1
|
Dong Z, Li S, Jiang Y, Wang S, Xing J, Ding D, Zheng H, Wang H, Huang C, Yin D, Zhao B, Hao J. Health-Oriented Emission Control Strategy of Energy Utilization and Its Co-CO 2 Benefits: A Case Study of the Yangtze River Delta, China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:12320-12329. [PMID: 38973717 DOI: 10.1021/acs.est.3c10693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
Reducing air pollutants and CO2 emissions from energy utilization is crucial for achieving the dual objectives of clean air and carbon neutrality in China. Thus, an optimized health-oriented strategy is urgently needed. Herein, by coupling a CO2 and air pollutants emission inventory with response surface models for PM2.5-associated mortality, we shed light on the effectiveness of protecting human health and co-CO2 benefit from reducing fuel-related emissions and generate a health-oriented strategy for the Yangtze River Delta (YRD). Results reveal that oil consumption is the primary contributor to fuel-related PM2.5 pollution and premature deaths in the YRD. Significantly, curtailing fuel consumption in transportation is the most effective measure to alleviate the fuel-related PM2.5 health impact, which also has the greatest cobenefits for CO2 emission reduction on a regional scale. Reducing fuel consumption will achieve substantial health improvements especially in eastern YRD, with nonroad vehicle emission reductions being particularly impactful for health protection, while on-road vehicles present the greatest potential for CO2 reductions. Scenario analysis confirms the importance of mitigating oil consumption in the transportation sector in addressing PM2.5 pollution and climate change.
Collapse
Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dian Ding
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
2
|
Xian Y, Zhang Y, Liu Z, Wang H, Xiong T. Characterization of winter PM 2.5 source contributions and impacts of meteorological conditions and anthropogenic emission changes in the Sichuan Basin, 2002-2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174557. [PMID: 38977099 DOI: 10.1016/j.scitotenv.2024.174557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/04/2024] [Accepted: 07/04/2024] [Indexed: 07/10/2024]
Abstract
In this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality-Integrated Source Apportionment Method (CMAQ-ISAM) were utilized, which were integrated with the Multiresolution Emission Inventory for China (MEIC) emission inventory, to simulate winter PM2.5 concentrations, regional transport, and changes in emission source contributions in the Sichuan basin (SCB) from 2002 to 2020, considering variations in meteorological conditions and anthropogenic emissions. The results indicated a gradual decrease in the basin's winter average PM2.5 concentration from 300 μg/m3 to 120 μg/m3, with the most significant decrease occurring after 2014, reflecting the actual impact of China's air pollution control measures. Spatially, the main pollution area shifted from Chongqing to Chengdu and the western basin. The sources of PM2.5 at the eastern and western margins of the basin have remained stable and have been dominated by local emissions for many years, while the sources of PM2.5 in the central part of the basin have evolved from a multiregional co-influenced source during the early period to a high proportion of local emissions; except for boundary condition sources, residential sources were the main PM2.5 sources in the basin (approximately 29.70 %), followed by industrial sources (approximately 14.11 %). Industrial sources exhibited higher contributions in Chengdu and Chongqing and gradually stabilized with residential sources over the years, while residential sources dominated in the eastern and western parts of the basin and exhibited a declining trend. Meteorological conditions exacerbated pollution in the whole basin from 2008 to 2014, especially in the west (21-40 μg/m3). The eastern basin and Chongqing exhibited more years with alleviated meteorological pollution, including a 40+ μg/m3 decrease in Chongqing from 2002 to 2005. Reduced anthropogenic emissions alleviated annual pollution levels, with a greater reduction (> -20 μg/m3) after 2011 due to pollution control measures.
Collapse
Affiliation(s)
- Yaohan Xian
- College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yang Zhang
- College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China; Key Laboratory of Atmospheric Environment Simulation and Pollution Control at Chengdu University of Information Technology of Sichuan Province, Chengdu 610225, China; Chengdu Plain Urban Meteorology and Environment Observation and Research Station of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, China.
| | - Zhihong Liu
- College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China; Key Laboratory of Atmospheric Environment Simulation and Pollution Control at Chengdu University of Information Technology of Sichuan Province, Chengdu 610225, China; Chengdu Plain Urban Meteorology and Environment Observation and Research Station of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, China
| | - Haofan Wang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
| | - Tianxin Xiong
- College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China
| |
Collapse
|
3
|
Du P, Du H, Zhang W, Lu K, Zhang C, Ban J, Wang Y, Liu T, Hu J, Li T. Unequal Health Risks and Attributable Mortality Burden of Source-Specific PM 2.5 in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10897-10909. [PMID: 38843119 DOI: 10.1021/acs.est.3c08789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Anthropogenic emissions, originating from human activities, stand as the primary contributors to PM2.5, which is recognized as a global health threat. The disease burden associated with PM2.5 has been extensively documented. However, the prevailing estimations have predominantly relied on PM2.5 exposure-response functions, neglecting the distinct risks posed by PM2.5 from various sources. China has experienced a significant reduction in the PM2.5 concentration due to stringent emission controls. With diverse sources and abundant mortality data, this situation provides a unique opportunity to estimate short-term source-specific attributable mortality. Our approach involves an integrated unequal health risk-oriented modeling in China, incorporating a source-oriented Community Multiscale Air Quality model, an adjustment and downscaling method for exposure measurement, a generalized linear model with random-effects meta-analysis, and premature mortality estimation. Adhering to the unequal health risk concept, we calculated the attributable mortality of multiple PM2.5 sources by determining the source risk-adjusted factor. In this study, we observed varying excess risks associated with multiple PM2.5 sources, with transportation-related PM2.5 exhibiting the most substantial association. An interquartile range increase (7.65 μg/m3) was linked to a 1.98% higher daily nonaccidental mortality. Residential use- and transportation-related PM2.5 emerged as the two principal sources of premature mortality. In 2018, a remarkable 53,381 avoiding deaths were estimated compared to 2013, and over 67% of these were attributed to reductions in coal-dependent sources. Notably, transportation-related PM2.5 emerged as the largest contributor to premature mortality in 2018. This study underscores the significance of a new source-oriented health risk assessment to support actions aimed at reducing air pollution. It strongly advocates for heightened attention to PM2.5 reductions in the transportation sector in China.
Collapse
Affiliation(s)
- Peng Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Hang Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Wenjing Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Kailai Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Can Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Jie Ban
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yiyi Wang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Ting Liu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
- School of Public Health, Nanjing Medical University, Nanjing 211166, China
| |
Collapse
|
4
|
Dong Z, Jiang Y, Wang S, Xing J, Ding D, Zheng H, Wang H, Huang C, Yin D, Song Q, Zhao B, Hao J. Spatially and Temporally Differentiated NO x and VOCs Emission Abatement Could Effectively Gain O 3-Related Health Benefits. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9570-9581. [PMID: 38781138 DOI: 10.1021/acs.est.4c01345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
The increasing level of O3 pollution in China significantly exacerbates the long-term O3 health damage, and an optimized health-oriented strategy for NOx and VOCs emission abatement is needed. Here, we developed an integrated evaluation and optimization system for the O3 control strategy by merging a response surface model for the O3-related mortality and an optimization module. Applying this system to the Yangtze River Delta (YRD), we evaluated driving factors for mortality changes from 2013 to 2017, quantified spatial and temporal O3-related mortality responses to precursor emission abatement, and optimized a health-oriented control strategy. Results indicate that insufficient NOx emission abatement combined with deficient VOCs control from 2013 to 2017 aggravated O3-related mortality, particularly during spring and autumn. Northern YRD should promote VOCs control due to higher VOC-limited characteristics, whereas fastening NOx emission abatement is more favorable in southern YRD. Moreover, promotion of NOx mitigation in late spring and summer and facilitating VOCs control in spring and autumn could further reduce O3-related mortality by nearly 10% compared to the control strategy without seasonal differences. These findings highlight that a spatially and temporally differentiated NOx and VOCs emission control strategy could gain more O3-related health benefits, offering valuable insights to regions with severe ozone pollution all over the world.
Collapse
Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dian Ding
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environment Sciences, Shanghai 200233, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qian Song
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
5
|
Yue H, Worrell E, Crijns-Graus W, Wagner F, Zhang S, Hu J. Air Quality and Health Implications of Coal Power Retirements Attributed to Industrial Electricity Savings in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9187-9199. [PMID: 38691631 DOI: 10.1021/acs.est.3c09517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
The coal-dominated electricity system, alongside increasing industrial electricity demand, places China into a dilemma between industrialization and environmental impacts. A practical solution is to exploit air quality and health cobenefits of industrial energy efficiency measures, which has not yet been integrated into China's energy transition strategy. This research examines the pivotal role of industrial electricity savings in accelerating coal plant retirements and assesses the nexus of energy-pollution-health by modeling nationwide coal-fired plants at individual unit level. It shows that minimizing electricity needs by implementing more efficient technologies leads to the phaseout of 1279 hyper-polluting units (subcritical, <300 MW) by 2040, advancing the retirement of these units by an average of 7 years (3-16 years). The retirements at different locations yield varying levels of air quality improvements (9-17%), across six power grids. Reduced exposure to PM2.5 could avoid 123,100 pollution-related cumulative deaths over the next 20 years from 2020, of which ∼75% occur in the Central, East, and North grids, particularly coal-intensive and populous provinces (e.g., Shandong and Jiangsu). These findings provide key indicators to support geographically specific policymaking and lay out a rationale for decision-makers to incorporate multiple benefits into early coal phaseout strategies to avoid lock-in risk.
