Quantifying and assessing nitrogen sources and transport in a megacity water supply watershed: Insights for effective non-point source pollution management with mixSIAR and SWAT models
The expansion and intensification of human activities have resulted in excessive nitrogen (N) in rivers, causing worldwide concern. To effectively manage agricultural non-point source pollution and ensure a safe drinking water supply in watersheds, it is crucial to trace and quantify the primary sou...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-02-01
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| Series: | Agricultural Water Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377423004869 |
| _version_ | 1827390423827480576 |
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| author | Zhuo Hao Yuanyuan Shi Xiaoying Zhan Bowei Yu Qing Fan Jie Zhu Lianhua Liu Qingwen Zhang Guangxiang Zhao |
| author_facet | Zhuo Hao Yuanyuan Shi Xiaoying Zhan Bowei Yu Qing Fan Jie Zhu Lianhua Liu Qingwen Zhang Guangxiang Zhao |
| author_sort | Zhuo Hao |
| collection | DOAJ |
| description | The expansion and intensification of human activities have resulted in excessive nitrogen (N) in rivers, causing worldwide concern. To effectively manage agricultural non-point source pollution and ensure a safe drinking water supply in watersheds, it is crucial to trace and quantify the primary sources and spatial distribution patterns of N. In response to the challenge of unclear sources and agricultural non-point source pollution, this study utilised the Bayesian isotope mixing model and Soil and Water Assessment Toolmodel to identify the dominant nitrate sources and transformation processes. These models were employed to quantify N retention by the mainstream and tributaries in the Bai River Basin, which directly impacts the safety of drinking water in Beijing. Total N (TN) concentrations in the Yunzhou Reservoir (4.22 ± 0.04 mg.L˗1) and Miyun Reservoir (2.88 ± 0.62 mg.L˗1) inlets in autumn and winter were V class (2 mg.L˗1) category as per GB 3838–2002 standards. This implies higher risks of eutrophication and algal blooms exceeding the standards at individual points and seasons in the Bai River. Fertilisers were the main nitrate source in the Bai River Basin, contributing 44.6% during the dry season and 62.9% during the wet season, suggesting that nitrate is more susceptible to leaching and runoff during rainy periods. Rainfall was not a major contributor, with only 3.2% and 2.9% originating from the Hebei and Beijing areas of the Bai River, respectively. Annually, the Bai River Basin exports loads of approximately 629.3 t.a˗1 of TN and 433.7 t.a˗1 of organic N (Org-N) from the Bai River Basin land to the river. The TN and Org-N loads at the final destination in the Miyun Reservoir were 521.3 t.a˗1 and 100.3 t.a˗1, respectively. Of the exported TN and Org-N, 17.16% and 76.87%, respectively, were retained in the river network. Consequently, N transformation occurred in the Bai River, with nitrification–denitrification being particularly dominant. Nitrification was more evident in the nitrate-enriched river. |
| first_indexed | 2024-03-08T16:52:29Z |
| format | Article |
| id | doaj.art-ea123d13195b4fdb8c3f31e757d1de68 |
| institution | Directory Open Access Journal |
| issn | 1873-2283 |
| language | English |
| last_indexed | 2024-03-08T16:52:29Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj.art-ea123d13195b4fdb8c3f31e757d1de682024-01-05T04:22:37ZengElsevierAgricultural Water Management1873-22832024-02-01291108621Quantifying and assessing nitrogen sources and transport in a megacity water supply watershed: Insights for effective non-point source pollution management with mixSIAR and SWAT modelsZhuo Hao0Yuanyuan Shi1Xiaoying Zhan2Bowei Yu3Qing Fan4Jie Zhu5Lianhua Liu6Qingwen Zhang7Guangxiang Zhao8Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaBeijing Municipal Research Institute of Eco-Environmental Protection, Beijing 100037, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaBeijing Municipal Research Institute of Eco-Environmental Protection, Beijing 100037, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Corresponding author.Water and soil conservation management station of Yanqing District Water Bureau of Beijing, Beijing 102100, ChinaThe expansion and intensification of human activities have resulted in excessive nitrogen (N) in rivers, causing worldwide concern. To effectively manage agricultural non-point source pollution and ensure a safe drinking water supply in watersheds, it is crucial to trace and quantify the primary sources and spatial distribution patterns of N. In response to the challenge of unclear sources and agricultural non-point source pollution, this study utilised the Bayesian isotope mixing model and Soil and Water Assessment Toolmodel to identify the dominant nitrate sources and transformation processes. These models were employed to quantify N retention by the mainstream and tributaries in the Bai River Basin, which directly impacts the safety of drinking water in Beijing. Total N (TN) concentrations in the Yunzhou Reservoir (4.22 ± 0.04 mg.L˗1) and Miyun Reservoir (2.88 ± 0.62 mg.L˗1) inlets in autumn and winter were V class (2 mg.L˗1) category as per GB 3838–2002 standards. This implies higher risks of eutrophication and algal blooms exceeding the standards at individual points and seasons in the Bai River. Fertilisers were the main nitrate source in the Bai River Basin, contributing 44.6% during the dry season and 62.9% during the wet season, suggesting that nitrate is more susceptible to leaching and runoff during rainy periods. Rainfall was not a major contributor, with only 3.2% and 2.9% originating from the Hebei and Beijing areas of the Bai River, respectively. Annually, the Bai River Basin exports loads of approximately 629.3 t.a˗1 of TN and 433.7 t.a˗1 of organic N (Org-N) from the Bai River Basin land to the river. The TN and Org-N loads at the final destination in the Miyun Reservoir were 521.3 t.a˗1 and 100.3 t.a˗1, respectively. Of the exported TN and Org-N, 17.16% and 76.87%, respectively, were retained in the river network. Consequently, N transformation occurred in the Bai River, with nitrification–denitrification being particularly dominant. Nitrification was more evident in the nitrate-enriched river.http://www.sciencedirect.com/science/article/pii/S0378377423004869Nitrate sourcesN retentionN fateDrinking water sources |
| spellingShingle | Zhuo Hao Yuanyuan Shi Xiaoying Zhan Bowei Yu Qing Fan Jie Zhu Lianhua Liu Qingwen Zhang Guangxiang Zhao Quantifying and assessing nitrogen sources and transport in a megacity water supply watershed: Insights for effective non-point source pollution management with mixSIAR and SWAT models Agricultural Water Management Nitrate sources N retention N fate Drinking water sources |
| title | Quantifying and assessing nitrogen sources and transport in a megacity water supply watershed: Insights for effective non-point source pollution management with mixSIAR and SWAT models |
| title_full | Quantifying and assessing nitrogen sources and transport in a megacity water supply watershed: Insights for effective non-point source pollution management with mixSIAR and SWAT models |
| title_fullStr | Quantifying and assessing nitrogen sources and transport in a megacity water supply watershed: Insights for effective non-point source pollution management with mixSIAR and SWAT models |
| title_full_unstemmed | Quantifying and assessing nitrogen sources and transport in a megacity water supply watershed: Insights for effective non-point source pollution management with mixSIAR and SWAT models |
| title_short | Quantifying and assessing nitrogen sources and transport in a megacity water supply watershed: Insights for effective non-point source pollution management with mixSIAR and SWAT models |
| title_sort | quantifying and assessing nitrogen sources and transport in a megacity water supply watershed insights for effective non point source pollution management with mixsiar and swat models |
| topic | Nitrate sources N retention N fate Drinking water sources |
| url | http://www.sciencedirect.com/science/article/pii/S0378377423004869 |
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