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

Abstract Quantitative identification of nitrate (NO 3 − -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 ( δ D-H 2 O, δ 18 O-H 2 O, δ 15 N-NO 3 − , and δ 18 O-NO 3 − ) analysis and the Markov Chain Monte Carlo (MCMC) mixing model to determine the proportions of riverine NO 3 − -N inputs from four potential NO 3 − -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 3 − -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 3 − -N pollution. The variations of the isotopic composition in NO 3 − -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 3 − -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 90 , to quantitatively characterize the uncertainties inherent in NO 3 − -N source apportionment and discussed the reasons behind the uncertainties.