Collapse
Affiliation(s)
- Hui Yue
- School of Management, Zhengzhou University, Science Avenue 100, 450001 Zhengzhou, China
- Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
| | - Ernst Worrell
- Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
| | - Wina Crijns-Graus
- Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
| | - Fabian Wagner
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Xueyuan Road 37, 100191 Beijing, China
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
| | - Jing Hu
- Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
| |
Collapse
|
6
|
Gen M, Zheng H, Sun Y, Xu W, Ma N, Su H, Cheng Y, Wang S, Xing J, Zhang S, Xue L, Xue C, Mu Y, Tian X, Matsuki A, Song S. Rapid hydrolysis of NO 2 at High Ionic Strengths of Deliquesced Aerosol Particles. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:7904-7915. [PMID: 38661303 DOI: 10.1021/acs.est.3c08810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Nitrogen dioxide (NO2) hydrolysis in deliquesced aerosol particles forms nitrous acid and nitrate and thus impacts air quality, climate, and the nitrogen cycle. Traditionally, it is considered to proceed far too slowly in the atmosphere. However, the significance of this process is highly uncertain because kinetic studies have only been made in dilute aqueous solutions but not under high ionic strength conditions of the aerosol particles. Here, we use laboratory experiments, air quality models, and field measurements to examine the effect of the ionic strength on the reaction kinetics of NO2 hydrolysis. We find that high ionic strengths (I) enhance the reaction rate constants (kI) by more than an order of magnitude compared to that at infinite dilution (kI=0), yielding log10(kI/kI=0) = 0.04I or rate enhancement factor = 100.04I. A state-of-the-art air quality model shows that the enhanced NO2 hydrolysis reduces the negative bias in the simulated concentrations of nitrous acid by 28% on average when compared to field observations over the North China Plain. Rapid NO2 hydrolysis also enhances the levels of nitrous acid in other polluted regions such as North India and further promotes atmospheric oxidation capacity. This study highlights the need to evaluate various reaction kinetics of atmospheric aerosols with high ionic strengths.
Collapse
Affiliation(s)
- Masao Gen
- Faculty of Frontier Engineering, Institute of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
| | - Haotian Zheng
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment Health Research, Tianjin 300350, China
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Wanyun Xu
- State Key Laboratory of Severe Weather, Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition and Environmental Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Nan Ma
- Institute for Environmental and Climate Research (ECI), Jinan University, Guangzhou 511443, China
| | - Hang Su
- Max Planck Institute for Chemistry, Mainz 55128, Germany
| | - Yafang Cheng
- Max Planck Institute for Chemistry, Mainz 55128, Germany
| | - Shuxiao Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuping Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Likun Xue
- Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Chaoyang Xue
- Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (LPC2E), CNRS - Université Orléans - CNES, Orléans Cedex 2 45071, France
| | - Yujing Mu
- Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Xiao Tian
- 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
| | - Atsushi Matsuki
- Institute of Nature and Environmental Technology, Kanazawa University, Kanazawa 920-1192, Japan
| | - Shaojie Song
- CMA-NKU Cooperative Laboratory for Atmospheric Environment Health Research, Tianjin 300350, China
- 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
- Harvard-China on Energy, Economy, and Environment, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| |
Collapse
|
7
|
Yang Y, Sun M, Wu G, Qi Y, Zhu W, Zhao Y, Zhu Y, Li W, Zhang Y, Wang N, Sheng L, Wang W, Yu X, Yu J, Yao X, Zhou Y. Characteristics of aerosol aminiums over a coastal city in North China: Insights from the divergent impacts of marine and terrestrial influences. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 918:170672. [PMID: 38316306 DOI: 10.1016/j.scitotenv.2024.170672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/07/2024]
Abstract
Aminium ions, as crucial alkaline components within fine atmospheric particles, have a notable influence on new particle formation and haze occurrence. Their concentrations within coastal atmosphere depict considerable variation due to the interplay of distinctive marine and terrestrial sources, further complicated by dynamic meteorological conditions. This study conducted a comprehensive examination of aminiums ions concentrations, with a particular focus on methylaminium (MMAH+), dimethylaminium (DMAH+), trimethylaminium (TMAH+), and triethylaminium (TEAH+) within PM2.5, over varying seasons (summer, autumn, and winter of 2019 and summer of 2021), at an urban site in the coastal megacity of Qingdao, Northern China. The investigations revealed that the total concentration of particulate aminium ions (∑Aminium) was 21.6 ± 23.6 ng/m3, exhibiting higher values in the autumn and winter compared to the two summer periods. Considering diurnal variations during autumn and winter, concentrations of particulate aminium ions (excluding TEAH+) exhibited a slight increase during the day compared to night, with a notable peak during the morning hours. However, it was not the case for TEAH+, which was argued to be readily oxidized by ambient oxidants in the afternoon. Additionally, the ∑Aminium within the summer demonstrated markedly elevated levels during the day compared to night, potentially attributed to daytime sea fog associated with sea-land breeze interactions. Positive matrix factorization results indicate terrestrial anthropogenic emissions, including vehicle emission mixed with road dust and primary pollution, as the primary sources of MMAH+ and DMAH+. Conversely, TMAH+ was predominantly emitted from agricultural and marine sources. With the dominance of sea breeze in summer, TMAH+ was identified as a primary marine emission correlated with sea salt, while MMAH+, DMAH+, and TEAH+ were postulated to undergo secondary formation. Furthermore, a notable inverse correlation was observed between TMAH+ and methanesulfonate in PM2.5, consistent with dynamic emissions of sulfur-content and nitrogen-content gases reported in the literature.
Collapse
Affiliation(s)
- Yiyan Yang
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Mingge Sun
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Guanru Wu
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Yuxuan Qi
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Wenqing Zhu
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Yunhui Zhao
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Yujiao Zhu
- Environment Research Institute, Shandong University, Qingdao 266237, China
| | - Wenshuai Li
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Yanjing Zhang
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Nana Wang
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China; Jiaozhou Meteorological Bureau, Qingdao Meteorological Bureau, Qingdao 266300, China
| | - Lifang Sheng
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Wencai Wang
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
| | - Xu Yu
- Division of Environment and Sustainability, Hong Kong University of Science and Technology, 999077, Hong Kong
| | - Jianzhen Yu
- Division of Environment and Sustainability, Hong Kong University of Science and Technology, 999077, Hong Kong; Department of Chemistry, Hong Kong University of Science and Technology, 999077, Hong Kong
| | - Xiaohong Yao
- Key Laboratory of Marine Environment and Ecology (MoE), Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ministry of Education, Ocean University of China, Qingdao 266100, China; Laboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Yang Zhou
- Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China; College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China.
| |
Collapse
|
8
|
Wen W, Su Y, Yang X, Liang Y, Guo Y, Liu H. Global economic structure transition boosts PM 2.5-related human health impact in Belt and Road Initiative. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 916:170071. [PMID: 38242465 DOI: 10.1016/j.scitotenv.2024.170071] [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: 09/01/2023] [Revised: 12/17/2023] [Accepted: 01/08/2024] [Indexed: 01/21/2024]
Abstract
The Belt and Road Initiative (BRI) is an open platform for international cooperation proposed by China to promote common global development and prosperity. The BRI can promote the optimal allocation of resources and promote in-depth cooperation in international trade. Meanwhile, it can establish a green supply chain cooperation network to help BRI countries achieve green transformation. BRI has made a notable contribution to the rapid growth of cross-border trade. However, it has also brought environmental impacts. Given that little attention has been paid to the trade-embodied particulate matter 2.5 related human health impacts (PM2.5-HHI) throughout the BRI, this study accounts for and traces the embodied PM2.5-HHI flows between the BRI countries and non-Belt and Road Initiative (non-BRI) countries. Moreover, this study also uncovers the critical socioeconomic drivers of PM2.5-HHI changes in BRI countries during 1990-2015, based on the multi-regional input-output based structural decomposition analysis (MRIO-SDA). Results show that, firstly, BRI countries had significantly increased their economic added value by exporting products to the non-BRI countries. They also have brought PM2.5-HHI to themselves. Secondly, the final demand of BRI countries was the largest potential driving force of PM2.5-HHI of BRI countries. Thirdly, the emission intensity change of BRI is the key socioeconomic factor for reducing PM2.5-HHI. While per capita final demand level change of BRI and production structure change of non-BRI are the key socioeconomic factors for increasing PM2.5-HHI. The study's findings on the one hand can help reduce the PM2.5-HHI and impacts of environmental pollution of BRI countries from a global perspective by providing scientific support. On the other hand, they can help provide relevant policy recommendations for the green transformation of BRI and the construction of green BRI.
