Structure-Based Prediction and Ranking of PFAS Groundwater Mobility for EU Drinking Water Regulation.
4 affiliations
- 1Department of Chemistry, University of Burgos, Burgos09001, Spain.
- 2ICCRAM (International Research Centre in Critical Raw Materials and Advanced Industrial Technologies), Burgos09001, Spain.
- 3Department of Physics, University of Burgos, Burgos09001, Spain.
- 4Department of Chemical and Paper Engineering, Western Michigan University, Kalamazoo, Michigan49008-5462, United States.
Abstract
The EU Drinking Water Directive (2020/2184) mandates monitoring of 20 priority per- and polyfluoroalkyl substances (PFAS) from January 2026, yet predicting which PFAS structures pose the greatest groundwater contamination risk remains challenging. We present a machine learning framework that simultaneously predicts the three physicochemical properties governing PFAS subsurface mobility─soil sorption (log KOC, R2 = 0.90), water solubility (log Sw, R2 = 0.90), and ionization class (93% accuracy)─from molecular structure alone. The models were trained on 122 PFAS spanning 37 subclasses using 44 molecular descriptors, including nine that encode the segmentation of the fluorinated backbone by ether linkages, and were validated by cross-validation, external test sets, leave-one-subclass-out analysis, Y-randomization, and a formal applicability domain. SHAP analysis indicates that ether-linkage descriptors are associated with lower predicted soil retention, providing a model-based explanation for the high mobility of GenX-type replacements. A composite Groundwater Mobility Index integrating the three end points identifies PFBA, PFPeA, and PFPeS as the most mobile EU-regulated compounds, consistent with field monitoring evidence, and flags several nonregulated PFAS with comparable mobility. An open-access prediction tool with applicability-domain screening accompanies this work.
Related
- Anthropogenic and Geogenic Factors Affecting Groundwater Arsenic and Potential Drinking Water Exposure in the San Luis Valley, Colorado.
- Molecular-Level Structure Regulation via Sequential Pre-Crosslinking and Friedel-Crafts Hypercrosslinking: Fabrication of Bimetallic Doped Porous Carbon Electrocatalysts
- Improved wideband electromagnetic performance in La3+ substituted Mn–Zn spinel ferrites: structure–microstructure regulation of grain-boundary non-Debye relaxation
- Multiscale Molecular Orientation in Polyacrylonitrile Precursors for Carbon Fibers: Processing Regulation, Characterization Methodologies, and Structure–Property Correlations
- Guidelines to Evaluate Structure-based Methods for Protein-Ligand Binding Prediction
BibTeX
@article{delahuertasainz2026structurebased,
title = {{Structure-Based Prediction and Ranking of PFAS Groundwater Mobility for EU Drinking Water Regulation.}},
author = {Sergio de-la-Huerta-Sainz and Ozge Ozkilinc and Valentín Diez-Cabanes and María Antonieta Escobedo-Monge and Pedro A Marcos and Alfredo Bol-Arreba and Mert Atilhan and Rocio Barros and Santiago Aparicio},
journal = {Environmental science \& technology},
year = {2026},
month = oct,
doi = {10.1021/acs.est.6c11513},
url = {https://europepmc.org/article/MED/42915986},
note = {CC BY},
}
RIS
TY - JOUR
TI - Structure-Based Prediction and Ranking of PFAS Groundwater Mobility for EU Drinking Water Regulation.
AU - Sergio de-la-Huerta-Sainz
AU - Ozge Ozkilinc
AU - Valentín Diez-Cabanes
AU - María Antonieta Escobedo-Monge
AU - Pedro A Marcos
AU - Alfredo Bol-Arreba
AU - Mert Atilhan
AU - Rocio Barros
AU - Santiago Aparicio
PY - 2026
DA - 2026/10/01
JO - Environmental science & technology
DO - 10.1021/acs.est.6c11513
UR - https://europepmc.org/article/MED/42915986
AB - The EU Drinking Water Directive (2020/2184) mandates monitoring of 20 priority per- and polyfluoroalkyl substances (PFAS) from January 2026, yet predicting which PFAS structures pose the greatest groundwater contamination risk remains challenging. We present a machine learning framework that simultaneously predicts the three physicochemical properties governing PFAS subsurface mobility─soil sorption (log KOC, R2 = 0.90), water solubility (log Sw, R2 = 0.90), and ionization class (93% accuracy)─from molecular structure alone. The models were trained on 122 PFAS spanning 37 subclasses using 44 molecular descriptors, including nine that encode the segmentation of the fluorinated backbone by ether linkages, and were validated by cross-validation, external test sets, leave-one-subclass-out analysis, Y-randomization, and a formal applicability domain. SHAP analysis indicates that ether-linkage descriptors are associated with lower predicted soil retention, providing a model-based explanation for the high mobility of GenX-type replacements. A composite Groundwater Mobility Index integrating the three end points identifies PFBA, PFPeA, and PFPeS as the most mobile EU-regulated compounds, consistent with field monitoring evidence, and flags several nonregulated PFAS with comparable mobility. An open-access prediction tool with applicability-domain screening accompanies this work.
SN - 0013-936X
SN - 1520-5851
ER -