Evaluating Geologic Sources of Arsenic in Well Water in Virginia (USA)

Tiffany VanDerwerker, Lin Zhang, Erin Ling, Brian Benham and Madeline Schreiber
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Tiffany VanDerwerker: Department of Geosciences, Virginia Tech, Blacksburg, VA 24061, USA
Lin Zhang: Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA
Erin Ling: Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA
Brian Benham: Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA
Madeline Schreiber: Department of Geosciences, Virginia Tech, Blacksburg, VA 24061, USA

IJERPH, 2018, vol. 15, issue 4, 1-17

Abstract: We investigated if geologic factors are linked to elevated arsenic (As) concentrations above 5 μg/L in well water in the state of Virginia, USA. Using geologic unit data mapped within GIS and two datasets of measured As concentrations in well water (one from public wells, the other from private wells), we evaluated occurrences of elevated As (above 5 μg/L) based on geologic unit. We also constructed a logistic regression model to examine statistical relationships between elevated As and geologic units. Two geologic units, including Triassic-aged sedimentary rocks and Triassic-Jurassic intrusives of the Culpeper Basin in north-central Virginia, had higher occurrences of elevated As in well water than other geologic units in Virginia. Model results support these patterns, showing a higher probability for As occurrence above 5 μg/L in well water in these two units. Due to the lack of observations (<5%) having elevated As concentrations in our data set, our model cannot be used to predict As concentrations in other parts of the state. However, our results are useful for identifying areas of Virginia, defined by underlying geology, that are more likely to have elevated As concentrations in well water. Due to the ease of obtaining publicly available data and the accessibility of GIS, this study approach can be applied to other areas with existing datasets of As concentrations in well water and accessible data on geology.

Keywords: groundwater management; drinking water; water quality; statistical modeling; logistic regression (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
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