Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies

Environmental Science and Technology
By: , and 

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Abstract

Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.

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Publication type Article
Publication Subtype Journal Article
Title Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies
Series title Environmental Science and Technology
DOI 10.1021/acs.est.0c05239
Volume 55
Issue 8
Year Published 2021
Language English
Publisher American Chemical Society
Contributing office(s) New England Water Science Center, Toxic Substances Hydrology Program, WMA - Office of Planning and Programming
Description 12 p.
First page 5012
Last page 5023
Country United States
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