Collapse
Affiliation(s)
- Wen Wen
- School of Humanities and Social Sciences, Beijing Institute of Technology, Beijing 100081, China
| | - Yang Su
- School of Information Management, Beijing Information Science & Technology University, Beijing 100010, China
| | - Xuechun Yang
- Institute of Circular Economy, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
| | - Yuhan Liang
- Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, Guangdong 510006, China.
| | - Yangyang Guo
- Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China
| | - Hongrui Liu
- Unit 32182 of People's Liberation Army, Beijing 100042, China
| |
Collapse
|
9
|
Zheng H, Li S, Jiang Y, Dong Z, Yin D, Zhao B, Wu Q, Liu K, Zhang S, Wu Y, Wen Y, Xing J, Henneman LRF, Kinney PL, Wang S, Hao J. Unpacking the factors contributing to changes in PM 2.5-associated mortality in China from 2013 to 2019. ENVIRONMENT INTERNATIONAL 2024; 184:108470. [PMID: 38324930 DOI: 10.1016/j.envint.2024.108470] [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: 06/27/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
From 2013 to 2019, a series of air pollution control actions significantly reduced PM2.5 pollution in China. Control actions included changes in activity levels, structural adjustment (SA) policy, energy and material saving (EMS) policy, and end-of-pipe (EOP) control in several sources, which have not been systematically studied in previous studies. Here, we integrate an emission inventory, a chemical transport model, a health impact assessment model, and a scenario analysis to quantify the contribution of each control action across a range of major emission sources to the changes in PM2.5 concentrations and associated mortality in China from 2013 to 2019. Assuming equal toxicity of PM2.5 from all the sources, we estimate that PM2.5-related mortality decreased from 2.52 (95 % confidence interval, 2.13-2.88) to 1.94 (1.62-2.24) million deaths. Anthropogenic emission reductions and declining baseline incidence rates significantly contributed to health benefits, but population aging partially offset their impact. Among the major sources, controls on power plants and industrial boilers were responsible for the highest reduction in PM2.5-related mortality (∼80 %), followed by industrial processes (∼40 %), residential combustion (∼40 %), and transportation (∼30 %). However, considering the potentially higher relative risks of power plant PM2.5, the adverse effects avoided by their control could be ∼2.4 times the current estimation. Our power plant sensitivity analyses indicate that future estimates of source-specific PM2.5 health effects should incorporate variations in individual source PM2.5 effect coefficients when available. As for the control actions, while activity levels increased for most sources, SA policy significantly reduced the emissions in residential combustion and industrial boilers, and EOP control dominated the contribution in health benefits in most sources except residential combustion. Considering the emission reduction potential by source and control actions in 2019, our results suggest that promoting clean energy in residential combustion and enforcing more stringent EOP control in the iron and steel industry should be prioritized in the future.
Collapse
Affiliation(s)
- Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Kaiyun Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Ye Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yifan Wen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Lucas R F Henneman
- Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA
| | - Patrick L Kinney
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
10
|
Zhang S, Xiong Y, Liang X, Wang F, Liang S, Wu Y. Spatial and Cross-Sectoral Transfer of Air Pollutant Emissions from the Fleet Electrification in China by 2030. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21249-21259. [PMID: 38054598 DOI: 10.1021/acs.est.3c04496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Fleet electrification shifts emission sources from the tailpipe to electricity generation and automotive supply chains subsequently, with emission transfer among regions. Such a spatial and cross-sectoral transfer of air pollutant emissions might embody uncertain environmental benefits spatially, which has not been comprehensively quantified, mainly due to the complexity of manufacturing processes of electric vehicle (EV) components (e.g., battery). We developed a hybrid life cycle assessment by combining inventory data of major processes and cross-sectoral input-output information and identified how China's EV deployment would influence the spatial redistribution of air pollutant emissions currently (2017) and in the future (2030). The results indicate that fleet electrification could readily reduce life cycle nitrogen oxides (NOx) and nonmethane volatile organic compound (NMVOC) emissions by 12-93%, and the reductions are estimated to be concentrated in major cities and urban agglomerations. However, increased demand for electricity and power battery production could increase PM2.5 and SO2 emissions in 17-55% of grids under all the scenarios, which emerge in coal-rich (e.g., Inner Mongolia, Shanxi) and industrial (e.g., Shandong, Henan, Jiangsu) provinces. By tracing the upstream, 31-55% of vehicle-cycle emissions are from deep supply chains but exhibit diverse sources. It suggests the necessity to relieve emissions leakage of fleet electrification by synchronizing effective environmental management across multiple sectors through EV supply chains.
Collapse
Affiliation(s)
- Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| | - Yiling Xiong
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Xinyu Liang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Fang Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Sai Liang
- School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, Guangdong 510006, China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, China
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| |
Collapse
|
11
|
Zhang W, Zhao J, Zhang Z, Liu M, Li R, Xue W, Xing J, Cai B, Jiang L, Zhang J, Hu X, Zhong L, Jiang H, Wang J, Bi J. The economy-employment-environmental health transfer and embedded inequities of China's capital metropolitan area: a mixed-methods study. Lancet Planet Health 2023; 7:e912-e924. [PMID: 37940211 DOI: 10.1016/s2542-5196(23)00218-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/19/2023] [Accepted: 09/22/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Metropolitan areas have complex trade linkages internally and externally. This complexity stimulates the unequal spatial transfer of environmental health consequences, economy, and employment embodied in internal trade or trade with the outside regions, resulting in unequal exchange. Existing research has rarely discussed this issue at a refined scale, hindering targeted inequity alleviation policies. METHODS We conducted a mixed-methods study, focusing on the most polluted metropolitan area in the world, the Beijing-Tianjin-Hebei region of China, and developed an integrated modelling framework to downscale the analysis of the trade-driven unequal transfer of PM2·5- related premature deaths, value added, and job opportunities to the city scale within Beijing-Tianjin-Hebei. The study couples a nested Multi-Regional Input-Output model table containing data from 13 Beijing-Tianjin-Hebei cities and 28 outer provinces in 2017 with a bottom-up emission inventory, value added and employment statistical data, the Weather Research and Forecasting-Comprehensive Air Quality Model with Extensions, the Global Exposure Mortality Model, and human capital methods. We also constructed two indices measuring unequal exchanges between PM2·5-related deaths and economic and employment gains embodied in trades between cities in Beijing-Tianjin-Hebei and trades with outside regions. FINDINGS The Beijing-Tianjin-Hebei region as a single entity shifted 14 985 (95% CI 12 800-16 948) net deaths to regions outside the Beijing-Tianjin-Hebei through trade, most of which occurred in the central region of China. The industrial-based peripheral Beijing-Tianjin-Hebei cities suffered the most serious inequities when trading with other Beijing-Tianjin-Hebei cities and outside regions. While gaining equivalent local jobs, these industrial-based peripheral cities had 250% higher PM2·5-related deaths (10·2 PM2·5-related deaths for obtaining 1000 local jobs) than core cities (2·9 PM2·5-related deaths for obtaining 1000 local jobs) and 57·7% higher PM2·5-related deaths than agricultural-based peripheral cities (6·5 PM2·5-related deaths for obtaining 1000 local jobs). While gaining equivalent value added, industrial-based peripheral cities had 50·6% higher PM2·5-related deaths (¥13·9 of reduced human capital due to PM2·5-related premature deaths to obtain ¥1000 local value added) than core cities (¥9·2 of reduced human capital due to PM2·5-related premature deaths to obtain ¥1000 local value added) and 67·4% higher PM2·5-related deaths than agricultural-based peripheral cities (¥8·3 of reduced human capital due to PM2·5-related premature deaths to obtain ¥1000 local value added). INTERPRETATION Treating metropolitan areas as a single entity obscured internal heterogeneity, potentially misleading policy makers into imposing strict regulations on the whole metropolitan area to alleviate the inequities it posed on outside regions. However, several peripheral Beijing-Tianjin-Hebei cities were disadvantaged in their trade with core Beijing-Tianjin-Hebei cities and outside regions. Therefore, policies should be tailored for particular cities within metropolitan areas. Future targeted policies should include, but not be limited to, making ecological compensations and incorporating the environment and health costs into the price of pollution-intensive goods and services. FUNDING National Key Research and Devlopment Program of China, National Natural Science Foundation of China, and Jiangsu Natural Science Foundation.
Collapse
Affiliation(s)
- Wei Zhang
- The Center for Beijing-Tianjin-Hebei Regional Environment and Ecology, Chinese Academy of Environmental Planning, Beijing, China; State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, China
| | - Jing Zhao
- The Center for Beijing-Tianjin-Hebei Regional Environment and Ecology, Chinese Academy of Environmental Planning, Beijing, China; State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, China
| | - Zhuoying Zhang
- State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
| | - Miaomiao Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China.
| | - Ruoqi Li
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China
| | - Wenbo Xue
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, China; Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing, China.
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
| | - Beiming Cai
- Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng, China
| | - Ling Jiang
- School of Government, Central University of Finance and Economics, Beijing, China
| | - Jing Zhang
- The Center for Beijing-Tianjin-Hebei Regional Environment and Ecology, Chinese Academy of Environmental Planning, Beijing, China; State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, China
| | - Xi Hu
- The Center for Beijing-Tianjin-Hebei Regional Environment and Ecology, Chinese Academy of Environmental Planning, Beijing, China; State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, China
| | - Lingjia Zhong
- The Center for Beijing-Tianjin-Hebei Regional Environment and Ecology, Chinese Academy of Environmental Planning, Beijing, China; State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, China
| | - Hongqiang Jiang
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, China
| | - Jinnan Wang
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, China
| | - Jun Bi
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China
| |
Collapse
|
12
|
Huang L, Zhao B, Wang S, Chang X, Klimont Z, Huang G, Zheng H, Hao J. Global Anthropogenic Emissions of Full-Volatility Organic Compounds. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:16435-16445. [PMID: 37853753 DOI: 10.1021/acs.est.3c04106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
Traditional global emission inventories classify primary organic emissions into nonvolatile organic carbon and volatile organic compounds (VOCs), excluding intermediate-volatility and semivolatile organic compounds (IVOCs and SVOCs, respectively), which are important precursors of secondary organic aerosols. This study establishes the first global anthropogenic full-volatility organic emission inventory with chemically speciated or volatility-binned emission factors. The emissions of extremely low/low-volatility organic compounds (xLVOCs), SVOCs, IVOCs, and VOCs in 2015 were 13.2, 10.1, 23.3, and 120.5 Mt, respectively. The full-volatility framework fills a gap of 18.5 Mt I/S/xLVOCs compared with the traditional framework. Volatile chemical products (VCPs), domestic combustion, and on-road transportation sources were dominant contributors to full-volatility emissions, accounting for 30, 30, and 12%, respectively. The VCP and on-road transportation sectors were the main contributors to IVOCs and VOCs. The key emitting regions included Africa, India, Southeast Asia, China, Europe, and the United States, among which China, Europe, and the United States emitted higher proportions of IVOCs and VOCs owing to the use of cleaner fuel in domestic combustion and more intense emissions from VCPs and on-road transportation activities. The findings contribute to a better understanding of the impact of organic emissions on global air pollution and climate change.
