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

IF 10.3 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environment International Pub Date : 2025-02-01 DOI:10.1016/j.envint.2025.109290

Xinmiao Huang , Huijuan Wang , Xiaoyong Song , Zilin Han , Yilan Shu , Jiaheng Wu , Xiaohui Luo , Xiaowei Zheng , Zhengqiu Fan

{"title":"Ecological risks of PFAS in China’s surface water: A machine learning approach","authors":"Xinmiao Huang , Huijuan Wang , Xiaoyong Song , Zilin Han , Yilan Shu , Jiaheng Wu , Xiaohui Luo , Xiaowei Zheng , Zhengqiu Fan","doi":"10.1016/j.envint.2025.109290","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":308,"journal":{"name":"Environment International","volume":"196 ","pages":"Article 109290"},"PeriodicalIF":10.3000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environment International","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0160412025000418","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

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.

Abstract Image

查看原文本刊更多论文

中国地表水PFAS的生态风险：机器学习方法

地表水中全氟烷基和多氟烷基物质的持续存在可能对生态系统构成风险，而由于数据有限，大规模全氟烷基物质的发生、风险和未来趋势仍然未知。本研究基于167篇已发表论文的浓度数据，利用机器学习模型研究了不同共享社会经济路径下中国地表水中PFAS的生态风险。结果表明，目前大部分流域以长链PFAS为主，风险较大，尤其是PFOA。人口密度和温度是影响长链PFAS风险的关键因素，第二产业和降水分别对PFBS和PFHxS的风险影响显著。未来，长链PFAS的生态风险总体呈下降趋势，其中SSP5年PFOA和PFOS的风险概率显著下降（分别比2020年下降8.15%和14.95%）。预计短链PFAS的风险会增加，但在SSP1后期趋于稳定。尽管如此，长链PFAS的风险仍然高于短链PFAS，高风险地区集中在中国东南部。该研究揭示了未来气候和人类活动驱动下PFAS生态风险的变化，为风险管理提供了新的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Environment International 环境科学-环境科学

CiteScore

21.90

自引率

3.40%

发文量

734

审稿时长

2.8 months

期刊介绍： Environmental Health publishes manuscripts focusing on critical aspects of environmental and occupational medicine, including studies in toxicology and epidemiology, to illuminate the human health implications of exposure to environmental hazards. The journal adopts an open-access model and practices open peer review. It caters to scientists and practitioners across all environmental science domains, directly or indirectly impacting human health and well-being. With a commitment to enhancing the prevention of environmentally-related health risks, Environmental Health serves as a public health journal for the community and scientists engaged in matters of public health significance concerning the environment.