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

Quantitative identification of nitrate pollution sources and uncertainty analysis based on dual... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Pollution Elsevier

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

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0269-7491
D.O.I.
10.1016/j.envpol.2017.06.100
Publisher site
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Abstract

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.

Journal

Environmental PollutionElsevier

Published: Oct 1, 2017

References

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