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Pan T, Zhang Y, Yang F, Liao H, Feng W, Sun F, Jiang W, Wang Q, Ji M, Yang C, Leppäranta M. Characteristics of the presence and migration patterns of DOM between ice and water in the cold and arid Daihai Lake. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:170876. [PMID: 38367733 DOI: 10.1016/j.scitotenv.2024.170876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/19/2024]
Abstract
Seasonal ice cover plays a crucial role in shaping the physical characteristics of lakes in cold and arid regions. Moreover, the ice significantly affects the level and quality of dissolved organic matter (DOM) in the water column. We utilized spectroscopy and mass spectrometry to analyze the molecular composition and distribution of DOM in ice cores and under-ice water in Daihai Lake. We identified the main environmental factors affecting DOM migration through structural equation modelling (SEM). The freezing process created a repulsive effect on DOM, with water samples demonstrating a greater DOM content than ice. The dominant part of the DOM in the ice cores was mainly comprised of protein-like materials (71.45 %), whereas water consisted of humus-like materials (54.81 %). The average molecular weight of the ice cover DOM (m/z = 396.77) was smaller than in the under-ice water (m/z = 405.42). While low-molecular and low-aromatic protein-like material tended to be trapped in the ice layer during ice formation, large-molecular and highly aromatic humic substances were more easily expelled into the water. Interestingly, condensed aromatic hydrocarbons were found to occur less frequently in the ice phase (11 %) compared to the aqueous phase (13 %). Both the lipid and protein/aliphatic compound structures exhibited slightly higher ratios in the ice (6 % and 8 %, respectively) than in water (1 % and 5 %, respectively). SEM between the ice cover environment and DOM indicated that the ice can influence the distribution pattern of DOM through the regulation of internal solute factors and other chemicals. The nature of the DOM and the rate of ice growth also play critical roles in determining the distribution mechanism of DOM for ice and water. The pollutant distribution characteristics and migration patterns between ice and water are essential for comprehending environmental water pollution and promoting pollution management and protection measures in cold region lakes.
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Affiliation(s)
- Ting Pan
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Yimeng Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Shandong Huankeyuan Environmental Engineering Co., Ltd, Jinan 250000, China
| | - Fang Yang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Haiqing Liao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Weiying Feng
- School of Space and Environment, Beihang University, Beijing 100191, China
| | - Fuhong Sun
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Weilong Jiang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Qianqian Wang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Meichen Ji
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Chenglei Yang
- Shandong Huankeyuan Environmental Engineering Co., Ltd, Jinan 250000, China
| | - Matti Leppäranta
- Institute for Atmospheric and Earth System Research, University of Helsinki, 00014 Helsinki, Finland
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Ma J, Zhou M, Peng Y, Tuo Y, Zhou C, Liu K, Huang Y, He F, Lai Q, Zhang Z, Kinouchi T, Li S, Xu X, Wu X, Lin X, Li W, Wang G. Instability in a carbon pool driven by multiple dissolved organic matter sources in a eutrophic lake basin: Potential factors for increased greenhouse gas emissions. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 350:119697. [PMID: 38035504 DOI: 10.1016/j.jenvman.2023.119697] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/06/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023]
Abstract
Lakes serve as vital reservoirs of dissolved organic matter (DOM) and play pivotal roles in biogeochemical carbon cycles. However, the sources and compositions of DOM in freshwater lakes and their potential effects on lake sediment carbon pools remain unclear. In this study, seven inflowing rivers in the Lake Taihu basin were selected to explore the potential effects of multi-source DOM inputs on the stability of the lake sediment carbon pool. The results showed the high concentrations of dissolved organic carbon in the Lake Taihu basin, accompanied by a high complexity level. Lignins constituted the majority of DOM compounds, surpassing 40% of the total, while the organic carbon content was predominantly composed of humic acids (1.02-3.01 g kg-1). The high amounts of lignin oxidative cleavage led to CHO being the main molecular structure in the DOM of the seven rivers. The carbon constituents within the sediment carbon reservoir exhibited a positive correlation with dissolved CH4 and CO2, with a notable emphasis on humic acid and dissolved CH4 (R2 = 0.86). The elevated concentration of DOM, coupled with its intricate composition, contributed to the increases in dissolved greenhouse gases (GHGs). Experiments showed that the mixing of multi-source DOM can accelerate the organic carbon mineralization processes. The unit carbon emission efficiency was highest in the mixed group, reaching reached 160.9 μmol∙Cg-1, which also exhibited a significantly different carbon pool. The mixed decomposition of DOM from different sources influenced the roles of the lake carbon pool as source and sink, indicating that the multi-source DOM of this lake basin was a potential driving factor for increased carbon emissions. These findings have improved our understanding of the sources and compositions of DOM in lake basins and revealed their impacts on carbon emissions, thereby providing a theoretical basis for improving assessments of lake carbon emissions.
