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北卡罗来纳州地下水中砷含量的空间建模。

Spatial modeling for groundwater arsenic levels in North Carolina.

机构信息

Department of Public Administration, North Carolina Central University, 215 Whiting CJ Building, Durham, North Carolina 27707, USA.

出版信息

Environ Sci Technol. 2011 Jun 1;45(11):4824-31. doi: 10.1021/es103336s. Epub 2011 Apr 29.

Abstract

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.

摘要

为了研究地下水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 471 口私人水井的井水砷浓度数据和水井建设数据进行了整合。在统计分析中,根据岩石类型和解释的沉积/就位模式,将地质单元简化为四个广义类别。将源自原生火山碎屑源的岩石与火山碎屑沉积源的岩石之间的地质过渡指定为折线。将数据拟合到左删失回归模型中,以确定地下水砷含量的关键决定因素。然后,开发了一个贝叶斯空间随机效应模型,以将地下水砷残差中的任何空间模式纳入模型估计中。统计模型结果表明:(1)靠近过渡带或断层的水井更有可能含有可检测到的砷;(2)焊接凝灰岩和热液石英体与相对较高的地下水砷浓度有关,而与靠近深成岩体的那些则更高;(3)较深的水井更有可能含有较高的砷。这项建模工作通过为该地区的任何位置和深度创建地下水砷预测水平的三维地图,为政策干预提供了信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12a5/3855354/0237496261b9/nihms521345f1.jpg

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