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Updated: May 14, 2025

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

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Published on: April 18, 2019

Modeling PFAS Sorption in Soils Using Machine Learning.

Joel Fabregat-Palau¹, Amirhossein Ershadi¹, Michael Finkel¹

¹Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, Tübingen 72076, Germany.

Environmental Science & Technology

|April 11, 2025

Summary

This summary is machine-generated.

A new machine learning tool, PFASorptionML, accurately predicts per- and polyfluoroalkyl substances (PFAS) sorption in soils. It identifies key factors like molecular weight and organic carbon, aiding environmental risk assessments.

Keywords:

Kd sensitivity data set spatial map speciation stacking model

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Published on: October 16, 2018

Area of Science:

Environmental Chemistry
Soil Science
Computational Chemistry

Background:

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants.
Understanding PFAS sorption in soils is critical for risk assessment.
Existing models for predicting PFAS sorption coefficients (Kd) have limitations.

Purpose of the Study:

To develop and validate a novel machine learning (ML) tool, PFASorptionML, for predicting PFAS solid-liquid distribution coefficients (Kd) in soils.
To identify key PFAS and soil properties influencing sorption behavior.
To provide a user-friendly platform for environmental risk assessment.

Main Methods:

Developed PFASorptionML using a dataset of 1,274 Kd entries for various PFAS in soils and sediments.
Incorporated PFAS properties (molecular weight, hydrophobicity, pKa) and soil characteristics (pH, texture, organic carbon, CEC).
Performed sensitivity analysis to determine the influence of different parameters on Kd values.

Main Results:

PFASorptionML demonstrated high predictive performance with RPD > 3.16, outperforming existing tools.
Molecular weight, hydrophobicity, and soil organic carbon content were identified as the most significant factors influencing PFAS sorption.
PFAS chain length and functional group significantly impacted Kd, with longer chains and higher hydrophobicity increasing sorption.

Conclusions:

PFASorptionML is a robust and accurate tool for predicting PFAS Kd in soils.
The model's ability to integrate location-specific data enables spatial Kd mapping.
PFASorptionML serves as a valuable resource for environmental risk assessment and management of PFAS contamination.