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

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.