Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model
Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversio...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cao, Shengwei [verfasserIn] Fei, Yuhong [verfasserIn] Tian, Xia [verfasserIn] Cui, Xiangxiang [verfasserIn] Zhang, Xueqing [verfasserIn] Yuan, Ruoxi [verfasserIn] Li, Yasong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Environmental science and pollution research - Berlin : Springer, 1994, 28(2021), 35 vom: 27. Apr., Seite 48343-48361 |
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| Übergeordnetes Werk: |
volume:28 ; year:2021 ; number:35 ; day:27 ; month:04 ; pages:48343-48361 |
| Links: |
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| DOI / URN: |
10.1007/s11356-021-14083-2 |
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| 520 | |a Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. Our results provide a theoretical basis for the development of measures to guarantee long-term water supply of the South to North Water Diversion Project. | ||
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10.1007/s11356-021-14083-2 doi (DE-627)SPR04496921X (SPR)s11356-021-14083-2-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Cao, Shengwei verfasserin aut Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. Our results provide a theoretical basis for the development of measures to guarantee long-term water supply of the South to North Water Diversion Project. Nitrate contamination (dpeaa)DE-He213 South to North Water Diversion Project (dpeaa)DE-He213 Stable isotope (dpeaa)DE-He213 Groundwater (dpeaa)DE-He213 Fei, Yuhong verfasserin aut Tian, Xia verfasserin aut Cui, Xiangxiang verfasserin aut Zhang, Xueqing verfasserin aut Yuan, Ruoxi verfasserin aut Li, Yasong verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 28(2021), 35 vom: 27. Apr., Seite 48343-48361 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:28 year:2021 number:35 day:27 month:04 pages:48343-48361 https://dx.doi.org/10.1007/s11356-021-14083-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 28 2021 35 27 04 48343-48361 |
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10.1007/s11356-021-14083-2 doi (DE-627)SPR04496921X (SPR)s11356-021-14083-2-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Cao, Shengwei verfasserin aut Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. Our results provide a theoretical basis for the development of measures to guarantee long-term water supply of the South to North Water Diversion Project. Nitrate contamination (dpeaa)DE-He213 South to North Water Diversion Project (dpeaa)DE-He213 Stable isotope (dpeaa)DE-He213 Groundwater (dpeaa)DE-He213 Fei, Yuhong verfasserin aut Tian, Xia verfasserin aut Cui, Xiangxiang verfasserin aut Zhang, Xueqing verfasserin aut Yuan, Ruoxi verfasserin aut Li, Yasong verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 28(2021), 35 vom: 27. Apr., Seite 48343-48361 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:28 year:2021 number:35 day:27 month:04 pages:48343-48361 https://dx.doi.org/10.1007/s11356-021-14083-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 28 2021 35 27 04 48343-48361 |
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10.1007/s11356-021-14083-2 doi (DE-627)SPR04496921X (SPR)s11356-021-14083-2-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Cao, Shengwei verfasserin aut Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. Our results provide a theoretical basis for the development of measures to guarantee long-term water supply of the South to North Water Diversion Project. Nitrate contamination (dpeaa)DE-He213 South to North Water Diversion Project (dpeaa)DE-He213 Stable isotope (dpeaa)DE-He213 Groundwater (dpeaa)DE-He213 Fei, Yuhong verfasserin aut Tian, Xia verfasserin aut Cui, Xiangxiang verfasserin aut Zhang, Xueqing verfasserin aut Yuan, Ruoxi verfasserin aut Li, Yasong verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 28(2021), 35 vom: 27. Apr., Seite 48343-48361 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:28 year:2021 number:35 day:27 month:04 pages:48343-48361 https://dx.doi.org/10.1007/s11356-021-14083-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 28 2021 35 27 04 48343-48361 |
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10.1007/s11356-021-14083-2 doi (DE-627)SPR04496921X (SPR)s11356-021-14083-2-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Cao, Shengwei verfasserin aut Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. Our results provide a theoretical basis for the development of measures to guarantee long-term water supply of the South to North Water Diversion Project. Nitrate contamination (dpeaa)DE-He213 South to North Water Diversion Project (dpeaa)DE-He213 Stable isotope (dpeaa)DE-He213 Groundwater (dpeaa)DE-He213 Fei, Yuhong verfasserin aut Tian, Xia verfasserin aut Cui, Xiangxiang verfasserin aut Zhang, Xueqing verfasserin aut Yuan, Ruoxi verfasserin aut Li, Yasong verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 28(2021), 35 vom: 27. Apr., Seite 48343-48361 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:28 year:2021 number:35 day:27 month:04 pages:48343-48361 https://dx.doi.org/10.1007/s11356-021-14083-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 28 2021 35 27 04 48343-48361 |
