotinus was also the only emerged species distributed in the wetland and the river. Profiles of PFAAs in the emerged species were like the submerged species. PFBA was still dominant, followed by PFBS. T. angustifolia and J. serotinus had higher portions for PFBA (89.5% and 83.4%, respectively), while N. nucifera and P. australis had higher portions for PFBS (34.9% and 33.6%, respectively). The artificial wetland is very different from natural processes. Different sections in the wetland were constructed at different times, which could lead to changes in sedimentation processes and affect the sorption of PFAAs. The size of the bunches would influence the effect of water flowing through the plants. The landscape building would also disturb the aquatic environment. Furthermore, although the roots of the emerged aquatic plants might accumulate considerable amounts of PFAAs, previous studies demonstrated that the root did not certainly present higher PFAAs levels than other parts (Zhou et al. 2017). If the emerged plants were used to remove PFAAs

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Fig. 6.25 Concentrations of the dominant PFAAs in sequential emerged plants in the wetland (The two species of submerged plants were distributed all over the wetland)

from the polluted water, it would be much more practical to harvest the stems and leaves other than the roots. Bioaccumulation efficiency. In this study, the BAF was calculated with the concentrations of PFAAs in aquatic plants (ng/g, dw) to the concentrations in insitu water (ng/L) and converted to its log10 value. Emerged plants showed higher BAF for the short-chain PFBA and PFBS while lower BAF for the long-chain PFOA and PFOS than submerged plants. J. serotinus presented the highest BAF for both PFBA (3.77–4.34, mean 4.16) and PFBS (2.97–3.85, mean 3.26), while C. demersum presented the highest BAF for PFOA (2.99–3.82, mean 3.55), and V. natans presented the highest BAF for PFOS (4.50–4.68, mean 4.59). Even though the PFAAs levels varied greatly among different species and sites, the BAF showed consistent trends. For all the aquatic plants in this study, BAF generally increased with the increasing chain lengths of PFAAs. This trend was consistent with the uptake of PFAAs in aquatic plants under controlled conditions (Pi et al. 2017). Besides the carbon chain length, other factors might also influence the BAF of PFAAs, including coefficients of membrane-water distribution, protein-water distribution and organic-water partition (Pi et al. 2017), and plant age (Gonzaga et al. 2007). However, the BAF of PFAAs in terrestrial plants showed a decreasing trend with increasing carbon chain length of PFAAs (Ghisi et al. 2018; Lan et al. 2018; Scher et al. 2018). Nevertheless, this trend would reverse to be consistent with the trend in aquatic plants when the terrestrial plants were cultured hydroponically (Felizeter et al. 2014). This phenomenon is because short-chain PFAAs have higher mobility than long-chain PFAAs. In studies on the sorption mechanism of PFAS, it was found that additional CF2 moiety would

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decrease the aqueous solubility and increase the OC-normalized sorption coefficients (Higgins and Luthy 2006; Liu and Lee 2007). Thus, cultivation of terrestrial plants in the soil would lead to more adsorption of long-chain PFAAs to organic matters and less sorption by root, while hydroponic cultivation could bring more sorption of long-chain PFAAs by root from the spiked solution. The mobility of PFAAs might also explain why the leaf could accumulate more short-chain PFAAs than the root (Ghisi et al. 2018), as the leaf usually contains more water. Thus, water content could be a simple yet valuable indicator for the bioaccumulation potential of PFAAs in plants, which requires further detailed study.

6.4.5 Evaluation and Exploration on Effective Removal of PFAAs The wetland and the F-WWTP were used for pollution control by fluorochemical industry parks. For the F-WWTP, even the conventional processes increased PFCAs levels notably, the exclusive biological aerated filter and UASB processes were proved to be effective for counteraction. However, the PFAAs levels in the outlet water were still very high. Therefore, there is still a big gap for the more efficient removal of PFAAs and other fluorochemical pollutants from the fluorochemical industrial wastewater. Specific technologies with a significant removal efficiency of PFAAs were not equipped in the F-WWTP. For example, adding powdered activated carbon to a membrane bioreactor could generate over 90% of removal efficiency for both PFOS and PFOA, with a dosage of 100 mg/L (Yu et al. 2014). Meanwhile, the two D-WWTPs showed limited removal of PFAAs due to a lack of corresponding processes. A comparison of the PFAAs levels from upstream to downstream of the wetland showed minimal PFAAs removal. In this study, sequences of the bioaccumulation efficiency were established for better selection of aquatic species (Fig. 6.26). Surprisingly, the species with the highest BAF for short-chain PFAAs (J. serotinus) were only cultivated in small bunches at AW3. The two submerged plants (P. crispus and C. demersum) entering the wetland with water flow also provided considerable bioaccumulation for PFAAs and were distributed fast without cost. Considering the high levels of the PFAAs in the wetland source water, the aquatic plant tolerance to the pollutants could not be neglected (Guittonny-Philippe et al. 2015). More studies are needed to check if the levels of pollutants have caused damage to aquatic plant species.

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Fig. 6.26 Sequences of the bioaccumulation efficiency for all the aquatic plant species in this study

6.5 Effects on Aquatic Animals in the River-Estuary-Sea Environment 6.5.1 Research Design and Sampling This study examined the water transportation pathway of PFAAs, starting from the emission source (the FIP) to the adjacent Laizhou Bay. Freshwater and marine aquatic animals were sampled along the pathway (Fig. 6.27). Little aquatic organisms were observed in the tributary receiving wastewater from the FIP. Sites DY1 and DY2 located upstream and downstream of the FIP were selected for monitoring PFAAs emission. Sites XQ1–XQ8 were set in the Xiaoqing River from upstream to the estuary. Two fish species and one crab species were collected to evaluate the transport of PFAAs through the river-estuary-sea environment. The greatest effort in collecting the widest range of freshwater species was focused on site XQ4, located downstream of the confluence point of the tributary and the Xiaoqing River. Site XQ-S was located in Laizhou Bay within an intensive fishery area, where the widest possible range of marine organisms was collected. Further description of these sampling sites can be found in Wang et al. (2016). The sampling campaign was conducted in October 2015, after the start of the oceanic fishing season (closed from May to September). Freshwater organisms were caught by fixed fishing nets combined with cast fishing nets, whereas marine organisms were caught by local fishing boats (from shore to site XQ-S). Only common local species were collected at their normal (adult) sizes. The sampled aquatic organisms were maintained in clean water for a short period (minutes to hours, benthic species took longer time) to reduce the effect of in-situ water. All aquatic taxa were identified at the species level. Depending on size and availability, a few individuals

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Fig. 6.27 Sampling of aquatic organisms in the Xiaoqing River and Laizhou Bay

of each species were homogenized to generate the sample material before extraction. Only the edible tissues were used for PFAA measurements to assess the human health risk, depending on the dietary habit (muscle, whole body without shell, or whole body, etc.). All the samples were freeze-dried and ground before extraction. Water samples (XQ1–XQ8, XQ-S, DY1–DY2) were collected in pre-rinsed 1 L PP bottles at 1 implies that a substance is biomagnifying. The carbon-to-nitrogen ratio (C:N) was proved sufficient to normalize the untreated δ13 C for lipid content, specifically applicable to aquatic organisms (Post et al. 2007). The normalization was conducted with Eq. 6.4: δ 13 Cnor mali zed = δ 13 Cuntr eated − 3.32 + 0.99 × C:N

(6.4)

Then the normalized δ 13 C was further used to determine the carbon source of the aquatic species with Eq. 6.5: Carbon source

) ( δ 13 Czooplankton − δ 13 Cconsumer + △δ 13 C T P consumer − T P zooplankton =1− δ 13 Czooplankton − δ 13 Cbenthic (6.5)

The hypothesis is that the zooplankton represent the pelagic source, △δ 13 C is the trophic enrichment factor for consumers, which is set as a constant of 1.3 ‰ (McKinney et al. 2012). The benthic source in this study is represented using the benthic species with the lowest δ 13 Cbenthic values including Eriocheir sinensis (−27.1) for the freshwater species and Zoarces slongatus (−25.0) for the marine species. Carbon source values closer to 0 indicate more benthic feeding, and values closer to 1 indicate more pelagic feeding.

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6.5.3 PFAAs in Aquatic Organisms PFAAs were detected in all freshwater and marine aquatic organisms. Only PFOA had a detection ratio of 100%. All the long-chain PFAAs had detection ratios over 90%, whereas the short-chain PFAAs had relatively low ratios, except for PFBA (93%). PFOA concentrations were much higher than other PFAAs (Table 6.2). Previous studies provided limited information on PFAAs in aquatic animals directly affected by fluorochemical emissions. Usually, PFOS receives the most attention. In this study, the PFOS concentrations in water were relatively low (max: 3.55 ng/L), but in aquatic animals, the concentration was up to 9.73 ng/g ww. This disparity is due to the high bioaccumulation potential of PFOS (Ahrens and Bundschuh 2014). The aquatic animals had low concentrations and detection ratios of short-chain PFAAs, which were likely related to their low bioconcentration factors (BCFs) (Martin et al. 2003) and possibly to depuration treatment in clean water (Cerveny et al. 2018; Zhong et al. 2019). In contrast, aquatic plants affected by fluorochemical industry emissions accumulate substantially higher amounts of short-chain PFAAs than the aquatic animals analyzed in this study (Wang et al. 2019). Thus, the following sections are focused on PFOA. PFOA in freshwater species. PFOA concentrations in the crucian carp (C. auratus) were only 0.33 and 0.38 ng/g (ww) at the upstream sites XQ2 and XQ3, Table 6.2 Summary of PFAA concentrations (ng/g, ww) in aquatic organisms (n = 43, freshwater species; and n = 42, marine species) Analytes

Carbon number

Concentration, ng/g, ww Min

Median

Mean

Detection ratio, % Max

PFCAs PFBA

4

nd

0.55

1.32

11.1

93

PFPeA

5

nd

0.11

0.21

1.20

25

PFHxA

6

nd

0.20

0.77

9.46

29

PFHpA

7

nd

0.09

0.54

6.35

55

PFOA

8

0.11

5.58

2161

100

PFNA

9

nd

0.18

64.6 0.42

5.02

91

PFDA

10

nd

0.71

1.51

12.3

91

PFUnDA

11

nd

0.69

0.86

4.64

99

PFDoDA

12

nd

0.36

0.66

3.20

91

PFSAs PFBS

4

nd

0.03

0.09

0.51

16

PFHxS

6

nd

0.06

0.06

0.29

20

PFOS

8

nd

1.16

2.16

9.73

98

2196

100

∑PFAAs Note nd, indicates below LOQ

0.24

14.6

71.8

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respectively, but 99.0 ng/g (ww) at XQ4. Among the sixteen freshwater species monitored at site XQ4, the highest PFOA concentration was found in a mollusk, the winkle (C. chinensis) (2,161 ng/g, ww), followed by a fish, the loach (M. anguillicaudatus) (340 ng/g, ww), and a crustacean, i.e., a crayfish species (P. clarkii) (241 ng/g, ww), which belong to three different phyla. The nine fish species showed relatively high PFOA concentrations, ranging from 7.04 to 340 ng/g (ww). The two amphibians, a toad (B. raddei) and a turtle (T. sinensis), had the lowest PFOA concentrations of 0.42 ng/g and 0.41 ng/g (ww), respectively) (Fig. 6.28). PFOA in marine species. As the seawater dilutes the river water PFOA levels in Laizhou Bay, the forty marine aquatic species at XQ-S had lower PFOA concentrations than the freshwater species. The highest PFOA concentration was found in a mollusk, the fur clam (S. subcrenata) (642 ng/g, ww), followed by a fish, the fang goby (O. rubicundus) (349 ng/g, ww), and a macroinvertebrate sea worm (U. unicinctus) (167 ng/g, ww). Like the findings with the freshwater species, the distribution of PFOA concentrations among the species was not associated with their phylum or type. Fourteen out of eighteen fish species had PFOA concentrations of less than 1.00 ng/g (ww) (Fig. 6.29).

Fig. 6.28 PFOA concentrations (ng/g, ww) in the freshwater species at site XQ4 (the value for the river crab (E. sinensis) was obtained from muscle tissue of males)

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Fig. 6.29 PFOA concentrations (ng/g, ww) in marine species collected between the shore and site XQ-S (values for the two sea crabs (P. trituberculatus and C. japonica) were obtained from muscle tissue of males)

6.5.4 Factors Affecting the Bioaccumulation of PFOA in Aquatic Animals PFOA levels in the water. Compared with our previous studies, the water PFOA levels kept very high and showed an increasing trend in recent years, with mean

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values from 3,100 ng/L in September 2011 (Wang et al. 2014), to 17,361 ng/L in June 2013 (Wang et al. 2016) and 40,368 ng/L in October 2015, measured at sites XQ6 to XQ8, respectively. The trend was corroborated by other studies conducted in the same area in 2014 (Heydebreck et al. 2015; Shi et al. 2015). Moreover, a study by Chen et al. (2016) suggested broad dissemination of PFOA in the entire Laizhou Bay area, indicating a continuous influence of PFOA on the local aquatic environment. The river-estuary-sea environment. Three common species, including crucian carp, river crab, and sea bass, were selected to evaluate the influence of the riverestuary-sea environment. In the section of the Xiaoqing River with heavy PFOA pollution (sites XQ4–XQ8), the crucian carp had the highest PFOA concentrations (mean: 90.4 ng/g, ww, range: 33.0–156 ng/g, ww), followed by the river crab (muscle tissue of males, mean: 38.9 ng/g, ww, range: 3.98–150 ng/g, ww) and sea bass (mean: 10.2 ng/g, ww, range: 4.19–15.1 ng/g, ww). Fluctuations of PFOA concentrations were observed at different sites (Fig. 6.30a). However, despite that the PFOA concentration decreased almost tenfold from site XQ7 (78.0 μg/L) to site XQ8 (8.56 μg/L), PFOA concentrations in the three species collected from the estuary (site XQ8) were substantially higher than those from upstream sites, especially in the river crab. The pairwise correlations between PFOA concentrations in the three species and PFOA concentrations in water, along with four water parameters (temperature, pH, salinity, and dissolved oxygen), showed that there were no strong correlations, except that there was a significant negative correlation (p < 0.05) between PFOA concentrations in sea bass and water pH. Typically, the water pH is positively correlated with salinity and cation concentrations, but it was found that the increasing cation content

Fig. 6.30 a PFOA concentrations in the three aquatic species (ng/g, ww) of the river-estuary-sea environment and water (ng/L); b The change of trophic levels along with carbon sources of the three species