Collapse
Affiliation(s)
- Lyuyin Huang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xing Chang
- Laboratory of Transport Pollution Control and Monitoring Technology, Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China
| | - Zbigniew Klimont
- International Institute for Applied Systems Analysis (IIASA), Laxenburg 2361, Austria
| | - Guanghan Huang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
13
|
Wu D, Zheng H, Li Q, Wang S, Zhao B, Jin L, Lyu R, Li S, Liu Y, Chen X, Zhang F, Wu Q, Liu T, Jiang J, Wang L, Li X, Chen J, Hao J. Achieving health-oriented air pollution control requires integrating unequal toxicities of industrial particles. Nat Commun 2023; 14:6491. [PMID: 37838777 PMCID: PMC10576764 DOI: 10.1038/s41467-023-42089-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 09/29/2023] [Indexed: 10/16/2023] Open
Abstract
Protecting human health from fine particulate matter (PM) pollution is the ambitious goal of clean air actions, but current control strategies largely ignore the role of source-specific PM toxicity. Here, we proposed health-oriented control strategies by integrating the unequal toxic potencies of the most polluting industrial PMs. Iron and steel industry (ISI)-emitted PM2.5 exhibit about one order of magnitude higher toxic potency than those of cement and power industries. Compared with the current mass-based control strategy (prioritizing implementation of ultralow emission standards in the power sector), the proposed health-oriented control strategy (priority control of the ISI sector) could generate 5.4 times higher reduction in population-weighted toxic potency-adjusted PM2.5 exposure among polluting industries in China. Furthermore, the marginal abatement cost per unit of toxic potency-adjusted mass of ISI-emitted PM2.5 is only a quarter of that of the other two sectors under ultralow emission scenarios. We highlight that a health-oriented air pollution control strategy is urgently required to achieve cost-effective reductions in particulate exposure risks.
Collapse
Affiliation(s)
- Di Wu
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Qing Li
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China.
- Shanghai Institute of Eco-Chongming (SIEC), 20 Cuiniao Road, Chenjia Town, Chongming District, Shanghai, 202162, China.
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China.
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China.
| | - Bin Zhao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Ling Jin
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Rui Lyu
- China Huaneng Clean Energy Research Institute, Beijing, 102209, China
| | - Shengyue Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Yuzhe Liu
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Xiu Chen
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Fenfen Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Tonghao Liu
- China National Environmental Monitoring Center, Beijing, 100012, China
| | - Jingkun Jiang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Lin Wang
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
| | - Xiangdong Li
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jianmin Chen
- Department of Environmental Science and Engineering, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, Shanghai, 200433, China
- Shanghai Institute of Eco-Chongming (SIEC), 20 Cuiniao Road, Chenjia Town, Chongming District, Shanghai, 202162, China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| |
Collapse
|
14
|
Zhang D, Wang Q, Song S, Chen S, Li M, Shen L, Zheng S, Cai B, Wang S, Zheng H. Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition. iScience 2023; 26:107652. [PMID: 37680462 PMCID: PMC10480617 DOI: 10.1016/j.isci.2023.107652] [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: 05/27/2022] [Revised: 01/18/2023] [Accepted: 08/14/2023] [Indexed: 09/09/2023] Open
Abstract
Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional approaches often rely on complicated chemical transport models that require extensive expertise and computational resources. In this study, we develop a machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations directly from a high-resolution fossil energy use dataset. Applications of the framework with Chinese data reveal highly heterogeneous health benefits of avoiding premature mortality by reducing fossil fuel use in different sectors and regions in China with a mean of $19/tCO2 and a standard deviation of $38/tCO2. Reducing rural and residential coal use offers the highest co-benefits with a mean of $151/tCO2. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition toward a carbon-neutral energy system.
Collapse
Affiliation(s)
- Da Zhang
- Institute of Energy, Economy, and Environment, Tsinghua University, Beijing, China
- Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qingyi Wang
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shaojie Song
- 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, Tianjin 300350, China
- Harvard-China on Energy, Economy, and Environment, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Simiao Chen
- Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany
- Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingwei Li
- Institute of Energy, Economy, and Environment, Tsinghua University, Beijing, China
- Center for Policy Research on Energy and the Environment, Princeton University, Princeton, NJ, USA
| | - Lu Shen
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
| | - Siqi Zheng
- Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, China
| | - Shenhao Wang
- Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Haotian Zheng
- CMA-NKU Cooperative Laboratory for Atmospheric Environment Health Research, Tianjin 300350, China
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
| |
Collapse
|
15
|
Tian X, Xiong Y, Mi Z, Zhang Q, Tian K, Zhao B, Dong Z, Wang S, Ding D, Xing J, Zhu Y, Long S, Zhang P. Mismatched Social Welfare Allocation and PM 2.5-Related Health Damage along Value Chains within China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:12689-12700. [PMID: 37587658 DOI: 10.1021/acs.est.3c00181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Value chains have played a critical part in the growth. However, the fairness of the social welfare allocation along the value chain is largely underinvestigated, especially when considering the harmful environmental and health effects associated with the production processes. We used fine-scale profiling to analyze the social welfare allocation along China's domestic value chain within the context of environmental and health effects and investigated the underlying mechanisms. Our results suggested that the top 10% regions in the value chain obtained 2.9 times more social income and 2.1 times more job opportunities than the average, with much lower health damage. Further inspection showed a significant contribution of the "siphon effect"─major resource providers suffer the most in terms of localized health damage along with insufficient social welfare for compensation. We found that inter-region atmosphere transport results in redistribution for 53% health damages, which decreases the welfare-damage mismatch at "suffering" regions but also causes serious health damage to more than half of regions and populations in total. Specifically, around 10% of regions have lower social welfare and also experienced a significant increase in health damage caused by atmospheric transport. These results highlighted the necessity of a value chain-oriented, quantitative compensation-driven policy.
Collapse
Affiliation(s)
- Xin Tian
- School of Environment, Beijing Normal University, Beijing 100875, China
- State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing 100875, China
| | - Yiling Xiong
- School of Environment, Beijing Normal University, Beijing 100875, China
| | - Zhifu Mi
- The Bartlett School of Sustainable Construction, University College London, London WC1E 7HB, U.K
| | - Qianzhi Zhang
- School of Environment, Beijing Normal University, Beijing 100875, China
| | - Kailan Tian
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yun Zhu
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Shicheng Long
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Pingdan Zhang
- Business School, Beijing Normal University, Beijing 100875, China
| |
Collapse
|
16
|
Zhang Q, Zhu J, Mulder J, Wang Q, Liu C, He N. High environmental costs behind rapid economic development: Evidence from economic loss caused by atmospheric acid deposition. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 334:117511. [PMID: 36801691 DOI: 10.1016/j.jenvman.2023.117511] [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: 11/21/2022] [Revised: 02/05/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
The rapid growth of energy-intensive and high-emission industries has propelled China's economy but has also led to massive levels of air pollutant emissions and ecological problems, such as acid deposition. Despite recent declines, atmospheric acid deposition in China is still severe. Long-term exposure to high levels of acid depositions has a substantial negative impact on the ecosystem. Evaluating these hazards and incorporating this issue into planning and decision-making processes is critical to achieving sustainable development goals in China. However, the long-term economic loss caused by atmospheric acid deposition and its temporal and spatial variation in China is unclear. Hence, the aim of this study was to assess the environmental cost of acid deposition in the agriculture, forestry, construction, and transportation industries from 1980 to 2019, using long-term monitoring, integrated data, and the dose-response method with localization parameters. The results showed that the estimated cumulative environmental cost of acid deposition was USD 230 billion, representing 0.27% of the gross domestic product (GDP) in China. This cost, was particularly high for building materials, followed by crops, forests, and roads. Temporally, the environmental cost and the ratio of environmental cost to GDP decreased from their peaks by 43% and 91%, respectively, because of emission controls targeting acidifying pollutants and promotion of clean energy. Spatially, the largest environmental cost occurred in developing provinces, indicating that more stringent emission reduction measures should be implemented in these regions. These findings highlight the huge environmental costs behind rapid development; however, the implementation of reasonable emission reduction measures can effectively reduce these environmental costs, providing a promising paradigm for other undeveloped and developing countries.
