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首页> 外文期刊>Environmental Science & Technology >Spatial Modeling for Groundwater Arsenic Levels in North Carolina
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Spatial Modeling for Groundwater Arsenic Levels in North Carolina

机译：北卡罗莱纳州地下水砷水平的空间模拟

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摘要

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 volcanidastic 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.
机译：为了检查地下水中砷的环境和地质决定因素，通过地理信息系统将详细的地质数据与北卡罗来纳州奥兰治县的471口私人井的井水砷浓度数据和井建设数据集成在一起。为了进行统计分析，根据岩石类型和解释的沉积/沉积模式将地质单位简化为四个广义类别。从原始火山碎屑起源的岩石到火山成岩沉积起源的岩石的地质过渡被称为多义线。将数据拟合到左删失回归模型中，以确定地下水中砷含量的关键决定因素。然后开发了贝叶斯空间随机效应模型，以捕获地下水砷残留物中的任何空间模式，并将其用于模型估计。统计模型结果表明：（1）靠近过渡带或断层的井更可能含有可检测的砷； （2）凝灰岩和热液石英体与较高的地下水砷浓度有关，而与岩体近端的砷浓度甚至更高。 （3）深度更大的井更可能含有升高的砷。该建模工作通过创建该区域任何位置和深度的地下水中砷预测水平的三维图来为政策干预提供依据。

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  • 来源
    《Environmental Science & Technology》 |2011年第11期|p.4824-4831|共8页
  • 作者

    Dohyeong Kim; Marie Lynn Miranda; Joshua Tootoo; Phil Bradley; Alan E. Gelfand;

  • 作者单位

    Department of Public Administration, North Carolina Central University, 215 Whiting CJ Building, Durham, North Carolina 27707,United States,Nicholas School of the Environment, Duke University, Box 90328, Durham, North Carolina 27708, United States;

    Nicholas School of the Environment, Duke University, Box 90328, Durham, North Carolina 27708, United States;

    Nicholas School of the Environment, Duke University, Box 90328, Durham, North Carolina 27708, United States;

    North Carolina Geological Survey, 1620 Mail Service Center, Raleigh, North Carolina 27699, United States;

    Department of Statistical Science, Duke University, Box 90251, Durham, North Carolina 27708, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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