Predicting the Sorption Capacity of Perfluoroalkyl and Polyfluoroalkyl Substances in Soils: Meta-Analysis and Machine Learning Modeling

Predicting the soil sorption capacity
for perfluoroalkyl
and polyfluoroalkyl
substances (PFAS) is pivotal for environmental risk assessment. However,
traditional experimental methods are inefficient, necessitating computational
model development. We compiled a comprehensive data set including
44 PFAS and 405 soils from 35 literature reports, conducted a meta-analysis,
and constructed robust machine learning models. Machine learning models
using LightGBM with RDKit or PaDEL descriptors achieved R2 of 0.89, 0.88, and 0.72, RMSE of 0.28, 0.28, and 0.36,
and MAE of 0.18, 0.19, and 0.28 for cross-validation, internal test
set, and external test set, respectively. SHapley Additive exPlanation
(SHAP) analysis identified PFAS properties as the primary influence
on sorption, followed by environmental conditions and soil properties.
We found that low SOC (<0.56%) minimally affects PFAS sorption.
A pH of 6 is the boundary point where anionic PFAS are mainly attracted
or repelled by electrostatic interaction, and higher pH may enhance
the PFAS soil sorption through cation bridges. Although van der Waals
forces and polar interactions enhance the sorption of PFAS with carbon
chains ≥8, the introduction of polar structures containing
oxygen, nitrogen, and sulfur into PFAS will lower hydrophobicity and
sorption affinity. This study provides accurate predictive models,
which are helpful for environmental decision-making.