Collapse
Affiliation(s)
- Qiongyu Zhang
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, 300072, China; Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jianxing Zhu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jan Mulder
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, 1431, Norway
| | - Qiufeng Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Congqiang Liu
- 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, 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China; Earth Critical Zone and Flux Research Station of Xing'an Mountains, Chinese Academy of Sciences, Daxing'anling, 165200, China.
| |
Collapse
|
17
|
Zheng H, Chang X, Wang S, Li S, Zhao B, Dong Z, Ding D, Jiang Y, Huang G, Huang C, An J, Zhou M, Qiao L, Xing J. Sources of Organic Aerosol in China from 2005 to 2019: A Modeling Analysis. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:5957-5966. [PMID: 36994990 DOI: 10.1021/acs.est.2c08315] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Organic aerosol (OA) is a key component of fine particulate matter (PM2.5) and affects the human health and leads to climate change. With strict control measures for air pollutants during the last decade, the OA concentration in China declined slowly, while its sources remain unclear. In this study, we simulate the primary OA (POA) and secondary OA (SOA) concentrations from 2005 to 2019 with a state-of-the-art air quality model, Community Multiscale Air Quality (CMAQ, version 5.3.2) coupled with a Two-Dimensional Volatility Basis Set (2D-VBS) module, and a long-term emission inventory of full-volatility organic compounds in China and conduct source apportionment and sensitivity analysis. The simulation results show that, from 2005 to 2019, the OA concentration in China decreased from 24.0 to 12.8 μg/m3 with most of the reduction from POA. The OA pollution from residential biomass burning declined 75% from 2005 to 2019, while it is still the major OA source in China. OA pollution from VCP increased by more than 2-fold and became the largest SOA source in China. From 2014 to 2019, the NOx control in China slightly offset the decrease of SOA concentration due to elevated oxidation capacity.
Collapse
Affiliation(s)
- Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of the Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xing Chang
- Transport Planning and Research Institute, Ministry of Transport, Laboratory of Transport Pollution Control and Monitoring Technology, Beijing 100028, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dian Ding
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Guanghan Huang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of the Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Jingyu An
- State Environmental Protection Key Laboratory of the Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Min Zhou
- State Environmental Protection Key Laboratory of the Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Liping Qiao
- State Environmental Protection Key Laboratory of the Formation and Prevention of the Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
18
|
Chang X, Zheng H, Zhao B, Yan C, Jiang Y, Hu R, Song S, Dong Z, Li S, Li Z, Zhu Y, Shi H, Jiang Z, Xing J, Wang S. Drivers of High Concentrations of Secondary Organic Aerosols in Northern China during the COVID-19 Lockdowns. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:5521-5531. [PMID: 36999996 DOI: 10.1021/acs.est.2c06914] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
During the COVID-19 lockdown in early 2020, observations in Beijing indicate that secondary organic aerosol (SOA) concentrations increased despite substantial emission reduction, but the reasons are not fully explained. Here, we integrate the two-dimensional volatility basis set into a state-of-the-art chemical transport model, which unprecedentedly reproduces organic aerosol (OA) components resolved by the positive matrix factorization based on aerosol mass spectrometer observations. The model shows that, for Beijing, the emission reduction during the lockdown lowered primary organic aerosol (POA)/SOA concentrations by 50%/18%, while deteriorated meteorological conditions increased them by 30%/119%, resulting in a net decrease in the POA concentration and a net increase in the SOA concentration. Emission reduction and meteorological changes both led to an increased OH concentration, which accounts for their distinct effects on POA and SOA. SOA from anthropogenic volatile organic compounds and organics with lower volatility contributed 28 and 62%, respectively, to the net SOA increase. Different from Beijing, the SOA concentration decreased in southern Hebei during the lockdown because of more favorable meteorology. Our findings confirm the effectiveness of organic emission reductions and meanwhile reveal the challenge in controlling SOA pollution that calls for large organic precursor emission reductions to rival the adverse impact of OH increase.
Collapse
Affiliation(s)
- Xing Chang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- Transport Planning and Research Institute, Ministry of Transport, Laboratory of Transport Pollution Control and Monitoring Technology, Beijing 100028, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Chao Yan
- Joint International Research Laboratory of Atmospheric and Earth System Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00560, Finland
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Ruolan Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shaojie Song
- 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
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Zeqi Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yun Zhu
- Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, Guangzhou 510006, China
| | - Hongrong Shi
- Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100045, China
| | - Zhe Jiang
- Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100045, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
19
|
Li Y, Xue L, Tao Y, Li Y, Wu Y, Liao Q, Wan J, Bai Y. Exploring the contributions of major emission sources to PM 2.5 and attributable health burdens in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 322:121177. [PMID: 36731741 DOI: 10.1016/j.envpol.2023.121177] [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: 05/15/2022] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
Ambient fine particulate matter (PM2.5) pollution is the principal environmental risk factor for health burdens in China. Identifying the sectoral contributions of pollutant emissions sources on multiple spatiotemporal scales can help in the formulation of specific strategies. In this study, we used sensitivity analysis to explore the specific contributions of seven major emission sources to ambient PM2.5 and attributable premature mortality across mainland China. In 2016, about 60% of China's population lived in areas with PM2.5 concentrations above the Chinese Ambient Air Quality Standard of 35 μg/m3. This percentage was expected to decrease to 35% and 39% if industrial and residential emissions were fully eliminated. In densely populated and highly polluted regions, residential sources contributed about 50% of the PM2.5 exposure in winter, while industrial sources contributed the most (29-51%) in the remaining seasons. The three major sectoral contributors to PM2.5-related deaths were industry (247,000 cases, 35%), residential sources (219,000 cases, 31%), and natural sources (87,000, 12%). The relative contributions of the different sectors varied in the different provinces, with industrial sources making the largest contribution in Shanghai (65%), while residential sources predominated in Heilongjiang (63%), and natural sources dominated in Xinjiang (82%). The contributions of the agricultural (11%), transportation (6%), and power (3%) sources were relatively low in China, but emissions mitigation was still effective in densely populated areas. In conclusion, to effectively alleviate health burdens across China, priority should be given to controlling residential emissions in winter and industrial emissions all year round, taking additional measures to curb emissions from other sources in urban hotspots, and formulating air pollution control strategies tailored to local conditions.
Collapse
Affiliation(s)
- Yong Li
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China; Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China
| | - Liyang Xue
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China; Gansu Ecological Environment Emergency and Accident Investigation Center, Lanzhou, 730030, China
| | - Yan Tao
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
| | - Yidu Li
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Yancong Wu
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Qin Liao
- Key Laboratory of Western China's Environmental Systems, Ministry of Education, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China; Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Junyi Wan
- School of Natural Science, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Yun Bai
- School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China
| |
Collapse
|
20
|
Liu M, Lei Y, Wang X, Xue W, Zhang W, Jiang H, Wang J, Bi J. Source Contributions to PM 2.5-Related Mortality and Costs: Evidence for Emission Allocation and Compensation Strategies in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:4720-4731. [PMID: 36917695 DOI: 10.1021/acs.est.2c08306] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The emissions from various pollution sources were not proportional to their contributions to ambient PM2.5 concentrations and associated health burdens. That means even with the same total abatement targets, different abatement allocation strategies across emission sources can have distinct health benefits. Insufficient knowledge of various sources' contributions to health burdens in China, the country suffering substantial PM2.5-related deaths, hindered the government from seeking optimized abatement allocation strategies. In this context, we separated the contributions of 155 emission sources (31 provinces × 5 sectors) to PM2.5-related mortality across China in 2017 by coupling the Comprehensive Air Quality Model with Extensions (CAMx), Weather Research and Forecasting model (WRF), and health impact assessment model. We further identified the priority-control emission sources and quantified interprovincial ecological compensation volumes to alleviate inequality induced by emission allocation strategies. Results showed that PM2.5 pollution caused 899,443 excess deaths and around 127 billion USD costs in 2017. Approximately half of the deaths and costs were attributable to emissions from sources outside the boundary of the regions where the deaths occurred. Twenty-five out of 155 emission sources that contributed to the top 60% mortality burdens and had high marginal abatement efficiencies in China shall be the priority-control emission sources. A 1 μg/m3 decrease of PM2.5 concentration in regions where these key emission sources occur shall be compensated by 76-153 million USD in their receptor regions. Our study sheds light on the sources' contributions to mortality burdens and costs and provides scientific evidence for optimizing the emission allocation and compensation strategies in China. It also has wide implications for other countries suffering similar problems.
Collapse
Affiliation(s)
- Miaomiao Liu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Yu Lei
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Xin Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Wenbo Xue
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, 100041, China
- Center of Air Quality Simulation and System Analysis, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Wei Zhang
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, 100041, China
- The Center for Beijing-Tianjin-Hebei Regional Ecology and Environment, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Hongqiang Jiang
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, 100041, China
- The Center for Beijing-Tianjin-Hebei Regional Ecology and Environment, Chinese Academy of Environmental Planning, Beijing 100041, China
| | - Jinnan Wang
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy of Environmental Planning, Beijing, 100041, China
| | - Jun Bi
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| |
Collapse
|
21
|
Jiang Y, Ding D, Dong Z, Liu S, Chang X, Zheng H, Xing J, Wang S. Extreme Emission Reduction Requirements for China to Achieve World Health Organization Global Air Quality Guidelines. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:4424-4433. [PMID: 36898019 DOI: 10.1021/acs.est.2c09164] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A big gap exists between current air quality in China and the World Health Organization (WHO) global air quality guidelines (AQG) released in 2021. Previous studies on air pollution control have focused on emission reduction demand in China but ignored the influence of transboundary pollution, which has been proven to have a significant impact on air quality in China. Here, we develop an emission-concentration response surface model coupled with transboundary pollution to quantify the emission reduction demand for China to achieve WHO AQG. China cannot achieve WHO AQG by its own emission reduction for high transboundary pollution of both PM2.5 and O3. Reducing transboundary pollution will loosen the reduction demand for NH3 and VOCs emissions in China. However, to meet 10 μg·m-3 for PM2.5 and 60 μg·m-3 for peak season O3, China still needs to reduce its emissions of SO2, NOx, NH3, VOCs, and primary PM2.5 by more than 95, 95, 76, 62, and 96% respectively, on the basis of 2015. We highlight that both extreme emission reduction in China and great efforts in addressing transboundary air pollution are crucial to reach WHO AQG.
