Explainable machine learning for uncovering PFAS adsorption mechanisms in soils

LIU Bing; ZOU Ke-ke; LIU Hui; SHI Kai-ge; CHEN Yan-min; LU Xin; HAN Shuai-jun; GU Li

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China Environmental Science ›› 2026, Vol. 46 ›› Issue (3) : 1460-1474.

Soil Pollution Control

Explainable machine learning for uncovering PFAS adsorption mechanisms in soils

LIU Bing¹, ZOU Ke-ke², LIU Hui¹, SHI Kai-ge¹, CHEN Yan-min¹, LU Xin¹, HAN Shuai-jun¹, GU Li³

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Abstract

The soil-water partition coefficient (K_d) of per- and polyfluoroalkyl substances (PFAS) was predicted using machine learning (ML) models, and their partitioning behavior in soils was elucidated. A dataset comprising 1,227 samples of 47 PFAS was employed, with 16 PFAS physicochemical properties and soil parameters used as input features. It was demonstrated that the lgK_d values ranged from -1.40 to 3.95. When the number of CF₂ groups exceeded 5and the molecular weight (MW) was greater than 400g/mol, significant linear relationships were observed between lgKd and both the CF₂ number and MW, with R² values of 0.96 and 0.94, respectively. The influence of soil properties on PFAS adsorption was found to be dependent on the type of PFAS: non-zwitterionic PFAS were mainly affected by organic carbon content and silt content, whereas zwitterionic PFAS were primarily influenced by silt and sand content. After parameter optimization, the ML model exhibited good predictive performance, achieving an R² of 0.85, an RMSE of 0.35, and an MAE of 0.26. MW, organic carbon content, water solubility (lgS), and net charge density were identified as the most important features, with contribution rates of 31.6%, 29.8%, 19.7%, and 10.0%, respectively. Three-dimensional interaction analysis indicated that when soils with high organic carbon content, high cation exchange capacity (CEC), and low pH were combined with PFAS of large MW, lgK_d exceeded 0.40, reflecting strong adsorption. Conversely, under conditions where CEC was below 8.00 cmol/kg, organic matter content was less than 1.00%, and MW was below 380g/mol, lgK_d was lower than 0.40, indicating weaker adsorption and higher potential for environmental migration. The findings provide a reliable tool for predicting the adsorption behavior of PFAS.

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per-and polyfluoroalkyl substances / explainable machine learning / soil-water partition coefficient / prediction

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LIU Bing, ZOU Ke-ke, LIU Hui, SHI Kai-ge, CHEN Yan-min, LU Xin, HAN Shuai-jun, GU Li. Explainable machine learning for uncovering PFAS adsorption mechanisms in soils[J]. China Environmental Science. 2026, 46(3): 1460-1474

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