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Xu M, Wang M, Zhao M, Weng Z, Tong F, Pan Y, Liu X, Xie Y. Uncovering the differentiated impacts of carbon neutrality and clean air policies in multi-provinces of China. iScience 2024; 27:109966. [PMID: 38832014 PMCID: PMC11144726 DOI: 10.1016/j.isci.2024.109966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/09/2024] [Accepted: 05/09/2024] [Indexed: 06/05/2024] Open
Abstract
Ambitious action plans have been launched to address climate change and air pollution. Through coupling the IMED|CGE, GAINS, and IMED|HEL models, this study investigate the impacts of implementing carbon neutrality and clean air policies on the energy-environment-health-economy chain in the Beijing-Tianjin-Hebei-Henan-Shandong-Shanxi region of China. Results show that Shandong holds the largest reduction in energy consumption and carbon emissions toward the 1.5°C target. Shandong, Henan, and Hebei are of particularly prominent pollutant reduction potential. Synergistic effects of carbon reduction on decreasing PM2.5 concentration will increase in the future, specifically in energy-intensive regions. Co-deployment of carbon reduction and end-of-pipe technologies are beneficial to decrease PM2.5-related mortalities and economic loss by 4.7-12.9% in 2050. Provincial carbon reduction cost will be higher than monetary health benefits after 2030, indicating that more zero-carbon technologies should be developed. Our findings provide scientific enlightenment on policymaking toward achieving carbon reduction and pollution mitigation from multiple perspectives.
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Affiliation(s)
- Meng Xu
- School of Management, Wuhan Institute of Technology, Wuhan 430205, China
| | - Minghao Wang
- China Institute of Marine Technology and Economy, Beijing 100081, China
| | - Mengdan Zhao
- School of Economics and Management, Beihang University, Beijing 100191, China
| | - Zhixiong Weng
- Institute of Circular Economy, Beijing University of Technology, Beijing 100124, China
| | - Fan Tong
- School of Economics and Management, Beihang University, Beijing 100191, China
- Laboratory for Low-carbon Intelligent Governance, Beihang University, Beijing 100191, China
- Peking University Ordos Research Institute of Energy, Ordos City, Inner Mongolia 017000, China
| | - Yujie Pan
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Xin Liu
- Energy Foundation China, Beijing 100004, China
| | - Yang Xie
- School of Economics and Management, Beihang University, Beijing 100191, China
- Laboratory for Low-carbon Intelligent Governance, Beihang University, Beijing 100191, China
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2
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Cui H, Li J, Sun Y, Milne R, Tao Y, Ren J. A novel framework for quantitative attribution of particulate matter pollution mitigation to natural and socioeconomic drivers. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:171910. [PMID: 38522549 DOI: 10.1016/j.scitotenv.2024.171910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 03/26/2024]
Abstract
Quantifying drivers contributing to air quality improvements is crucial for pollution prevention and optimizing local policies. Despite advances in machine learning for air quality analysis, their limited interpretability hinders attribution on global and local scales, vital for informed city management. Our study introduces an innovative framework quantifying socioeconomic and natural impacts on mitigation of particulate matter pollution in 31 Chinese major cities from 2014 to 2021. Two indices, formulated based on the additivity of Shapley additive explanations, are proposed to measure driver contributions globally and locally. Our analysis explores the self-contained and interactive effects of these drivers on particulate levels, pinpointing critical threshold values where these drivers trigger shifts in particulate matter levels. It is revealed that SO2, NOx, and dust emission reductions collectively account for 51.58 % and 51.96 % of PM2.5 and PM10 decreases at the global level. Moreover, our findings unveil a significant heterogeneity in driver contributions to pollutant mitigation across distinct cities, which can be instrumental in crafting location-specific policy recommendations.
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Affiliation(s)
- Hao Cui
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Jian Li
- School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Yutong Sun
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Russell Milne
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton T6G 2G1, Alberta, Canada
| | - Yiwen Tao
- School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, Henan, China.
| | - Jingli Ren
- School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, Henan, China.