Collapse
Affiliation(s)
- Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuchang Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- Institute for Atmospheric and Climate Science, ETH Zurich, CH-8092 Zurich, Switzerland
| | - Xing Chang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- Transport Planning and Research Institute, Ministry of Transport, Laboratory of Transport Pollution Control and Monitoring Technology, Beijing 100028, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
22
|
Wu Q, Han L, Li S, Wang S, Cong Y, Liu K, Lei Y, Zheng H, Li G, Cai B, Hao J. Facility-Level Emissions and Synergistic Control of Energy-Related Air Pollutants and Carbon Dioxide in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:4504-4512. [PMID: 36877596 DOI: 10.1021/acs.est.2c07704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Boilers involve ∼60% of primary energy consumption in China and emit more air pollutants and CO2 than any other infrastructures. Here, we established a nationwide, facility-level emission data set considering over 185,000 active boilers in China by fusing multiple data sources and jointly using various technical means. The emission uncertainties and spatial allocations were significantly improved. We found that coal-fired power plant boilers were not the most emission-intensive boilers with regard to SO2, NOx, PM, and mercury but emitted the highest CO2. However, biomass- and municipal waste-fired combustion, regarded as zero-carbon technologies, emitted a large fraction of SO2, NOx, and PM. Future biomass or municipal waste mixing in coal-fired power plant boilers can make full use of the advantages of zero-carbon fuel and the pollution control devices of coal-fired power plants. We identified small-size boilers, medium-size boilers using circulating fluidized bed boilers, and large-size boilers located in China's coal mine bases as the main high emitters. Future focuses on high-emitter control can substantially mitigate the emissions of SO2 by 66%, NOx by 49%, PM by 90%, mercury by 51%, and CO2 by 46% at the most. Our study sheds light on other countries wishing to reduce their energy-related emissions and thus the related impacts on humans, ecosystems, and climates.
Collapse
Affiliation(s)
- Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Licong Han
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yan Cong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Kaiyun Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yu Lei
- Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Guoliang Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Bofeng Cai
- Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
23
|
Wang Y, Wen Y, Zhang S, Zheng G, Zheng H, Chang X, Huang C, Wang S, Wu Y, Hao J. Vehicular Ammonia Emissions Significantly Contribute to Urban PM 2.5 Pollution in Two Chinese Megacities. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:2698-2705. [PMID: 36700651 DOI: 10.1021/acs.est.2c06198] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Ammonia (NH3) plays a vital role in the formation of fine particulate matter (PM2.5). Prior studies have primarily focused on the control of agricultural NH3 emissions, the dominant source of anthropogenic NH3 emissions. The air quality impact from vehicular NH3 emissions, which could be particularly important in urban areas, has not been adequately evaluated. We developed high-resolution vehicular NH3 emission inventories for Beijing and Shanghai based on detailed link-level traffic profiles and conducted atmospheric simulations of ambient PM2.5 concentrations contributed by vehicular NH3 emissions. We found that vehicular NH3 emissions shared high proportions among total anthropogenic NH3 emissions in the urban areas of Beijing (86%) and Shanghai (45%), where vehicular NH3 was primarily emitted by gasoline vehicles. Local vehicular NH3 emissions could be responsible for approximately 3% of urban PM2.5 concentrations during wintertime, and the contributions could be much higher during polluted periods (∼3 μg m-3). We also showed that controlling vehicular NH3 emissions will be effective and feasible to alleviate urban PM2.5 pollution for megacities in the near future.
Collapse
Affiliation(s)
- Yunjie Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
| | - Yifan Wen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
| | - Shaojun Zhang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing100084, China
| | - Guangjie Zheng
- Minerva Research Group, Max Planck Institute for Chemistry, Mainz55128, Germany
| | - Haotian Zheng
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
| | - Xing Chang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai200233, China
| | - Shuxiao Wang
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, China
| | - Ye Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing100084, China
| | - Jiming Hao
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, China
- Beijing Laboratory of Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing100084, China
| |
Collapse
|
24
|
Dong Z, Wang S, Jiang Y, Xing J, Ding D, Zheng H, Hao J. An acid rain-friendly NH 3 control strategy to maximize benefits toward human health and nitrogen deposition. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160116. [PMID: 36379329 DOI: 10.1016/j.scitotenv.2022.160116] [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: 10/12/2022] [Revised: 11/05/2022] [Accepted: 11/06/2022] [Indexed: 06/16/2023]
Abstract
Ammonia (NH3) abatement remains controversial in China owing to its effectiveness in reducing PM2.5 pollution and nitrogen deposition but with the potential risk of promoting acid rain formation, necessitating scientific guidance. Here, we propose a novel method for designing an NH3 control strategy to mitigate both air pollution and nitrogen deposition without significantly exacerbating acid rain. This method involves extending the response surface model (RSM) to deposition using a delicately developed polynomial response function of deposition (i.e., dep-RSM). The Yangtze River Delta (YRD) dep-RSM application reveals that 16 out of 41 cities have NH3 control potentials from 15 % to 71 %. Excellent NH3 control potentials have been noted between April and June (78 %-92 %). From 2013 to 2017, the effective SO2 and NOx control significantly reduced wet sulfur and oxidized nitrogen deposition, providing considerable NH3 abatement potentials (15 %-24 %) to further reduce PM2.5 and nitrogen deposition by up to 2 % and 9 %, respectively, without acid rain exacerbation (the wet neutralization factor was maintained). Additionally, 57 % and 73 % NH3 emission reduction potentials were obtained under acid rain constraints with 75 % and 86 % reductions in the other precursors to reduce the average PM2.5 concentration below 25 and 15 μg/m3, and an additional 8408 and 14,459 premature deaths could only be avoided at an extra cost of 8.7 and 19.7 billion CNY, respectively. Meanwhile, the N deposition considerably reduced by 10 and 13 kgN/ha·yr. However, the YRD region could still simultaneously obtain substantial amounts of PM2.5 and N deposition mitigation using the strategy proposed herein. The expanded optimization system can be directly adopted by policymakers to implement coordinated control in regions or countries facing the same NH3 control conundrum.
Collapse
Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki 00014, Finland
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
25
|
Chen L, Liao H, Zhu J, Li K, Bai Y, Yue X, Yang Y, Hu J, Zhang M. Increases in ozone-related mortality in China over 2013-2030 attributed to historical ozone deterioration and future population aging. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159972. [PMID: 36356763 DOI: 10.1016/j.scitotenv.2022.159972] [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: 07/31/2022] [Revised: 10/18/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
We systematically examine historical and future changes in premature respiratory mortalities attributable to ozone (O3) exposure (O3-mortality) in China and identify the leading cause of respective change for the first time. The historical assessment for 2013-2019 is based on gridded O3 concentrations generated by a multi-source-data-fusion algorithm; the future prediction for 2019-2030 uses gridded O3 concentrations projected by four Coupled Model Intercomparison Project Phase 6 (CMIP6) models under three Shared Socioeconomic Pathways (SSP) scenarios. During 2013-2019, national annual O3-mortality is 176.3 thousand (95%CI: 123.5-224.0 thousand) averaged over 2013-2019 with an increasing trend of 14.1 thousand yr-1 (95%CI: 10.2-17.4 thousand yr-1); sensitivity experiments show that the O3-mortality varies at a rate of +12.7 (95%CI: 9.2-15.6), +5.8 (95%CI: 4.0-7.4), +1.0 (95%CI: 0.7-1.2), -5.4 (95%CI: -6.9 to -3.7) thousand yr-1, owing to changes in O3 concentration, population age structure, population size, mortality rate for respiratory disease, respectively. The deterioration of O3 air quality, shown as significant increase in O3 concentration, is identified as the primary factor which contributes 90.1 % of 2013-2019 O3-mortality rise. Compared with O3-mortality estimated in this study, the widely-used O3-mortality assessment method based on urban-site-dominant O3 measurements generates close national O3-mortality but overestimates (underestimates) provincial O3-mortality in coastal (central) provinces. From 2019 to 2030, national O3-mortality is projected to increase by 50.4-103.7 thousand under different SSP scenarios. The change in age structure (i.e. population aging) alone will result in significant O3-mortality rises of 137.9-160.5 thousand. Compared with 2013-2019 rapid O3 increase (+2.5 μg m-3 yr-1 at national level), O3 concentrations are projected to increase at a lower rate (+0.4 μg m-3 yr-1 in SSP5-8.5) or even decrease (-0.7 μg m-3 yr-1 in SSP1-2.6) from 2019 to 2030. Therefore, population aging, in place of O3 air quality deterioration, will become the leading cause of future O3-mortality rises during the coming decade.