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(e.g., Ca2+ and Na+ ) in water could decrease the BCF of PFOA in aquatic organisms (Xiao et al. 2015). Sex and tissue differences in PFOA levels of crabs. Sex-specific differences exist in the accumulation of PFAS within the same species (Cerveny et al. 2018), but these differences may not be comparable with those between species (Babut et al. 2017). Besides, sex was not a critical factor in most aquatic species for human consumption, except in crabs. In this study, PFOA concentrations were measured in the edible parts of crabs, i.e., in the muscle and fat of male crabs and the muscle and roe of female crabs. For the river crab species, PFOA levels were generally higher in males than females. Among male river crabs, PFOA levels were slightly higher in muscle (mean: 38.9 ng/g, ww) than in fat (mean: 31.5 ng/g, ww), even in crabs from site XQ8 with much higher PFOA concentrations. The same trend was also found in male sea crabs. However, the trend was the opposite in female river crabs, in which PFOA levels were slightly higher in roe (mean: 8.14 ng/g, ww) than in muscle (mean: 6.07 ng/g, ww). A portion of the PFOA burden is likely disseminated into the eggs, which requires further detailed studies. Influence of TL. The TLs ranged from 2.21 to 5.57 for freshwater species (3.85 ± 0.8) and from 2.35 to 4.83 for marine species (3.45 ± 0.6). The most contaminated species had relatively low TLs, i.e., in freshwater species, the TL was 4.02 for winkle, 2.29 for loach, and 3.62 for crayfish; and in marine species, the TL was 2.38 for fur clam, 3.35 for fang goby, and 2.60 for a sea worm, respectively. The TMF of PFOA was 1.10 (p = 0.60) for freshwater species and 1.28 (p = 0.29) for marine species. This indicated that PFOA was biomagnifying, but the trend was not significant, which was a limitation of field sampling for the analysis of bioaccumulation potential (Franklin 2015). Among the three species living in the river-estuary-sea environment, the sea bass had the highest TL, followed by the crucian carp and river crab. This trend was consistent along the river from site XQ4 to XQ6, whereas their TLs did not differ much at the estuary site (XQ8) (Fig. 6.30b). The freshwater winkle had the highest PFOA level in this study. According to its biology, the winkle grazes on algae and lives on the sediment. PFOA has a high sorption coefficient from water to sediment (Ahrens et al. 2010), and high PFOA levels have been recorded in Xiaoqing River sediments (Wang et al. 2016). Furthermore, Robinson et al. (1984) found that the lipids of aquatic organisms can merge with sediment, generating sediment with an increased nutritive value that is ingested by some aquatic species. This phenomenon could explain the comparable TLs between the winkle and some predator fishes. Influence of carbon source. The carbon source is related to the food and habitat of aquatic organisms (Borgå et al. 2012). In this study, the carbon source was 0.30 ± 0.23 for freshwater species and 1.11 ± 0.37 for marine species. This implied that freshwater species were more benthic feeding, while marine species were more pelagic feeding. Combining the TL with carbon source, it seemed that benthic feeding would lead to higher TLs than pelagic feeding. The three species of the river-estuarysea environment showed different trends in their TLs and carbon sources (Fig. 6.30b). The crucian carp showed relatively consistent trends of both TL and carbon sources. However, the TL and carbon source of the river crab showed opposite trends, and there was a little sex-specific difference. This might be related to the habitat of the

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river crab, which lives in both the river and riverbank. The carbon sources of the sea bass showed a jump from sea to the river, with carbon sources altered from pelagic to benthic, and TL increased. The migration of this fish is associated with specific physiological adaptations, such as developmental status, fluid osmotic pressure, etc. How the changing carbon sources or adaptation can affect the bioaccumulation of PFOA in the sea bass or how PFOA can influence the sea bass migration require further studies.

6.5.5 Ecological Risk Evaluation of PFOA The pollution of PFOA would pose a potential ecological risk to the aquatic environment. Valsecchi et al. (2017) derived annual average environmental quality standards (AA-EQS) for PFOA. In this study, the PFOA concentrations in both freshwater (XQ4–XQ7) and estuary (XQ8) almost all exceeded the AA-EQS for the protection of the pelagic community in freshwater (30 μg/L) and sea water (3 μg/L), respectively. As mentioned above, the PFOA emission has continued to increase in recent years. This might explain the poor richness of phytoplankton and zooplankton in the Xiaoqing River. Besides, the PFOA concentrations in water from sites XQ4–XQ8 and XQ-S all vastly exceeded the AA-EQS for the protection of predators (0.1 μg/L in freshwater and 0.02 μg/L in seawater, respectively). Moreover, 72% of the PFOA concentrations in aquatic organisms exceeded the AA-EQS to protect predators (0.9 ng/g, ww). Thus, more studies are needed to investigate the influence of PFOA on the local predator species, especially avians. And in such a monitoring campaign, both whole fish and fillet are necessary to obtain sufficient information on ecological risk evaluation (Mazzoni et al. 2019).

6.5.6 Managing Health Risks of PFOA Exposure via Consumption of Aquatic Food Establishment of the screening values of PFOA. Aquatic food consumption screening values (ACSV) of PFOA (ng/g; ww) were calculated using Eq. 6.6: AC SV P F O A = FC SV P F O S × R P F O A/P F O S

(6.6)

The FCSV PFOS (ng/g, ww) is the fish consumption screening value (FCSV) of PFOS derived by the Michigan Department of Health and Human Services (MDHHS) (State of Michigan 2016), which includes a series of concentration ranges of PFOS that provide guidelines for fish consumption frequency. The RPFOA/PFOS is the PFOAto-PFOS ratio extracted from published health guidelines with both PFOA and PFOS

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in the same subject. We hypothesized that the RPFOA/PFOS could explain the difference between PFOA and PFOS, and thus, the ACSV PFOA could provide guidance for the consumption of aquatic food with PFOA contamination. Calculation of the estimated daily intake of PFOA. The EDI of PFOA (ng/kg/day) based on the high end of the ranges for ACSV PFOA was further calculated with Eq. 6.7: E D I P F O A = AC SV P F O A × F × R I R/BW

(6.7)

where F is the consumption frequency (converted to a daily basis) of the aquatic food that is obtained from the meal category of the FCSV PFOS . RIR/BW (g/kg) is the ratio of the ingestion rate (IR) per meal (g) to the body weight (BW; kg) of consumers. Differences in IR and BW exist among different populations. However, the IR and BW values given by the MDHHS are divided into age groups and in one-to-one correspondence, which generates a constant ratio (0.10 oz/kg converted to 2.84 g/kg). Assessment of consumption screening values of PFOA. Previous studies have demonstrated that the consumption of aquatic food is a major pathway for human exposure to PFAAs. However, the health risk of PFOA depends not only on PFOA concentrations in the food but also on dietary habits. In the Michigan Fish Consumption Advisory Program for PFOS by the MDHHS, the dietary habit includes 1–16 meals per month and 6 meals per year as limited meal categories and a do-not-eat meal category. The TDI values for PFOA and PFOS from published health guidelines changed much by different countries/regions in recent years (Table 6.3). For example, the European Food Safety Authority (EFSA) updated the TDI values in 2018 that were much lower than those set in 2008 (EFSA 2008; 2018). This indicated that with more epidemiological findings, the toxicity of PFOA and PFOS was more serious, and the toxicity of PFOA became even higher than that of PFOS. A total of four scenarios were developed to explore risk assessment and management options for PFOA in this study. The percentage (P, %) of PFOA concentrations in aquatic animals that fall into the corresponding ACSV ranges were also calculated (Table 6.4). Scenario 1: The RPFOA/PFOS is equal to 8 based on the TDI values for PFOA and PFOS set by the FSANZ (2017). Results were presented as ACSV1 , EDI1, and P1 . Scenario 2: The RPFOA/PFOS is equal to 1 based on the oral non-cancer reference doses (RfDs) for PFOA and PFOS set by the United States Environmental Protection Agency (USEPA) (USEPA 2017). Results were presented as ACSV2 , EDI2, and P2 . Scenario 3: The RPFOA/PFOS is equal to 0.44 based on the TDI values for PFOA and PFOS set by the EFSA (EFSA 2018). Results were presented as ACSV3 , EDI3, and P3 . Scenario 4: The EDI was set as a constant (0.8 ng/kg bw/day). Results were presented as ACSV4 and P4 . The calculated EDI values were well within the corresponding TDI values in each scenario for scenario 1 and scenario 2, but exceeded the corresponding TDI values in scenario 3. And scenario 4 was designed to adjust scenario 3. From scenario 1

MRL = 20 ng/kg bw/day

RfD = 20 ng/kg bw/day

TDI = 20 ng/kg bw/day

TDI = 150 ng/kg bw/day TDI = 1.8 ng/kg bw/day

MRLa = 30 ng/kg bw/day

RfD b = 20 ng/kg bw/day

TDI = 160 ng/kg bw/day

TDI = 1500 ng/kg bw/day TDI = 0.8 ng/kg bw/day

2015

2016

2017

2008

2018

Agency for Toxic Substances and Disease Registry (ASTDR)

United States Environmental Protection Agency (USEPA)

Food Standards Australia and New Zealand (FSANZ)

European Food Safety Authority (EFSA)

European Food Safety Authority (EFSA)

b

MRL: provisional minimal risk level RfD: oral non-cancer reference dose

TDI = 30 ng/kg bw/day

TDI = 100 ng/kg bw/day

2015

Danish Environmental Protection Agency (Danish EPA)

a

PFOS

PFOA

Year

Regulatory agency

Table 6.3 Calculation of the PFOA/PFOS ratio based on the published health guideline values

0.44

10

8

1

1.5

3.3

PFOA/PFOS

(EFSA 2018)

(EFSA 2008)

(FSANZ 2017)

(USEPA 2017)

(ATSDR 2018)

(Danish EPA 2015)

Reference

6 Evaluating the Comprehensive Effects of PFAAs Emited … 307

1200

2400

>2400

0.033

0.016

0

1

6 meals/year

Do not eat

304

P1

109

112

114

114

115

115

118

0

1.18

1.18

2.35

3.53

5.88

3.53

82.4

%

>300

300

150

75

38

19

13

9

ng/g

14

14

14

14

14

15

14

ng/kg bw/d

EDI2

4.71

4.71

8.24

2.35

4.71

11.8

7.06

56.5

%

P2

>133

133

67

33

17

8

6

4

ng/g

ACSV3

Scenario 3

6

6

6

6

6

7

6

ng/kg bw/d

EDI3

10.6

7.06

2.35

4.71

20.0

4.71

7.06

43.5

%

P3

>17.1

17.1

8.45

4.23

2.11

1.06

0.70

0.53

ng/g

ASCV4

Scenario 4

24.7

20.0

10.6

11.8

4.71

7.06

2.35

18.8

%

P4

Note The meal categories match the ranges provided by the MDHHS (State of Michigan 2016); F is the consumption frequency converted from the meal category; ACSV1 , ACSV2 , ACSV3 and ACSV4 represent the upper limit of the concentration ranges; P is the percentage of PFOA concentrations in aquatic organisms measured in this study that fall into the corresponding ACSV ranges

600

0.133

0.067

152

4

0.267

8

72

104

ng/kg bw/d

ACSV2

EDI1

ACSV1

ng/g

Scenario 2

Scenario 1

2

0.533

0.400

16

Meals/day

Meals/month

12

F

Meal category

Table 6.4 ASCV (ng/g, ww) and EDI (ng/kg bw/d) adjusted for PFOA

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Fig. 6.31 Percentage (%) of PFOA concentrations (ng/g, ww) in the aquatic organisms measured in this study that fall into the corresponding ACSV ranges from the four scenarios

to scenario 4, the human health risk increased with decreasing TDI and EDI values, and more aquatic organisms fell into stricter meal categories, especially the do-noteat category, from zero in scenario 1–24.7% in scenario 4 (Fig. 6.31). These results brought critical challenges for human health risk assessment and management of PFOA via aquatic food consumption with high PFOA residue. Stricter guidelines are better for protecting human health but might also be more challenging to manage sufficiently by local governments. The governments need to choose proper guidelines for the protection of the health of residents and work with the manufacturers to reduce PFOA emissions. For aquatic food consumers, taking appropriate options can also mitigate health risks. Health risk evaluation of PFAAs. The aquatic animals marked as ‘edible’ in Figs. 6.28 and 6.29 are routinely consumed, and the species with top PFOA levels require special attention to the dietary habit to mitigate the health risk. The factors affecting the bioaccumulation of PFOA suggested that certain benthic species accumulate higher levels of PFOA than other species. If these species appear on the list of food preferences, the ACSV of PFOA can provide valuable suggestions on meal frequency. The ACSV results are more suitable to describe the limits for a single food. Thus, even if these values might be within the TDI guidelines, adding other food sources with substantial PFOA residue might lead to PFOA overconsumption and increased health risks. Considering that other PFAAs examined in this study had much lower levels than PFOA, the ACSV of PFOA could be considered a summary of the 12 PFAAs for health suggestions. However, many more PFAS, such as HFPO-DA and chlorinated polyfluorinated ether sulfonate (F53B), were also detected at notable levels in the same study area (Heydebreck et al. 2015; Shi et al. 2015). Although the

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information on the health effects of these emerging pollutants is still limited, there are rising concerns, and including them in future risk assessments would improve the ASCV results. Furthermore, the trend to lower health guideline values indicates an urgent need to protect human health from PFAS pollution. Treatment of aquatic food with PFAA pollution. There are additional measures for mitigating the health risk of PFAAs, including proper treatment of contaminated aquatic food, which we summarized from previous studies. Maintaining contaminated aquatic organisms alive in clean water for a short period before cooking would decrease the pollution to some extent; especially, it would clean the contaminated in-situ water as it did in this study. Extending the cultivation period would benefit the detoxification processes (Cerveny et al. 2018). Moreover, treating edible tissues of aquatic products with uncontaminated water could lead to the depuration of pollutants (Taylor et al. 2017). Selection of the editable parts of the contaminated aquatic animals is also essential. It was found that PFAS concentrations were two to three times higher in the whole fish than in the fillets (Fair et al. 2019). However, cooking cannot consistently reduce PFAS residue. Although the concentrations might change, it is likely due to the treatment, while the amounts of PFAS usually remain unchanged during cooking (Bhavsar et al. 2014; Vassiliadou et al. 2015; Taylor et al. 2019).

6.6 Effects on Home and Commercially Produced Chicken Eggs 6.6.1 Sampling Design and Collection The FIP is located in a densely populated county. In 2013, the county had a population of about 0.5 million, and 81% lived in rural areas. 16 chicken egg samples were collected from households around the FIP in October 2015 (Fig. 6.32). Some residents raise chickens at home and get the eggs (n = 4, site 1–4), while others buy eggs from nearby supermarkets (n = 12, site 5–16). These egg samples were taken to the laboratory and extracted within a week. Each sample consisted of 3 individual eggs.