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3
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Liu R, Shao M, Wang Q. Multi-timescale variation characteristics of PM 2.5 in different regions of China during 2014-2022. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:171008. [PMID: 38369160 DOI: 10.1016/j.scitotenv.2024.171008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/31/2024] [Accepted: 02/14/2024] [Indexed: 02/20/2024]
Abstract
Over the past decade, China has achieved a significant reduction in PM2.5 concentrations. Due to the diversity of natural and artificial factors, regional differences are remarkable in the variation characteristics and have not been well addressed in previous studies. Based on hourly observed PM2.5 concentrations from 2014 to 2022, this study conducted a comprehensive analysis of variation characteristics on annual, seasonal, and diurnal scales, with a special focus on differences across major regions. Driving factors of the variations, the effectiveness of air pollution control efforts as well as future priorities were discussed. The annual PM2.5 concentrations in all regions showed an overall downward trend from 2014 to 2022, but the decline rates differed notably across the regions, with the maximum value nearly two times higher than the minimum value. The seasonal decline rates also differ from region to region, which could be partially attributed to the burning of crop residues and dust events. Northeast China was significantly affected by the burning of crop residues and experienced a big drop in the number of fire points in autumn, but a remarkable increase in spring. The spring dust events may greatly contribute to PM2.5 concentrations in northern and western China. For diurnal variation, nighttime concentrations were generally greater than daytime concentrations, and the nighttime concentrations were likely to increase in eastern regions and decrease in western regions. Furthermore, the daytime and nighttime ratios (calculated by daytime/nighttime concentration divided by the daily-mean concentration) exhibited different interannual trends, with the daytime ratios decreasing and nighttime ratios increasing, especially in the northeastern and western regions. The findings indicate that the air pollution control efforts have been generally successful, but with large regional disparities, and highlight the importance of controlling crop residue burning, dust events, and nighttime emissions for specific seasons and regions.
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Affiliation(s)
- Rui Liu
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210023, China
| | - Min Shao
- School of Environment, Nanjing Normal University, Nanjing 210046, China
| | - Qin'geng Wang
- State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University, Nanjing 210023, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China.
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4
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Men Y, Li Y, Luo Z, Jiang K, Yi F, Liu X, Xing R, Cheng H, Shen G, Tao S. Interpreting Highly Variable Indoor PM 2.5 in Rural North China Using Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18183-18192. [PMID: 37150969 DOI: 10.1021/acs.est.3c02014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Household air pollution associated with solid fuel use is a long-standing public concern. The global population mainly using solid fuels for cooking remains large. Besides cooking, large amounts of coal and biomass fuels are burned for space heating during cold seasons in many regions. In this study, a wintertime multiple-region field campaign was carried out in north China to evaluate indoor PM2.5 variations. With hourly resolved data from ∼1600 households, key influencing factors of indoor PM2.5 were identified from a machine learning approach, and a random forest regression (RFR) model was further developed to quantitatively assess the impacts of household energy transition on indoor PM2.5. The indoor PM2.5 concentration averaged at 120 μg/m3 but ranged from 16 to ∼400 μg/m3. Indoor PM2.5 was ∼60% lower in families using clean heating approaches compared to those burning traditional coal or biomass fuels. The RFR model had a good performance (R2 = 0.85), and the interpretation was consistent with the field observation. A transition to clean coals or biomass pellets can reduce indoor PM2.5 by 20%, and further switching to clean modern energies would reduce it an additional 30%, suggesting many significant benefits in promoting clean transitions in household heating activities.