Collapse
Affiliation(s)
- Lei Chen
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hong Liao
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Jia Zhu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Ke Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Yang Bai
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Xu Yue
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Yang Yang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jianlin Hu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Meigen Zhang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
26
|
Qi L, Zheng H, Ding D, Wang S. Responses of sulfate and nitrate to anthropogenic emission changes in eastern China - in perspective of long-term variations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 855:158875. [PMID: 36126708 DOI: 10.1016/j.scitotenv.2022.158875] [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: 06/23/2022] [Revised: 09/07/2022] [Accepted: 09/15/2022] [Indexed: 06/15/2023]
Abstract
We investigate responses of sulfate (SO42-) and nitrate (NO3-) to anthropogenic emission changes in 2006-2017 by fixing meteorology at the 2009 level using nested 3D chemical transport model GEOS-Chem. We find that sulfate concentration decreases following SO2 emissions, but with a relatively smaller reduction rate (by 16 % in North China Plain (NCP) and 28 % in Yangtze River Delta (YRD)) due to larger sulfur oxidation ratio (SOR) at lower SO2 level. SOR follows a power law with SO2 emissions in general except in winter in NCP, when and where both SO2 emission reduction and atmospheric oxidation capacity are critical to the inter-annual variations of SOR. Nitrate concentration ([pNO3-]) decreases along with NOx emission reduction in summer, but increases slightly in winter in 2011-2017. Equilibrium with gas phase HNO3, NO3- in particle phase (pNO3-) is determined by total HNO3 (TN = [pNO3-] + [gHNO3]) oxidized from NO2 and gas-particle partitioning (ε(NO3-) = [pNO3-]/TN). TN is decreasing faster in summer (~33 %) than in winter (~25 %) in 2011-2017. In contrast, ε(NO3-) changes marginally in summer (within 5 %) but increases by 36 % in NCP and by 51 % in YRD in winter in 2006-2017. The increasing of ε(NO3-) in winter is attributed to the strong reduction of [pSO42-], which increases the relative abundance of NH3 and thus favors partitioning of NO3- to the particle phase. The effect of increasing ε(NO3-) overcomes that of decreasing TN in winter. We suggest reduce SO2 emissions to further reduce [pSO42-] in eastern China. In addition, we recommend reduce NOx emissions in summer, and reduce atmospheric oxidation capacity and relative abundance of NH3 in winter to reduce [pNO3-].
Collapse
Affiliation(s)
- Ling Qi
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Dian Ding
- Institute for Atmospheric and Earth System Research (INAR)/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| |
Collapse
|
27
|
Dong Z, Xing J, Zhang F, Wang S, Ding D, Wang H, Huang C, Zheng H, Jiang Y, Hao J. Synergetic PM 2.5 and O 3 control strategy for the Yangtze River Delta, China. J Environ Sci (China) 2023; 123:281-291. [PMID: 36521990 DOI: 10.1016/j.jes.2022.04.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 06/17/2023]
Abstract
PM2.5 concentrations have dramatically reduced in key regions of China during the period 2013-2017, while O3 has increased. Hence there is an urgent demand to develop a synergetic regional PM2.5 and O3 control strategy. This study develops an emission-to-concentration response surface model and proposes a synergetic pathway for PM2.5 and O3 control in the Yangtze River Delta (YRD) based on the framework of the Air Benefit and Cost and Attainment Assessment System (ABaCAS). Results suggest that the regional emissions of NOx, SO2, NH3, VOCs (volatile organic compounds) and primary PM2.5 should be reduced by 18%, 23%, 14%, 17% and 33% compared with 2017 to achieve 25% and 5% decreases of PM2.5 and O3 in 2025, and that the emission reduction ratios will need to be 50%, 26%, 28%, 28% and 55% to attain the National Ambient Air Quality Standard. To effectively reduce the O3 pollution in the central and eastern YRD, VOCs controls need to be strengthened to reduce O3 by 5%, and then NOx reduction should be accelerated for air quality attainment. Meanwhile, control of primary PM2.5 emissions shall be prioritized to address the severe PM2.5 pollution in the northern YRD. For most cities in the YRD, the VOCs emission reduction ratio should be higher than that for NOx in Spring and Autumn. NOx control should be increased in summer rather than winter when a strong VOC-limited regime occurs. Besides, regarding the emission control of industrial processes, on-road vehicle and residential sources shall be prioritized and the joint control area should be enlarged to include Shandong, Jiangxi and Hubei Province for effective O3 control.
Collapse
Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Fenfen Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Hongli Wang
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
28
|
Wang J, Li J, Li X, Fang C. Characteristics of Air Pollutants Emission and Its Impacts on Public Health of Chengdu, Western China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192416852. [PMID: 36554731 PMCID: PMC9779229 DOI: 10.3390/ijerph192416852] [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: 10/13/2022] [Revised: 12/01/2022] [Accepted: 12/13/2022] [Indexed: 05/06/2023]
Abstract
Pollution caused by PM2.5 and O3 are common environmental problems which can easily affect human health. Chengdu is a major central city in Western China, and there is little research on the regional emissions and health effects of air pollution in Chengdu. According to the Multi-resolution Emissions Inventory of the Chinese Model, 2017 (MEIC v1.3), this study compiled the air pollutant emission inventory of Chengdu. The results show that the pollutant emission of Chengdu is generally higher in winter than in summer. The southeast area of Chengdu is the key area where emissions of residential and industrial sectors are dominant. Through air quality simulation with a Weather Research and Forecasting model, coupled with the Community Multiscale Air Quality (WRF-CMAQ), the health effects of PM2.5 and O3 in winter and summer in Chengdu of 2017 were investigated. The primary pollutant in winter is PM2.5 and O3 in summer. PM2.5 pollution accounted for 351 deaths in January and July 2017, and O3 pollution accounted for 328 deaths in the same period. There were 276 deaths in rural areas and 413 in urban areas. In January and July 2017, the health economic loss caused by PM2.5 accounted for 0.0974% of the gross regional product (GDP) of Chengdu in 2017, and the health economic loss caused by O3 accounted for 0.0910%.
Collapse
Affiliation(s)
- Ju Wang
- College of New Energy and Environment, Jilin University, Changchun 130012, China
- Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130012, China
- Jilin Province Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130012, China
- Correspondence: ; Tel.: +86-131-0431-7228
| | - Juan Li
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Xinlong Li
- College of New Energy and Environment, Jilin University, Changchun 130012, China
| | - Chunsheng Fang
- College of New Energy and Environment, Jilin University, Changchun 130012, China
- Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130012, China
- Jilin Province Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130012, China
| |
Collapse
|
29
|
Liu K, Wu Q, Ren Y, Wang S. Air Pollutant Emissions from Residential Solid Fuel Combustion in the Pan-Third Pole Region. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:15347-15355. [PMID: 36288504 DOI: 10.1021/acs.est.2c04150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
As the largest emission source in the Pan-Third Pole region, residential solid fuel combustion gains increasing public concern regarding air pollution-associated health impacts. This study firstly developed emission inventories by combining energy statistics, fuel-mix survey, and detailed emission factors considering different fuel types, stove types, and altitudes, and we achieved full regional coverage and increased spatial resolution from 9 × 9 km to 1 km × 1 km. Total CO2, CO, PM2.5, SO2, and NOx emissions (coefficient of variation) were estimated to be 823 Mt (24%), 53 Mt (28%), 4525 kt (48%), 1388 kt (55%), and 1275 kt (46%) in 2020. India, Pakistan, and Bangladesh combined contributed 73, 57, 65, 67, and 69% of total CO2, CO, PM2.5, SO2, and NOx emissions, respectively, due to the large population. The Qinghai-Tibet Plateau had the second-highest emission intensity, mainly due to the high fuel consumption per capita. Unlike the emissions of the Pan-Third Pole in existing Asian inventories, dung cake combustion dominated total PM2.5, SO2, and NOx emissions rather than firewood combustion with proportions of 54, 70, and 67%, respectively. The effect of altitude on combustion efficiencies increased PM2.5 emissions by about 21% from the region. The method and results can provide technical guidance for emission inventory refinement in the Pan-Third Pole and other regions.
Collapse
Affiliation(s)
- Kaiyun Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing100084, China
| | - Yujia Ren
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing100084, China
| |
Collapse
|
30
|
Ding S, Wei Z, He J, Liu D, Zhao R. Estimates of PM 2.5 concentrations spatiotemporal evolution across China considering aerosol components in the context of the Reform and Opening-up. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 322:115983. [PMID: 36058070 DOI: 10.1016/j.jenvman.2022.115983] [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: 12/06/2021] [Revised: 07/12/2022] [Accepted: 08/07/2022] [Indexed: 06/15/2023]
Abstract
With astonishing and rapid development in China since the Reform and Opening-up in 1978, serious air pollution has become a great challenge. A better understanding of the response of PM2.5 pollution to socioeconomic development after the Reform and Opening-up policy is benefit for pollution control. However, heterogeneous influences of biophysical and socioeconomic activities on PM2.5 pollution pose great challenges in statistical simulation of PM2.5. Few statistical model regards aerosol species as the explanatory variables for heterogeneous formation mechanism to retrieve PM2.5 concentration. In this research, monthly PM2.5 concentration in China during 1980-2020 was reconstructed by a novel statistical strategy considering aerosol components (AC-RF). Three cross-validation (CV) methods, sample-based CV, spatial-based CV and temporal-based CV results indicated satisfactory performance of AC-RF model with correlation coefficient (R) of 0.92, 0.90, 0.86, respectively. A three-stage concluded on PM2.5 concentration annual variation in China was drawn as followed: Before 2000, PM2.5 level in China represented smooth evolution and mainly influenced by natural events with polluted region locating in Xinjiang province, North China and Central China. Since 2000, PM2.5 concentration increased to high level in the context of rapid socioeconomic development. Severe air pollution covered Jing-Jin-Ji agglomeration, Central China and Sichuan Basin. During 2012-2020, PM2.5 declined and polluted region shrank, which was benefited by the strictest-ever air pollution control measures. Based on aerosol components analysis, sulfate aerosol exhibited the most significant increase trend in recent 40 years and black aerosol variation is the most closely related to PM2.5 pollution. In conclusion, unsustainable development is the culprit for air quality deterioration. Strict and continuous air pollution control strategies are effective for air quality improvement.
