Grantee Research Project Results
2022 Progress Report: Predicting and Communicating PFAS Exposure Risks from Rural Private Wells
EPA Grant Number: R840081Title: 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: North Carolina State University , Emory University , Research Triangle Institute , University of Southern California
Current 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, 2021 through August 31,2022
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: 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. In this reporting period, our research team completed detailed fate-and-transport model construction, performed model calibrations, trained Bayesian networks (BNs), and developed a framework for integrating the models. Our team completed a conceptual site model representing the characteristics and processes that control PFAS fate and transport using synthesized available data in the North Carolina (NC) study area. We used this conceptual site model to complete construction on a 3-layer numerical groundwater fate-and-transport model, which we then calibrated for steady state groundwater flow. Our research team used the multi-media dataset that was created in the first year to train BNs to predict the probability of having PFAS concentration exceeding local health advisory values at the Minnesota case study site. We also developed a framework for integrating the outputs from a physics-based fate and transport model into a probabilistic machine-learned BN model.
Objective 2: Conduct Rural PFAS Citizen-Science Monitoring Campaign. In this reporting period, we collected and analyzed 270 well water samples across four study regions (Spokane County, WA; Washington County, MN; Monroe County, IN; and Robeson County, NC). In addition, we collected samples from farms and wastewater treatment plants at the Indiana case study site for PFAS quantification.
Objective 3: Disseminate User-friendly Maps and Tools for PFAS Risk Management. During the reporting period, we curated nationally representative data sources that can be used to estimate PFAS risks in private well water. We completed a thorough de-duplication procedure. These data will be used, along with the results of our integrated modeling approach, to predict PFAS exposure risks nationally.
Future Activities:
Objective 1: Build Scalable Models of PFAS Exposure Risks in Rural Private Wells. Model integration and validation will focus on the North Carolina case study site. In the next reporting period, RTI will complete the groundwater flow and transport model calibrations. Outputs from the calibrated numerical groundwater model will be used as an additional input to our previously trained BN model for the North Carolina site to create an integrated model. We will also finish the publication process for the submitted manuscript and submit another manuscript focused on predicting temporal variation in PFAS exposure risks in well water at the Minnesota study site.
Objective 2: Conduct Rural PFAS Citizen-Science Monitoring Campaign. In the next reporting period, we will finalize report-back of participant data to hard-to-reach study participants. We will also develop a manuscript for peer-review reporting the results of the citizen science sampling campaign. In addition, wastewater, runoff, and soil samples will be collected from multiple Robeson County farms potentially affected by the Chemours facility. These and all wastewater and agricultural samples collected from the Indiana case study site will be analyzed for PFAS.
Objective 3: Create User-Friendly Maps and Tools for PFAS Risk Communication and Management. In the next reporting period, we will finalize our computational approach to calculating cumulative risk from known/suspected PFAS sources at a national scale and develop an interactive, online map based on the results. We will apply modified fate and transport models to simulate PFAS concentration at defined receptors under multiple representative scenarios for PFAS contamination and evaluate the influence of sorbent additions on PFAS exposure risk based on the simulated PFAS concentrations. We will then train BN models to simulate developed scenarios. In addition, we will perform a cost analysis for all stabilization options. We will also construct a risk assessment model using Analytica Enterprise software to estimate the contribution of PFAS exposure from drinking water and food compared to other sources in rural areas relying on private wells.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
Other project views: | All 3 publications | 3 publications in selected types | All 3 journal articles |
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Type | Citation | ||
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Li R, MacDonald Gibson J. Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks. Frontiers in Environmental Science 2022; 10:958784. |
R840081 (2022) |
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Supplemental Keywords:
PFAS, Bayesian network, well water, drinking water, groundwater modeling, machine-learning, citizen-scienceRelevant Websites:
Clean Water for Kids: Forever Chemicals Exit
Progress and Final Reports:
Original AbstractThe 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.