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.