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Poster
in
Affinity Workshop: New In ML

Spatial-Temporal Trends and Source Tracing of Per- and Polyfluoroalkyl Substances in Water Systems: A Comprehensive Review


Abstract:

This comprehensive review examines the spatial-temporal trends and source tracing methodologies for Per- and Polyfluoroalkyl Substances (PFAS) in water systems. PFAS contamination has emerged as a widespread environmental concern, with detection frequencies exceeding 70% for several compounds in drinking water sources nationwide. The review synthesizes evidence on occurrence patterns, revealing that 71-95 million Americans potentially rely on groundwater with detectable PFAS concentrations. Geographic distribution analyses identify distinct regional hotspots associated with industrial facilities, wastewater treatment plants, and military sites, with concerning environmental justice implications in the spatial distribution of contamination sources. The review evaluates advanced source identification techniques, including industrial point source characterization, multivariate statistical approaches, and chemical fingerprinting that enable accurate attribution of contamination to specific sources. Emerging predictive modeling frameworks employing machine learning algorithms demonstrate promising capabilities for forecasting PFAS distribution while incorporating hydrogeological parameters that govern transport. Analysis of exposure pathways highlights drinking water and fish consumption as significant routes, with bioaccumulation patterns showing preferential accumulation of specific compounds in biological systems despite minimal water detection. Health risk assessments document associations between PFAS exposure and immunotoxicity, thyroid dysfunction, and increased cancer risk, underscoring the significance of this persistent public health challenge.

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