Skip to main content

A Spatial Autologistic Model to Predict the Presence of Arsenic in Private Wells Across Gaston County, North Carolina Using Geology, Well Depth, and pH

Abstract

Chronic exposure to arsenic-contaminated drinking water is detrimental to human health. We develop an autologistic regression model to evaluate if the geology, pH, and well depth can improve our ability to predict the presence of arsenic at and above detectable levels (≥ 5 µg/L) found in private wells. We use arsenic samples measured in private well water across Gaston County, North Carolina, from 2011 to 2017. We use kriging to map the probability of arsenic at detectable levels across Gaston County. Arsenic at detectable levels was reported at 78 private wells. The median pH for samples containing detectable levels of arsenic was 7.3 and for samples with arsenic < 5 µg/L was 7.1. Our spatial autologistic model suggests that arsenic at detectable levels is positively associated with pH. In addition, private wells set in Mica schist (ЄZms) were associated with arsenic, suggesting a local-scale geologic source influence of arsenic in the county. Our kriging map shows that the northwestern section of the county has more than a 50% probability to have arsenic at detectable levels. In conclusion, based on our results, we recommend increased testing for wells in the Mica schist area. The map of probability of arsenic at and above detectable levels can be used to implement cost-effective targeted interventions.

This is a preview of subscription content, access via your institution.

Fig. 1

source: North Carolina Department of Environmental Quality 2020)

Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

Download references

Acknowledgements

We acknowledge Samantha Dye, the Environmental Health Administrator for Gaston County Health and Human Services, Division of Environmental Health for her collaboration in obtaining the arsenic data. We are grateful to the Centers for Disease Control and Prevention, National Center for Environmental Health for providing funding to support our research as part of the Environmental Health Services Support for Public Health Drinking Water Programs to Reduce Drinking Water Exposures, Grant #CDC-RFA-EH15-1507.

Author information

Authors and Affiliations

Authors

Contributions

CO conceived the idea of modeling the presence of arsenic in private wells in Gaston County, North Carolina. CO designed the overall study, prepared the arsenic, pH, well depth and geology data with technical support from ED, GS, DV and RP. AB provided extensive input in the relevant geology data needed to conduct the analysis. The first draft of the manuscript was written by CO, and all authors provided extensive inputs on different versions of the manuscripts. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Eric Delmelle.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Owusu, C., Silverman, G.S., Vinson, D.S. et al. A Spatial Autologistic Model to Predict the Presence of Arsenic in Private Wells Across Gaston County, North Carolina Using Geology, Well Depth, and pH. Expo Health 13, 195–206 (2021). https://doi.org/10.1007/s12403-020-00373-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12403-020-00373-6

Keywords

  • Arsenic
  • Autologistic regression
  • Geology
  • GIS
  • Private wells
  • Water