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Affiliation(s)
- Jie Ma
- Ministry of Ecology and Environment, Nanjing Institute of Environment Sciences, Nanjing, 210042, China
| | - Muchun Zhou
- Department of Applied Physics and Chemical Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan
| | - Yu Peng
- Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, Tokyo, 152-8550, Japan
| | - Ya Tuo
- Environmental Development Center of the Ministry of Ecology and Environment, Beijing, 100029, China
| | - Chuanqiao Zhou
- Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, Tokyo, 152-8550, Japan.
| | - Kexin Liu
- Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, Tokyo, 152-8550, Japan
| | - Yilin Huang
- Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, Tokyo, 152-8550, Japan
| | - Fei He
- Ministry of Ecology and Environment, Nanjing Institute of Environment Sciences, Nanjing, 210042, China.
| | - Qiuying Lai
- Ministry of Ecology and Environment, Nanjing Institute of Environment Sciences, Nanjing, 210042, China
| | - Zhihui Zhang
- School of Environment, Nanjing Normal University, Nanjing, 210023, China
| | - Tsuyoshi Kinouchi
- Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, Tokyo, 152-8550, Japan
| | - Shuyin Li
- Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, Tokyo, 152-8550, Japan; Yangtze River Basin Ecological Environment Monitoring and Scientific Research Center, Yangtze River Basin Ecological Environment Supervision and Administration Bureau, Ministry of Ecological Environment, Wuhan, 430010, China
| | - Xiaoguang Xu
- School of Environment, Nanjing Normal University, Nanjing, 210023, China
| | - Xiaodong Wu
- College of Urban and Environmental Sciences, Hubei Normal University, Huangshi, 435002, China
| | - Xiaowen Lin
- College of Urban and Environmental Sciences, Hubei Normal University, Huangshi, 435002, China
| | - Weixin Li
- Ministry of Ecology and Environment, Nanjing Institute of Environment Sciences, Nanjing, 210042, China
| | - Guoxiang Wang
- School of Environment, Nanjing Normal University, Nanjing, 210023, China
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Ding F, Zhang W, Cao S, Hao S, Chen L, Xie X, Li W, Jiang M. Optimization of water quality index models using machine learning approaches. WATER RESEARCH 2023; 243:120337. [PMID: 37473509 DOI: 10.1016/j.watres.2023.120337] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/09/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
To optimize the water quality index (WQI) assessment model, this study upgraded the parameter weight values and aggregation functions. We determined the combined weights based on machine learning and game theory to improve the accuracy of the models, and proposed new aggregation functions to reduce the uncertainty of the model. A new water quality assessment system was established, and took the Chaobai River Basin as a case study. To optimize the weight, two combined weights were established based on game theory. The weight CWAE was combined by the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). The weight CWAL was combined by AHP and machine learning (LightGBM). CWAL was judged to be an optimal composite weight by comparing the coefficient of variation (CV) values and the Kaiser-Meyer-Olkin (KMO) extracted values. To reduce the uncertainty of the model, we proposed two aggregation functions, the Sinusoidal Weighted Mean (SWM) and the Log-weighted Quadratic Mean (LQM). The three water quality assessment models (WQIS, WQIL and WQIW) were established based on the optimal weights besides. All three models had good reliability. Both WQIS and WQIW models had low eclipsing problems (25.49% and 18.63%). The accuracy of the models was ranked as WQIS > WQIW > WQIL. The uncertainty of WQIs (0.000) in assessing poor water quality was low, and so was WQIW (0.259) in assessing good water quality. Overall, the WQIS model was recommended for assessing poor water quality and the WQIW model was recommended for assessing good water quality. The assessment results of WQIS showed that the Chaobai River Basin was "slightly polluted", and the water quality upstream was better than that downstream. TN was the main pollutant in the basin, and there was slight pollution with CODMn, CODCr, BOD5, etc. There was little metal contamination, only a few months exceeded Class I. The model established in this study can provide a reference for the same type work of water quality assessment. The assessment results can provide a scientific basis for the protection of the regional water environment.
