Spatial Modeling for Groundwater Arsenic Levels in North Carolina

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• 作者：Dohyeong Kim ; Marie Lynn Miranda ; Joshua Tootoo ; Phil Bradley ; Alan E. Gelfand
• 刊名：Environmental Science & Technology
• 出版年：2011
• 出版时间：June 1, 2011
• 年：2011
• 卷：45
• 期：11
• 页码：4824-4831
• 全文大小：947K
• 年卷期：v.45,no.11(June 1, 2011)
• ISSN：1520-5851

文摘

To examine environmental and geologic determinants of arsenic in groundwater, detailed geologic data were integrated with well water arsenic concentration data and well construction data for 471 private wells in Orange County, NC, via a geographic information system. For the statistical analysis, the geologic units were simplified into four generalized categories based on rock type and interpreted mode of deposition/emplacement. The geologic transitions from rocks of a primary pyroclastic origin to rocks of volcaniclastic sedimentary origin were designated as polylines. The data were fitted to a left-censored regression model to identify key determinants of arsenic levels in groundwater. A Bayesian spatial random effects model was then developed to capture any spatial patterns in groundwater arsenic residuals into model estimation. Statistical model results indicate (1) wells close to a transition zone or fault are more likely to contain detectible arsenic; (2) welded tuffs and hydrothermal quartz bodies are associated with relatively higher groundwater arsenic concentrations and even higher for those proximal to a pluton; and (3) wells of greater depth are more likely to contain elevated arsenic. This modeling effort informs policy intervention by creating three-dimensional maps of predicted arsenic levels in groundwater for any location and depth in the area.