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Predicting soil-water partition coefficients of PFAS using machine learning: Model development, interpretation, and

Yue Zhou¹, Hao Chen¹, Xi Wang¹

¹MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.

Environmental Pollution (Barking, Essex : 1987)

|July 14, 2026

Summary

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Machine learning models predict polyfluoroalkyl substances (PFAS) partitioning in soil and water. This efficient tool aids environmental risk assessment for persistent contaminants.

Area of Science:

Environmental Chemistry
Computational Chemistry
Environmental Science

Background:

Polyfluoroalkyl substances (PFAS) are persistent environmental contaminants.
Experimental determination of PFAS soil-water partition coefficients (K_d) is costly and time-consuming.

Purpose of the Study:

To develop efficient machine-learning models for predicting PFAS soil-water partitioning.
To provide an interpretable tool for environmental risk assessment and contaminated site management.

Main Methods:

Developed five machine-learning regression models using 2057 literature-derived batch adsorption data points.
Integrated PFAS structural descriptors, soil properties, and concentration data.
Utilized extreme gradient boosting (XGBoost) and Shapley additive explanations (SHAP) for model development and analysis.

Keywords:

Machine learning Per- and polyfluoroalkyl substances (PFAS)SHAP analysis Soil-water partitioning

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Main Results:

The XGBoost model demonstrated high performance with R² values of 0.83 and 0.86.
SHAP analysis revealed dominant hydrophobic interactions at low concentrations and headgroup hydrophilicity at higher concentrations.
Independent validation showed prediction deviations within one order of magnitude for contaminated site soils.

Conclusions:

The proposed machine-learning model offers an efficient and interpretable method for predicting PFAS soil-water partitioning.
The model supports environmental risk assessment and management of sites contaminated with PFAS.
This approach reduces the need for costly and time-consuming experimental K_d determination.