Quantitative identification of nitrate pollution sources and uncertainty analysis based on dual isotope approach in an agricultural watershed.

Quantitative identification of nitrate (NO3--N) sources is critical to the control of nonpoint source nitrogen pollution in an agricultural watershed. Combined with water quality monitoring, we adopted the environmental isotope (δD-H2O, δ18O-H2O, δ15N-NO3-, and δ18O-NO3-) analysis and the Markov Chain Monte Carlo (MCMC) mixing model to determine the proportions of riverine NO3--N inputs from four potential NO3--N sources, namely, atmospheric deposition (AD), chemical nitrogen fertilizer (NF), soil nitrogen (SN), and manure and sewage (M&S), in the ChangLe River watershed of eastern China. Results showed that NO3--N was the main form of nitrogen in this watershed, accounting for approximately 74% of the total nitrogen concentration. A strong hydraulic interaction existed between the surface and groundwater for NO3--N pollution. The variations of the isotopic composition in NO3--N suggested that microbial nitrification was the dominant nitrogen transformation process in surface water, whereas significant denitrification was observed in groundwater. MCMC mixing model outputs revealed that M&S was the predominant contributor to riverine NO3--N pollution (contributing 41.8% on average), followed by SN (34.0%), NF (21.9%), and AD (2.3%) sources. Finally, we constructed an uncertainty index, UI90, to quantitatively characterize the uncertainties inherent in NO3--N source apportionment and discussed the reasons behind the uncertainties.