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

Quantitative identification of nitrate (NO-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-HO, δO-HO, δN-NO, and δO-NO) analysis and the Markov Chain Monte Carlo (MCMC) mixing model to determine the proportions of riverine NO-N inputs from four potential NO-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 NO-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 NO-N pollution. The variations of the isotopic composition in NO-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 NO-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, UI, to quantitatively characterize the uncertainties inherent in NO-N source apportionment and discussed the reasons behind the uncertainties.