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Affiliation(s)
- Fei Ding
- Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China.
| | - Wenjie Zhang
- Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
| | - Shaohua Cao
- State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu, China
| | - Shilong Hao
- Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
| | - Liangyao Chen
- Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
| | - Xin Xie
- State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China
| | - Wenpan Li
- State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China
| | - Mingcen Jiang
- State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China.
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Wang W, Zhao L, Li W, Chen J, Wang S. Response mechanism of sediment organic matter of plateau lakes in cold and arid regions to climate change: a case study of Hulun Lake, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:26778-26790. [PMID: 36370313 DOI: 10.1007/s11356-022-24097-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 05/18/2022] [Indexed: 06/16/2023]
Abstract
Lake organic matter is one of the important forms of terrestrial carbon, and its sedimentary evolution is affected by many factors such as climate and sources. However, few studies have been conducted on the feedback mechanism of the sedimentary evolution of organic matter to climate change in cold and arid lakes. Historical variations and compositions of sources of the sediment organic matter (SOM) of Hulun Lake, a typical lake in the cold and arid region of China, were studied by multiple methods. The interactions and fee7dback mechanisms between the sedimentary evolution of SOM and climate change, and compositions of SOM source change, were also discussed. Overall, the characteristic indexes of the SOM, including total organic carbon (TOC), carbon stable isotope (δ13C), carbon to nitrogen ratio (C/N), and fluorescence intensity (FI) of the protein-like component in water extractable organic matter (WEOM), showed obvious and uniform characteristics of periodical changes. The indexes were relatively stable before 1920, and fluctuated from 1920 to 1979. Since the 1980s, values of TOC, δ13C, and FI of the protein-like component in WEOM has increased, while C/N decreased. The absolute dominant contribution of terrestrial source to the SOM had changed, and the relative average contribution rate of autochthonous source increased from 17.6% before 1920 to 36.9% after 2000. The increase of temperature, strong evaporation concentration effect, and change of compositions of SOM sources are the important driving factors of the sedimentary evolution of organic matter in Hulun Lake.
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Affiliation(s)
- Wenwen Wang
- MOE Key Laboratory of Resources and Environmental Systems Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China
| | - Li Zhao
- National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
- State Environment Protection Key Laboratory for Lake Pollution Control, Chinese Research Academy of Environmental Science, 8 Dayangfang Rd., Chaoyang District, Beijing, 100012, China
| | - Wei Li
- MOE Key Laboratory of Resources and Environmental Systems Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China
| | - Junyi Chen
- National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
- State Environment Protection Key Laboratory for Lake Pollution Control, Chinese Research Academy of Environmental Science, 8 Dayangfang Rd., Chaoyang District, Beijing, 100012, China
| | - Shuhang Wang
- National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
- State Environment Protection Key Laboratory for Lake Pollution Control, Chinese Research Academy of Environmental Science, 8 Dayangfang Rd., Chaoyang District, Beijing, 100012, China.
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