6.6.2 Occurrence of PFAAs in Chicken Egg Yolks We have detected 12 PFAAs calculated on a wet weight (ww) basis in chicken egg yolks, egg whites, and whole egg samples. PFBS and PFHxS were not detected in any sample and will not be discussed further. In yolks of home-produced eggs (HPEs) (site 1–4), C4 PFCA, C8–C12 PFCAs, and PFOS were detected100%, and C5, C6, and C7 PFCAs were 75%, 50%, and 25%, respectively. The concentrations of ∑PFAAs (sum of 10 PFAAs except for PFBS

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Fig. 6.32 Sampling sites for home and commercially produced eggs around the FIP

and PFHxS) were 482, 162, 63.7, and 8.99 ng/g for sites 1, 2, 3 and 4, respectively, which suggested a decrease with increasing distance from the FIP. Concentrations of C9–C12 PFCAs and PFOS were similar for samples taken from these four sites. PFOA (5.11–368 ng/g) and PFBA (1.75–110 ng/g) were the first and second most abundant congeners, contributing 69% and 22% of ∑PFAAs, respectively (Fig. 6.33). PFOS (0.73–1.39 ng/g) contributed 3.6% of the total, and the remaining seven PFCAs accounted for 5%. Both PFOA and PFBA concentrations declined with increasing distance from the FIP. In the environmental media around the FIP, the concentrations and contributions to ∑PFAAs of PFPeA, PFHxA, and PFHpA were similar to PFBA. However, the egg yolks contained much lower proportions of these three homologues than that of PFBA. This may be due to higher PFBA concentrations in the chicken diet, for example, in maize from the farmland around the FIP. In yolks of commercially produced eggs (CPEs) (site 5–16), PFBA, PFOA, and PFOS were detected 100%, whereas other PFAAs were less frequently detected. PFHpA was not detected in any sample. ∑PFAAs concentrations (0.77–9.14 ng/g) were similar to that of the HPE yolk sample from site 4, 20 km away from the FIP. The hens living environment and eating habits may explain the difference between PFAAs levels in HPEs and CPEs. For example, laying hens in commercial farms usually eat processed or packaged food, while free-foraging hens consume soil, earthworms, insects, weeds, food leftovers, and local water. In the yolks of CPEs, contributions of PFBA (27–62%, with a mean of 39%), PFOA (6–51%, 38%), and PFOS (9–56%, 30%) to ∑PFAAs were comparable with each other (Fig. 6.33). The contribution of the remaining PFCAs to ∑PFAAs was about 3%. PFOA (0.09–2.66 ng/g) and

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Fig. 6.33 PFAAs in egg yolks, egg whites, and whole eggs of HPEs and CPEs

PFBA (0.31–5.64 ng/g) concentrations in CPEs yolks were lower than or similar to that of HPE yolk sample from site 4, respectively. PFOS concentrations were similar between CPE and HPE yolks.

6.6.3 Occurrence of PFAAs in Egg Whites, Whole Eggs and Distribution Pattern of PFAAs in Eggs In all 16 egg whites, PFBA and PFOA were detected in 16 and 14 samples, respectively. The remaining PFCAs and PFOS were detected at low frequencies or were not detected. Unlike the PFAAs congener pattern in egg yolks, PFBA was the predominant form in egg whites, contributing 77% of ∑PFAAs, followed by PFOA (17%) (Fig. 6.33). Concentrations of PFBA (0.24–6.97 ng/g) and PFOA (0.05–1.46 ng/g) in whites of HPEs declined with increasing distance from the FIP, and PFBA (0.16– 0.52 ng/g) and PFOA (90% of PFOA were found in the egg yolks. PFBA was examined in eggs and showed little difference from the other PFAAs. An average of 20% of PFBA (ranging from 6 to 48%) was found in egg whites, although most of it was still distributed in egg yolks. The different affinity between individual PFAA and the proteins in egg yolk and egg white parts might be a reason for the distribution pattern. PFAAs concentrations were 2–8 times lower in all whole egg samples than in corresponding egg yolk samples, while the congener patterns were similar (Fig. 6.33). Analysis of PFAAs in yolks solely would underestimate the health risk on account of the relatively higher proportion of PFBA in egg white. Some groups consume large

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amounts of egg whites and control the intake of egg yolks. These include athletes, patients at risk of vascular disease and diabetics, etc. As a result, only analysis of yolks was recommended when studying the PFSAs in eggs, while yolks and whites should be analyzed separately when examining the PFCAs.

6.6.4 Human Exposure to PFAAs via Egg Consumption PFAAs uptake via HPEs could be an important exposure pathway for the residents near FIP. According to Shandong Statistic Year Book, the average egg consumption in the studied area was 37.5 g/day/person. Applying an average body weight (bw) of 59.4 kg for adults and 21.1 kg for children (Zhang et al. 2010), daily intakes of several main PFAAs and ∑PFAAs via HPEs and CPEs consumption were estimated (Table 6.5). EDI of PFAAs were calculated by EDI (ng/kg bw/day) = egg consumption (g/day) × PFAAs’ concentration (ng/g)/body weight (kg). PFAAs’ concentration refers to the sum of PFAAs in egg yolks and egg whites. As for EDI calculation of CPEs, arithmetic means concentrations of PFAAs were used. The reference dose (RfD) values proposed by the Environmental Working Group (EWG) are 25 and 333 ng/kg bw/day for PFOS and PFOA, respectively (Thayer and Houlihan 2002). The EDIs of PFOS through all HPEs (0.46–3.01 ng/kg bw/day) and CPEs (0.54–1.52 ng/kg bw/day) were comparable, accounting for 1.8–12.0% of the RfD. It is alarming that the EDI of PFOA via HPEs from site 1 was 233 and 657 ng/kg bw/day for adults (close to the RfD) and children (2 times the RfD). Due to low concentrations of PFAAs found in eggs, reports on EDI via eggs are scarce. A recent study reported that the total dietary intake of PFOA was 11.95 ng/kg bw/day Table 6.5 EDI (ng/kg bw/day) of PFAA via HPEs and CPEs consumption Site

Distance (km)

PFBA

PFOA

PFOS

∑PFAAs

2

73.6

233

1.07

310

PHPEs (n = 4) Adults

Child

1 2

5

10.8

88.4

0.86

103

3

10

16.5

23.1

0.46

41.9

4

20

1.26

3.26

0.70

5.92

1

2

207

657

3.01

873

2

5

30.4

249

2.43

291

3

10

46.5

65.1

1.30

118

4

20

3.54

9.19

1.97

16.7

CPEs (n = 12) Adults

Mean

1.30

0.79

0.54

2.69

Child

Mean

3.66

2.23

1.52

7.58

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P. Wang et al.

for the general population in China (Shan et al. 2016), being 20 times lower than the EDI via HPEs from site 1. The EDI of PFBA via egg consumption ranged from 1.26 to 73.6 ng/kg bw/day for adults and 3.54 to 207 ng/kg bw/day for children, respectively. There are few studies on human exposure and the toxicology effect of PFBA, which might be due to shorter serum elimination half-lives of PFBA, 3–4 days (Chang et al. 2008). However, a recent study on PFAAs in autopsy tissues from the general population in Catalonia, Spain, revealed high levels of short-chain PFAAs, especially PFBA (median values: 807 and 263 ng/g in lung and kidney, respectively) (Pérez et al. 2013). This implies that PFBA may accumulate in these human tissues rather than be excreted from the body. Thus, human exposure pathways of PFBA are worthy of attention, and the health effects of PFBA exposure to residents around the FIP via egg consumption need urgent further investigation.

6.7 Indoor and Outdoor Dust 6.7.1 Sampling Design and Collection The sampling sites are shown in Fig. 6.34a. With the FIP as the center, samples were taken with a radius of 2, 5, 10, and 20 km in four directions (East, E; South, S; West, W; North, N). 16 pairs of indoor and corresponding outdoor dust samples were collected from randomly selected homes at each sampling site in October of 2014. An outdoor dust sample was also collected from a road in the FIP simultaneously. Dust samples were swept with a pre-cleaned brush, wrapped in aluminum foil, and further sealed in polyethylene zip bags, and then transported to the laboratory and stored at −20 °C until analysis. Before chemical analysis, large debris and particles (visible hairs, fibers or grits, etc.) were removed from the samples by using a methanol-rinsed pair of tweezers.

6.7.2 PFAAs in Indoor Dusts ∑PFAAs concentrations in indoor dust samples ranged from 73 to 13,500 ng/g, with a median of 979 ng/g. C4 to C10 PFCAs and PFBS, PFHxS, and PFOS were detected 100% in all samples, and C11 and C12 PFCAs were 82% and 94%, respectively. The mean concentrations of ∑PFAAs at 2, 5, 10, and 20 km sampling circles were 6402, 1568, 812, and 243 ng/g, decreasing exponentially with the increase of distance from the FIP (r2 ≥ 0.990) (Fig. 6.35).

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Fig. 6.34 a Map of the study area and sampling sites; b Spatial distribution of PFAAs in indoor dusts; c Relative abundance of individual PFAA in indoor dusts; d Spatial distribution of PFAAs in outdoor dusts; e Relative abundance of individual PFAA in outdoor dusts; f Comparison of PFAAs concentration in indoor dust and outdoor dust. The lower and upper ends of the box are the 25th and 75th percentiles of the data. The horizontal solid line within the box is the median value, and the symbol ▲ represents the arithmetic mean value

For all indoor dust samples, PFOA (56–8873 ng/g) was the dominant congener, contributing 80.4% of ∑PFAAs, followed by short-chain PFCAs, including PFPeA (2.90–3362 ng/g, 6.3%), PFBA (5.82–220 ng/g, 5.0%), PFHpA (1.34–662 ng/g, 3.9%), PFHxA (4.75–424 ng/g, 3.8%) (Fig. 6.34b, c). For the other long-chain PFCAs and all PFSAs (2.16–18.5 ng/g), the contribution was lower than 1%. The congener pattern of PFAAs in dust was consistent with surface water and sediment in this area

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Fig. 6.35 Decline in C4–C8 PFCAs and ∑PFAAs concentrations in indoor dust samples with the distance from the FIP. (Note The decline curve was based on the arithmetic mean concentration. The lower and upper ends of the box are the 25th and 75th percentiles of the data. The horizontal solid line within the box is the median value, and the symbol ▲ represents the arithmetic mean value.)

(Wang et al. 2016). Like the congener pattern in the present study, PFOA was found to be the predominant PFCA in house dust samples in many other countries, such as Canada, Sweden, Spain, and Australia (Eriksson and Kärrman 2015). However, the median concentration of PFOA (852 ng/g) in house dust here was 1–2 orders of magnitude higher than that from these countries (9–21 ng/g).

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6.7.3 PFAAs in Outdoor Dust ∑PFAAs concentrations in outdoor dust ranged from 5 to 9495 ng/g (median: 62 ng/g). The highest ∑PFAAs concentration was found in the dust collected from the road located in the FIP. C4 to C9 and C12 PFCAs were detected at 100%, and C10 and C11 PFCAs were 94%, while PFBS and PFOS were 88%, and that PFHxS was 41%. With the increased distance from the FIP, ∑PFAAs concentrations in outdoor dust decreased (Fig. 6.34d). Mean concentrations of ∑PFAAs for 2, 5, 10, and 20 km from the FIP were 747, 319, 77.6, and 34.7 ng/g, which were about 13–274 times lower than that in dust from the FIP. The relative contributions of individual PFAAs for indoor and outdoor dust were similar. PFOA (4.29–8511 ng/g) was the dominant congener and contributed 79.5% of ∑PFAAs, followed by PFBA (0.53– 255 ng/g, 7.7%), PFPeA (0.22–521 ng/g, 4.8%), PFHxA (0.15–108 ng/g, 3.1%), PFHpA (0.03–82.2 ng/g, 3.1%) (Fig. 6.34e). Concentrations of C9–C12 PFCAs and all PFSAs ranged from 0.17 to 16.6 ng/g. For each site, the concentrations of PFAAs measured in indoor dust exceeded that measured in outdoor dust (Fig. 6.36f). This may be related to the different compositions of indoor and outdoor dust or the environment in which they were located. Indoor dust particles have different properties from outdoor such as particle size or organic content, potentially making them more attractive sorbents for PFAAs. Meanwhile, house dust is located in different environmental conditions from outdoor dust (e.g., wind and rain dispersal, runoff, moisture, sunlight). Therefore, the elimination or degradation of contaminants associated with dust is assumed to be slower indoors than outdoors (Vorhees et al. 1999; Mahler et al. 2009).

Fig. 6.36 Schematic diagram of sources of PFAAs in the dust around the FIP

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6.7.4 Source Identification of PFAAs in Dust Associations among different PFAAs concentrations in dust were explored. PFCAs from C4 (PFBA) to C8 (PFOA) were highly correlated (p < 0.01, correlation coefficients > 0.85) with each other. Furthermore, a significant correlation (p < 0.05) was observed among the remaining PFCAs, while associations within PFSAs and between PFCAs and PFSAs, were less significant. The associations among individual PFAAs indicated that these congeners share similar origins or fates. Correlation analysis of PFAA congeners between the central FIP (C) and the other dust sampling sites showed that they were similar in the signature profiles (p < 0.01, correlation coefficients > 0.76). Based on our previous examination (Wang et al. 2014), the FIP was the only point source in the study area and is the most likely source of the PFAAs contaminants in dust samples. The FIP is a self-sufficient manufacturer so that the PFCAs could be generated and released through their direct production, the production of fluoropolymers, or many intermediates (Wang et al. 2016). Spatially, PFAAs concentrations of dust samples in the west were higher than that in the other three directions, which were comparable to each other (Fig. 6.34b, d). The wind rose plot for the study area shows that the E (east) wind and the ESE (eastsouth-east) wind are the primary wind directions (Fig. 6.34a). Hence, the downwind location may be the main reason for higher concentrations of PFAAs at the sites in the west. So, air transport and deposition were the most likely pathway for PFAAs from the FIP to the households in the surrounding areas, similar to the APFO transportation in environmental media near a fluorochemical manufacturing facility (Davis et al. 2007). Correlations of PFAAs in indoor and outdoor dust at each sample site were also investigated. The total PFAAs in the indoor dust samples correlated well with that in the corresponding outdoor dust samples (p < 0.01, correlation coefficients > 0.74), which implies similar sources. Outdoor dust may be walked into the houses by the residents. Indoor and outdoor air exchange may also contribute to the organic contaminants in indoor dust. The clothing and skin of workers close to the FIP are possibly another source of PFAAs in the dust. These sources of PFAAs in the dust around the FIP (Fig. 6.36) are different from those in ordinary homes where the source is the use, wear, and abrasion of consumer products inside the home.

