Modeling PFAS Sorption in Soils Using Machine Learning.

ABSTRACT: 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 behav

SUBMITTER: Fabregat-Palau J 

PROVIDER: S-EPMC12020356 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature