Explainable AI Reveals Statistical Associations Between Industrial Activity and PFAS Contamination of Public Water Systems

Recent United States Environmental Protection Agency (USEPA) studies detected Per- and polyfluoroalkyl substances (PFAS) in ~45% of U.S. tap water highlighting the widespread environmental and public health concern. Although industrial activity in general and aqueous film forming foam (AFFF) usage are known contributors to the contamination, the specific industry sectors driving it remain unclear. Here, we apply eXplainable-AI (XAI) methods to move beyond coarse industrial categorizations and uncover the sectors most strongly associated with PFAS contamination. Using the national PFAS monitoring (UCMR5) data, industrial geolocations and socioeconomicfeatures we achieved strong predictive ability (F1-score = 0.84). SHAP analysis identified metal treatment, fabrication, and polymer manufacturing as dominant contributors, corroborating prior studies, while revealing specialty chemical manufacturing as a major yet previously overlooked predictor of PFAS contamination—often surpassing AFFF in influence. A paradoxical socioeconomic pattern also emerged: PFAS contamination was more likely in affluent regions (higher income, education, and professional employment).