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Jiang C, Jiang C, Wang Q, Liu H, Li D, Zhu Q, Liu F. Seasonal characteristics of groundwater discharge controlled by precipitation and its environmental effects in a coal mining subsidence lake, eastern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 915:170067. [PMID: 38242470 DOI: 10.1016/j.scitotenv.2024.170067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 01/21/2024]
Abstract
Many regions have formed subsidence lakes due to underground mining in the world. However, seasonal variations of lacustrine groundwater discharge (LGD) rate and solute fluxes in the coal mining subsidence were rarely reported. In this study, we conducted four seasonal samplings in a coal mining subsidence, during which samples for stable water (δ18O) and radioactive (222Rn) isotopes were collected to quantify the seasonal dynamics of LGD rates. The LGD rates estimated from the 222Rn mass balance model were 10.2 ± 8.7, 5.5 ± 3.2, 11.5 ± 7.8, and 7.8 ± 4.5 mm d-1 in summer, autumn, winter and spring, respectively. According to the 18O mass balance model, the corresponding LGD rates were 15.1, 7.3, 15.6, and 11.3 mm d-1 in summer, autumn, winter and spring, respectively. We found a significant correlation between precipitation and LGD rates, suggesting precipitation was recognized as the main control factor for seasonal variations of LGD rates. Based on this correlation, the extrapolated LGD rates over a year ranged from 3.1 to 12.7 mm d-1 with an average of 8.8 mm d-1. Moreover, the fluxes of dissolved silicon (DSi), iron (Fe), and manganese (Mn) from LGD in autumn were (1.6 ± 0.9) × 105, (1.9 ± 1.1) × 104, and (1.1 ± 0.6) × 104 mol a-1, respectively. Correspondingly, in winter they were (3.5 ± 2.4) × 105, (4.1 ± 2.8) × 103, and (2.8 ± 1.9) × 103 mol a-1, respectively. This study demonstrated significantly seasonal variations of LGD, with precipitation being the main control factor of LGD in the coal mining subsidence lake. The fluxes of dissolved substance (DSi, Fe, Mn) from LGD need to be emphasized because they may have important impacts on the ecological stability in coal mining subsidence lakes.
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Affiliation(s)
- Chenghong Jiang
- School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Chunlu Jiang
- School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China; School of Resources and Geoscience, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China.
| | - Qianqian Wang
- Department of Earth Science, The University of Hong Kong, Hong Kong, China; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China.
| | - Hui Liu
- School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Desheng Li
- School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Qiyu Zhu
- School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China
| | - Feng Liu
- School of Resources and Environmental Engineering, Anhui University, Hefei 230601, Anhui, China
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Xi Y, Wang Q, Zhu J, Yang M, Hao T, Chen Y, Zhang Q, He N, Yu G. Atmospheric wet organic nitrogen deposition in China: Insights from the national observation network. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 898:165629. [PMID: 37467980 DOI: 10.1016/j.scitotenv.2023.165629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 07/21/2023]
Abstract
Organic nitrogen (N) is an important component of atmospheric reactive N deposition, and its bioavailability is almost as important as that of inorganic N. Currently, there are limited reports of national observations of organic N deposition; most stations are concentrated in rural and urban areas, with even fewer long-term observations of natural ecosystems in remote areas. Based on the China Wet Deposition Observation Network, this study regularly collected monthly wet deposition samples from 43 typical ecosystems from 2013 to 2021 and measured related N concentrations. The aim was to provide a more comprehensive assessment of the multi-component characteristics of atmospheric wet N deposition and reveal the influencing factors and potential sources of wet dissolved organic N (DON) deposition. The results showed that atmospheric wet deposition fluxes of NO3-, NH4+, DON and dissolved total N (DTN) were 4.68, 5.25, 4.32, and 13.05 kg N ha-1 yr-1, respectively, and that DON accounted for 30 % of DTN deposition (potentially up to 50 % in remote areas). Wet DON deposition was related to anthropogenic emissions (agriculture, biomass burning, and traffic), natural emissions (volatile organic compound emissions from vegetation), and precipitation processes. The wet DON deposition flux was higher in South, Central, and Southwest China, with more precipitation and intensive agricultural activities or more vegetation cover, and lower in Northwest China and Inner Mongolia, with less precipitation and human activities or vegetation cover. DON was the main contributor to DTN deposition in remote areas and was possibly related to natural emissions. In rural and urban areas, DON may have been more influenced by agricultural activities and anthropogenic emissions. This study quantified the long-term spatiotemporal patterns of wet N deposition and provides a reference for future N addition experiments and N cycle studies. Further consideration of DON deposition is required, especially in the context of anthropogenic control of NO2 and NH3.
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Affiliation(s)
- Yue Xi
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Qiufeng Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Jianxing Zhu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
| | - Meng Yang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Tianxiang Hao
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Yanran Chen
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Qiongyu Zhang
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
| | - Nianpeng He
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Vegetation Ecology, Ministry of Education, Northeast Normal University, Changchun, China
| | - Guirui Yu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
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Zeng J, Han G, Zhang S, Qu R. Nitrate dynamics and source identification of rainwater in Beijing during rainy season: Insight from dual isotopes and Bayesian model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 856:159234. [PMID: 36208764 DOI: 10.1016/j.scitotenv.2022.159234] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/30/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Anthropogenic reactive nitrogen emissions have a significant impact on atmospheric chemical composition and earth surface ecosystem. As one of the most important sinks of atmospheric nitrogen, the wet deposition of nitrate (rainwater NO3-) has been widely concerned. Yet, the sources and transformation processes of wet deposited NO3- were not well revealed in megacity during rainy season in the context of global climate change. Here, we investigated the concentrations of nitrogen components and dual isotopes of rainwater nitrate collected in Beijing during July to August 2021 (rainy season). The main findings showed that the concentrations of NH4+-N, NO3--N, and NO2--N ranged 0.5- 6.7 mg L-1, 0.3- 4.5 mg L-1, and 0.05- 0.18 mg L-1, respectively, with the average relative percentages of 69 %, 29 %, and 2 %. The stoichiometry analysis of characteristic ion ratios indicated that the contribution of municipal wastes and agricultural sources to rainwater NH4+-N is relatively significant, while traffics were the major contributor of NO3--N instead of the fixed emission. Rainwater δ15N-NO3- and δ18O-NO3- presented slightly 15N-depleted characteristic compared to previous studies with the average values of -3.9 ± 3.1 ‰ and 58.7 ± 12.6 ‰. These isotope compositions suggesting an origin of rainwater NO3- from the mixing of multi-sources and was mainly generated via the pathway of OH radical oxidization. Further source apportionment of rainwater NO3- by Bayesian mixing model evaluated that traffic (30.3 %) and soil (30.3 %) emissions contributed mostly to NO3-, while the contribution of biomass burning (18.8 %) and coal combustion (20.6 %) were relatively lower. This study highlighted the important role of dual isotopes in rainwater nitrate source identification and formation processes in megacity.
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Affiliation(s)
- Jie Zeng
- Institute of Earth Sciences, China University of Geosciences (Beijing), Beijing 100083, China
| | - Guilin Han
- Institute of Earth Sciences, China University of Geosciences (Beijing), Beijing 100083, China.
| | - Shitong Zhang
- Institute of Earth Sciences, China University of Geosciences (Beijing), Beijing 100083, China
| | - Rui Qu
- Institute of Earth Sciences, China University of Geosciences (Beijing), Beijing 100083, China
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