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Lu Y, Jin L, Chen H, Luo A, Ehrlich E, Li S, Wilkinson DM, Sha Z, Yang J. Urbanization leads to convergent succession and homogenization of phytoplankton functional traits in a subtropical watershed over 11 years. ENVIRONMENTAL RESEARCH 2025; 271:121097. [PMID: 39938632 DOI: 10.1016/j.envres.2025.121097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2024] [Revised: 01/22/2025] [Accepted: 02/09/2025] [Indexed: 02/14/2025]
Abstract
Urbanization can significantly drive biodiversity loss in river ecosystems, yet the underlying mechanisms require further study. Here, we used a trait-based approach to investigate temporal succession and variation in the dissimilarity of phytoplankton community functional traits along an urbanizing subtropical river over 11 years - during which time the downstream of catchment underwent rapid urbanization. Our results indicated that urbanization altered the interannual succession of phytoplankton. The phytoplankton communities in the rural region were mainly shaped by a specialist trade-off between extreme lotic strategies (single cell, high maximum growth rate and high silica demand) in river habitat, and extreme lentic strategies (colonial, toxin production and nitrogen fixation abilities) in reservoir habitat. Conversely, in the urban region, generalist strategies with intermediate trait combinations (moderate mobility and mixotrophic ability) dominated the communities in both river and reservoir habitats. Time-lag analysis of functional dissimilarity showed lower, or even no significant variations of functional beta diversity in the urban region. Further decomposition of functional beta diversity indicated a reduced rate of functional turnover in urban river compared with that in rural river and a decrease in functional nestedness in urban reservoir. Paired differences between river and reservoir in the urban region exhibited convergent succession by functional turnover. The convergent succession and homogenization in the urban region made the variation in phytoplankton functional structure more unpredictable in a random forest model, and diminished the relationship between functional dissimilarity and environmental factors compared to the rural region. Our study shows how urbanization shapes the phytoplankton functional structure and causes homogenization in functional trait composition. The insight gained enhance our ability to assess and predict the environmental impacts of urbanization on aquatic ecosystems.
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Affiliation(s)
- Yifan Lu
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, State Key Laboratory for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Jin
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, State Key Laboratory for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Life Sciences, Hebei University, Baoding 071000, China
| | - Huihuang Chen
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, State Key Laboratory for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Anqi Luo
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, State Key Laboratory for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Elias Ehrlich
- Institute of Biochemistry and Biology, University of Potsdam, Potsdam 14469, Germany; Department of Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin 12587, Germany
| | - Shuzhen Li
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, State Key Laboratory for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - David M Wilkinson
- School of Natural Sciences, University of Lincoln, Lincoln LN6 7TS, UK
| | - Zhansen Sha
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, State Key Laboratory for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Life Sciences, Hebei University, Baoding 071000, China
| | - Jun Yang
- Aquatic EcoHealth Group, Fujian Key Laboratory of Watershed Ecology, State Key Laboratory for Ecological Security of Regions and Cities, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China.
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Liu Y, Zhang Y, Lv H, Zhao L, Wang X, Yang Z, Li R, Chen W, Song G, Gu H. Research on the traceability and treatment of nitrate pollution in groundwater: a comprehensive review. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2025; 47:107. [PMID: 40053144 DOI: 10.1007/s10653-025-02412-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2024] [Accepted: 02/19/2025] [Indexed: 04/02/2025]
Abstract
The preservation of groundwater quality is essential for maintaining the integrity of the water ecological cycle. The preservation of groundwater quality is crucial for sustaining the integrity of the water ecological cycle. Nitrate (NO3-) has emerged as a pervasive contaminant in groundwater, attracting significant research attention due to its extensive distribution and the potential environmental consequences it poses. The primary sources of NO3- pollution include soil organic nitrogen, atmospheric nitrogen deposition, domestic sewage, industrial wastewater, landfill leachate, as well as organic and inorganic nitrogen fertilizers and manure. A comprehensive understanding of these sources is imperative for devising effective strategies to mitigate NO3- contamination. Technologies for tracing NO3--polluted groundwater include hydrochemical analysis, nitrogen and oxygen isotope techniques, microbial tracers, and numerical simulations. Quantitative isotope analysis frequently necessitates the application of mathematical models such as IsoSource, IsoError, IsoConc, MixSIR, SIAR, and MixSIAR to deduce the origins of pollution. This study provides a summary of the application scenarios, as well as the strengths and limitations of these models. In terms of remediation, pump and treat and permeable reactive barrier are predominant technologies currently employed. These approaches are designed to remove or reduce NO3- concentrations in groundwater, thereby restoring its quality. The study offers a systematic examination of NO3- pollution, encompassing its origins, detection methodologies, and remediation approaches, highlighting the role of numerical simulations and integrating multidisciplinary knowledge. Additionally, this review delves into technological advancements and future trends concerning the detection and treatment of NO3- pollution in groundwater. It proposes methods to control the spread of pollution and acts as a guide for identifying and preventing pollution sources.
