Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model

Javad Roostaei; Sarah Colley; Riley Mulhern; Andrew A May; Jacqueline MacDonald Gibson

doi:10.1016/j.jhazmat.2021.125075

Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model

J Hazard Mater. 2021 Jun 5:411:125075. doi: 10.1016/j.jhazmat.2021.125075. Epub 2021 Jan 7.

Authors

Javad Roostaei¹, Sarah Colley², Riley Mulhern², Andrew A May³, Jacqueline MacDonald Gibson⁴

Affiliations

¹ Department of Environmental and Occupational Health, Indiana University, Bloomington, IN 47405, United States.
² Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill 27516, United States.
³ Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH 43210, United States.
⁴ Department of Environmental and Occupational Health, Indiana University, Bloomington, IN 47405, United States. Electronic address: jacmgibs@iu.edu.

PMID: 33858085
DOI: 10.1016/j.jhazmat.2021.125075

Abstract

Per- and polyfluoroalkyl substances (PFAS) are emerging contaminants that pose significant challenges in mechanistic fate and transport modeling due to their diverse and complex chemical characteristics. Machine learning provides a novel approach for predicting the spatial distribution of PFAS in the environment. We used spatial location information to link PFAS measurements from 1207 private drinking water wells around a fluorochemical manufacturing facility to a mechanistic model of PFAS air deposition and to publicly available data on soil, land use, topography, weather, and proximity to multiple PFAS sources. We used the resulting linked data set to train a Bayesian network model to predict the risk that GenX, a member of the PFAS class, would exceed a state provisional health goal (140 ng/L) in private well water. The model had high accuracy (ROC curve index for five-fold cross-validation of 0.85, 90% CI 0.84-0.87). Among factors significantly associated with GenX risk in private wells, the most important was the historic rate of atmospheric deposition of GenX from the fluorochemical manufacturing facility. The model output was used to generate spatial risk predictions for the study area to aid in risk assessment, environmental investigations, and targeted public health interventions.

Keywords: Bayesian network; Drinking water; GenX; Machine-learning; PFAS; Well water.