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.
Environmental Pollution – Elsevier
Published: Oct 1, 2017
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