A geospatial and binomial logistic regression model to prioritize sampling for per- and polyfluorinated alkyl substances in public water systems

As health-based drinking water standards for per- and polyfluorinated alkyl substances (PFAS) continue to evolve, public health and environmental protection decision-makers must assess exposure risks associated with all public drinking water systems in the United States (US). Unfortunately, current knowledge regarding the presence of PFAS in environmental systems is limited. In this study, a screening approach was established to: (1) identify and direct attention toward potential PFAS hot spots in drinking water sources, (2) prioritize sampling locations, and (3) provide insights regarding the potential PFAS sources that contaminate groundwater and surface water. Our approach incorporates geospatial data from public sources, including the US Environmental Protection Agency's Toxic Release Inventory, to identify locations where PFAS may be present in drinking water sources. An indicator factor (also known as “risk factor”) was developed as a function of distance between potential past and/or present PFAS users (e.g., military bases, industrial sites, and airports) and the public water system, which generates a heat map that visualizes potential exposure risks. A binomial logistic regression model indicates whether PFAS are likely to be detected in public water systems. The results obtained using the developed screening approach aligned well (with a 76% overall model accuracy) with PFAS sampling and chemical analysis data from 81 public drinking water systems in the state of Kentucky. This study proposes this screening model as an effective decision aid to assist key decision-makers in identifying and prioritizing sampling locations for potential PFAS exposure risks in the public drinking water sources in their service areas. Integr Environ Assess Manag 2023;19:163–174.