Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks

Abstract

In the past two decades, global manufacturing of per- and polyfluoroalkyl substances (PFAS) has shifted from long-chain compounds to short-chain alternatives in response to evidence of the health hazards of long-chain formulations. However, accumulating data indicate that short-chain PFAS also pose health risks and are highly mobile and persistent in the environment. Because short-chain PFAS are relatively new chemicals, comprehensive knowledge needed to predict their environmental fate is lacking. This study evaluated the capacity of machine-learned Bayesian networks (BNs) to predict risks of exposure to short-chain PFAS in a Minnesota region affected by PFAS releases from the 3M Cottage Grove facility. Models were trained using long-term monitoring data provided by the Minnesota Department of Health (n = 12,406), which we coupled to a comprehensive dataset created by curating 88 other variables that describe potential PFAS sources, soil and hydrogeologic characteristics, and land use. Model performance was assessed using the area under the receiver-operating characteristic curve (AUC), a common measure of the accuracy of machine-learned classification algorithms. In addition, exposure risks were visualized spatially by coupling model predictions to a geographic information system. We found that machine-learned BN models had robust predictive performance, with AUCs above 0.96 in cross-validation. Significant risk factors identified by the BNs include distance to the 3M factory, distance to a former landfill, and areal extent of wetlands and developed land. We also found that risks of exposure to and the areal extent of perfluorosulfonic acids were greater than for perfluorocarboxylic acids with the same carbon number. The results suggest that machine-learned BNs could provide a promising screening tool for assessing short-chain PFAS exposure risks in groundwater.

Application Of Machine Learning To Understand Pfas Occurrence, Distribution, Transport And Removal In Water

“…Kwon et al, (2023) predicted bioactivities of PFAS. Other data sources include the United states environmental protection agency's water quality portal (USEPA) (Azhagiya Singam et al, 2020;DeLuca et al, 2023;Dong et al, 2023), PubChem Bioassay Database (Kwon et al, 2023), Pennsylvania Water quality network (Breitmeyer et al, 2023), from previously published studies on PFAS (Karbassiyazdi et al, 2022;Kibbey et al, 2020;Patel et al, 2022), lake and river data (Antell et al, 2023;Stults et al, 2023), Minnesota department of health (MDH) Government agency data (Breitmeyer et al, 2023;Fernandez et al, 2023;Li and Gibson, 2023) and experimental data (Cao et al, 2022;Sörengård et al, 2022;Wang et al, 2022). Some authors combined several public data for their machine learning predictions.…”

Prediction of 35 Target Per- and Polyfluoroalkyl Substances (PFASs) in California Groundwater Using Multilabel Semisupervised Machine Learning

“…Several ML methods have been successfully applied to predict and monitor environmental pollutants in groundwater. Logistic regression, random forests, and Bayesian networks are commonly used for analytes such as heavy metals, nitrate, fluoride, and PFAS. − ML PFAS studies have predicted the occurrence of PFAS in water resources. The PFAS source and cocontaminants , were considered the most important features for these ML predictions.…”

An Artificial Intelligence Platform for Automated PFAS Subgroup Classification: A Discovery Tool for PFAS Screening

“…PFAS (per-and polyfluoroalkyl substances) are a class of chemicals that are widely used as commercial products, but at the same time pose high risks to the environment and human health due to their high persistence (P), bioaccumulation (B), and/or toxicity (T). In recent years, machine learning models have been used to assist PFAS research in many areas, including PFAS source allocation [1][2][3] , water contamination identification [4][5][6] , analytical method development 7 , substitutes design 8 , adsorption removal 9 , protein binding 10,11 , and bioactivity and toxicity prediction [12][13][14] .…”