黄爽兵*,刘昌蓉
Abstract
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 AFNISmodel
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 arsenicmobilization in
groundwaters with lowarsenic 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
phosphatecompetitive 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.