6.7.5 Human Exposure to PFAAs via Dust Ingestion and Dermal Absorption Humans can be exposed to PFAAs in dust via ingestion and dermal absorption. The EDI (ng/kg bw/day) of PFAAs through dust was calculated by averaging the intake dose over body weight, with equations and exposure/ingestion factors recommended by the Environmental Protection Agency of the United States (USEPA 2011; Zhang

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et al. 2010). Considering that body weights and consumption rates vary by age, we estimated the EDI of PFAAs for five age groups: infants (0–1 years), toddlers (2– 5 years), children (6–10 years), teenagers (11–17 years), and adults (≥18 years). As for the EDI calculation of each sampling circle, arithmetic means concentrations were used. The EDIs of several main PFAAs in the study area via dust ingestion and dermal absorption varied, depending on the age group and the distance of the residents from the FIP. The EDI of PFAAs through dust ingestion was approximately 4–14 times higher than that through dermal absorption. The total exposure of PFBA, PFPeA, PFHxA, PFHpA, PFOA, and ∑PFAAs via dust was 0.184, 0.997, 0.196, 0.293, 4.42, and 6.09 ng/kg bw/day for adults living 2 km away from the FIP, and corresponding exposures were 1.10, 5.81, 1.14, 1.70, 26.0 and 35.9 ng/kg bw/day for toddlers, respectively. As expected, the EDI for toddlers was higher than those for other age groups in each sample circle (Fig. 6.37) due to more frequent hand-to-mouth contact, indicating that the dust imposes more potential health risks on this age group. Compared to the current recommended TDI values of 100 to 1500 ng/kg bw/day for PFOA proposed by several countries, the EDI of PFOA via dust for residents in the study area are all below these thresholds. However, it is essential to note that there is an ongoing discussion about the relevance of these TDIs. Some argue that these values are insufficiently protective and may be several 100-fold too high (Grandjean and Budtz-Jørgensen 2013; Grandjean and Clapp 2015). PTFE production has been expanded in the FIP with an average annual growth rate of 25% since 2001, and without suitable substitutes for PFOA in the production of most fluoropolymers, high exposure is likely to continue for the residents.

Fig. 6.37 EDI (ng/kg bw/day) of PFOA via indoor dust for residents around the FIP

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6.8 Effects on the Atmospheric Environment: Air, Dust, and Rain 6.8.1 Research Design and Sampling The original design on the sampling sites was set around the FIP in four directions (east, north, west, and south). Different distances away from the FIP were set to evaluate the influence range. The actual sampling locations were adjusted based on field conditions, yet they still followed the principles of the directions and range of influence (Fig. 6.38). Active high-volume sampler (Qingdao Hengyuan S.T. Development Co., Ltd) combined with a Tissuquartz™ filter (PALL Co., Port Washington, NY) were used to establish air concentrations of PFAAs within a short period. Previous studies found breakthroughs in gaseous and particulate PFAAs on the filter at both tracelevel loadings (Johansson et al. 2017) and saturated sampling medium (Melymuk et al. 2014). As a result, the amount of PFAAs adsorbed to filters was neither a measure of particulate-bound PFAAs nor the total PFAAs. Currently, with no applicable correction factor for filter breakthrough of PFAAs during air sampling, this

Fig. 6.38 Geographical information on air sampling (this study), outdoor dust sampling (Su et al. 2016), and rain sampling (Liu et al. 2017) around the FIP

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study hypothesizes that the filter may capture both the particulate and the gaseous PFAAs, although potentially resulting in an underestimation of the concentrations. The filter was made of binder-free pure quartz, with a 25 × 20 cm dimension and a typical thickness of 432 μm. The air sampler was operated at a flow rate of 800 L/min for 20 h to collect the sample, which allowed for an air collection volume of 960 m3 for each sample. In total, 12 air samples at 12 sites (A1–A12) were collected in November 2017. Data on PFAA concentrations in dust and EDI of PFOA via dust ingestion (Su et al. 2016) and PFAA concentrations in the rain (Liu et al. 2017) was extracted from our previous studies renumbered. Seventeen outdoor dust samples were collected at 17 sites (D1–D16, and D0 was located within the FIP) in October 2014, and 20 rain samples at five sites (R1–R5) from several precipitation events collected from October to November 2014. The units of the PFAA concentration were pg/m3 for air, ng/g for outdoor dust, and ng/L for rain.

6.8.2 Measurement of PFAAs in Air Among the 12 PFAAs, PFOA and short-chain PFCAs presented a detection ratio (DR) of 100%, and the long-chain PFCAs and PFSAs∑ presented in both low concentration and DR (Table 6.6). Concentrations of the 12 PFAAs ranged from 77.7 to 15,092 pg/m3 . C8 PFOA was dominant (42.8–9,730 pg/m3 ), followed by the C5 PFPeA (8.29–3,013 pg/m3 ), C4 PFBA (10.3–1,743 pg/m3 ), C6 PFHxA (5.43– 493 pg/m3 ) and C7 PFHpA (3.27–185 pg/m3 ). Previous studies focused more on the volatile/neutral PFAS than ionic PFAAs in the atmosphere (Stock et al. 2004; Li et al. 2011). However, in this study, strong atmospheric emissions of PFAAs were observed, and the profiles of PFAAs in the air were like those measured in the aquatic environment of the same area (Wang et al. 2016). This indicated a concerted emission of PFAAs from the fluorochemical manufacturer.

6.8.3 Comparisons Among Air, Outdoor Dust, and Rain To evaluate the compositional distribution ∑ of the dominant PFAAs, the percentage contribution (%) of each PFAA to the 12 PFAAs was calculated for air, outdoor dust, and rain, respectively. The percentage of PFOA in the air was the lowest (mean ± SD: 60 ± 21%, the same below) compared to those in the dust (80 ± 7%) and rain (76 ± 9%). However, the SD values were more prominent in the air (2–21%) than in dust (1–7%) and rain (2–9%), indicating that the compositional distribution of the dominant PFAAs was more stable in the dust and rain than in the air. Remarkably, the larger the percentage, the larger the SD, implying that the concentrations may influence the PFAAs compositional distribution.

3.90

44.7

110

804

100

Min

Median

Mean

Max

DR

255

100

Max

33.9

Mean

DR

0.53

4.20

Min

Median

100

DR

100

420

57.2

27.7

2.84

100

521

46.6

2.66

0.22

100

3013

483

62.1

8.29

PFPeA

100

554

69.2

29.1

2.41

100

108

13.2

2.95

0.15

100

493

131

25.9

5.43

PFHxA

100

944

117

43.8

2.03

88

82.3

13.5

5.40

nd

100

185

41.5

28.0

3.27

PFHpA

Note nd indicates below LOQ; DR is the detection ratio (%)

Rain (n = 20)

Outdoor dust (n = 17)

459

1743

Mean

Max

10.3

105

Min

Air (n = 12)

Median

PFBA

Statistics

Medium

100

2752

786

615

44.7

100

8512

728

50.8

4.29

100

9730

1610

451

42.8

PFOA

95

9.79

2.34

1.44

nd

94

4.55

0.73

0.49

nd

92

5.27

1.64

1.20

nd

PFNA

85

7.39

1.68

1.05

nd

94

6.41

0.87

0.37

nd

50

9.92

2.96

1.67

nd

PFDA

50

1.58

0.44

0.28

nd

82

2.20

0.34

0.14

nd

0

Nd

Nd

Nd

Nd

PFUnDA

15

0.39

0.24

0.18

nd

94

3.27

0.46

0.20

nd

8

0.48

0.48

0.48

nd

PFDoDA

Table 6.6 Summary of PFAAs concentrations in air (pg/m3), outdoor dust (ng/g) and rain (ng/L) around the FIP

25

1.33

0.67

0.32

nd

47

0.10

0.06

0.05

nd

58

2.89

1.21

0.79

nd

PFBS

55

4.46

0.72

0.12

nd

41

0.06

0.03

0.03

nd

8

0.75

0.75

0.75

nd

PFHxS

60

1.35

0.60

0.53

nd

88

0.54

0.15

0.13

nd

67

2.74

1.24

1.01

nd

PFOS

100

4862

1145

803

59.4

100

9496

836

61.6

5.32

100

15,092

2729

1046

77.7

12 PFAAs

∑

322 P. Wang et al.

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To further evaluate the trends of the percentage contribution with increasing concentrations in air, dust, and rain, a linear model was developed and tested with one-way ANOVA. The results showed no clear trend for PFAAs in the air, especially for PFOA. Notably, decreasing trends were observed for PFBA, PFHxA, and PFHpA, and an increasing trend for PFOA in the dust, yet all the trends were insignificant (P > 0.05). However, significant increasing trends for PFHxA and PFHpA and a significant decreasing trend for PFOA (P < 0.05) were found in the rain (Fig. 6.39). This indicated that rain droplets were more efficiently scavenging PFOA with increasing concentrations than short-chain ∑ PFCAs. The concentrations of 12 PFAAs in air, outdoor dust, and rain were converted into their log10 values to evaluate the spatial distribution of PFAAs (Fig. 6.40). The three media showed different trends in terms ∑ of direction. Excluding the sites within the FIP, the highest concentrations of 12 PFAAs were found in the west for air, the north for outdoor dust, and the southeast for rain (mean values from the several precipitation ∑ events). Regarding influence ranges, decreasing trends on concentrations of 12 PFAAs away from the FIP were evident for outdoor dust in all directions within the 20 km radius, as observed in the study by Su et al. soils (2016). Trends in outdoor dust were also consistent with those in farmland ∑ around the FIP (Liu et al. 2017). For air, changes in the concentrations of 12 PFAAs with increasing distance from the FIP were inconsistent. These disparities were primarily due to the sampling approaches. Air samples collected via high-volume air samplers and rain samples collected from precipitation events were both short-period captures for PFAAs in the atmosphere, which would be strongly influenced by the emission activities of the manufacturer as well as the meteorological conditions. For example, the rain samples collected at the same site (R1) from six precipitation events were measured separately, showing large variances of PFAAs concentrations, with a minimum of 76.3 ng/L and a maximum of 4862 ng/L. On the contrary, outdoor dust accumulates over a more extended period, reflecting the diffusion trend more clearly than other media.

6.8.4 Human Health Risk Evaluation of PFOA The PFOA concentrations in outdoor air could be used to estimate the concentrations in indoor air (Lorber and Egeghy 2011). In this study, with a direct impact of the fluorochemical manufacturer, the concentrations of PFOA in indoor air were estimated under two scenarios: Scenario 1: The indoor/outdoor air ratio was set as 1, assuming that the indoor and outdoor air was thoroughly exchanged. This scenario was based on the fact that the rural residential area has limited housing airtightness. This could be considered a good-case scenario. Scenario 2: The indoor/outdoor air ratio was set as 14.4, the median value of the indoor/outdoor dust ratio of the same site in the same study area (Su et al. 2016). This scenario was made based on the fact that the air PFOA sampled in this study

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∑ Fig. 6.39 Trends on the percentages of dominant PFAAs to 12PFAAs with increasing concentrations in air, dust, and rain (P < 0.05 indicates that the trend is significant)

6 Evaluating the Comprehensive Effects of PFAAs Emited …

Fig. 6.40 Comparison of the atmospheric diffusion of and rain

∑

325

12PFAAs (in log10 values) in air, dust,

was a mixture of gaseous and particulate PFOA. Thus, it was closely connected to the dust PFOA. This could be considered a bad-case scenario. The human intake of PFOA via air inhalation (ng/day) was estimated with Eq. 6.8. I ntake P F O A = C ∗ R ∗ T ∗ AF

(6.8)

where C is the concentration of PFOA in the air (with unit converted to ng/m3 ); R is the inhalation rate (m3 /day); T is the activity patterns, including time indoors (day) and time outdoors (day); and AF is the absorption fraction, which is set as 0.5 for PFOA (Lorber and Egeghy 2011). The EDI of PFOA via air inhalation was further calculated with Eq. 6.9. E D I P F O A = I ntake P F O A /BW

(6.9)

where BW is the body weight for corresponding age group (kg). The intake and EDI of PFOA via inhalation of outdoor and indoor air by five age groups were estimated separately (Table 6.7). The PFOA intake via the inhalation of indoor air was much higher than via the inhalation of outdoor air across all age groups under the two scenarios. This was because the time indoors was much more than the time outdoors. PFOA intakes via the inhalation of outdoor air showed a steady trend with increasing ages, with the highest values in the adult group (0.06– 13.0, mean: 2.15 ng/day). The trend was slightly different from the indoor air, where the highest values were found in the teenager group (0.28–64.7, mean: 10.7 ng/day under scenario 1, and 4.10–932, mean: 154 ng/day under scenario 2).

3.80

23.0

0.14

0.86

Mean

Max

331

54.7

15.3

1.46

0

0.01

0.02

0.12

Min

Median

Mean

Max

3.15

0.52

0.15

0.01

45.3

7.50

2.10

0.20

EDI (ng/kg bw/day)

1.06

0.04

Median

0.10

0

Intake (ng/day)

S1

0.29

0.05

0.01

0

4.58

0.76

0.21

0.02

2.67

0.44

0.12

0.01

41.6

6.89

1.93

0.18

S1

I

O

S2

O

I

Toddlers (2 to grain crops. For the same species, leaves had larger BAF than roots. This indicated that the plants with higher water contents accumulated PFAAs more easily, and larger leaf areas would lead to more sorption of PFAAs from atmospheric deposition. For the three grains, soybean showed the highest BAFs, followed by wheat and corn, which may be related to decreasing lipid and protein content. For aquatic animals, the species with top PFOA levels were all benthic. Freshwater species generally had higher PFOA concentrations than marine species due to higher PFOA concentrations in the river water. Results of the carbon source indicated that the freshwater species were more benthic feeding, while the marine species were more pelagic feeding. Comparing the TL with the carbon source, it seemed that benthic feeding would lead

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to higher TLs than pelagic feeding. Analysis of the PFAAs in eggs found that HPEs were much more polluted than CPEs, and most of the PFAAs were distributed in egg yolks. All these findings fill the gap in our knowledge of the bioaccumulation of PFAAs under serious pollution. PFOA concentrations in water showed a high ecological risk to the pelagic community, while PFOA concentrations in both water and aquatic organism showed a very high ecological risk to air-breathing predators. For the concern over the human health of local residents, most of the analysis used the calculation of EDI and then compared it to the published TDI. This allowed for a direct health risk assessment. The results revealed considerable risk via limited food sources, especially for the younger aged, because of their higher consumption per body weight. The combined intake of PFOA via more food sources and inhalation pathways implied more severe risks. Besides, there are extra considerations of the health risk evaluation. Firstly, the published health guideline values are not unchanged. On the contrary, the values have decreased dramatically in recent years due to more toxicology findings. This requires the necessary modification of the existing evaluation results. Secondly, with the restrictions on well-known pollutants, alternatives emerged, and scientists must keep up with more emerging pollutants. As a step forward for the health risk assessment, we enhanced the screening values of PFOA to manage its health risks, with different scenarios on the suggested consumption frequency of PFOA-contaminated aquatic food based on the 1-to-16 meals-per-month and 6 meals-per-year categories, as well as the do-not-eat category. Nonetheless, the control of the emission sources is still fundamental, as the current wastewater treatment technologies had limited capacity for the removal of PFAAs, and neither did the waste gas treatment. Therefore, it is imperative for the government and industry to adopt effective measures to reduce the emission of emerging pollutants and to protect the environment and human health.

