Grantee Research Project Results

2024 Progress Report: Predicting and Communicating PFAS Exposure Risks from Rural Private Wells

EPA Grant Number: R840081
Title: Predicting and Communicating PFAS Exposure Risks from Rural Private Wells
Investigators: MacDonald Gibson, Jacqueline , Salamova, Amina , Redmon, Jenny , Livanapatirana, Chamindu , de Bruin, Wandi Bruine
Institution: Emory University , North Carolina State University , University of Southern California , Research Triangle Institute
EPA Project Officer: Packard, Benjamin H
Project Period: September 1, 2020 through August 31, 2023 (Extended to August 31, 2025)
Project Period Covered by this Report: September 1, 2023 through August 31,2024
Project Amount: $1,584,420
RFA: National Priorities: Research on PFAS Impacts in Rural Communities and Agricultural Operations (2020) RFA Text | Recipients Lists
Research Category: Endocrine Disruptors , Drinking Water , Water

Objective:

The main purpose of this project is to develop data and tools for managing PFAS risks to private well water. There are three main objectives: (1) build scalable models to predict PFAS exposure risks in rural private wells, (2) conduct a rural citizen-science PFAS monitoring campaign, and (3) create user-friendly maps and tools for PFAS risk communication and management.

Progress Summary:

Objective 1: Build Scalable Models of PFAS Exposure Risks in Rural Private Wells. We built a traditional mechanistic model to predict concentrations of GenX around a PFAS manufacturing facility and validated the model using previously collected measurements of GenX concentrations in 1205 private wells from the region. We also trained a machine-learned Bayesian network model to predict the probability of GenX concentrations exceeding a health-based guideline concentration (140 ng/L) established by the North Carolina Division of Public Health before the new national maximum contaminant level (MCL, 10 ng/L) for drinking water was promulgated. We tested two different methods for integrating the two models, and we compared the accuracy of the four different models for predicting which wells had GenX concentrations above 140 ng/L. We also compared computing time required to run each model. We found that the machine-learned Bayesian network was more accurate than the mechanistic model while requiring a fraction of the computing time. Small gains in accuracy were achieved by integrating mechanistic model outputs with the Bayesian network. We are now replicating these results using the new MCL as a threshold.

Objective 2: Conduct Rural PFAS Citizen-Science Monitoring Campaign. We completed a citizen-science study that engaged 271 private well owners in four communities in four different states to collect their own water samples and ship them to our team for PFAS analysis. During this reporting period, we administered a post-participation survey to the participants to determine the perceived usefulness of the evidence-based risk communication materials we developed to report results to the participants. We also assessed whether the results influenced their decisions to drink or not drink and to treat or not treat their well water. We submitted a manuscript for potential publication to Environmental Health Perspectives. Overall, 63% of the tested wells had detectable PFAS. Median PFAS concentrations (sum of 25 tested) were significantly greater in Spokane County, WA, Washington County, MN, and Robeson County, NC (the three sites with known PFAS sources), than in Monroe County, IN, where there were no known point sources. In the MN wells, PFBA made up 87% of the total PFAS detected on average, while the WA, IN, and NC sites showed a greater diversity of PFAS. In 49% of wells, PFAS was detected above a health guideline set either by the EPA or one of the four states where participants lived. Higher total PFAS concentrations were significantly associated with nearness to PFAS production facilities, Superfund sites, spill sites, and federal facilities, whereas distances to wastewater discharges, toxic release inventory sites, and facilities generating biosolids were not significant in any of the models. Among those participating in the final survey, a recommendation to start using a filter significantly increased filter usage. Of the 66 survey responses (24% of all participants), 94% found the results report helpful.

Objective 3: Disseminate User-friendly Maps and Tools for PFAS Risk Management. During the reporting period, we finalized a computational approach and web-based mapping tool to calculate cumulative risk from known/suspected PFAS sources based on nationally representative data sources curated to estimate PFAS risks in private well water. A manuscript to document the computational approach used is in process, and the mapping tool will be released to the public after the manuscript is published. We also completed and analyzed results from a Monte Carlo simulation model to estimate the contribution of private well water contamination to total PFAS exposure in different age groups in our four case study areas. The fraction of total PFAS exposure from private well water ranged from approximately 1-6 % in Monroe County, IN, to 17-54 % in Washington County, MN, depending on age group.

Future Activities:

Objective 1: Build Scalable Models of PFAS Exposure Risks in Rural Private Wells. Integrated Bayesian network model and mechanistic model results will be finalized to predict GenX exceedance risk for the North Carolina (NC) study area using the new GenX MCL. At least one journal article will be submitted for publication.

Objective 2: Conduct Rural PFAS Citizen-Science Monitoring Campaign. This part of the project has been completed.

Objective 3: Create User-Friendly Maps and Tools for PFAS Risk Communication and Management. In the next reporting period, we will submit a journal manuscript describing the probabilistic modeling approach for estimating PFAS exposure risks in private well water. Once the manuscript is accepted for publication, the interactive, web-based mapping tool will be publicly released. We also will finalize a journal article, “Contribution of Private Well Water Contamination to Total PFAS Exposure,” for submission to the journal Risk Analysis.

References:

Master's Thesis

Banks Grubbs; Estimating the Contribution of Private Well Water Consumption to Total PFAS Exposure

Journal Articles:

No journal articles submitted with this report: View all 4 publications for this project

Supplemental Keywords:

PFAS, Bayesian network, well water, drinking water, groundwater modeling, machine-learning, citizen-science

Relevant Websites:

Project Website Exit

Progress and Final Reports:

Original Abstract

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.