Integration of Per- and Polyfluoroalkyl Substance (PFAS) Fingerprints in Fish with Machine Learning for PFAS Source Tracking in Surface Water

Per- and polyfluoroalkyl substances (PFASs) are a class of environmental contaminants that originate from various sources. The unique chemical fingerprints associated with many commercial products and industrial applications make PFASs ideal candidates for machine learning (ML)-assisted environmental forensics. Here, we propose a novel use of PFAS fingerprints in fish tissue from surface water systems to classify exposure from multiple sources of PFASs using a proof-of-concept demonstration. Three supervised ML classification techniques (k-nearest neighbors (KNN), decision trees, support vector machines) implementing two predictive features are used to classify literature-reported PFAS fingerprints in fish (n = 1057). The importance of additional predictive features was explored using brute force optimization of a multifeature KNN algorithm. The multiclass classification considered exposure to aqueous film-forming foam-impacted water, paper industry wastewater, diffuse sources, or PFASs undergoing long-range transport. The optimized classifiers demonstrated 85%–94% classification accuracy for this first known multiclass classification of PFASs for environmental forensics. The optimized classifiers also demonstrated 79%–92% classification accuracy with a set of independent external validation data (n = 192). Our results demonstrate that PFAS fingerprints in fish tissue may be an effective means of PFAS source tracking in surface water systems. The source code is provided for guidance on best practices for ML-assisted environmental forensics.