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Affiliation(s)
- Yuhao Liu
- Department of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
| | - Yu Zhang
- Department of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Haiyang Lv
- Department of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Lei Zhao
- Department of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Xinyi Wang
- Department of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Ziyan Yang
- Department of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Ruihua Li
- Department of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Weisheng Chen
- Department of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
| | - Gangfu Song
- Department of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Haiping Gu
- School of Forestry, Henan Agricultural University, Zhengzhou, 450002, China
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Gao H, Wang G, Fan Y, Wu J, Yao M, Zhu X, Guo X, Long B, Zhao J. Tracing groundwater nitrate sources in an intensive agricultural region integrated of a self-organizing map and end-member mixing model tool. Sci Rep 2024; 14:16873. [PMID: 39043782 PMCID: PMC11266494 DOI: 10.1038/s41598-024-67735-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 07/15/2024] [Indexed: 07/25/2024] Open
Abstract
The traceability of groundwater nitrate pollution is crucial for controlling and managing polluted groundwater. This study integrates hydrochemistry, nitrate isotope (δ15N-NO3- and δ18O-NO3-), and self-organizing map (SOM) and end-member mixing (EMMTE) models to identify the sources and quantify the contributions of nitrate pollution to groundwater in an intensive agricultural region in the Sha River Basin in southwestern Henan Province. The results indicate that the NO3--N concentration in 74% (n = 39) of the groundwater samples exceeded the WHO standard of 10 mg/L. According to the results of EMMTE modeling, soil nitrogen (68.4%) was the main source of nitrate in Cluster-1, followed by manure and sewage (16.5%), chemical fertilizer (11.9%) and atmospheric deposition (3.3%). In Cluster-2, soil nitrogen (60.1%) was the main source of nitrate, with a significant increase in the contribution of manure and sewage (35.5%). The considerable contributions of soil nitrogen may be attributed to the high nitrogen fertilizer usage that accumulated in the soil in this traditional agricultural area. Moreover, it is apparent that most Cluster-2 sampling sites with high contributions of manure and sewage are located around residential land. Therefore, the arbitrary discharge and leaching of domestic sewage may be responsible for these results. Therefore, this study provides useful assistance for the continuous management and pollution control of groundwater in the Sha River Basin.
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Affiliation(s)
- Hongbin Gao
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
- Henan International Joint Laboratory of Green Low Carbon Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
| | - Gang Wang
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
- Henan International Joint Laboratory of Green Low Carbon Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
- School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Yanru Fan
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China.
- Henan International Joint Laboratory of Green Low Carbon Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China.
| | - Junfeng Wu
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China.
- Henan International Joint Laboratory of Green Low Carbon Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China.
