Ecological risks of PFAS in China's surface water: A machine learning approach.

Journal: Environment international
PMID: 39984226

Abstract

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.

Authors

  • Xinmiao Huang
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
  • Huijuan Wang
    Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Delft, The Netherlands.
  • Xiaoyong Song
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
  • Zilin Han
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
  • Yilan Shu
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
  • Jiaheng Wu
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
  • Xiaohui Luo
    School of Computer and Software Engineering, Xihua University, Chengdu 610039, China.
  • Xiaowei Zheng
    School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
  • Zhengqiu Fan
    Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China. Electronic address: zhqfan@fudan.edu.cn.

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