APCS-MLR model: A convenient and fast method for quantitative identification of nitrate pollution sources in groundwater

Abstract

Nitrate (NO3−) contamination in groundwater has diverse sources and complicated transformation processes. To effectively control NO3− pollution in groundwater systems, quantitative and accurate identification of NO3− sources is critical. In this work, we applied hydrochemical characteristics and isotope analysis to determine NO3− source apportionment. For the first time, the NO3− source contributions were calculated using hydrochemical indicators combined with multivariate statistical model (PCA-APCS-MLR). The results interpret that chemical fertilizers (58.11%) and natural sources (22.69%) were the primary NO3− sources in the vegetable cultivation area (VCA) which were rather close to the estimation by Bayesian isotope mixing model (SIAR). In particular, the contributions of chemical fertilizers in the VCA differed by only 3.79% between the two methods. Compared with previous approaches e.g. SIAR, the key advantage of the proposed PCA-APCS-MLR model is that it only requires the hydrochemical indicators which can be easily measured. A series of complicated experiments including measurement of isotope data of NO3− in groundwater, monitoring of in-situ pollution source information and calculation of isotopic enrichment factor can be simply avoided. The PCA-APCS-MLR model offers a much more convenient and faster method to determine the contribution rates of NO3− pollution sources in groundwater.