Determining the probability of arsenic in groundwater using a parsimonious model
Resource
Environmental Science & Technology 43: 6662-6668
Journal
Environmental Science and Technology
Journal Volume
43
Journal Issue
17
Pages
6662-6668
Date Issued
2009
Author(s)
Abstract
Spatial distributions of groundwater quality are commonly heterogeneous, varying with depths and locations, which is important in assessing the health and ecological risks. Owing to time and cost constraints, it is not practical or economical to measure arsenic everywhere. A predictive model is necessary to estimate the distribution ofaspecific pollutant in groundwater. This study developed a logistic regression (LR) model to predict the residential well water quality in the Lanyang plain. Six hydrochemical parameters, pH, NO 3--N, NO2--N, NH4 +-N, Fe, and Mn, and a regional variable (binary type) were used to evaluate the probability of arsenic concentrations exceeding 10 μg/L in groundwater. The developed parsimonious LR model indicates that four parameters in the Lanyang plain aquifer, (pH, NH4+, Fe(aq), and a component to account for regional heterogeneity) can accurately predict probability of arsenic concentration ?10 μg/L in groundwater. These parameters provide an explanation for release of arsenic by reductive dissolution of As-rich FeOOH in NH4+ containing groundwater. A comparison of LR and indicator kriging (IK) show similar results in modeling the distributions of arsenic. LR can be applied to assess the probability of groundwater arsenic at sampled sites without arsenic concentration data apriori. However, arsenic sampling is still needed and required in arsenic-assessment stages in other areas, and the need for long-term monitoring and maintenance is not precluded. ? 2009 American Chemical Society.
Other Subjects
Apriori; Arsenic concentration; Arsenic in groundwater; Cost constraints; Ecological risks; Groundwater quality; Hydrochemical parameters; Indicator kriging; Logistic regression models; Long term monitoring; Predictive models; Reductive dissolution; Regional variable; Residential wells; Sampled sites; Spatial distribution; Aquifers; Dissolution; Electric reactors; Groundwater pollution; Groundwater resources; Health risks; Hydraulic models; Hydrogeology; Manganese; Manganese compounds; Probability; Risk perception; Water quality; Water wells; Arsenic; arsenic; ground water; accuracy assessment; aquifer; arsenic; concentration (composition); dissolution; environmental risk; groundwater pollution; hydrochemistry; parsimony analysis; probability; regression analysis; spatial distribution; water quality; accuracy; article; comparative study; concentration (parameters); kriging; logistic regression analysis; pH; predictor variable; probability; sampling; Taiwan; water analysis; water quality; water sampling; Arsenic; Fresh Water; Logistic Models; Probability; Taiwan; Water Pollutants, Chemical; Water Supply
Type
journal article
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