Grantee Research Project Results
2021 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, 2020 through August 31,2021
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. These models will be created through a combination of physical fate-and-transport modeling and machine-learning from existing data sets. In this reporting period, we began detailed fate-and-transport model construction beginning with synthesizing available data in one of our four case study areas (Robeson, Cumberland, and Bladen counties, North Carolina) to create a conceptual site model. Using the conceptual site model, we are building a groundwater flow and transport model. We also finished data curation for a second case study area (Washington County, Minnesota) and have begun using machine-learning methods to explore patterns in variables predictive of PFAS occurrence. The curated database includes 172 variables.
Objective 2: Conduct Rural PFAS Citizen-Science Monitoring Campaign. During the reporting period, we created all materials necessary to recruit and enroll participants, including the study enrollment website and mail-out recruitment materials. We sent four rounds of mailers to over 3,800 private well owners in three of four study regions. In the fourth area (in North Carolina), we collaborated with American Indiana Mothers to recruit participants, and 42 households enrolled and collected water samples. Enrollment is on track to meet the study goal of 300 total participants.
Future Activities:
Objective 1: Build Scalable Models of PFAS Exposure Risks in Rural Private Wells. In the next reporting period, we will complete the groundwater flow and transport model using one of the MODFLOW family of USGS simulators and MT3DMS to capture regional/local hydrogeologic features and PFAS fate and transport. We then will integrate output from this model with a curated data set representing 1,207 private wells previously tested for PFAS. We will compare the predictive accuracy of the integrated model with a model that relies on machine learning along and with the fate-and-transport model alone.
Objective 2: Conduct Rural PFAS Citizen-Science Monitoring Campaign. In the next reporting period, we will complete enrollment and water sample collection for 300 study participants in all four study regions. We will then analyze the samples for 25 PFAS using targeted and non-targeted methods and report back results to participants. In addition, to further inform development of PFAS risk models, we will recruit and collect wastewater, runoff, and soil samples from 24 farmers in Monroe County, Indiana. American Indian Mothers, Inc., will recruit and train 24 farmers in Robeson County, North Carolina, to collect similar samples.
Objective 3: Create User-Friendly Maps and Tools for PFAS Risk Communication and Management. Part of this task includes using the fate-and-transport models created under Objective 1 to simulate PFAS concentration at defined receptors under multiple representative scenarios for PFAS contamination. This part of the task will be completed in the next reporting period.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
| Other project views: | All 4 publications | 3 publications in selected types | All 3 journal articles |
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Roostaei J, Colley S, Mulhem R, May A, Gibson J. Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model. JOURNAL OF HAZARDOUS MATERIALS 2021;411:125075. |
R840081 (2021) |
Exit Exit |
Supplemental Keywords:
PFAS, Bayesian network, well water, drinking water, groundwater modeling, machine-learning, citizen-scienceRelevant Websites:
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.