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Many US households with private wells are unaware of arsenic risks. This study developed a machine learning model predicting arsenic (As) exposure, guiding targeted testing and well placement in underserved communities.

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Area of Science:

  • Environmental Science
  • Public Health
  • Geochemistry

Background:

  • Private wells in the US lack federal regulation, leading to widespread undetected arsenic (As) contamination.
  • Chronic arsenic exposure poses significant health risks, with contamination levels varying greatly over short distances and well depths.
  • Existing arsenic modeling approaches have limitations in resolution and data requirements, hindering their application in undersampled regions.

Purpose of the Study:

  • To develop a novel machine learning model for predicting arsenic exposure risk in private wells.
  • To utilize surficial variables from remote sensing and global datasets for arsenic risk prediction.
  • To provide detailed risk maps and depth profiles to guide public health actions in Minnesota.

Main Methods:

  • Developed a machine learning model using surficial remote sensing and global datasets for arsenic risk prediction.
  • Selected variables based on mechanistic links to redox conditions and arsenic mobilization, including surface water hydrology and geomorphology.
  • Emphasized local training data and sensitive surficial geology variables to enhance model accuracy.

Main Results:

  • The model accurately predicts arsenic concentrations above the 10 μg/L maximum contaminant level, generating detailed risk maps and depth profiles.
  • Model accuracy is dependent on the density of local training data, with 0.07 wells/km² identified as a practical target for stable county-level performance.
  • Identified priority areas for arsenic testing, particularly benefiting rural communities with historically limited sampling efforts.

Conclusions:

  • The developed machine learning model effectively predicts arsenic exposure risk using accessible data, overcoming limitations of previous methods.
  • The findings support targeted public health interventions, including guidance on well placement, testing strategies, and outreach for treatment.
  • Improved spatial resolution of arsenic risk assessment is crucial for protecting public health in areas with private well usage.