Grantee Research Project Results
2023 Progress Report: Predicting Drinking Water Contamination from Extreme Weather to Reduce Early Life Contaminant Exposures
EPA Grant Number: R840181Title: Predicting Drinking Water Contamination from Extreme Weather to Reduce Early Life Contaminant Exposures
Investigators: Hochard, Jacob
Institution: University of Wyoming
EPA Project Officer: Aja, Hayley
Project Period: December 1, 2020 through November 30, 2023 (Extended to November 30, 2024)
Project Period Covered by this Report: December 1, 2022 through November 30,2023
Project Amount: $799,952
RFA: Contaminated Sites, Natural Disasters, Changing Environmental Conditions and Vulnerable Communities: Research to Build Resilience (2019) RFA Text | Recipients Lists
Research Category: Drinking Water , Endocrine Disruptors , Human Health , Safer Chemicals , Sustainable and Healthy Communities , Children's Health
Objective:
Researchers are working on a multidisciplinary approach involving atmospheric science, economics, ecological engineering design, pediatrics, microbiology, and soil ecology to:
- Predict groundwater contamination that leads to human exposures.
- Engage with county health offices to notify at-risk households based on predictive models.
- Assess the impact of the risk messenger on risk mitigation choices
Researchers hypothesize:
- Chemical concentrations and coliform bacteria in wells relate to proximity to contaminated sites during precipitation events.
- Bacterial contamination depends on source and seasonality.
- Homes with aging wells are more vulnerable to contaminants.
- Risk communication from an ECU pediatrician promotes households’ risk mitigation behaviors.
Progress Summary:
Human Health Interventions and Well Water Testing:
During the third year, our work primarily focused on targeted human health interventions and well water testing. We partnered with the Orange County Health Department in North Carolina to pilot this portion of the project. Using GIS data and available birth records, we identified households whose drinking water source was private wells and prioritized sending notifications to locations that were likely to have children and where wells had not been recently constructed. Notifications were sent when weather forecasts predicted high temperatures above 90°F for seven consecutive days, a period identified as having elevated risk for bacterial contamination in private groundwater wells. We sent 3,491 letters offering free well water testing for bacteria. Of these letters, half were signed by a pediatrician from ECU’s Brody School of Medicine and the other half by a graduate student on our team. This pilot resulted in 48 completed tests, most of which were negative for coliform bacteria. Residents receiving positive results were provided with well shock chlorination kits, and the grant covered retesting after chlorination. The Orange County Health Department deemed the pilot a success and is assisting with expanding the program in 2024. Notably, many sampled homes were in affluent, primarily white neighborhoods. To improve inclusivity, the 2023 letters were bilingual (English and Spanish), and we are revising the process to include WhatsApp for test requests. In 2024, the program will expand to Pitt and Beaufort Counties, aiming to enhance participation and effectiveness.
Modeling and Prediction:
Our team published a study in the Journal of the American Water Resources Association on using machine learning models combined with weather and soil data to predict nitrate contamination risk. The study revealed that the best predictive models were in the coastal plain region of North Carolina, although overall prediction accuracy was lower than previous efforts using geospatial data alone. This finding underscores the need for additional geospatial data to enhance model performance. We made significant progress in refining the hydrological model SWAT+ for the Cape Fear River basin. The model can now predict groundwater nitrate contamination levels, which we are validating against private well water samples from the state laboratory. Calibration for river flow at observation stations closest to animal feeding operations yielded a Nash-Sutcliffe Efficiency (NSE) of 0.54, considered good for a watershed of this size. Cross-validation showed acceptable NSE values at other observation stations, and we are finalizing a manuscript based on this work.
Risk Communication and Stakeholder Engagement:
Scheduled to launch in May 2023, our analytical predictions of groundwater contamination events will be used to initiate targeted communications and interventions for at-risk households. We co-produced risk communication letters with our partners at NCDHHS and the Orange County Health Department. These letters, tested for readability and designed with professional graphic design, were translated into two languages to ensure inclusivity and accessibility. The letters will also serve as a medium for a risk communication randomized control trial (RCT), comparing the impact of communications from research scientists versus pediatricians on enrollment in voluntary well water testing.
Future Activities:
In the next reporting period, we plan to finalize our community sampling efforts in Orange County, NC, and complete our final publications and reporting. Our analytical predictions of groundwater contamination will continue to improve, and we will facilitate our RCT risk communications study. The project's dedicated web platform will also launch, enhancing access to our findings and resources.
Journal Articles on this Report : 2 Displayed | Download in RIS Format
Other project views: | All 5 publications | 5 publications in selected types | All 5 journal articles |
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Type | Citation | ||
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Etheridge R, Pascual‐Gonzalez J, Hochard J, Peralta AL, Vogel TJ. Predicting nitrate exposure from groundwater wells using machine learning and meteorological conditions. JAWRA Journal of the American Water Resources Association. 2024 Apr;60(2):639-51. |
R840181 (2023) |
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Drewry KR, Jones CN, Hayes W, Beighley RE, Wang Q, Hochard J, Mize W, Fowlkes J, Goforth C, Pieper KJ. Using Inundation Extents to Predict Microbial Contamination in Private Wells after Flooding Events. Environmental Science & Technology. 2024; 58(12):5220-8. |
R840181 (2023) |
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Supplemental Keywords:
Community-based risk management, averting behavior, predictive modelingProgress 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.