| | - Mengyang Yao
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
- Henan International Joint Laboratory of Green Low Carbon Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
| | - Xinfeng Zhu
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
- Henan International Joint Laboratory of Green Low Carbon Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
| | - Xiang Guo
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
- Henan International Joint Laboratory of Green Low Carbon Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
| | - Bei Long
- School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Jie Zhao
- Henan Key Laboratory of Water Pollution Control and Rehabilitation Technology, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
- Henan International Joint Laboratory of Green Low Carbon Water Treatment Technology and Water Resources Utilization, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467036, China
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Chen X, Ren M, Li G, Zhang J, Xie F, Zheng L. Identification of nitrate accumulation mechanism of surface water in a mining-rural-urban agglomeration area based on multiple isotopic evidence. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169123. [PMID: 38070569 DOI: 10.1016/j.scitotenv.2023.169123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 11/28/2023] [Accepted: 12/03/2023] [Indexed: 01/18/2024]
Abstract
The accumulation of nitrate (NO3-) in surface waters resulting from mining activities and rapid urbanization has raised widespread concerns. Therefore, it is crucial to develop a nitrate transformation information system to elucidate the nitrogen cycle and ensure sustainable water quality management. In this study, we focused on the main river and subsidence area of the Huaibei mining region to monitor the temporal and spatial variations in the NO3- content. Multiple isotopes (δD, δ18O-H2O, δ15N-NO3-, δ18O-NO3-, and δ15N-NH4+) along with water chemistry indicators were employed to identify the key mechanisms responsible for nitrate accumulation (e.g., nitrification and denitrification). The NO3- concentrations in surface water ranged from 0.28 to 7.50 mg/L, with NO3- being the predominant form of nitrogen pollution. Moreover, the average NO3- levels were higher during the dry season than during the wet season. Nitrification was identified as the primary process driving NO3- accumulation in rivers and subsidence areas, which was further supported by the linear relationship between δ15N-NO3- and δ15N-NH4+. The redox conditions and the relationship between δ15N-NO3- and δ18O-NO3-, and lower isotope enrichment factor of denitrification indicated that denitrification was weakened. Phytoplankton preferentially utilized available NH4+ sources while inhibiting NO3- assimilation because of their abundance. These findings provide direct evidence regarding the mechanism underlying nitrate accumulation in mining areas, while aiding in formulating improved measures for effectively managing water environments to prevent further deterioration.
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Affiliation(s)
- Xing Chen
- School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China; Anhui Province Engineering Laboratory for Mine Ecological Remediation, Anhui University, Hefei 230601, China
| | - Mengxi Ren
- School of Biological and Environmental Engineering, Chaohu University, Chaohu 238000, China; Anhui Province Engineering Laboratory for Mine Ecological Remediation, Anhui University, Hefei 230601, China
| | - Guolian Li
- School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China
| | - Jiamei Zhang
- School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China
| | - Fazhi Xie
- School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China.
| | - Liugen Zheng
- Anhui Province Engineering Laboratory for Mine Ecological Remediation, Anhui University, Hefei 230601, China.
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Matiatos I, Lazogiannis K, Papadopoulos A, Skoulikidis NT, Boeckx P, Dimitriou E. Stable isotopes reveal organic nitrogen pollution and cycling from point and non-point sources in a heavily cultivated (agricultural) Mediterranean river basin. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 901:166455. [PMID: 37607634 DOI: 10.1016/j.scitotenv.2023.166455] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 08/24/2023]
Abstract
The Pinios River Basin (PRB) is the most intensively cultivated area in Greece, which hosts numerous industries and other anthropogenic activities. The analysis of water samples collected monthly for ∼1 ½ years in eight monitoring sites in the PRB revealed nitrate pollution of organic origin extending from upstream to downstream and occurring throughout the year, masking the signal from the application of synthetic fertilizers. Nitrate concentrations reached up to 3.6 mg/l as NO3--N, without exceeding the drinking water threshold of ∼11.0 mg/l (as NO3--N). However, the water quality status was "poor" or "bad" in ∼50 % of the samples based on a local index, which considers the potential impact of nitrate on aquatic biological communities. The δ15Ν-ΝΟ3- and δ18O-NO3- values ranged from +4.4 ‰ to +20.3 ‰ and from -0.5 ‰ to +14.4 ‰, respectively. The application of a Bayesian model showed that the proportional contribution of organic pollution from industries, animal breeding facilities and manure fertilizers exceeded 70 % in most river sites with an overall uncertainty of ∼0.3 (UI90 index). The δ18O-NO3- and its relationship with δ18O-H2O revealed N-cycling and mixing processes, which were difficult to identify apart from the uptake of nutrients by phytoplankton during the growing season and metabolic activities. The strong correlation of δ15Ν-ΝΟ3- values with a Land Use Index (LUI) and a Point Source Index (PSI) highlighted not only the role of non-point nitrate sources but also of point sources of nitrate pollution on water quality degradation, which are usually overlooked. The nitrification of organic wastes is the dominant nitrate source in most rivers in Europe. The systematic monitoring of rivers for nitrate isotopes will help improve the understanding of N-cycling and the impact of these pollutants on ecosystems and better inform policies for protection measures so to achieve good ecological status.
