Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model
Per- and polyfluoroalkyl substances (PFAS), commonly known as “forever chemicals”, are ubiquitous in surface waters and potentially threaten human health and ecosystems. Despite extensive monitoring efforts, PFAS risk in European surface waters remain poorly understood, as performing PFAS analyses i...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412025002557 |
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| author | Li Zhao Jian Chen Jiaqi Wen Yangjie Li Yingjie Zhang Qunyue Wu Gang Yu |
| author_facet | Li Zhao Jian Chen Jiaqi Wen Yangjie Li Yingjie Zhang Qunyue Wu Gang Yu |
| author_sort | Li Zhao |
| collection | DOAJ |
| description | Per- and polyfluoroalkyl substances (PFAS), commonly known as “forever chemicals”, are ubiquitous in surface waters and potentially threaten human health and ecosystems. Despite extensive monitoring efforts, PFAS risk in European surface waters remain poorly understood, as performing PFAS analyses in all surface waters is remarkably challenging. This study developed two machine-learning models to generate the first maps depicting the concentration levels and ecological risks of PFAS in continuous surface waters across 44 European countries, at a 2-km spatial resolution. We estimated that nearly eight thousand individuals were affected by surface waters with PFAS concentrations exceeding the European Drinking Water guideline of 100 ng/L. The prediction maps identified surface waters with high ecological risk and PFAS concentration (>100 ng/L), primarily in Germany, the Netherlands, Portugal, Spain, and Finland. Furthermore, we quantified the distance to the nearest PFAS point sources as the most critical factor (14%–19%) influencing the concentrations and ecological risks of PFAS. Importantly, we determined a threshold distance (4.1–4.9 km) from PFAS point sources, below which PFAS hazards in surface waters could be elevated. Our findings advance the understanding of spatial PFAS pollution in European surface waters and provide a guideline threshold to inform targeted regulatory measures aimed at mitigating PFAS hazards. |
| format | Article |
| id | doaj-art-006596a3a1c64d45b6a8e89fee6018fe |
| institution | OA Journals |
| issn | 0160-4120 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj-art-006596a3a1c64d45b6a8e89fee6018fe2025-08-20T02:14:24ZengElsevierEnvironment International0160-41202025-05-0119910950410.1016/j.envint.2025.109504Unveiling PFAS hazard in European surface waters using an interpretable machine-learning modelLi Zhao0Jian Chen1Jiaqi Wen2Yangjie Li3Yingjie Zhang4Qunyue Wu5Gang Yu6Guangdong Institute for Drug Control, Guangzhou 510006, China; School of Environment, South China Normal University, Guangzhou 510006, ChinaState Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, 999077, Hong Kong Special Administrative Region; Corresponding authors.Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, United StatesGuangdong Institute for Drug Control, Guangzhou 510006, China; Corresponding authors.State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, 999077, Hong Kong Special Administrative RegionGuangdong Institute for Drug Control, Guangzhou 510006, ChinaAdvanced Interdisciplinary Institute of Environment and Ecology, Guangdong Provincial Key Laboratory of Wastewater Information Analysis and Early Warning, Beijing Normal University, Zhuhai 519087, ChinaPer- and polyfluoroalkyl substances (PFAS), commonly known as “forever chemicals”, are ubiquitous in surface waters and potentially threaten human health and ecosystems. Despite extensive monitoring efforts, PFAS risk in European surface waters remain poorly understood, as performing PFAS analyses in all surface waters is remarkably challenging. This study developed two machine-learning models to generate the first maps depicting the concentration levels and ecological risks of PFAS in continuous surface waters across 44 European countries, at a 2-km spatial resolution. We estimated that nearly eight thousand individuals were affected by surface waters with PFAS concentrations exceeding the European Drinking Water guideline of 100 ng/L. The prediction maps identified surface waters with high ecological risk and PFAS concentration (>100 ng/L), primarily in Germany, the Netherlands, Portugal, Spain, and Finland. Furthermore, we quantified the distance to the nearest PFAS point sources as the most critical factor (14%–19%) influencing the concentrations and ecological risks of PFAS. Importantly, we determined a threshold distance (4.1–4.9 km) from PFAS point sources, below which PFAS hazards in surface waters could be elevated. Our findings advance the understanding of spatial PFAS pollution in European surface waters and provide a guideline threshold to inform targeted regulatory measures aimed at mitigating PFAS hazards.http://www.sciencedirect.com/science/article/pii/S0160412025002557PFAS contaminationAffected populationEcosystem safetyInterpretable machine learningTipping point |
| spellingShingle | Li Zhao Jian Chen Jiaqi Wen Yangjie Li Yingjie Zhang Qunyue Wu Gang Yu Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model Environment International PFAS contamination Affected population Ecosystem safety Interpretable machine learning Tipping point |
| title | Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model |
| title_full | Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model |
| title_fullStr | Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model |
| title_full_unstemmed | Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model |
| title_short | Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model |
| title_sort | unveiling pfas hazard in european surface waters using an interpretable machine learning model |
| topic | PFAS contamination Affected population Ecosystem safety Interpretable machine learning Tipping point |
| url | http://www.sciencedirect.com/science/article/pii/S0160412025002557 |
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