Developing, and piloting in the Columbia Basin, a prioritization model and screening approach for PFAS contamination
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Jurisdictions are currently challenged with efficiently identifying and characterizing the extent of per- and polyfluoroalkyl substances (PFAS) contamination and human exposure to PFAS. While PFAS contamination can be presumed near well-studied sources, the number of potential sources in larger regions, unknown facilities’ PFAS use, and uncertain fate and transport, make sampling prioritization overwhelming and complex. In the Columbia Basin, particularly in and around the region’s tribal lands, the availability of PFAS occurrence data is limited and exposure is less characterized than in other parts of the United States. This project seeks to develop and pilot a cost-effective approach to investigate and monitor for the presence and extent of PFAS contamination, with a focus on identifying PFAS contamination in fish tissue. In phase one, a geospatial prioritization workflow was developed that leverages existing PFAS measurements in fish tissue, along with information on potential sources of PFAS including industry facilities, fire training sites, and landfills. Machine learning and statistical models were employed to predict areas in natural waters of the Columbia Basin in which high PFAS concentrations are likely to occur in fish tissue, but sampling has not yet confirmed. Based on these model predictions, information on population vulnerability, and regional partner fish collection activities, additional fish tissue data will be generated in phase two of this pilot study. A novel tiered analytical chemistry strategy will be applied to screen for PFAS in these collected specimens and results will be used to evaluate the geospatial model predictions.