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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 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.
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 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.
Ecological risks of PFAS in China’s surface water: A machine learning approach
Xinmiao Huang (author) / Huijuan Wang (author) / Xiaoyong Song (author) / Zilin Han (author) / Yilan Shu (author) / Jiaheng Wu (author) / Xiaohui Luo (author) / Xiaowei Zheng (author) / Zhengqiu Fan (author)
2025
Article (Journal)
Electronic Resource
Unknown
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