A three-pronged approach for identifying source and extent of nitrate contamination in groundwater

A method to delineate the nitrate (NO3-) contamination of a study area by sequential and statistical use of dual isotope data, principal component analysis (PCA), and land use/land cover (LULC) data was demonstrated using data from Eumseong, Korea. First, a dual isotope approach was applied to identify the possible NO3- sources and quantify their contribution to NO3- contamination using Bayesian statistics. Second, a PCA was performed to discriminate and evaluate the impact of NO3- contamination on chemical evolution in the aquifer. Lastly, we incorporated the LULC data into a regression analysis to identify the contribution of various land uses to NO3- recharge. Some samples had NO3- and iron (Fe) concentrations above the local drinking water quality standard, and the distributions of potassium (K+), sulfate (SO42-), Fe, and manganese (Mn) were skewed significantly. Trends from the dual isotope analysis suggested three major sources of NO3- contamination. Among the three sources, Bayesian statistics indicated that the NO3- contamination was largely attributable to influx from manure/sewage and soil nitrogen (N). Based on the PCA, following screening for skewed data, a contamination indicator was extracted. The indicator exhibited positive correlations with NO3-, chlorine (Cl-), strontium (Sr2+), calcium (Ca2+), sodium (Na+), magnesium (Mg2+), K+, and SO42-, suggesting a single strong source. The regression analysis, using the LULC information, determined that agriculture activities in nonpaddy areas were responsible for the NO3- recharge. This study identified the benefits of combining dual isotope analysis, PCA, and LULC data for discriminating and evaluating sources of NO3- contamination when diverse contaminants are involved in geochemical evolution.