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Affiliation(s)
- Yatai Men
- MOE Key Lab for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Yaojie Li
- MOE Key Lab for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Zhihan Luo
- MOE Key Lab for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Ke Jiang
- MOE Key Lab for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Fan Yi
- Beijing Key Lab Plant Resources Research and Development, Beijing Technology and Business University, Beijing 100048, China
| | - Xinlei Liu
- MOE Key Lab for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Ran Xing
- MOE Key Lab for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Hefa Cheng
- MOE Key Lab for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Guofeng Shen
- MOE Key Lab for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
- School of Ecology and Environment, Zhengzhou University, Zhengzhou 45001, China
| | - Shu Tao
- MOE Key Lab for Earth Surface Process, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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Wang Z, Zhao H, Xu H, Li J, Ma T, Zhang L, Feng Y, Shi G. Strategies for the coordinated control of particulate matter and carbon dioxide under multiple combined pollution conditions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165679. [PMID: 37481086 DOI: 10.1016/j.scitotenv.2023.165679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/29/2023] [Accepted: 07/18/2023] [Indexed: 07/24/2023]
Abstract
Air pollutants represented by fine particulate matter (PM2.5) and the greenhouse effect caused by carbon dioxide (CO2), are both urgent threats to public health. Tackling the synergistic reduction of PM2.5 and CO2 is critical to achieving improvements in clean air worldwide. A persistent issue is the identification of their common sources and integrated impacts under different environmental conditions. In this study, we investigated the characteristics of the pollution types captured by combined analysis through a comprehensive observational dataset for 2017-2020, and applied machine learning algorithms to quantify the effects of drivers on air pollutants and CO2 formation. More importantly, detailed conclusions were drawn for the joint control of PM2.5-CO2 in multiple pollution types by using ensemble traceability technique. We demonstrated that reducing coal combustion emissions was an effective measure to maximize the benefits of PM2.5-CO2 in weather with low CO2 levels and no PM2.5 pollution. Correspondingly, on days with severe PM2.5 episodes, prioritizing control of vehicle emissions can simultaneously mitigate PM2.5 and CO2. Similar conclusions were found at high CO2 levels, accompanied by a more extensive role of vehicle emissions. Furthermore, a comparison of the differences in source impacts between PM2.5-CO2 and individual species suggests that focusing only on the sources that contribute significantly to one species may result in an underestimation or overestimation of PM2.5-CO2 source impacts. One such implication, as evidenced by our findings, is that synergistic controlling common sources of pollutants should be efficient. Thereby, common source management targeting PM2.5-CO2 under multiple pollution types is a more workable solution to alleviate environmental pollution.
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Affiliation(s)
- Zhenyu Wang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Huan Zhao
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Han Xu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Jie Li
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Tong Ma
- Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Linlin Zhang
- China National Environmental Monitoring Centre, Beijing 100012, China.
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Guoliang Shi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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Xiang W, Wang W, Du L, Zhao B, Liu X, Zhang X, Yao L, Ge M. Toxicological Effects of Secondary Air Pollutants. Chem Res Chin Univ 2023; 39:326-341. [PMID: 37303472 PMCID: PMC10147539 DOI: 10.1007/s40242-023-3050-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/13/2023] [Indexed: 06/13/2023]
Abstract
Secondary air pollutants, originating from gaseous pollutants and primary particulate matter emitted by natural sources and human activities, undergo complex atmospheric chemical reactions and multiphase processes. Secondary gaseous pollutants represented by ozone and secondary particulate matter, including sulfates, nitrates, ammonium salts, and secondary organic aerosols, are formed in the atmosphere, affecting air quality and human health. This paper summarizes the formation pathways and mechanisms of important atmospheric secondary pollutants. Meanwhile, different secondary pollutants' toxicological effects and corresponding health risks are evaluated. Studies have shown that secondary pollutants are generally more toxic than primary ones. However, due to their diverse source and complex generation mechanism, the study of the toxicological effects of secondary pollutants is still in its early stages. Therefore, this paper first introduces the formation mechanism of secondary gaseous pollutants and focuses mainly on ozone's toxicological effects. In terms of particulate matter, secondary inorganic and organic particulate matters are summarized separately, then the contribution and toxicological effects of secondary components formed from primary carbonaceous aerosols are discussed. Finally, secondary pollutants generated in the indoor environment are briefly introduced. Overall, a comprehensive review of secondary air pollutants may shed light on the future toxicological and health effects research of secondary air pollutants.
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Affiliation(s)
- Wang Xiang
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190 P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049 P. R. China
| | - Weigang Wang
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190 P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049 P. R. China
| | - Libo Du
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190 P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049 P. R. China
| | - Bin Zhao
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190 P. R. China
- College of Chemistry and Material Science, Hebei Normal University, Shijiazhuang, 050024 P. R. China
| | - Xingyang Liu
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190 P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049 P. R. China
| | - Xiaojie Zhang
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190 P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049 P. R. China
| | - Li Yao
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190 P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049 P. R. China
| | - Maofa Ge
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190 P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049 P. R. China
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Guo Q, He Z, Wang Z. Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors. TOXICS 2023; 11:210. [PMID: 36976975 PMCID: PMC10056825 DOI: 10.3390/toxics11030210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.
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Affiliation(s)
- Qingchun Guo
- School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
- Institute of Huanghe Studies, Liaocheng University, Liaocheng 252000, China
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
| | - Zhenfang He
- School of Geography and Environment, Liaocheng University, Liaocheng 252000, China
- Institute of Huanghe Studies, Liaocheng University, Liaocheng 252000, China
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Zhaosheng Wang
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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