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Mapping PFAS Exceedance Risk in China's Surface Water: A Machine Learning Approach Informed by Source Distribution.

Jungang Wang1, Shuai Shao1, Qiufeng Gao1

  • 1Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.

Environmental Science & Technology
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

Per- and polyfluoroalkyl substances (PFAS) contaminate China's surface water, posing a public health risk. A new model maps high-risk areas, revealing hotspots and impacting millions.

Keywords:
PFAScontamination sourcemachine learningpopulation risks

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Area of Science:

  • Environmental Science
  • Public Health
  • Chemical Contamination

Background:

  • Per- and polyfluoroalkyl substances (PFAS) are persistent pollutants causing widespread surface water contamination.
  • China's significant role in fluorinated chemical production necessitates understanding nationwide PFAS distribution.
  • Sparse monitoring data presents a challenge for assessing national-level PFAS contamination risks.

Purpose of the Study:

  • To develop a predictive model for mapping PFAS exceedance risk in China's surface water.
  • To identify high-risk areas and hotspots of PFAS contamination nationwide.
  • To inform targeted mitigation strategies and optimize PFAS management.

Main Methods:

  • Development of a Geographically Weighted Random Forest (GWR-RF) model.
  • Integration of a comprehensive, spatially explicit inventory of over 280,000 potential PFAS sources.
  • Generation of a 1 km resolution spatial map of PFAS exceedance risk.

Main Results:

  • The GWR-RF model showed strong predictive performance (accuracy = 0.83, ROC-AUC = 0.91).
  • Significant surface water areas in China are identified as high-risk for PFAS contamination.
  • Hotspots are concentrated in eastern coastal plains and industrial provinces, affecting an estimated 80-90 million people.

Conclusions:

  • Proximity to PFAS users and annual precipitation are key predictors of increased contamination risk.
  • The study provides crucial scientific evidence for developing targeted PFAS mitigation and management strategies.
  • Vulnerable populations in high-risk areas require prioritized protection efforts.