Development, Evaluation, and Application of Machine Learning Models for Accurate Prediction of Root Uptake of Per- and Polyfluoroalkyl Substances

Abstract

Machine learning (ML) models were developed for understanding the root uptake of per- and polyfluoroalkyl substances (PFASs) under complex PFAS-crop-soil interactions. Three hundred root concentration factor (RCF) data points and 26 features associated with PFAS structures, crop properties, soil properties, and cultivation conditions were used for the model development. The optimal ML model, obtained by stratified sampling, Bayesian optimization, and 5-fold cross-validation, was explained by permutation feature importance, individual conditional expectation plot, and 3D interaction plot. The results showed that soil organic carbon contents, pH, chemical logP, soil PFAS concentration, root protein contents, and exposure time greatly affected the root uptake of PFASs with 0.43, 0.25, 0.10, 0.05, 0.05, and 0.05 of relative importance, respectively. Furthermore, these factors presented the key threshold ranges in favor of the PFAS uptake. Carbon-chain length was identified as the critical molecular structure affecting root uptake of PFASs with 0.12 of relative importance, based on the extended connectivity fingerprints. A user-friendly model was established with symbolic regression for accurately predicting RCF values of the PFASs (including branched PFAS isomerides). The present study provides a novel approach for profound insight into the uptake of PFASs by crops under complex PFAS-crop-soil interactions, aiming to ensure food safety and human health.