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Chapter 7

Environmental Health Policy Implications and Future Perspectives Yonglong Lu , Jingjing Yuan, and Guizhen He

7.1 Overview Rapid urbanization in China has brought threats to the surrounding environment, and the consequent changes have implications for rural and urban livelihood and wellbeing. Several strategies relating to agricultural reform and rural–urban migration must be considered at the national level for sustainable cities. The approaches for handling the delicate rural–urban relationship for smooth social transformation are also suggested. As the world’s largest manufacturer and the second largest consumer of chemicals, China has transitioned from small and medium chemical factories to chemical industrial parks as a solution for the prevention and control of environmental pollution, which requires substantially higher levels of public interaction and cooperation. Hence, understanding and considering public concern, attitude, and response are undoubtedly critical for the chemical industrial project, planning, and policy implementation. Chemical accidents have become a major contributor to health and environmental risks with the accelerating expansion of chemical industries in China. It is commonly observed that many serious chemical accidents have been caused by inappropriate Y. Lu (B) State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems, International Institute for Sustainability Science, College of the Environment and Ecology, Xiamen University, Fujian 361102, China e-mail: [email protected]; [email protected] Y. Lu · G. He State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China J. Yuan Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems and Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Fujian 361102, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 Y. Lu et al. (eds.), Ecological Risks of Emerging Pollutants in Urbanizing Regions, https://doi.org/10.1007/978-981-19-9630-6_7

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responses or delayed actions following emergencies. Here we review the existing accident databases in China, analyze data on major hazardous chemical accidents, and identify the improvements required for developing a chemical emergency response system. As China has grown in economic strength and influence, it has identified the need for strengthening the management of toxic and hazardous chemicals. Responsible chemicals use and management cut across industry and enterprise, national and international regulatory controls, scientific research and technology transfer, and public participation. This Chapter is intended to provide a comprehensive profile of the impacts of urbanization on environmental and human health, public perception and attitude toward chemical industrial parks and emissions in China, the emergency response system for environmental accidents, and the policy and legislation for the management of emerging contaminants. Ecosystem-based management is proposed for promoting sustainable urbanization.

7.2 Urbanization, Rural Development, and Environmental Health For a long time, China has prioritized urban development at the expense of agriculture and the rural economy, widening the gap between urban and rural areas. Rapid urbanization has brought about risks to the surrounding environment, and the resulting changes impact livelihood and well-being in rural and urban areas. Urbanization has increased the pressure on farmers to make traditional farming less economical while providing opportunities for alternative and high-value agricultural enterprises to take advantage of urban markets. With more rural people migrating to cities, occupational hazards in the working environment, low awareness of health risks, and poor living conditions make them more vulnerable to health risks than other groups. Several strategies for sustainable cities must be considered at the national level, particularly those related to agricultural reform and rural–urban migration. How to deal with the delicate relationship between urban and rural areas and realize social transformation smoothly is also suggested (Yuan et al. 2018).

7.2.1 Urbanization and its Impacts on Environmental Change With the reform and opening-up policy in 1978, China has been progressing in rapid urbanization. The level of urban development increased from 17.92% in 1978 to 63.89% in 2020 (Fig. 7.1). Five major urban agglomerations were identified as (i) Beijing-Tianjin-Hebei urban agglomeration in the Haihe River Watershed; (ii)

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Fig. 7.1 Urbanization and industrialization level in China from 1978 to 2020

Cheng-Yu urban agglomeration in the Yangtze River Watershed-; (iii) urban agglomeration in the middle Yangtze River Watershed; (iv) Yangtze River Delta urban agglomeration in the Yangtze River Watershed; and (v) Pearl River Delta urban agglomeration in the Pearl River Watershed (Fig. 7.2). Large-scale urbanization has traditionally been accompanied by industrialization. Numerous studies have identified the coercing effects between increasing urbanization and environmental change. The main changes in the water environment during urbanization include groundwater depletion and groundwater table decline, rivers and springs drying up, seawater intrusion, and water quality deterioration (Wu and Tan 2012). In general, surface water was lightly polluted. However, river water adjacent to cities, especially the major urban agglomerations, was seriously polluted in 2016 (Ministry of Environment Protection of China 2016). Comparing the surface water quality of the three major watersheds in which urban agglomerations have developed, pollution of Haihe River was the most severe (ratio of water quality worse than the water quality standard V was 41%), and the water quality of the Pearl River and Yangtze River was relatively good. The water quality of four major bays (Bohai Bay, Hangzhou Bay, Yangtze River Estuary and Pearl River Estuary) adjacent to megacities were seriously polluted and the associated environment severely damaged. In addition to conventional pollutants such as BOD, ammonium, and phosphorus, these rivers were also polluted by emerging organic pollutants, such as PFAS and PBDEs. The more urbanized the region, the worse the air quality (Xia et al. 2014). According to a national air quality survey, the primary pollutants were PM2.5 , PM10, and NO2 (Ministry of Environment Protection of China 2014). The days exceeding standards per year in Beijing, Shanghai and Guangzhou were 48.0%, 67.4%, and 71.0%, respectively. The most polluted, highly urbanized area was the Beijing-Tianjin-Hebei region. Among 74 cities included in the first monitoring stage according to the new standard, the highest air quality indicator (AQI) was

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Fig. 7.2 Five urban agglomerations in China

also found in the Beijing-Tianjin-Hebei region (Ministry of Environment Protection of China 2016). Spatial analysis showed that PM2.5 pollution in the Central Plain urban agglomeration, Cheng-Yu urban agglomeration, Yangtze River Delta urban agglomeration, and Pearl River Delta urban agglomeration was the most severe. Urbanization also increased the risk of soil contamination from waste disposal and acid deposition (Chen 2007; Yuan et al. 2020). The five-year national soil assessment program completed in 2013 showed that soil pollution in South China was more severe than that in North China. The soil pollution in the Yangtze River Delta, Pearl River Delta, and Northeast Old Industrial Base was extremely serious, mainly from inorganic pollutants. In addition, as analyzed in previous chapters, there is a positive correlation between urbanization and pollution by emerging organic pollutants.

7.2.2 Impacts of Urbanization on Rural Development 7.2.2.1

Opportunities for Rural Development Under Urbanization

Urbanization, along with the increasing residential population and the expansion of non-agricultural business and industry, has increased the pressure on existing farmers and made traditional farming less economical. At the same time, it offers alternative

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and high-value agricultural businesses the opportunity to take advantage of nearby urban markets (Larson et al. 2001). Population migration. With less than 0.1 hectares of arable land per farmer, rural areas have little opportunity to boost income growth through agricultural space expansion. There are certain situations that urbanization can reduce the number of poor agricultural people and the overall number of small-farm holders and open up possibilities to accelerate rural development through large-scale and modernized farming practices. The development of urban agglomerations is accelerating the migration of millions of poor rural workers to urban areas. Rural–urban migration and the decline of the agricultural population will profoundly change the costs and increase the efficiency of agricultural production. Although the rural population is smaller than the urban population and is declining, the land used for rural residents is increasing rapidly. Four times as much rural land is used for construction as urban land, often inefficiently. More than 7.58 million hectares of potentially arable land could be realized by optimizing rural residential land use, for example, by clearing up discarded residential plots (Ministry of Land and Resources of China 2016). Agricultural industrialization. Urbanization can stimulate the transformation of agricultural modernization from closed subsistence agriculture to open market agriculture and increase farmers’ income. With the increase in urban population and related economic size, the agricultural consumption market will expand, thus stimulating the demand for agricultural products and improving the quantity and quality of agricultural products. Localized agriculture can shift from the production of primary agricultural products such as grain and feed to more valuable products such as horticulture and food production that enter the value-added processing chain. Agricultural industrialization has led to the emergence of many township enterprises, providing a link for the economic relations between urban and rural areas (Satterthwaite et al. 2010). In the urbanization process, the agricultural modernization level was improved by promoting specialized agricultural production, integrated operation, and enterprise management (Satterthwaite et al. 2010). Ann increasing role for supermarkets in agricultural trade will benefit larger (often non-local) agricultural producers and lead to a shift in employment within the agricultural system, with fewer people working in agriculture and more people working in transport, wholesale, retail, food processing, and vending (Kennedy et al. 2004, Cohen and Garrett 2010). The expansion of the agricultural market also gave birth to various agricultural functions and business models, such as agricultural parks, tourism, and leisure agriculture, focusing on the transformation from quantity to quality and economic benefits (Long et al. 2009). Urbanization will promote the transformation of agriculture from single production to multiple functions of production, consumption, and ecological protection, which is conducive to the coordinated development of rural population, resources, and environment. Technology and supporting services. Urbanization can provide educational resources for farmers, create opportunities and improve their quality of life. Urbanization promotes the continuous improvements of agricultural facilities, machinery,

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technology, pre-production and post-production services, financial services, agricultural policies, and other supporting services and promotes the large-scale intensive industrialization of agriculture (Qiu 2010). Education and innovation can also improve agricultural technology and efficiency. Developed information resources, network channels, and transportation infrastructure provide improving conditions for the circulation and trade of agricultural products.

7.2.2.2

Challenges for Rural Development Under Urbanization

Urbanization in China will also bring many challenges to the sustainable development of rural areas, such as labor shortages, loss of arable land and increase in uncultivated land, pollution of arable land, and the neglected rights of farmers. Labor shortage. Over the past 40 years of reform and opening up, China’s rapid urbanization has led to the loss of large amounts of farmland which was converted into industrial plants, and rural labor has been converted into industrial labor. At the same time, more and more rural people are migrating to urban areas in search of economic opportunities, leading to massive depopulation in rural areas. By 2017, 286 million people had migrated to cities. Most rural–urban migrants are men who seek higher wages in cities but have to leave their children, spouses, and elderly parents behind in the countryside. Left-behind children, women, and aging parents in rural areas account for more than 22% of China’s rural population (Yang 2013). Loss of arable land. Official figures show that China’s total area of arable land has been steadily shrinking since the late 1950s (Smil 1999). By the end of 2016, there were 645.2 million hectares of agricultural land, with arable land accounting for 20.9% (134.96 million hectares). Even after the redline of 120 million hectares of arable land was set by the Chinese Central Government in 2013, the area of arable land still declined from 135.16 million hectares in 2013 to 134.96 million hectares in 2016. Two national land surveys were conducted from 1984 to 1997 and from 2007 to 2009, respectively, in China. For statistics for the three periods since 1978, using different criteria and techniques, it is impossible to establish an overall trend curve. Nevertheless, the arable land area still presents an apparent general trend of decline in these three periods (Fig. 7.3). In recent years, the trend of rapid loss of arable land has been slowed because of the strict control. There were about 12.97 million hectares of arable land loss in China from 1978 to 2012, and about half of its urban growth came at the expense of arable land, raising concerns about food security (Peng 2011). In 2016, China’s arable land per capita was 0.10 hectares, 40% less than the global average. According to China’s agricultural development strategy, seven grain-producing areas have been designated. The area of arable land in all prominent grain producing areas showed a trend of decline from 1996 to 2008, except for Xinjiang and Heilongjiang. The Huang-Huai-Hai Plain and the Yangtze River Basin Plain, the biggest grain-producing provinces, accounting for 46.39% of the total grain production. There was a decline of about 5.24% of arable land in these grain-producing

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Fig. 7.3 Changes in total arable land area in China from 1978 to 2016

areas, and urbanization would be an important reason (Fig. 7.4). These provinces are more developed and have the densest development zones and city clusters. The loss of arable land, the shortage of water resources, and the acceleration of urbanization make agriculture face difficult situations and continuous potential threats. Arable land conversion. As a critical link in urbanization, arable land is being converted to other uses at a rapid rate. A total of 2.85 million hectares of the built-up area were converted from arable land from 2001 to 2015 in China (Ministry of Land and Resources of China 2001–2015). The built-up areas in 135 cities increased by 5.7 km2 annually. This number is even much bigger in big cities. For example, nearly half of Beijing’s first designated green space in 2005 had been converted to built-up areas, due to the firm and growing trend of urban area expansion (Yang and Zhou 2007). From 1996 to 2016, the amount of arable land in Beijing declined by 35.45%. Land

Fig. 7.4 Arable land loss from 1996 to 2008 and grain-producing areas in China

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expropriated by local governments is heavily used to promote urban development and financial infrastructure. Rapid urbanization has dramatically changed the transfer of arable land in rural areas. Arable land pollution. Urbanization is also increasing the risk of arable land pollution. In the first national soil pollution survey in 2014,19.4% of arable land was found to be heavily polluted, mainly by heavy metals, DDT, and PAHs (Ministry of Environment Protection of China 2014). Peri-urban zones in China had a lot of waste-polluted lands and untreated sewage used for irrigation. With rapid urbanization in China, increasing municipal solid waste (MSW) has led to severe soil pollution. The total amount of untreated MSW in 214 large and medium-sized cities in China was 11.7 million tons in 2016 (Ministry of Environment Protection of China 2017). Industrial and municipal sewage and surface water polluted through irrigation and flooding also contribute to arable land pollution. The total amount of municipal sewage discharge increased from 18 billion tons in 1985 to nearly 37 billion tons in 2013 (Lu et al. 2015a). In water-shortage regions, industrial and municipal sewage has to be used to irrigate farmland, orchards, and vegetable gardens. Many site-specific studies were conducted on arable land pollution in China’s highly urbanized regions, such as the Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta.

7.2.3 Impacts of Urbanization on Human Health 7.2.3.1

Urbanization and Infectious Diseases in China

Public health in China has been improving since 1990, and the prevalence of infectious diseases has generally declined (Fig. 7.5). The annual mortality rate of infectious diseases declined rapidly from 1990 to 2016, from 20.47 cases per 100,000 to 6.46 cases per 100,000 in urban areas, and from 35.08 cases per 100,000 to 7.76 cases per 100,000 in rural areas. In general, the mortality rate of infectious diseases in rural areas was higher than that in urban areas, and the gap was large before 2005 but gradually narrowed down since 2010.

7.2.3.2

Urbanization and Chronic Diseases in China

In China, the pattern for causes of death has shifted from infectious diseases to noncommunicable chronic diseases in a much shorter time than in many other countries (Yang et al. 2008). According to the five National Health Service Surveys, the prevalence of chronic diseases increased from 20.7% in 1993 to 33.1% in 2013 (Fig. 7.6). Chronic diseases mainly include hypertension, diabetes, ischemic heart disease, cerebrovascular disease, and intervertebral disk disease. The prevalence of chronic diseases in urban areas is much higher than in rural areas, which has increased

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Fig. 7.5 Mortality rate of infectious diseases in rural and urban China from 1990 to 2016

Fig. 7.6 Prevalence of chronic diseases in rural and urban China from 1993 to 2013

significantly since 2003. Nation-wide wealth has increased with urbanization, which has led to an increase in chronic diseases in both rural and urban areas. The social and economic changes brought about by urbanization increase the risk factors for chronic diseases. The urban resident diet is shifting towards the highcalorie and low-nutrition Western diet (Tilman and Clark 2014). Urban life is more sedentary than rural life in China. Occupational, physical activity is less common in urban areas than in rural areas, where automobile use is more common. High-calorie foods and lack of physical activity are major risk factors for overweight or obesity. In addition, the fast-paced city life and crowded environment increase the psychological stress of urban residents, which can lead to hypertension (Ibrahim and Damasceno 2012).

