Modeling PFAS Sorption in Soils Using Machine Learning

In this study, we introduce PFASorptionML, a novel machine learning (ML) tool developed to predict solid–liquid distribution coefficients (K_d) for per- and polyfluoroalkyl substances (PFAS) in soils. Leveraging a data set of 1,274 K_d entries for PFAS in soils and sediments, including compounds such as trifluoroacetate, cationic, and zwitterionic PFAS, and neutral fluorotelomer alcohols, the model incorporates PFAS-specific properties such as molecular weight, hydrophobicity, and pK_a, alongside soil characteristics like pH, texture, organic carbon content, and cation exchange capacity. Sensitivity analysis reveals that molecular weight, hydrophobicity, and organic carbon content are the most significant factors influencing sorption behavior, while charge density and mineral soil fraction have comparatively minor effects. The model demonstrates high predictive performance, with RPD values exceeding 3.16 across validation data sets, outperforming existing tools in accuracy and scope. Notably, PFAS chain length and functional group variability significantly influence K_d, with longer chain lengths and higher hydrophobicity positively correlating with K_d. By integrating location-specific soil repository data, the model enables the generation of spatial K_d maps for selected PFAS species. These capabilities are implemented in the online platform PFASorptionML, providing researchers and practitioners with a valuable resource for conducting environmental risk assessments of PFAS contamination in soils.

A comprehensive dataset of PFAS solid−liquid distribution coefficients (K_d) in soils was used to develop a machine learning model, offering K_d (PFAS) predictions that are useful for environmental management.