Statistical modeling of global geogenic arsenic contamination in groundwater
Statistical modeling of global geogenic arsenic contamination in groundwater
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DOI：
10.1021/es702859e
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发表时间：
2008-05-15
影响因子：
11.4
通讯作者：
Johnson, C. Annette
中科院分区：
文献类型：
--
作者：
Amini, Manouchehr;Abbaspour, Karim C.;Johnson, C. Annette
地下水受到地质成因砷的污染，对数以百万计的人口构成重大健康风险。尽管砷释放的主要地球化学机制已为人熟知，但全球受影响地区的范围仍不明确。在本研究中，我们利用一个庞大的全球地下水砷浓度测量数据库（约20,000个数据点），以及土壤、地质、气候和海拔等物理特征的数字地图，来构建全球砷污染概率图。我们采用了一种基于规则的全新统计方法，将物理数据与专家知识相结合，划分出砷释放的两个过程区域：“还原”区域和“高pH/氧化”区域。在每个区域，我们通过回归分析和自适应神经模糊推理对砷浓度进行建模，随后采用拉丁超立方抽样进行不确定性传播分析，以生成概率图。所推导的全球砷模型若能获取更精确的地质信息以及含水层化学/物理信息，效果会更佳。不过，利用一些替代性地表信息，这些模型能够解释还原区域77%的砷含量变化以及高pH/氧化区域68%的砷含量变化。基于上述模型生成的概率图与全球已知的污染区域吻合度良好，并圈定了一些新的、未经检测但砷污染概率较高的区域。其中值得注意的地区包括亚洲的中国东南部和西北部、澳大利亚中部、新西兰、阿富汗北部，以及非洲的马里北部和赞比亚。
Contamination of groundwaters with geogenic arsenic poses a major health risk to millions of people. Although the main geochemical mechanisms of arsenic mobilization are well understood, the worldwide scale of affected regions is still unknown. In this study we used a large database of measured arsenic concentration in groundwaters (around 20,000 data points) from around the world as well as digital maps of physical characteristics such as soil, geology, climate, and elevation to model probability maps of global arsenic contamination. A novel rule-based statistical procedure was used to combine the physical data and expert knowledge to delineate two process regions for arsenic mobilization: "reducing" and "high-pH/oxidizing". Arsenic concentrations were modeled in each region using regression analysis and adaptive neuro-fuzzy inferencing followed by Latin hypercube sampling for uncertainty propagation to produce probability maps. The derived global arsenic models could benefit from more accurate geologic information and aquifer chemical/physical information. Using some proxy surface information, however, the models explained 77% of arsenic variation in reducing regions and 68% of arsenic variation in high-pH/oxidizing regions. The probability maps based on the above models correspond well with the known contaminated regions around the world and delineate new untested areas that have a high probability of arsenic contamination. Notable among these regions are South East and North West of China in Asia, Central Australia, New Zealand, Northern Afghanistan, and Northern Mali and Zambia in Africa.