7.2.3.3

Health Issue Faced by Rural-To-Urban Migrant Workers in China

Urbanization in China is associated with a large rural–urban migration. Nearly 40% of urban residents are migrants, and the total number of migrants is about 289 million.

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While migrants play a crucial role in rapid economic growth, they are largely excluded from most social benefits, including public health services. Occupational hazards in the work environment, poor housing conditions, low awareness of health risks, and poor economic conditions make migrants more vulnerable to health risks than other groups (Therese et al. 2008; Mou et al. 2010). Occupational injuries and diseases are of great concern to rural–urban migrants, especially those working in hazardous environments. In general, migrants take jobs with high health risks that urban workers reject, such as construction, manufacturing, and mining. Reports of work-related injuries and diseases, such as disability, pneumoconiosis, and acute/chronic poisoning, are regular. A survey in Shanghai showed that 38.3% of migrant workers had experience with work-related injuries, and 62.9% needed work-related injury prevention services (Yin 2012). In 2013, 23,152 cases of pneumoconiosis were diagnosed nationwide, accounting for 87.72% of the total reported occupational diseases. Pneumoconiosis, which is preventable but difficult to cure, is a common disease among mining and construction workers. Lack of occupational protection may be the main reason for severe disease epidemics. Despite significant progress in controlling infectious diseases, these diseases remain one of the major causes of morbidity and mortality in China (Wang et al. 2008). Due to low immunization rates, crowded living conditions, and high mobility, migrant workers are both victims and vectors of infectious diseases. While childhood immunization is the most cost-effective means of ensuring healthy growth and development, the coverage rates for all recommended vaccines for migrant children aged 0 to 6 years are lower than for the non-migrant child population.

7.2.4 Narrowing Down the Urban-Rural Gap for Sustainable Urbanization To realize the dream of new urbanization, China faces three major policy challenges: land, people, and the environment (Bai et al. 2014). In the past, local governments continued to introduce industrial and real estate projects regardless of the carrying capacity of the natural environment, leading to ecological degradation and environmental pollution. Consequently, population urbanization lagged behind land urbanization, and a large number of empty cities or ghost cities emerged. Urban planning is playing an increasingly important role in promoting sustainable urbanization. But corresponding plans are formulated by planning departments in different administrative areas, such as national and provincial scales. Since many of the problems arising from urbanization are cross-regional, especially in industrial development and environmental protection, the central government has proposed a strategy for urban agglomeration development and carried out across-provincial planning. Several strategies need to be considered at the national level for sustainable cities. First, health education should be carried out in rural and urban areas. Previous reports have shown that education can change lifestyle and is positively associated with

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health status (Albert and Davia 2011; Lu et al. 2015b). Second, there is a need to improve further the living conditions of urban residents, such as increasing green spaces and sports facilities or improving the transportation system. Thirdly, the gap in access to public services, particularly health and education, between rural and urban migrants and local citizens should be reduced. Narrowing or eliminating the gap between rural and urban areas is the key to realizing the sustainable development of urbanization. To this end, the following actions must be taken. Strengthening rural capacity building. Basic agricultural infrastructure, including water conservancy infrastructures like reservoirs, rivers, lakes, canal networks and irrigation networks, and rural roads, should be improved, and agricultural mechanization to increase production should be promoted (Lu et al. 2015a). The government’s goal is to increase the overall mechanization rate of farming, planting, and harvesting to 70%. In the urbanization process, incentive and benefit-sharing mechanisms should be established and improved to facilitate sending agricultural science and technology personnel to the countryside, transferring agricultural innovation results, and promoting advanced agricultural technologies. Urbanization will provide more resources for agricultural science and technology innovation, especially genetic engineering, precision agriculture, and cultivation technology innovation (Lu et al. 2015b). Providing financial services for agriculture, rural areas, and farmers is the driving force for rural development. The government should also develop and improve the agricultural insurance system and encourage social capital to invest in rural development. Rural education, including technology training and information communication, helps improve the quality and skills of the workforce. Redline for protecting arable land. The government should strictly control the misuse and return of arable land and speed up the transformation of low-yielding farmland and the construction of high-standard farmland. Industrial upgrading from the secondary industry to the tertiary industry helps reduce the transfer of farmland. In terms of land management, the government should adhere to the basic position of household operation in agriculture and develop a variety of management modes, such as household farms and collective agricultural enterprises. A unified land market will be established to ensure that farmers fairly share the benefits of value-added land. As for the pollution of arable land, the government should identify the pollution source and take strict pollution control measures to eliminate it. Optimizing the scale of agricultural economy. The existing smallholder agriculture must be transformed into a larger-scale agriculture, devoted to developing high-yield, high-quality, efficient, ecologically sound, and safe agriculture. As a link between urban and rural areas, the township enterprises need to be upgraded to realize the diversification of rural industrial structure and increase the income of the rural population. The whole life cycle of agricultural products production and supply chain, including low-temperature storage, graded packaging, transportation, and e-commerce, should be well developed to meet the growing demand for food and strict environmental management. With regional advantages, rural communities

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should focus on the development of characteristic agriculture, such as urban, intensive, and tourism agriculture. Multiple agricultural industries are also necessary for agricultural industrialization, which is significant for dealing with economic crises or natural disasters (Long et al. 2009; Siciliano 2012). Ensuring living security for rural migrants. After the Hukou (household registration system) reform in 2014, agricultural and non-agricultural hukou was abolished, but the requirements for migrants to settle in megacities are still rigorous. Fundamental reform of hukou policy needs to take into account land rights and property rights. The moderately loose household registration policy will allow more rural migration to cities during urbanization and ensure equal access to economic opportunities and social welfare. Equal exchange factors of production and balanced allocation of public resources between urban and rural areas should be promoted to protect the rights and interests of rural migrant workers. Affordable housing, job training, and the migrant labor market are necessary to ensure their living and jobs. Rural communities and mass organizations should be encouraged to protect the rights of farmers or migrant workers, promote the rules of urban life, and properly handle the delicate relationship between rural and urban areas for a smooth social transformation.

7.3 Public Perception and Attitude Towards Chemical Industry Park China is the world’s largest manufacturer and the second largest consumer of chemicals, with over 25,000 large-scale chemical companies and recorded usage of 45,000 types of chemicals (Ministry of Environmental Protection (MEP) 2013). The (chemical) industrial park policy has also become a centrally important component in the Chinese concept of a Circular Economy in the 1990s, aiming to reconcile rapid industrialization with social-environmental sustainability (He et al. 2014). By the end of 2015, 502 major Chemical Industrial Park (CIPs) were built across China, in which over 15 thousand chemical enterprises were set up and operated. However, potential environmental risks will arise because many enterprises in the CIPs produce and use hazardous substances simultaneously in centralized area, especially in coastal cities. In recent decades, accidents with harmful substances and hazardous chemicals have become major problems in China (He et al. 2011, 2014). In the past decade, the chemical infrastructure construction led to conflicts between local communities on the one hand and the project implementation organization and higher-level authorities, on the other hand, resulting in protests and the postponement or cancelation of the project. Empirical evidence suggests that sufficient and reliable information and two-way communication would build citizen trust in governmental agencies, which could enhance public acceptance of programs and projects (Fiorino 1990; Yuan et al. 2011). Understanding public concern, attitude,

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and response is undoubtedly a key to implementing a chemical industrial project, planning, and policy direction (He et al. 2018).

7.3.1 Location of Chemical Industrial Parks In this study, a face-to-face questionnaire survey was conducted in Dalian city, Liaoning Province, one of the coastal cities in China. Dalian is selected as a typical study city considering its chemical industrial parks, the high risks of chemical companies, and public concerns about chemical accidents. The chemical industry is one of the pillar industrial sectors in Dalian. Dalian has four CIPs: Changxing Island, Songmudao, Dagushan, and Zhuanghe. The survey was taken in Dagushan CIP in Jinzhou District and Songmudao CIP in Wafangdian District. This research only investigated the residents living within 10 km of nearby CIP. Five villages close to Songmudao CIP and four communities around Dagushan CIP, with a total of 440 interviewees, were selected. A total of 418 valid questionnaires were returned. The questionnaire consisted of three sections: (1) socio-demographic characteristics of the interviewees; (2) perceived environmental impacts and problem awareness of local CIPs.

7.3.2 Public Perceived Risk, Impacts, and Benefits of a Chemical Industrial Park Nearly 40 and 29% of the respondents agreed that the centralized construction of chemical plants in a CIP could mitigate obviously and mitigate to a certain degree of the environmental impacts and risks (such as air pollution, water pollution, marine pollution, soil pollution, solid waste discharge, ecosystem degradation, crops damage, toxic substances discharge, emergent safety, and environmental accident, and human health impacts) in comparison to the decentralized and individual chemical factories in a city. Over 21% of the respondents thought chemical enterprises in a CIP would increase the environmental impacts and risks. Other 11% of the respondents held neutral opinions. Spearman correlation analysis showed that the farther the respondents’ residence from a CIP was, the lower level of the environmental impacts and risks of CIP was perceived (P < 0.001). Respondents’ opinions on the roles of the CIPs construction ranged from 1 (strongly decrease/worsen) to 7 (strongly increase/improve). Results showed that 45.0 and 39.5% of the respondents thought that the CIPs had played positive roles (strongly increase + increase + relatively increase) for the local industrial and economic development. Over 78% of the respondents regarded job opportunities as a negative benefit (strongly decrease + decrease + relatively decrease). More than 62% of the respondents considered that the CIPs had a negative role in local

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Fig. 7.7 Perceived roles of the chemical industrial parks by the respondents (n = 418)

energy saving, emissions reduction, and circular economy (Fig. 7.7). Average scores for these six aspects ranged from 2.88 to 4.29. Local employment was ranked the least one, and local industrial development played the most positive function. The perceived worsening role (37.6% of respondents) of CIPs in the local environmental pollution control was fairly bigger than their improving role (33% of respondents), with an average score of 3.89.

7.3.3 Public Awareness of Chemical Industrial Parks The perceived policy and project outcomes are influenced by the extent to which the people are aware of the problems. Five items were used to assess the problem awareness of the respondents. The scales ranged from 1 (not at all problematic) to 7 (very problematic). Average scores were 4.66, 4.86, 5.15, 5.38, and 5.38 for the number and size of CIPs, impacts on the local environmental quality, impacts on the local ecosystems, the local human health, and the local coast and marine environment. Regarding the impacts level of CIPs on different target objects, the coast and marine environment was considered the most problematic (78.9% of respondents selected somewhat problematic, problematic, and very problematic), followed by the local ecosystems (77.3%), the local human health (76.6%), and the local environmental quality (73.7%) (Fig. 7.8). The number and size of CIPs was thought as less problematic (61.7%), while only 18.4% of the respondents selected it as not at all problematic, very slightly problematic, and slightly problematic. Notably, the CIPs

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Fig. 7.8 Public awareness of the chemical industrial parks in Dalian (n = 418)

impacts on the local environmental quality were thought to be the most “somewhat problematic,” with 44.3% of the respondents’ selection. This case study in Dalian illustrated that the chemical industrial transition to the integrated CIP in China indeed falls into the implementation controversies. While national policies and plans are all set for chemical plants relocation to CIPs to reduce the decentralized risks and enhance environmental efficiency and performance, the implementation outcomes of these plans and policies in different regions need to be systematically observed and evaluated carefully in the long run. Not only do many challenges remain in addressing cumulative health and environmental impacts of major chemical industrial parks, but environmental and health improvements of such projects, more than incidentally, occur after major public controversies and conflicts. Moreover, a sound environmental emergency management plan and risk early warning system should be established in CIPs. The enterprises in the chemical industrial park have to adopt advanced production technologies and strengthen their environmental management system.

7.4 Chemical Accidents and Emerging Response System From their very inception, chemical industries have been controversial due to the high risks they impose on the safety of human beings and the environment. With the accelerating expansion of chemical industries, chemical accidents have become a major contributor to health and environmental risks in China (Liu et al. 2005). In case of an accident, the response to emergencies largely determines the consequences and effects of the accidents. It is commonly observed that many serious chemical accidents have been caused by inappropriate responses or delayed actions following emergencies.

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A series of laws, regulations, and standards on chemical accidents have been promulgated and implemented with the increasing number of chemical accidents in recent years in China. In the opinion on strengthening Environmental Emergency Response Management (No. 130 of 2009), the MEP recognized the big gap between the demand for effective risk management and the actual capacity within government and industry to meet that demand. To bring chemical risks under effective control, comprehensive, standardized, and geo-referenced information on chemical risks and accidents is crucial for decision-making, supervision, assessment, insurance, and management (Nivolianitou et al. 2006). Here we aim to review the existing accident databases in China, analyze data on major hazardous chemical accidents, and identify the improvements required for developing a chemical emergency management policy.

