Predicting the relationship between PFAS component signatures in water and non-water phases through mathematical transformation: Application to machine learning classification

Abstract

Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, as a result of decades of use across a range of applications. While PFAS contamination often enters the environment in the aqueous phase, PFAS is regularly detected in a range of different phases, including soils, sediments and biota. Although PFAS at a given site may originate from the same sources, the compositions observed in different phases are nearly always different, a fact that can complicate source allocation efforts. This paper presents a quantitative method for prediction of the relative composition of PFAS in different phases for components for which differences in behavior are primarily driven by hydrophobicity. The derived equations suggest that under these conditions, the relative compositions in different phases in contact with water should be independent of overall affinity for the phase, and as such should be the same for all non-water phases. This result is illustrated with data from individual samples, as well as from site-wide evaluations for a range of different phases. The results of the work provide a useful tool to reconcile PFAS composition differences in different phases, and provide a baseline for recognizing cases where hydrophobicity is not the primary driver of differences in distribution between phases. Furthermore, the results may be useful in forensic applications for classification of PFAS across different phases. The use of the resulting equations to transform water data to train a supervised learning algorithm for forensic analysis of PFAS in non-water phases is illustrated.