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Affiliation(s)
- Ioannis Matiatos
- Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, 19013 Anavissos Attikis, Greece.
| | - Konstantinos Lazogiannis
- Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, 19013 Anavissos Attikis, Greece
| | - Anastasios Papadopoulos
- Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, 19013 Anavissos Attikis, Greece
| | - Nikolaos Th Skoulikidis
- Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, 19013 Anavissos Attikis, Greece
| | - Pascal Boeckx
- Isotope Bioscience Laboratory-ISOFYS, Department of Green Chemistry and Technology, Ghent University, Belgium
| | - Elias Dimitriou
- Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, 19013 Anavissos Attikis, Greece
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Zhao G, Sun T, Wang D, Chen S, Ding Y, Li Y, Shi G, Sun H, Wu S, Li Y, Wu C, Li Y, Yu Z, Chen Z. Treated wastewater and weak removal mechanisms enhance nitrate pollution in metropolitan rivers. ENVIRONMENTAL RESEARCH 2023; 231:116182. [PMID: 37201708 DOI: 10.1016/j.envres.2023.116182] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/07/2023] [Accepted: 05/15/2023] [Indexed: 05/20/2023]
Abstract
The focus of urban water environment renovation has shifted to high nitrate (NO3-) load. Nitrate input and nitrogen conversion are responsible for the continuous increase in nitrate levels in urban rivers. This study utilized nitrate stable isotopes (δ15N-NO3- and δ18O-NO3-) to investigate NO3- sources and transformation processes in Suzhou Creek, located in Shanghai. The results demonstrated that NO3- was the most common form of dissolved inorganic nitrogen (DIN), accounting for 66 ± 14% of total DIN with a mean value of 1.86 ± 0.85 mg L-1. The δ15N-NO3- and δ18O-NO3- values ranged from 5.72 to 12.42‰ (mean value: 8.38 ± 1.54‰) and -5.01 to 10.39‰ (mean value: 0.58 ± 1.76‰), respectively. Based on isotopic evidence, the river received a significant amount of nitrate through direct exogenous input and sewage ammonium nitrification, while nitrate removal (denitrification) was insignificant, resulting in nitrate accumulation. Analysis using the MixSIAR model revealed that treated wastewater (68.3 ± 9.7%), soil nitrogen (15.7 ± 4.8%) and nitrogen fertilizer (15.5 ± 4.9%) were the main sources of NO3- in rivers. Despite the fact that Shanghai's urban domestic sewage recovery rate has reached 92%, reducing nitrate concentrations in treated wastewater is crucial for addressing nitrogen pollution in urban rivers. Additional efforts are needed to upgrade urban sewage treatment during low flow periods and/or in the main stream, and to control non-point sources of nitrate, such as soil nitrogen and nitrogen fertilizer, during high flow periods and/or tributaries. This research provides insights into NO3- sources and transformations, and serves as a scientific basis for controlling NO3- in urban rivers.
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Affiliation(s)
- Guanghui Zhao
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Taihu Sun
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Dongqi Wang
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China; Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Shanghai, 200241, China.
| | - Shu Chen
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China; College of Smart Energy, Shanghai Jiao Tong University, Shanghai, 200240, China; Research Institute of Carbon Neutrality, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yan Ding
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Yilan Li
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Guitao Shi
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Hechen Sun
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Shengnan Wu
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Yizhe Li
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Chenyang Wu
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Yufang Li
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China
| | - Zhongjie Yu
- Department of Natural Resources and Environmental Sciences, University of Illinois Urbana-Champaign, Urbana, 61801, IL, USA
| | - Zhenlou Chen
- School of Geographical Sciences, Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China.
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