7.4.1 Existing Chemical Accident Databases in China The importance of an integrated chemical accident database has long been recognized worldwide. Chemical accident databases can serve as foundations for policy and decision-making by national and international regulators, financial and insurance companies, industries, and the public. It can also serve as a reference and knowledge pool in responding to similar emergencies (Gomez et al. 2008; Zhang et al. 2008). Currently, data regarding chemical accidents in China are collected and stored mainly by two departments: MEP and the State Administration of Work Safety (SAWS). SAWS is responsible for collecting and analyzing information on all industrial production accidents throughout the country and regularly releasing data to the public. The SAWS and its affiliations, namely the National Registration Center for Chemicals (NRCC) and the China Chemical Safety Association (CCSA), own and manage three different chemical safety-related databases: the Accident Inquiry System (AIS) of SAWS, the Chemical Accident Cases (CAC) of CCSA affiliated to SAWS, and the Daily Accidents Information (DAI) of NRCC affiliated to SAWS. These three databases are publicly and freely accessible via online inquiry systems. MEP has one database on so-called environmental accidents. The China Environmental Yearbooks of MEP report annually only aggregate data, which are not freely accessible and originate from the local Environmental Protection Bureaus (EPBs); MEP data on environmental accidents are also not searchable online. Hence, no accident specific-data is openly available following the MEP database. Table 7.1 summarizes the four existing Chinese databases that include information on chemical accidents. AIS is one of the main official databases for all types of work safety accidents in China, including coal mining safety accidents, metal and non-metal mining accidents, industrial and commercial enterprises accidents, and chemical accidents. CAC and DAI focus on chemical accidents in different stages, such as production, transportation, and storage. The information included within these databases varies

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Table 7.1 Major providers of data and information on chemical accidents in China Database Provider Starting name time

Number Forms of Type of Sources of Description of presentation accidents information accidents

AIS

SAWS

July 2000

20,000

Online

All types Local of WSBs accidents

Authoritative, timely, brief introduction

CAC

CCSA

January 2007

2428

Online

Chemical Other accidents media, Association members

Indirect data, timely, more details about the accidents

DAI

NRCC, SAWS

September 2006

790

Online

Chemical Internet, accidents newspaper, SAWS, etc

Indirect information, not timely

Statistic

MEP

1991

Paper

Environ. Local EPBs Only accidents statistical data

32,549

according to the owners purpose (e.g., whether the database is developed in accordance with legislative obligations, scientific research, or as a means to exchange information among industries). Together these three databases cover chemical accidents throughout China with different details of description and information and different reporting forms, containing both numerical (number of deaths/injuries, economic loss) and qualitative information (substances involved, type of process). Since the databases get data and information from different sources, the content and form of a single accident can be different in each database. For work safety accidents and environmental accidents, the classification criteria are different. According to Regulations on the Reporting, Investigation and Disposition of Work Safety Accidents (2007) and Emergency Plan for Hazardous Chemicals Accidents (2006), work safety accidents are graded by four levels, based on casualties and direct economic losses (Table 7.2). According to Measures on the Reporting of Environmental Emergency Accidents (Trial), environmental accidents are also categorized according to four levels based on more criteria. Before 2006, the State Environmental Protection Administration/SEPA (which became MEP in 2008) applied the Interim Measures on Reporting Environmental Pollution and Damage Accidents. Thus, environmental accidents that used to be reported as major accidents may now be reported as ordinary, which partly explains the declining number of accidents reported by authorities compared with those before 2006. In both SAWS and MEP definitions, if an accident belongs according to one of the criteria (deaths, injuries, direct economic losses, etc.) to level I, it is classified into Level I. SAWS and MEP use different reporting systems to collect information on chemical accidents, based on two departmental regulations: Measures on the Reporting and Disposition of Work Safety Accidents Information 2009 by SAWS and Measures on the Reporting of Environmental Emergency Accidents (Trial) 2006 by MEP. Figure 7.9 illustrates the current reporting procedures involving SAWS and MEP

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Table 7.2 Categories of the work safety accidents (1 Yuan is appr. 0.155 USD) Level of severity of accidents

No. of deaths

No. of serious injuries

Direct economic losses (million Yuan)

I

30

100

100

II

10–29

50–99

50–99

III

3–9

10–49

10–49

IV

0–2

0–9

0–10

through their networks, depending on whether the nature of the accident is (interpreted as) a work safety accident or a pollution accident. According to Article 6, 7, 8, and 9 of SAWS Measures, the responsible manager on site should report the accident to the county WSB in one hour when work safety accident occurs. At the same time, he/she should report the accident to the provincial WSB when it is at least a level III accident, and he/she should report the accident to SAWS when it is a level I or II accident. When the county WSB receives the accident information, it should report the accident to the higher level WSB and the same level government, who continue reporting to higher levels. The WSB and SAWS are also responsible for contacting other related agencies such as the Public Security Bureau and the Labor Security Office. Only when the accident is classified as a pollution accident or an environmental accident caused by a work safety accident will the reporting system of the EPB function. According to Article 7 of MEP Measures, when the local EPB receives information on an accident, it should report the accident to a higher level EPB and the same level of government, who both continue reporting to higher levels. At the same time, it should directly report the accident to the provincial EPB when it is an accident of level III or higher and should report the accident to MEP when it is a level I accident. In the whole process, the higher level WSBs and EPBs would give direction, assist in accident management and control, and minimize the consequences. If necessary, SAWS and MEP become the leaders of accident response and control. This leads to overlapping and missing reporting and to incompatible information on a single accident, for instance, when it starts as a workplace safety accident but turns into an environmental accident. The absence of an integrated and shared database has resulted in a situation where no one has a complete historical overview of chemical accidents in China. To conclude, China’s existing data about chemical accidents are fragmented and insufficient. Moreover, even in combination, they fall short in comprehensive and adequate information collection and provision. No common database integrates and uniforms information and data from different sources about a single accident, not to mention standardized working definitions and reporting procedures or specified requirements of comprehensive reporting on a single accident.

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Fig. 7.9 The chemical accident reporting system in China

7.4.2 Review of Existing Chemical Accidents Data in China Data on major chemical accidents from 1970 to 2009 were collected and analyzed. Since none of the existing databases (Table 7.1) fully offers the required data and information, we additionally consulted the book “Selected Cases of Major Chemical Accidents” (Zhang, 2007) for complementary information. Chemical accidents of levels I–III were included in the analysis based on the classification in Table 7.2. We reviewed chemical accidents concerning spatial and temporal patterns and damages. In total, 3673 chemical accidents could be identified from the above-mentioned sources. Only accidents of level I to III that occurred after 1970 and for which the number of deaths known was included in our review of accidents. After applying the selection criteria, 976 major (level I, level II, and level III as defined in Table 7.3) chemical accidents were singled out for review. Information on the severity of the accident is given with respect to the number of death, the number of serious injuries, the number of people evacuated, and the direct economic loss to property. According to The Classification for Casualty Accidents of Enterprise Staff and Workers (GB6441-1986), serious injury was defined as those injuries that cause loss of working days over 105. As to the direct economic loss, the Statistical Standard of Economic Losses from Injury–Fatal Accidents of Enterprise Staff and Workers (GB6721-1986) includes the cost of medical treatment, funeral expenses, pension, allowance, and relief funds, salary compensation, on-site and afterward disposal cost, penalty and compensation, and property loss.

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The geographical distribution of the major chemical accidents in China from1970 from 2009 is shown in Fig. 7.10. We could only position the accident at the provincial level. In total, there were significant chemical accidents reported in all 31 provinces/municipalities/autonomous regions. According to these reported data, the most affected provinces, considering the total number of major chemical accidents, are Shandong Province with 77 accidents, Zhejiang Province with 60 accidents, and Jiangsu Province with 59 accidents. The least-hit province is Qinghai with 2 reported accidents, followed by Tibet and Hainan with 3 reported accidents. Regarding the average number of deaths per accident, Chongqing, Jilin and Hunan provinces are at the top and Qinghai, Guizhou, and Gansu Provinces are at the bottom (Fig. 7.11). Although we have no indications as such, there might be differences in reporting practices among provinces that might have interfered with these data. We also related the chemical accidents and the deaths caused by accident to the size of the petrochemical factory, the gross petrochemical and chemical industry product, and the number of employees (Fig. 7.12). It is indicated that the more petrochemical and chemical factories, the more petrochemical, and chemical employees, and the higher the gross petrochemical and industrial chemical product the province has, the more accidents and deaths are reported in the databases. The most affected provinces by major chemical accidents are Shandong, Zhejiang, and Jiangsu, which have 1618, 1332, and 2613 petrochemical and chemical plants, respectively, and which produced a gross petrochemical and industrial chemical product of 163.71 billion, 82.14 billion,

Fig. 7.10 Major dangerous chemical accidents in China from 1970 to 2009

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Fig. 7.11 Average of deaths per major dangerous chemical accident in China from1970 to 2009

and 167.15 billion Yuan. At the same time, these provinces have the most employees in the petrochemical and chemical sector. Tibet, the province with the lowest number of chemical accidents, has the lowest number of chemical plants (5), the lowest gross petrochemical industrial product (10 million Yuan), and the lowest number of petrochemical employees (200 workers). For decision-making on the accident severity, accountability, establishing compensation, and developing insurance products (e.g., following the new Environmental Pollution Liability Insurance), detailed information on the damage caused by the accident is very important, among others (Mol et al. 2011). Here, it is only possible to get a rough idea of the damage caused by chemical accidents in terms of the number of dead and seriously injured persons. The temporal distribution of the major chemical accidents and their damage is presented in Figs. 7.13 and 7.14. In general, the number of chemical accidents, deaths, and serious injuries caused by the accidents have increased, especially after 2000, indicating the increasing risks of a rapidly developing chemical sector. Figure 7.13 shows that the number of accidents has increased over the years with the stable increase of the gross petro-chemical and chemical industry product. The number of deaths caused by accidents over time indicates a fluctuating trend till 1999 and a rapid increase since 2000, except for 2005–2006. This corresponds with a parallel increase in the gross industrial product of the petro-chemical and chemical industries. It seems that the number of petro-chemical and chemical factories has no direct relationship

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Fig. 7.12 The number of accidents, number of deaths caused by accidents, number of factories, gross industrial product, and number of employees of the petro-chemical industry, per province in China from 1978 to 2008

with the number of accidents, nor with the number of deaths caused by accident. The number of serious injuries, the number of the petro-chemical and chemical factories, and the gross petro-chemical and industrial chemical products from 1978 to 2008 are analyzed (Fig. 7.14). These historical analyses of major chemical accidents might be disturbed by improved information flows so that increased actual chemical accidents might not entirely cause increased numbers of reported chemical accidents. Our use of various data sources will limit that potential distortion, although it cannot rule it out completely. For the sustainable development of a fast-expanding chemical sector in China, it is strategically important to move from responding reactively to proactively preventing risks and accidents. This calls for more coordinated management of a comprehensive information system that gives more weight to environmental pollution information of chemical accidents. A comprehensive and publicly accessible information system would increase the efficiency and effectiveness of accident prevention and responses and help raise public awareness and gain support from society at large.

7.4.3 Chemical Emergency Management Policy The generation and disposition of risk are emblematic of modern society. The 2003 SARS epidemic was most influential in pushing the Chinese government to review how it handles emergencies. Following the epidemic, the Chinese authorities realized

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Fig. 7.13 Developments in the number of major chemical accidents, number of death, number of petro-chemical plant, and the gross petro-chemical industry product in China from 1978 to 2008 (1 Yuan is about 0.155 USD)

Fig. 7.14 Developments in the number of injury, gross petro-chemical industry product, and the number of petro-chemical plant in China from 1978 to 2008 (1 Yuan is about 0.155 USD)

that their approach to disaster preparedness and response was overly centralized and began establishing emergency management offices at multiple levels of government under the central State Emergency Management Office and the National Disaster Reduction Committee. From the first introduction of the term ‘emergency state’ in the modified Constitution 2004, the issuing of the “National Emergency Response Plan” by the State Council on January 8, 2006, the establishment of the National Emergency Office within the State Council, up to the formulation of emergency

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response plans at various levels and the enactment of “Emergency Response Law” in 2007, a risk management system has been emerging quickly in China. Ministry of Emergency Management was established in March 2018 according to a proposal by the State Council to take the lead in handling China’s major emergency responses and improve the public safety system. The new ministry will take over the responsibilities of the former State Administration of Work Safety, along with functions from other ministries, including firefighting from the Ministry of Public Security, disaster relief from the Ministry of Civil Affairs, geological disaster prevention from the Ministry of Land and Resources, drought and flood control from the Ministry of Water Resources and prairie fire control from the Ministry of Agriculture. China Earthquake Administration and State Administration of Coal Mine Safety were affiliated with the new ministry, while the State Administration of Work Safety was dismantled. The Ministry vowed to speed up the integration of the former State Administration of Work Safety’s emergency response system and firefighting departments to improve performance. Since World War II, emergency management has focused primarily on preparedness. Often this involved preparing for enemy attack. Community preparedness for all disasters and accidents requires identifying resources and expertise and planning how these can be used in a disaster and accidents. However, preparedness is only one phase of emergency management. Current thinking defines four phases of chemical emergency management: mitigation, preparedness, response, and recovery (Fig. 7.15) • Mitigation—Preventing future chemical emergencies or minimizing their effects. It includes any activities that prevent a chemical emergency, reduces the chance of an emergency, or reduce the damaging effects of unavoidable chemical emergencies. With a focus on chemical risk assessment, prevention, and planning, emergency response plans are compiled and reviewed at plant site, local, regional and national levels. These plans clarify the roles, responsibilities, and communication channels between groups. Site emergency plans must be approved before the site can operate. The adequacy of the plans is reviewed regularly. The basis of the Fig. 7.15 Phases of emergency management

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planned response is chemical risk assessment and understanding the scenarios that could lead to an incident and the potential impact. Buying environmental and fire insurance for you is a mitigation activity. Mitigation activities take place before and after emergencies. • Preparedness—Preparing to handle a chemical emergency. It includes plans or preparations to save lives and help response and rescue operations. Evacuation plans and stocking food and water are both examples of preparedness. An essential aspect of the effective chemical response is the capacity of responders. Specialized training, the provision of equipment and regular drills to test plans and inter-organizational communication are essential elements of “being prepared”. Preparedness activities take place before an emergency occurs. • Response—Responding safely to a chemical emergency. When an actual accident occurs, administer first aid or get medical attention for victims if necessary. It includes actions to save lives and prevent further property damage in an emergency. The response is putting your preparedness plans into action. Both response activities are seeking shelter or turning off gas valves in an accident. Transparent chains of command and interagency cooperation provide a coordinated and tiered response allowing for a rapid assessment and response at the point of the incident, plus appropriate escalation to regional and national teams. Response activities take place during an emergency. • Recovery—Recovering from a chemical emergency. It includes actions to return to a typical or even safer situation following an emergency. Recovery includes getting financial assistance to help pay for the repairs. In an accident, the polluter is responsible for cleaning up and compensation costs. Recovery activities take place after an emergency. Chemical and public information management systems can support effective chemical emergency management. Inventory management tracks the flow of manufactured and distributed chemicals, particularly toxic ones. The system also provides the necessary information for a quick and effective response if an accident happens, particularly when combined with consistent and available labeling that identifies the chemical’s human and environmental impacts. Public information systems provide information to the public about the hazards present under normal operations and timely information in the event of an emergency. A sophisticated and effective environmental emergency prevention and response system call for more institutional reforms in the legal framework, organizational arrangements, chemicals management, response plans, financial and incentive mechanisms, monitoring and reporting, information disclosure, community participation, remediation, and evaluation. China’s past practice in environmental emergency management shows that the local government primary focus has been mitigation after an incident. While this is a critically important part of any emergency response system, prevention is better than cure. Once an accident has occurred, the impact on the environment and human health becomes more difficult and more costly to control. Prevention of pollution by strictly enforcing appropriate policies and regulations is typically more cost-effective.

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7.5 Policy and Regulations for the Management of Emerging Contaminants As China has grown in economic strength and influence, it has identified the need for new measures on the management of toxic chemicals and environmental management of hazardous chemicals. Responsible chemicals use and management cut across national and international regulatory controls, industry and enterprise, scientific research and technology transfer, and public participation (McElwee 2011; Price et al. 2012).

