Determining the Probability of Arsenic in Groundwater Using a Parsimonious Model

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 of a specific 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, NO3- -N, NO2- -N, NH+ -N, Fe, and Mn, and a regional variable (binary type) were used to evaluate the probability of arsenic concentrations exceeding 10 microg/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 > or =1 microg/Lin 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.

Assessing how heavy metal pollution and human activity are related by using logistic regression and kriging methods

“…also indicate that the LR is another applicable approach to estimate the occurrence probability of soil pollutants at each sample without directly measure pollutants everywhere (Lee et al, 2009). Overall, the maps indicate that the highest variability is close to industrial plants and irrigation systems.…”

“…Based on logistic regression (LR), many studies have analyzed how the driving factors and soil pollution are related to determine the probability of soil pollution occurring (Lee et al, 2009;Liu et al, 2005;Tesoriero and Voss, 1997;Twarakavi and Kaluarachchi, 2005). Logistic regression analysis examines the probability of a contaminant concentration to exceed a threshold concentration for a set of possible sources.…”

“…Using the same approach, Twarakavi and Kaluarachchi (2005) predicted the susceptibility of heavy metal pollutants in the Sumas-Blaine Aquifer. Also using LR, Lee et al (2009) quantified how contaminant susceptibility of arsenic concentration and hydrochemical parameters are related. Their study determined the probability of the contaminant hazard at each location based on their drivers.…”

Bioavailability and risk assessment of arsenic in surface sediments of the Yangtze River estuary

“…Geostatistics and geographic information system (GIS) are also useful tools to study the distribution of contaminants and analyse the extent of pollution in large scale regions (Mamat et al, 2014;Wang et al, 2015). Geostatistics has been proven to be an effective methodological approximation for studying metal pollution in soil, sediment, mining areas and groundwater (Lee et al, 2009;Antunes and Albuquerque, 2013;Chica-Olmo et al, 2014). Ordinary Kriging is also widely used in predicting pollution and has also been utilized in the present study Wang et al, 2014;Wang et al, 2015).…”

Assessment of BTEX‐induced health risk under multiple uncertainties at a petroleum‐contaminated site: An integrated fuzzy stochastic approach

“…It is related to characteristics of contaminant transport and fate in subsurface and behavioral patterns of an individual or a population exposed to the pollutant [ Maxwell et al , 1998; Phan et al , 2010]. Site‐specific risk assessment is thus essential based on the modeling results of contaminant transport and fate simulation [ Ma , 2002; Maqsood et al , 2005; Chen and Ma , 2006; Lee et al , 2009]. Simulation‐based risk assessment requires extensive data and information which are inherently associated with multiple forms of uncertainties [ Bogen and Spear , 1987; Hoffman and Hammonds , 1994; Labieniec et al , 1997; Bennett et al , 1998; Carrington and Bolger , 1998; Daniels et al , 2000; Ma , 2002; Tam and Byer , 2002; Li , 2003; Kentel and Aral , 2004, 2005; Lopez et al , 2008].…”