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-H2 O, δ18 O-H2 O, δ15 N-NO3 - , and δ18 O-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.