a Institute of Hydrogeology and Environmental Geology , Chinese Academy of Geological Sciences , Shijiazhuang , PR China.
J Environ Sci Health A Tox Hazard Subst Environ Eng. 2014;49(4):478-89. doi: 10.1080/10934529.2014.854689.
The effects of various geochemical processes on arsenic enrichment in a high-arsenic aquifer at Jianghan Plain in Central China were investigated using multivariate models developed from combined adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR). The results indicated that the optimum variable group for the AFNIS model consisted of bicarbonate, ammonium, phosphorus, iron, manganese, fluorescence index, pH, and siderite saturation. These data suggest that reductive dissolution of iron/manganese oxides, phosphate-competitive adsorption, pH-dependent desorption, and siderite precipitation could integrally affect arsenic concentration. Analysis of the MLR models indicated that reductive dissolution of iron(III) was primarily responsible for arsenic mobilization in groundwaters with low arsenic concentration. By contrast, for groundwaters with high arsenic concentration (i.e., > 170 μg/L), reductive dissolution of iron oxides approached a dynamic equilibrium. The desorption effects from phosphate-competitive adsorption and the increase in pH exhibited arsenic enrichment superior to that caused by iron(III) reductive dissolution as the groundwater chemistry evolved. The inhibition effect of siderite precipitation on arsenic mobilization was expected to exist in groundwater that was highly saturated with siderite. The results suggest an evolutionary dominance of specific geochemical process over other factors controlling arsenic concentration, which presented a heterogeneous distribution in aquifers. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Environmental Science and Health, Part A, to view the supplemental file.
采用自适应神经模糊推理系统（ANFIS）和多元线性回归（MLR）相结合的方法，建立了多元模型，研究了江汉平原高砷含水层中各种地球化学过程对砷富集的影响。结果表明，最优的 ANFIS 模型变量组包括碳酸氢盐、铵、磷、铁、锰、荧光指数、pH 值和菱铁矿饱和度。这些数据表明，铁/锰氧化物的还原溶解、磷酸盐竞争吸附、pH 值依赖解吸和菱铁矿沉淀等过程可能综合影响砷的浓度。MLR 模型分析表明，在低砷浓度地下水中，铁（III）的还原溶解主要导致砷的迁移。相比之下，在高砷浓度（即>170μg/L）的地下水中，铁氧化物的还原溶解达到了动态平衡。随着地下水化学的演化，磷酸盐竞争吸附的解吸效应和 pH 值的增加表现出对砷的富集作用，优于铁（III）的还原溶解。菱铁矿沉淀对砷迁移的抑制作用预计将存在于高度饱和菱铁矿的地下水中。结果表明，在含水层中，特定地球化学过程对砷浓度的控制作用具有进化优势，这呈现出不均匀的分布。本文的补充材料可在期刊《环境科学与健康 A 辑》的在线版本中查看。