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10.1007/s11356-021-14083-2 doi (DE-627)SPR04496921X (SPR)s11356-021-14083-2-e DE-627 ger DE-627 rakwb eng 333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Cao, Shengwei verfasserin aut Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. Our results provide a theoretical basis for the development of measures to guarantee long-term water supply of the South to North Water Diversion Project. Nitrate contamination (dpeaa)DE-He213 South to North Water Diversion Project (dpeaa)DE-He213 Stable isotope (dpeaa)DE-He213 Groundwater (dpeaa)DE-He213 Fei, Yuhong verfasserin aut Tian, Xia verfasserin aut Cui, Xiangxiang verfasserin aut Zhang, Xueqing verfasserin aut Yuan, Ruoxi verfasserin aut Li, Yasong verfasserin aut Enthalten in Environmental science and pollution research Berlin : Springer, 1994 28(2021), 35 vom: 27. Apr., Seite 48343-48361 (DE-627)320517926 (DE-600)2014192-0 1614-7499 nnns volume:28 year:2021 number:35 day:27 month:04 pages:48343-48361 https://dx.doi.org/10.1007/s11356-021-14083-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.00 ASE 43.50 ASE 58.50 ASE AR 28 2021 35 27 04 48343-48361 |
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We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. 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Cao, Shengwei |
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Cao, Shengwei ddc 333.7 bkl 43.00 bkl 43.50 bkl 58.50 misc Nitrate contamination misc South to North Water Diversion Project misc Stable isotope misc Groundwater Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model |
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333.7 690 ASE 43.00 bkl 43.50 bkl 58.50 bkl Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model Nitrate contamination (dpeaa)DE-He213 South to North Water Diversion Project (dpeaa)DE-He213 Stable isotope (dpeaa)DE-He213 Groundwater (dpeaa)DE-He213 |
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Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model |
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Cao, Shengwei |
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Cao, Shengwei Fei, Yuhong Tian, Xia Cui, Xiangxiang Zhang, Xueqing Yuan, Ruoxi Li, Yasong |
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determining the origin and fate of nitrate in the nanyang basin, central china, using environmental isotopes and the bayesian mixing model |
| title_auth |
Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model |
| abstract |
Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. Our results provide a theoretical basis for the development of measures to guarantee long-term water supply of the South to North Water Diversion Project. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstractGer |
Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. Our results provide a theoretical basis for the development of measures to guarantee long-term water supply of the South to North Water Diversion Project. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Identifying sources of nitrate contamination has been a long-term challenge in areas with different land uses. We investigated the biogeochemical processes and quantified the contribution of potential nitrate sources in the Nanyang Basin, the source area of the South to North Water Diversion Project in China. Hydrogeochemical characteristics, the dual-isotope method ($ δ^{15} $N-$ NO_{3} $− and $ δ^{18} $O-$ NO_{3} $−), and the Bayesian mixing model (SIAR) were combined. The results for 160 samples indicated that mean nitrate concentrations of residential area (162.83 mg $ L^{−1} $) and farmland (75.71 mg $ L^{−1} $) were higher compared with those of surface water (16.15 mg $ L^{−1} $) and forest (36.25 mg $ L^{−1} $). Hydrochemical facies and molar ratios of major ions indicated that the natural environment was greatly impacted by anthropogenic activities. Nitrification, ammonium volatilization, and mixing effects were the dominant processes in nitrogen transformation. The contributions of different sources to nitrate contamination were 45.41%, 35.81%, 17.87%, and 0.91% for sewage and manure, soil organic nitrogen, synthetic fertilizer, and atmospheric deposition, respectively. Undeveloped infrastructure and sewage disposal in rural areas were the main causes of nitrate contamination. Our results provide a theoretical basis for the development of measures to guarantee long-term water supply of the South to North Water Diversion Project. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Determining the origin and fate of nitrate in the Nanyang Basin, Central China, using environmental isotopes and the Bayesian mixing model |
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| score |
7.4014387 |