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Article

Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach

1
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
2
Water Affairs Bureau of Jiangning District, Nanjing 211100, China
3
College of Urban Resource and Environment Sciences, Jiangsu Second Normal University, Nanjing 210013, China
4
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
5
Department of Geography and Environment Science, University of Reading, Reading RG6 6AB, UK
*
Authors to whom correspondence should be addressed.
Water 2021, 13(1), 83; https://doi.org/10.3390/w13010083
Received: 26 November 2020 / Revised: 23 December 2020 / Accepted: 29 December 2020 / Published: 1 January 2021
Excessive nutrient input to surface water, including nitrate, exacerbates water eutrophication. Clarifying the proportions of different nitrate sources in the aquatic environment is critical for improving the polluted water. However, nitrate sources in river basins are very complex and not clearly understood. In this study, nitrogen concentrations and nitrate isotopic compositions were determined to estimate the spatiotemporal variation in nitrate sources in the Yuntaishan River basin, Nanjing, East China, from March 2019 to January 2020. The results showed that the concentrations of total nitrogen (TN), ammonium (NH4+-N), and nitrate (NO3-N) changed in the ranges of 0.53–18.0 mg/L, 0.01–15.4 mg/L, and 0.06–9.3 mg/L, respectively, wherein NO3-N was the main nitrogen form. Higher nitrogen concentrations appeared in winter and in the downstream parts of the river. In the entire river basin, the NO3-N mainly originated from sewage (67%) and soil (26%), with clear spatial variations. NO3-N in the Yunba sub-watershed was mainly derived from sewage (78%), which was higher than that in other tributaries, i.e., Shengli River (44%) and Yangshan River (49%). This was due to the fact that that Shengli and Yangshan sub-watersheds were covered by urban areas and were equipped with a complete sewage treatment system. In addition, the contributions of sewage to NO3-N rose from 60% upstream to 86% downstream, suggesting the increasing influence of the point source of sewage. The results showed that 53% of NO3-N in the basin outlet originated from the point source of sewage near the M4 site. Sewage contributed 75% of NO3-N in the rainy season and 67% of NO3-N in the dry season, suggesting the weakly temporal variation. Our results highlight the spatiotemporal variations in sources of NO3-N. These results will aid in the development of measures needed to control nitrogen pollution in river basins. View Full-Text
Keywords: nitrate isotope; Bayesian isotope mixing model; source identification; eutrophication nitrate isotope; Bayesian isotope mixing model; source identification; eutrophication
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MDPI and ACS Style

Xu, Y.; Yuan, Q.; Zhao, C.; Wang, L.; Li, Y.; Ma, X.; Guo, J.; Yang, H. Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach. Water 2021, 13, 83. https://doi.org/10.3390/w13010083

AMA Style

Xu Y, Yuan Q, Zhao C, Wang L, Li Y, Ma X, Guo J, Yang H. Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach. Water. 2021; 13(1):83. https://doi.org/10.3390/w13010083

Chicago/Turabian Style

Xu, Yunyun; Yuan, Qiqi; Zhao, Chunfa; Wang, Lachun; Li, Yuhua; Ma, Xiaoxue; Guo, Jiaxun; Yang, Hong. 2021. "Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach" Water 13, no. 1: 83. https://doi.org/10.3390/w13010083

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