Aquifer vulnerability assessment to heavy metals using ordinal logistic regression

Abstract

A methodology using ordinal logistic regression is proposed to predict the probability of occurrence of heavy metals in ground water. The predicted probabilities are defined with reference to the background concentration and the maximum contaminant level. The model is able to predict the occurrence due to different influencing variables such as the land use, soil hydrologic group (SHG), and surface elevation. The methodology was applied to the Sumas-Blaine Aquifer located in Washington State to predict the occurrence of five heavy metals. The influencing variables considered were (1) SHG; (2) land use; (3) elevation; (4) clay content; (5) hydraulic conductivity; and (6) well depth. The predicted probabilities were in agreement with the observed probabilities under existing conditions. The results showed that aquifer vulnerability to each heavy metal was related to different sets of influencing variables. However, all heavy metals had a strong influence from land use and SHG. The model results also provided good insight into the influence of various hydrogeochemical factors and land uses on the presence of each heavy metal. A simple economic analysis was proposed and demonstrated to evaluate the cost effects of changing the land use on heavy metal occurrence.

Trace elements in streambed sediments of floodplains: consequences for water management measures

“…But agricultural land use in connection with intensive and often inappropriate water management change the hydrology and the equilibrium of geochemical processes resulting in contamination of groundwater and surface water (Böhlke et al 2002;Twarakavi and Kaluarachchi 2005). The hydrological situation of low water retention disturbs the natural sink function, especially that for redox sensitive trace metals.…”

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

“…The LR provides the probability of the presence of each pollution hazard at each location based on the driving factors (Lee, 2005;Tesoriero and Voss, 1997;Twarakavi and Kaluarachchi, 2005). The LR quantifies the relationships between hazard occurrence and the drivers, which is specified by:…”

“…By using LR, Tesoriero and Voss (1997) estimated aquifer vulnerability of nitrate contamination in the Puget Sound Basin. 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.…”

“…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.…”

A spatial, statistical approach to map the risk of milk contamination by β-hexachlorocyclohexane in dairy farms

“…Among other techniques, indicator kriging (IK) is a geostatistical tool which estimates the probability of a contaminant to exceed specific threshold concentrations in not sampled locations on the basis of data from surrounding areas, thus providing a probability map of spatial contamination (Van Meirvenne and Goovaerts, 2001;Brus et al, 2002;Ciotoli et al, 2007;Huang et al, 2007;Chu et al, 2010). On the other hand, classical statistical methods such as logistic regression (LR) combined with GIS have been used for the same purpose based on the presence of some environmental predictive factors (Liu et al, 2005;Twarakavi and Kaluarachchi, 2005;Lin et al, 2011).…”