7.5.1 National Regulations on Chemicals In the latter part of the twentieth century, there came the realization that chemicals can act as environmental pollutants. In China, chemicals supervision, management and control are allocated to different agencies. The legal system in pesticide handling includes laws, regulations, ordinances, and standards (Lu et al. 2007; Shi et al. 2005; Wang et al. 2005; Zhang et al. 2005). Till March 2013, 38 national laws, 48 State Council regulations, 196 national government department ordinances, and 324 national and industrial pesticide standards had been promulgated, targeting licensing/classification, production, storage/packaging, transportation, trade/import/export, use, disposal, and supervision (Table 7.3). About half of the laws and regulations target the licensing and production of pesticides. Broadly speaking, these laws and regulations impose obligations to make less comprehensive and more specific products in a safer, cleaner, more efficient way by using less hazardous raw materials, less energy and water, and producing fewer toxic wastes. Table 7.4 shows the essential functions of 18 ministries and commissions involved in pesticide management and supervision. Whereas the MoA is mainly responsible for registration, storage safety, and pesticide application, industrial pollution prevention and control of pesticides are the responsibility of MEP. Given the significant environmental and health impact caused by chemical enterprises, the MEP has listed the larger plants as key national enterprises for extra monitoring and supervision of pollutant discharge since 2009. This means that provincial, municipal, and county EPBs have strongly focused on regulating these plants. MEP assessed chemical plants on their environmental compliance according to the Notice on the Inspection and Verification of Environmental Protection of the Corporations Applying for Listing and the Listed Corporations Applying for Refinancing, enacted by the previous State Environmental Protection Administration (now MEP) in 2003. The SAWS plays a critical function in the safety supervision and inspection of chemical facilities. SAWS and MEP control emergency management of the chemical plant. In terms of chemical management, production, transportation, inventory, and supervision responsibilities are divided among various agencies without a unified management system or an effective coordination mechanism. Furthermore, local

1

11

Standards

International conventions

11

49

Regulations

8

Licensing

Ordinances

Laws

4

269

36

10

9

Production

4

7

4

2

Storage

1

5

31

5

2

Transport-ation

Table 7.3 Pesticide-relevant laws and regulations at the national level

2

2

29

8

6

Trade, export and import

2

21

11

4

5

Use

6

12

7

8

Disposal

2

13

40

2

2

Supervision

13

324

196

48

38

Total

7 Environmental Health Policy Implications and Future Perspectives 361

SAT

SAG

SAF

SAIC

MPS

MIIT

NDRC

MWR

MoT

MoH

MoC

MoA

MEP

GAC

CAA

AQSIQ

ACFSMC

Departmentsa

√

√

√

√

√

√

√ √

√

√ √

(continued)

Supervision

√

√

√

√

√

Disposal

√

√

√

√

√

√

Use

√ √

√

√

√

√

√

Trade √

√

√

√

√

√

√

√

√

√

Import/Export

√ √

√

√

√

√

√

Transportation

√

√

√

√

√

Storage

√

√

√

√

√

√

√

√

Production

√

√

Licensing √

Table 7.4 National governmental ministries and commissions and their functions in chemicals management in China

362 Y. Lu et al.

Licensing √

Production √

Storage √

Transportation √

Import/Export

Trade √

Use √

Disposal √

Supervision √

Note Abbreviations of governmental agencies: ACFSMC, All China Federation of Supply and Marketing Cooperatives; AQSIQ, General Administration of Quality Supervision, Inspection and Quarantine; CAA, Civil Aviation Administration; GAC, General Administration of Customs; MEP, Ministry of Environmental Protection; MoA, Ministry of Agriculture; MoC, Ministry of Commerce; MoH, Ministry of Health; MoT, Ministry of Transport; MWR, Ministry of Water Resources; NDRC, National Development and Reform Commission; MIIT, Ministry of Industry and Information Technology; MPS, Ministry of Public Security; SAIC, State Administration for Industry and Commerce; SAF, State Administration of Forestry; SAG, State Administration of Grain; SAT, State Administration of Taxation; SAWS, State Administration of Work Safety

SAWS

Departmentsa

Table 7.4 (continued)

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EPBs are currently under the direct management of local governments even though they also receive guidance from MEP. Under such a management system, the ability of local EPBs to handle local pollution incidents objectively and independently is always questionable.

7.5.2 Management and Regulations Related to POPs In the 1980s, the public and scientists started to be concerned over human health and environmental impacts caused by toxic substances such as pesticide POPs. Consequently, during the 1980s, some highly toxic pesticides, such as DDT and toxaphene, were banned in China. Over the past four decades, China has entered into more than 30 international treaties, such as the Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal (Basel Convention) and the Stockholm Convention on Persistent Organic Pollutants. In 1999, China signed the Rotterdam Convention on the Prior Informed Consent Procedure for Certain Hazardous Chemicals and Pesticides in International Trade (Rotterdam Convention), which came into effect in 2005, constituted the Regulation on Pesticide Management and Regulation on Safety Management of Dangerous Chemicals. Subsequently, China tightened its control on the hazardous chemicals by stipulating the Rule for Preventing Electric Power Equipment Containing PCBs from Polluting the Environment, which was promulgated to restrict the import, export, and use of electric equipment containing PCBs and regulate the collection, storage, and transportation of the wastes containing PCBs. The International Maritime Organization (IMO) has implemented an international convention to totally ban the use of organotin compounds in any antifouling paint formulation or antifouling system since September 2008. It is anticipated that the use of organotins as antifouling compounds and their environmental levels will be reduced in Chinese marine environments in the near future. China ratified the Stockholm Convention of Persistent Organic Pollutants, and the Convention entered into force in 2004. The Chinese central government developed the National Implementation Plan of China to implement the Stockholm Convention on Persistent Organic Pollutants. In May 2005, the National Coordination Group for Implementation of the Stockholm Convention was established, consisting of 11 ministries and agencies. With financial and technical support from Global Environment Facility, the Chinese Central Government has carried out a series of activities which include regional publicity and training to increase public awareness; POPs exposure impact assessment; development of action plans on pesticide POPs elimination, PCB reduction and disposal, and dioxin reduction and control; and capacity building of dioxin laboratory. The management and control of chemical substances (including POPs) are currently under the control of different ministries and administrations (He et al. 2014; Liu et al. 2010). For instance, pesticidal POPs are regulated under the MoA, POPs in food are governed under the MoH, and the use and regulation of chlordane

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and mirex are under the management of the Ministry of Housing and Urban–Rural Development. As listed in Table 7.5, several laws and regulations address environmental protection and pollution prevention of unintentional POPs in China. In addition, more and more standards and specifications were developed (Lau et al. 2012). For pesticides, a set of laws, regulations, and standards is developed to control pesticides packaging, handling, management, and labeling, while other sets of regulations are enacted to control the pesticide residue level in food products. In 2003, the SEPA issued the Measures on the Environmental Administration of New Chemical Substances (MEANCS). Similar to the current European program entitled “Registration, Evaluation, Authorization, and Restriction of Chemical Substances (REACH).” Remarkable achievements have been made since the implementation of these new measures. MEANCS improves the environmental management of the production and import of new chemical substances while it prevents and reduces the unregulated use of new chemical substances. On January 19, 2010, the MEP announced the revised version of the MEANCS (i.e., New-MEANCS), No.7 Order of MEP, which came into effect on October 15, 2010. On September 16, 2010, MEP issued six supportive documents for the New-MEANCS. These documents provide guidelines for declaring and registering new chemical substances in China. Given that e-waste recycling activities are one of the major causes of POPs pollution, the Chinese Central Government has initiated a national pilot program to determine the best model to manage e-waste and strengthen the prevention and control of e-wastes in China (Hicks et al. 2005). Since 2008, the Administrative Measures for the Prevention and Control of Environmental Pollution by Electronic Waste has been taken into effect to prevent and control environmental pollution by the dismantling, utilization, and disposal of e-waste. Regulations and standards targeting e-waste are implemented (Lau et al. 2012). While some laws and regulations concerning POPs management control the production, use, storage, import and export, packaging, storage, and transportation of POPs and other hazardous chemicals, China does not have any law or regulation which targets POPs specifically. To safeguard human health and protect ecosystem integrity in China, the government needs to evaluate the current status of POPs, perform a risk assessment on both human health and the ecosystems, prioritize the area of concern, and develop effective monitoring, management, and enforcement system for controlling POPs, emerging pollutants and new chemical substances.

7.6 Ecosystems-Based Management of Emerging Pollutants Along Urbanized Coasts Mapping the global distribution of nutrients, metals, and POPs contamination shows that the eastern Atlantic and western Pacific coasts are hot spots with the highest concentrations of several pollutants (Fig. 7.16). Worldwide high levels of PFAAs,

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dissolved inorganic phosphorus (DIP), Cu, Zn, Cd, and Pb were found around the Mediterranean Sea of the Eastern Atlantic Ocean. Top levels of PFAAs, OCPs and PCBs, DIP, and dissolved inorganic nitrogen (DIN) have been found in the Bohai and Yellow Seas of the Western Pacific Ocean. These hotspots are characterized by a high level of urbanization with a large population, developed industry, and (semi-) closed sea (Lu et al. 2018). Increasing pollution of emerging pollutants is the major consequence of land–ocean interaction in coastal areas. Reducing land-based emissions and enhancing coastal capacity are two ways to reduce the risk of POPs. Table 7.5 Laws and regulations related to POPs in China Law and regulations

Issued organization

Effective from

Environmental protection law

Standing committee of the national people’s congress (NPC)

1989 (revised in 2015)

Law on prevention and control of water Pollution

Standing committee of the NPC

1984 (revised in 1996, 2008, 2018)

Food safety law

Standing committee of the NPC

2009 (revised in 2015)

Law on the prevention and control of environmental pollution by solid waste

Standing committee of the NPC

1996 (revised in 2004, 2013, 2015)

Marine environment protection Standing committee of the law NPC

1982 (revised in 1999, 2013, 2016)

Law on the prevention and control of atmospheric pollution

Standing committee of the NPC

1987 (revised in 1995, 2000, 2016)

Environmental impact assessment law

Standing committee of the NPC

2003 (revised in 2016)

Law on the prevention and treatment of on occupational diseases

Standing committee of the NPC

2002 (revised in 2011, 2017)

Production safety law

Standing committee of the NPC

2002

Agricultural law of China

Standing committee of the NPC

1993 (revised in 2003)

Law on agricultural product quality safety

Standing committee of the NPC

2006

Regulation on pesticide management

The state council

1997 (revised in 2001, 2017)

Regulation on the supervision and administration of the quality and safety of dairy product

The state council

2008

(continued)

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Table 7.5 (continued) Law and regulations

Issued organization

Effective from

Regulation on the The state council administration of the recovery and disposal of waste electrical and electronic products

2011

Measures for the implementation of the regulation on pesticides management

2008

Ministry of agriculture (ministry of agriculture and rural affairs since 2018)

Measures for the administration Ministry of agriculture (ibid) of pesticide labels and manuals

2008

Administrative measures for Ministry of agriculture (ibid) the safety of places of origin of agricultural products

2006

Ministry of agriculture (ibid)

2006

Measures for the administration Ministry of agriculture (ibid) of geographical indications of agricultural products

2008

Management measures for the production of pesticides

2005

Administrative measures for the packaging and marking of agricultural products

National development and reform commission

Provision on the environmental State environmental protection administration of new chemical administration (ministry of ecological and environment substances since 2018)

2010

Measures on the administration State environmental protection of the certification for organic administration (ibid) foods

2001

Halogen elements, such as fluorine, chlorine, and bromine, are extracted from seawater and then added to the synthesis of organic compounds, such as pesticides, flame retardants, and fabric coatings. These halogenated organic chemicals were discovered more and more frequently in coastal environments with their extensive applications. Because of their properties of stable structure, bioaccumulation, and long-distance transport, some of these halogenated organic chemicals have been listed as POPs under the Stockholm Convention, including legacy contaminants such as OCPs, PCBs, and PBDEs, as well as emerging pollutants such as HBCDs, Dechlorane Plus (DP) and PFAS (Kim and Yoon 2014; Han and Currell 2016; Mwangi et al. 2016; Meng et al. 2017). Monitoring of POPs in coastal environments has been mainly concentrated in China and Europe, with few investigations in North and South America, Africa, and Oceania (Fig. 7.16). Existing monitoring results show higher levels of pollution along the coasts of China and Europe than in other areas, which may be linked to a lack of information in other parts of the world. The highest concentration of

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Fig. 7.16 Global distributions of contaminants along the coast. (Note Different shapes represent different contaminants. For each chemical, the concentration was logarithmically normalized and then divided into five groups at equal intervals. Different colors from green to red indicate different ranges, with red indicating the top 20% range. The cyclone map indicates the worst areas that suffered from storms. The thermometer shows that coastal waters are warmer than other areas.)

PBDEs in coastal surface sediment from the River Clyde estuary in the UK reached 1500 ng g−1 (Vane et al. 2010). The highest PCBs in sediments of the Lenga Estuary in Chile reached 13,000 ng g−1 , located in a nature reserve but influenced by heavy industry and economic development near the coast (Pozo et al. 2014). Moreover, the highest concentration of PFAAs in the coastal water reached 1240 ng L−1 at the Daling Estuary in China (Wang et al. 2016), which was contaminated by the upstream fluorine chemical parks (Wang et al. 2015; Zhu et al. 2015). Land-based emissions are considered as the main source of POPs in coastal areas, particularly in estuaries, while higher POPs usually occur in densely populated, urbanized, and industrialized areas. Integrating ecosystem services into environmental management is receiving increasing global attention. Ecosystem-based management (EBM) is an integrated approach that considers the cumulative impacts of the entire ecosystem, including humans and different sectors (McLeod et al. 2005; Curtin and Prellezo 2010). The focus is on maintaining ecosystem services and functions and managing human activities based on ecosystems. Taking coastal mining as an example, we should not only consider the distribution and area of exploitation and utilization but also consider its impact on the surrounding coastal ecosystem and the restoration of the ecosystem after mining activities. Most marine and coastal processes are affected by multiple stressors. Pollution of excess nutrients, metals, and POPs due to intensive human activities should be

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managed among different sectors and can be prevented by spatial planning of coastal zone, which can provide a method to determine which activity should be occurring were, with less impact and less user conflict by regulating different human purposes in a region. Degradation of salt marshes can be mitigated by regulating land reclamation activities, pollutants discharged into estuaries, and run-off of fertilizers. Spatially explicit management of multi-use areas through coastal zone spatial planning, such as designating marine protected areas, enables managers to protect areas that are most critical to ecosystem function and ecosystem services. It could also provide necessary conservation for areas vulnerable to extreme climate events, such as storm-induced coastal flooding. EBM can provide a robust management strategy that considers the cumulative impact of human activities. However, it usually considers an ecosystem ecological factors, processes, and stressors, such as energy, climate, habitat restoration, primary productivity, and fisheries. Although environmental sustainability and socioeconomic factors are included in the concept of EBM, pollutants such as nutrients, metals, and POPs are not taken into account in its application (Kluger et al. 2015; Thrush et al. 2016; Yáñezarancibia et al. 2015). However, these pollutants may act as potential stressors on coastal ecosystems through combined exposure, although each pollutant may be at very low levels. The legal frameworks currently established focus solely on pollution control and may not work well in the context of climate change. It is, therefore, necessary to assess the ecological impacts of nutrients, metals, and POPs and consider their cumulative effects under climate change. From the perspective of ecosystem stability and restoration, it is important to enhance scientific understanding of the accumulation, integration, and interaction of climate change and human activities, including eutrophication, toxic pollution, and habitat destruction.

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