Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach
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 u...
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MDPI AG
2021-01-01
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| author | Yunyun Xu Qiqi Yuan Chunfa Zhao Lachun Wang Yuhua Li Xiaoxue Ma Jiaxun Guo Hong Yang |
| author_facet | Yunyun Xu Qiqi Yuan Chunfa Zhao Lachun Wang Yuhua Li Xiaoxue Ma Jiaxun Guo Hong Yang |
| author_sort | Yunyun Xu |
| collection | DOAJ |
| description | 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 (NH<sub>4</sub><sup>+</sup>-N), and nitrate (NO<sub>3</sub><sup>−</sup>-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 NO<sub>3</sub><sup>−</sup>-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 NO<sub>3</sub><sup>−</sup>-N mainly originated from sewage (67%) and soil (26%), with clear spatial variations. NO<sub>3</sub><sup>−</sup>-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 NO<sub>3</sub><sup>−</sup>-N rose from 60% upstream to 86% downstream, suggesting the increasing influence of the point source of sewage. The results showed that 53% of NO<sub>3</sub><sup>−</sup>-N in the basin outlet originated from the point source of sewage near the M4 site. Sewage contributed 75% of NO<sub>3</sub><sup>−</sup>-N in the rainy season and 67% of NO<sub>3</sub><sup>−</sup>-N in the dry season, suggesting the weakly temporal variation. Our results highlight the spatiotemporal variations in sources of NO<sub>3</sub><sup>−</sup>-N. These results will aid in the development of measures needed to control nitrogen pollution in river basins. |
| first_indexed | 2024-03-10T13:33:53Z |
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| spelling | doaj.art-669cf7fd63954258b324f60ed5aaff062023-11-21T07:41:52ZengMDPI AGWater2073-44412021-01-011318310.3390/w13010083Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic ApproachYunyun Xu0Qiqi Yuan1Chunfa Zhao2Lachun Wang3Yuhua Li4Xiaoxue Ma5Jiaxun Guo6Hong Yang7School of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaWater Affairs Bureau of Jiangning District, Nanjing 211100, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaWater Affairs Bureau of Jiangning District, Nanjing 211100, ChinaCollege of Urban Resource and Environment Sciences, Jiangsu Second Normal University, Nanjing 210013, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210023, ChinaCollaborative 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, ChinaExcessive 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 (NH<sub>4</sub><sup>+</sup>-N), and nitrate (NO<sub>3</sub><sup>−</sup>-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 NO<sub>3</sub><sup>−</sup>-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 NO<sub>3</sub><sup>−</sup>-N mainly originated from sewage (67%) and soil (26%), with clear spatial variations. NO<sub>3</sub><sup>−</sup>-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 NO<sub>3</sub><sup>−</sup>-N rose from 60% upstream to 86% downstream, suggesting the increasing influence of the point source of sewage. The results showed that 53% of NO<sub>3</sub><sup>−</sup>-N in the basin outlet originated from the point source of sewage near the M4 site. Sewage contributed 75% of NO<sub>3</sub><sup>−</sup>-N in the rainy season and 67% of NO<sub>3</sub><sup>−</sup>-N in the dry season, suggesting the weakly temporal variation. Our results highlight the spatiotemporal variations in sources of NO<sub>3</sub><sup>−</sup>-N. These results will aid in the development of measures needed to control nitrogen pollution in river basins.https://www.mdpi.com/2073-4441/13/1/83nitrate isotopeBayesian isotope mixing modelsource identificationeutrophication |
| spellingShingle | Yunyun Xu Qiqi Yuan Chunfa Zhao Lachun Wang Yuhua Li Xiaoxue Ma Jiaxun Guo Hong Yang Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach Water nitrate isotope Bayesian isotope mixing model source identification eutrophication |
| title | Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach |
| title_full | Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach |
| title_fullStr | Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach |
| title_full_unstemmed | Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach |
| title_short | Identification of Nitrate Sources in Rivers in a Complex Catchment Using a Dual Isotopic Approach |
| title_sort | identification of nitrate sources in rivers in a complex catchment using a dual isotopic approach |
| topic | nitrate isotope Bayesian isotope mixing model source identification eutrophication |
| url | https://www.mdpi.com/2073-4441/13/1/83 |
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