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

Author

Rajib Paul, Claudio Owusu, David S. Vinson, Gary S. Silverman, Eric Delmelle, and Andy R. Bobyarchick

Subjects
inorganic chemicals, Chronic exposure, Hydrology, Autologistic regression, integumentary system, Health, Toxicology and Mutagenesis, Public Health, Environmental and Occupational Health, chemistry.chemical_element, Targeted interventions, Pollution, Human health, chemistry, Private well, Arsenic, Geology, Water Science and Technology
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

Published
2020

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