Ecological risks of PFAS in China’s surface water: A machine learning approach
The persistence of per- and polyfluoroalkyl substances (PFAS) in surface water can pose risks to ecosystems, while due to data limitations, the occurrence, risks, and future trends of PFAS at large scales remain unknown. This study investigated the ecological risks of PFAS in surface water in China...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412025000418 |
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| author | Xinmiao Huang Huijuan Wang Xiaoyong Song Zilin Han Yilan Shu Jiaheng Wu Xiaohui Luo Xiaowei Zheng Zhengqiu Fan |
| author_facet | Xinmiao Huang Huijuan Wang Xiaoyong Song Zilin Han Yilan Shu Jiaheng Wu Xiaohui Luo Xiaowei Zheng Zhengqiu Fan |
| author_sort | Xinmiao Huang |
| collection | DOAJ |
| description | The persistence of per- and polyfluoroalkyl substances (PFAS) in surface water can pose risks to ecosystems, while due to data limitations, the occurrence, risks, and future trends of PFAS at large scales remain unknown. This study investigated the ecological risks of PFAS in surface water in China under different Shared Socioeconomic Pathways (SSPs) using machine learning modeling, based on concentration data collected from 167 published papers. The results indicated that long-chain PFAS currently dominated in most basins and posed significant risks, especially PFOA. Population density and temperature were key factors influencing risks of long-chain PFAS, while secondary industry and precipitation affected the risks of PFBS and PFHxS significantly, respectively. In the future, the ecological risks of long-chain PFAS would overall decrease, with risk probabilities of PFOA and PFOS decreasing significantly in SSP5 (8.15 % and 14.95 % reduction compared to 2020, respectively). The risks of short-chain PFAS were expected to increase, but stabilize in the late stage of SSP1. Nevertheless, the risks of long-chain PFAS would remain higher than those of short-chain PFAS, with high-risk areas concentrated in Southeast China. This study suggests changes in ecological risks of PFAS driven by future climate and human activities, providing new insights for risk management. |
| format | Article |
| id | doaj-art-22d23448393f45818db90d3e8b2d0a10 |
| institution | DOAJ |
| issn | 0160-4120 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj-art-22d23448393f45818db90d3e8b2d0a102025-08-20T03:11:57ZengElsevierEnvironment International0160-41202025-02-0119610929010.1016/j.envint.2025.109290Ecological risks of PFAS in China’s surface water: A machine learning approachXinmiao Huang0Huijuan Wang1Xiaoyong Song2Zilin Han3Yilan Shu4Jiaheng Wu5Xiaohui Luo6Xiaowei Zheng7Zhengqiu Fan8Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, ChinaDepartment of Environmental Science and Engineering, Fudan University, Shanghai 200433, ChinaDepartment of Environmental Science and Engineering, Fudan University, Shanghai 200433, ChinaDepartment of Environmental Science and Engineering, Fudan University, Shanghai 200433, ChinaDepartment of Environmental Science and Engineering, Fudan University, Shanghai 200433, ChinaDepartment of Environmental Science and Engineering, Fudan University, Shanghai 200433, ChinaDepartment of Environmental Science and Engineering, Fudan University, Shanghai 200433, ChinaSchool of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Fudan Zhangjiang Institute, Shanghai 201203, China; Corresponding author at: School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Corresponding author.The persistence of per- and polyfluoroalkyl substances (PFAS) in surface water can pose risks to ecosystems, while due to data limitations, the occurrence, risks, and future trends of PFAS at large scales remain unknown. This study investigated the ecological risks of PFAS in surface water in China under different Shared Socioeconomic Pathways (SSPs) using machine learning modeling, based on concentration data collected from 167 published papers. The results indicated that long-chain PFAS currently dominated in most basins and posed significant risks, especially PFOA. Population density and temperature were key factors influencing risks of long-chain PFAS, while secondary industry and precipitation affected the risks of PFBS and PFHxS significantly, respectively. In the future, the ecological risks of long-chain PFAS would overall decrease, with risk probabilities of PFOA and PFOS decreasing significantly in SSP5 (8.15 % and 14.95 % reduction compared to 2020, respectively). The risks of short-chain PFAS were expected to increase, but stabilize in the late stage of SSP1. Nevertheless, the risks of long-chain PFAS would remain higher than those of short-chain PFAS, with high-risk areas concentrated in Southeast China. This study suggests changes in ecological risks of PFAS driven by future climate and human activities, providing new insights for risk management.http://www.sciencedirect.com/science/article/pii/S0160412025000418Per- and polyfluoroalkyl substancesHuman activitiesClimate changeRandom forestShared socioeconomic pathwaysInfluencing factors |
| spellingShingle | Xinmiao Huang Huijuan Wang Xiaoyong Song Zilin Han Yilan Shu Jiaheng Wu Xiaohui Luo Xiaowei Zheng Zhengqiu Fan Ecological risks of PFAS in China’s surface water: A machine learning approach Environment International Per- and polyfluoroalkyl substances Human activities Climate change Random forest Shared socioeconomic pathways Influencing factors |
| title | Ecological risks of PFAS in China’s surface water: A machine learning approach |
| title_full | Ecological risks of PFAS in China’s surface water: A machine learning approach |
| title_fullStr | Ecological risks of PFAS in China’s surface water: A machine learning approach |
| title_full_unstemmed | Ecological risks of PFAS in China’s surface water: A machine learning approach |
| title_short | Ecological risks of PFAS in China’s surface water: A machine learning approach |
| title_sort | ecological risks of pfas in china s surface water a machine learning approach |
| topic | Per- and polyfluoroalkyl substances Human activities Climate change Random forest Shared socioeconomic pathways Influencing factors |
| url | http://www.sciencedirect.com/science/article/pii/S0160412025000418 |
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