Optimizing Interpolation Methods for National-Scale PFAS Mapping

Abstract

Per- and polyfluoroalkyl substances (PFAS) are synthetic “forever chemicals” that are widely detected in U.S. drinking water and pose significant public health concerns. Characterizing their spatial distribution is essential for monitoring and remediation efforts. The Environmental Protection Agency’s Unregulated Contaminant Monitoring Rule 5 (UCMR5) provides national-scale PFAS data for more than 100,000 public water systems, though the data’s scale and spatial complexity provide challenges for assessment. This study evaluates spatial interpolation methods, Inverse Distance Weighting, Natural Neighbor, Ordinary Kriging, and Universal Kriging, for their effectiveness in estimating national PFAS concentrations. Hundreds of interpolations were generated and assessed using ArcPy workflows. Findings inform best practices for interpolating large environmental datasets and support the development of automated workflows for future UCMR releases.