ArticleThe Science of the total environment2019
Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study.
Article in The Science of the total environment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The trial behind it
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Who cites it
17 citing papers in PubMed.
- Drinking water nitrate, disinfection byproducts, and prostate cancer incidence in the Agricultural Health Study.Journal of the National Cancer Institute · 2026Article
- Ingested nitrate and nitrite, drinking water disinfection by-products, and incidence of thyroid cancer among licensed pesticide applicators and spouses in the Agricultural Health Study.Ecotoxicology and environmental safety · 2026Article
- Nitrate and disinfection by-products in drinking water and risk of ovarian cancer.Environmental epidemiology (Philadelphia, Pa.) · 2025Article
- A review of geospatial exposure models and approaches for health data integration.Journal of exposure science & environmental epidemiology · 2025Review
- Predicting Redox Conditions in Groundwater at a National Scale Using Random Forest Classification.Environmental science & technology · 2024Article
- Ingested nitrate and nitrite and end-stage renal disease in licensed pesticide applicators and spouses in the Agricultural Health Study.Journal of exposure science & environmental epidemiology · 2024Article
- US drinking water quality: exposure risk profiles for seven legacy and emerging contaminants.Journal of exposure science & environmental epidemiology · 2024Review
- The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities.Current environmental health reports · 2023Review
- Drinking water sources and water quality in a prospective agricultural cohort.Environmental epidemiology (Philadelphia, Pa.) · 2022Article
- Printed Potentiometric Nitrate Sensors for Use in Soil.Sensors (Basel, Switzerland) · 2022Article
- Examining Relationships Between Groundwater Nitrate Concentrations in Drinking Water and Landscape Characteristics to Understand Health Risks.GeoHealth · 2022Article
- Feature selection approaches for predictive modelling of cadmium sources and pollution levels in water springs.Environmental science and pollution research international · 2022Article
- Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study.International journal of environmental research and public health · 2021Article
- Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration.Sensors (Basel, Switzerland) · 2020Article
- Assessment of denitrification potential for coastal and inland sites using groundwater and soil analysis: the multivariate approach.Environmental monitoring and assessment · 2020Article
- Feature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patients.Diagnostics (Basel, Switzerland) · 2019Article
- Leveraging Automated Machine Learning for the Analysis of Global Public Health Data: A Case Study in Malaria.International journal of public healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Unregulated private wells in the United States are susceptible to many groundwater contaminants. Ingestion of nitrate, the most common anthropogenic private well contaminant in the United States, can lead to the endogenous formation of N-nitroso-compounds, which are known human carcinogens. In this study, we expand upon previous efforts to model private well groundwater nitrate concentration in North Carolina by developing multiple machine learning models and testing against out-of-sample prediction. Our purpose was to develop exposure estimates in unmonitored areas for use in the Agricultural Health Study (AHS) cohort. Using approximately 22,000 private well nitrate measurements in North Carolina, we trained and tested continuous models including a censored maximum likelihood-based linear model, random forest, gradient boosted machine, support vector machine, neural networks, and kriging. Continuous nitrate models had low predictive performance (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.