2026-10-06T13:39:52Z
https://api.figshare.com/v2/oai
oai:figshare.com:article/34027216
2026-09-29T18:09:03Z
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Structure-Based
Prediction and Ranking of PFAS Groundwater
Mobility for EU Drinking Water Regulation
Sergio de-la-Huerta-Sainz (23439816)
Ozge Ozkilinc (25138502)
Valentín Diez-Cabanes (24232650)
María Antonieta Escobedo-Monge (25138505)
Pedro A. Marcos (20391450)
Alfredo Bol-Arreba (17201607)
Mert Atilhan (1272906)
Rocio Barros (24487701)
Santiago Aparicio (1266183)
Biochemistry
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
machine learning framework
external test sets
domain screening accompanies
20 priority per
field monitoring evidence
access prediction tool
pfas structures pose
shap analysis indicates
molecular structure alone
formal applicability domain
2 </ sup
pfas groundwater mobility
mandates monitoring
based prediction
high mobility
comparable mobility
yet predicting
type replacements
simultaneously predicts
regulated compounds
polyfluoroalkyl substances
mobile eu
log sw
log koc
linkage descriptors
january 2026
ionization class
including nine
fluorinated backbone
based explanation
accuracy )
90 ),
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 mobilitysoil sorption (log KOC,
R<sup>2</sup> = 0.90), water solubility (log Sw, R<sup>2</sup> = 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.
2026-09-29T00:00:00Z
Dataset
Dataset
10.1021/acs.est.6c11513.s004
https://figshare.com/articles/dataset/Structure-Based_Prediction_and_Ranking_of_PFAS_Groundwater_Mobility_for_EU_Drinking_Water_Regulation/34027216
CC BY-NC 4.0