Geospatial modeling of iAs exposure

Univ Of North Carolina Chapel Hill, Chapel Hill NC

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Abstract

ABSTRACT: PROJECT 3 Chronic exposure to inorganic arsenic (iAs) has been linked to risk of type 2 diabetes (T2D) in epidemiologic studies and a major source of exposure is through unregulated private wells. Yet, there is a significant gap in methodologies that can be used to predict iAs contamination in untested private wells and determine major contributing factors (e.g. geogenic versus anthropogenic). Project 3 addresses this research gap and builds upon a strong scientific foundation. First, the team has developed various datasets that will be key to the study. This includes a database of the Superfund sites and their associated chemicals in the US and NC, the NCWELL database of >90, 000 private well measures. Second, we have developed innovative analytical methods that will be employed including a Bayesian maximum entropy (BME) approach that can be used to predict iAs levels in untested areas of NC and the Chemical and Social Stressors Integration Technique (CASS-IT) to identify areas where populations are at risk for elevated exposure to iAs, co-occurring contaminants and social stressors. We hypothesize that that geospatial models that incorporate geogenic and anthropogenic data, social vulnerability factors, and disease prevalence can predict locations of populations at risk for iAs- associated diabetes. To test this hypothesis, we will pursue three aims. In Aim 1, we will use geospatial modeling techniques to predict iAs levels in untested private drinking wells in NC and determine contribution from geogenic or anthropogenic point sources. In Aim 2, we will examine whether there are disparities in iAs levels in NC drinking wells in relation to sociodemographic and geospatial characteristics. In Aim 3 we will use ecologic analysis to estimate iAs-associated metabolic disease risk and associated economic burden. Our strong team has complementary expertise in geospatial modeling, environmental science, geology, environmental justice, and economic estimation. Together we use innovative techniques to characterize contributions of numerus factors to iAs-associated diabetes risk. Project 3 supports the UNC-SRP theme “Protecting vulnerable populations from arsenic-induced metabolic dysfunction with a vision for exposure reduction and disease prevention.” Project 3 is both significant and innovative, as it will use integrated geospatial methods for the prediction of elevated iAs and the identification of populations at most risk for iAs-induced adverse outcomes to guide future public health initiatives. By accomplishing this aim, we will enhance the understanding of the critical factors that contribute to the prediction of iAs levels in private wells, with implications for more than 44.5 million individuals across the US drinking from private wells.