Prediction of groundwater contamination with multivariate regression and probabilistic capture zones

Probabilistic capture zones are combined with a regression model and used as buffer zones around wells for Tobit regression analysis to predict contaminant concentration of groundwater in an agricultural region. A backward transport equation, which is a mathematical model based on the physical processes of solute transport, is used to delineate probabilistic capture zones. The probabilistic capture zone defines the area where contaminant discharge can have a direct influence, with pertinent probability, on the quality of groundwater pumped from a well. Tobit regression analysis is used to find the relationship between independent regression variables and a dependent variable, which is contaminant concentration in this study. The capture zone and the regression are combined into a model, and its applicability for prediction of nitrate concentration is tested in a small agricultural basin in Chuncheon, Korea, which is occupied mainly by vegetation fields, orchards, and small barns. Three cases of Model 1, Model 2, and Model 3 are compared in which buffer zones are circles, capture zones with probability over 0.1, and capture zones divided into sections with different probabilities, respectively. The resulting regression model describes nitrate concentration in terms of selected independent variables. When the concentrations are calculated with the model, the best fit with the observed concentrations was in Model 3. This result supports the applicability of the method proposed in this study to prediction of contaminant concentration of groundwater.     

Factors affecting the spatial pattern of nitrate contamination in shallow groundwater

The elevated level of nitrate in groundwater is a serious problem in Korean agricultural areas. To control and manage groundwater quality, the characterization of groundwater contamination and identification of the factors affecting the nitrate concentration of groundwater are significant. The characterization of groundwater contamination at a hydrologically complex agricultural site in Yupori, Chuncheon (Korea) was undertaken by analyzing the hydrochemical data of groundwater within a statistical framework. Multivariate statistical tools such as cluster analyses and Tobit regression were applied to investigate the spatial variation of nitrate contamination and to analyze the factors affecting the NO3-N concentration in a shallow groundwater system. The groundwater groups from the cluster analysis were consistent with the land use pattern of the study area. The clustered group of a gentle-slope area with lower elevations showed higher NO3-N contamination of groundwater than groups on a hillside with higher elevations. Tobit regression results indicated that the agricultural activity in the vegetable fields and barns were the major factors affecting the elevated NO3-N concentration while the land slopes and elevations were negatively correlated with the NO3-N concentration. This shows that topographic characteristics such as land slopes and elevations should be considered to evaluate the land use impact on shallow groundwater quality.