Collapse
Affiliation(s)
- Su Ding
- State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou, 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Hangzhou, 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China.
| | - Zhiwei Wei
- School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China; Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jianhua He
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China; Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, 430079, China
| | - Dianfeng Liu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China; Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, 430079, China
| | - Rong Zhao
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| |
Collapse
|
31
|
Lin Y, Li M, Lin R. Can urban rail transit reduce haze pollution? A spatial difference-in-differences approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:81430-81440. [PMID: 35732892 DOI: 10.1007/s11356-022-21490-6] [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: 02/10/2022] [Accepted: 06/11/2022] [Indexed: 06/15/2023]
Abstract
This paper explores the influence and mechanism of urban rail transit on haze pollution in mainland China. Based on the satellite remote sensing dataset released by the Earth Observing System of Data and Information System (EOSDIS) of the National Aeronautics and Space Administration (NASA) and urban rail transit network data of Robert Schwan, the prefecture-year urban rail transit and haze concentration-related dataset from 2001 to 2018 is collected. Considering the significant spatial autocorrelation of urban haze pollution, a spatial difference-in-differences (SDID) approach is applied to empirically investigate the influence of urban rail transit on haze pollution. The results show that the connection of urban rail transit significantly reduces the urban PM2.5 concentration, and the effect has significant regional heterogeneity. Furthermore, it is found that the substitution effect on motor vehicles is the mechanism in which urban rail transit impacts haze pollution. Based on our findings, accelerating urban rail transit network support, appropriately relaxing the subway application in some large cities, and taking comprehensive measures to attract more residents to choose subway travel is proposed.
Collapse
Affiliation(s)
- Yumei Lin
- School of Marxism, Shanghai University of International Business and Economics, Shanghai, China
| | - Meiling Li
- Department of Economics, University of Melbourne, Melbourne, Australia
| | - Ruofei Lin
- School of Economic and Management, Tongji University, Shanghai, China.
| |
Collapse
|
32
|
Shu Y, Hu J, Zhang S, Schöpp W, Tang W, Du J, Cofala J, Kiesewetter G, Sander R, Winiwarter W, Klimont Z, Borken-Kleefeld J, Amann M, Li H, He Y, Zhao J, Xie D. Analysis of the air pollution reduction and climate change mitigation effects of the Three-Year Action Plan for Blue Skies on the "2+26" Cities in China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 317:115455. [PMID: 35751259 DOI: 10.1016/j.jenvman.2022.115455] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 02/11/2022] [Accepted: 05/28/2022] [Indexed: 06/15/2023]
Abstract
City clusters play an important role in air pollutant and greenhouse gas (GHG) emissions reduction in China, primarily due to their high fossil energy consumption levels. The "2 + 26" Cities, i.e., Beijing, Tianjin and 26 other perfectures in northern China, has experienced serious air pollution in recent years. We employ the Greenhouse Gas and Air Pollution Interactions and Synergies model adapted to the "2 + 26" Cities (GAINS-JJJ) to evaluate the impacts of structural adjustments in four major sectors, industry, energy, transport and land use, under the Three-Year Action Plan for Blue Skies (Three-Year Action Plan) on the emissions of both the major air pollutants and CO2 in the "2 + 26" Cities. The results indicate that the Three-Year Action Plan applied in the "2 + 26" Cities reduces the total emissions of primary fine particulate matter with an aerodynamic diameter of ≤ 2.5 μm (PM2.5), SO2, NOx, NH3 and CO2 by 17%, 25%, 21%, 3% and 1%, respectively, from 2017 to 2020. The emission reduction potentials vary widely across the 28 prefectures, which may be attributed to the differences in energy structure, industrial composition, and policy enforcement rate. Among the four sectors, adjustment of industrial structure attains the highest co-benefits of CO2 reduction and air pollution control due to its high CO2 reduction potential, while structural adjustments in energy and transport attain much lower co-benefits, despite their relatively high air pollutant emissions reductions, primarily resulting from an increase in the coal-electric load and associated carbon emissions caused by electric reform policies..
Collapse
Affiliation(s)
- Yun Shu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Jingnan Hu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Beijing, 100191, China; International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria
| | - Wolfgang Schöpp
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria
| | - Wei Tang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Jinhong Du
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Janusz Cofala
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria
| | - Gregor Kiesewetter
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria
| | - Robert Sander
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria
| | - Wilfried Winiwarter
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria; Institute of Environmental Engineering, University of Zielona Góra, Zielona Góra, 65-417, Poland
| | - Zbigniew Klimont
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria
| | - Jens Borken-Kleefeld
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria
| | - Markus Amann
- International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361, Laxenburg, Austria
| | - Haisheng Li
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
| | - Youjiang He
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Jinmin Zhao
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Deyuan Xie
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| |
Collapse
|
33
|
Liu K, Wu Q, Wang S, Chang X, Tang Y, Wang L, Liu T, Zhang L, Zhao Y, Wang Q, Chen J. Improved atmospheric mercury simulation using updated gas-particle partition and organic aerosol concentrations. J Environ Sci (China) 2022; 119:106-118. [PMID: 35934455 DOI: 10.1016/j.jes.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/29/2022] [Accepted: 04/06/2022] [Indexed: 06/15/2023]
Abstract
The gaseous or particulate forms of divalent mercury (HgII) significantly impact the spatial distribution of atmospheric mercury concentration and deposition flux (FLX). In the new nested-grid GEOS-Chem model, we try to modify the HgII gas-particle partitioning relationship with synchronous and hourly observations at four sites in China. Observations of gaseous oxidized Hg (GOM), particulate-bound Hg (PBM), and PM2.5 were used to derive an empirical gas-particle partitioning coefficient as a function of temperature (T) and organic aerosol (OA) concentrations under different relative humidity (RH). Results showed that with increasing RH, the dominant process of HgII gas-particle partitioning changed from physical adsorption to chemical desorption. And the dominant factor of HgII gas-particle partitioning changed from T to OA concentrations. We thus improved the simulated OA concentration field by introducing intermediate-volatility and semi-volatile organic compounds (I/SVOCs) emission inventory into the model framework and refining the volatile distributions of I/SVOCs according to new filed tests in the recent literatures. Finally, normalized mean biases (NMBs) of monthly gaseous element mercury (GEM), GOM, PBM, WFLX were reduced from -33%-29%, 95%-300%, 64%-261%, 117%-122% to -13%-0%, -20%-80%, -31%-50%, -17%-23%. The improved model explains 69%-98% of the observed atmospheric Hg decrease during 2013-2020 and can serve as a useful tool to evaluate the effectiveness of the Minamata Convention on Mercury.
Collapse
Affiliation(s)
- Kaiyun Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Xing Chang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yi Tang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Long Wang
- Institute of Atmospheric Environment, Guangdong provincial academy of environmental science, Guangzhou 510045, China
| | - Tonghao Liu
- China National Environmental Monitoring Centre, Beijing 100012, China
| | - Lei Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Yu Zhao
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Qin'geng Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Jinsheng Chen
- Center for Excellence in Regional Atmos. Environ., Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| |
Collapse
|
34
|
Dong Z, Xing J, Wang S, Ding D, Ge X, Zheng H, Jiang Y, An J, Huang C, Duan L, Hao J. Responses of nitrogen and sulfur deposition to NH 3 emission control in the Yangtze River Delta, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 308:119646. [PMID: 35718044 DOI: 10.1016/j.envpol.2022.119646] [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: 03/16/2022] [Revised: 06/11/2022] [Accepted: 06/14/2022] [Indexed: 06/15/2023]
Abstract
NH3 emission control has proven to be of great importance in reducing PM2.5 concentrations in China, while how it affects nitrogen/sulfur (N/S) deposition is still unclear. This study expanded the response surface model method to quantify the responses of N/S deposition to the emission control of precursors (NOx, SO2, NH3, VOCs and primary PM2.5) in the Yangtze River Delta, China. NH3 control was found to have higher efficiency in reducing N/S deposition than NOx and SO2 alone. The reduced N deposition response to NH3 emission control was higher in the northern part of the YRD region, whereas oxidized N deposition decreased sharply in the region with a low N critical load. Synergetic effect was found in reducing N deposition when we controlled the NH3 and NOx emissions simultaneously. Compared with the sum effect of individual NH3 and NOx emission control, the extra benefits from the synergy controls accounted for 4.4% (1.23 kg N·ha-1·yr-1) of the total N deposition, of which 81% came from the oxidized N deposition. The YRD region could receive the largest synergetic effect with a 1:1 ratio of NOx:NH3 emission reduction. The NH3 emission control increases the dry deposition of acid substances and worsens acid rain though it reduces the wet S/oxidized N deposition. These findings highlight the effectiveness of NH3 emission control and suggest a multi-pollutant control strategy for reducing N/S deposition. The response surface model method for deposition also provides a reference for other regions in China and other countries.
Collapse
Affiliation(s)
- Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China.
| | - Dian Ding
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China; Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014, Helsinki, Finland
| | - Xiaodong Ge
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Yueqi Jiang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Jingyu An
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Cheng Huang
- State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Lei Duan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, China
| |
Collapse
|
35
|
Yin S. Decadal changes in PM 2.5-related health impacts in China from 1990 to 2019 and implications for current and future emission controls. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155334. [PMID: 35452723 DOI: 10.1016/j.scitotenv.2022.155334] [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: 02/02/2022] [Revised: 04/04/2022] [Accepted: 04/13/2022] [Indexed: 06/14/2023]
Abstract
In China, the rapid development of the economy and implementation of multiple emission control policies in recent decades have been accompanied by dramatic changes in air quality. In this study, PM2.5 concentrations estimated by using MERRA-2 reanalysis data were integrated into the Global Exposure Mortality Model (GEMM) to explore the spatiotemporal variation of nationwide PM2.5-related premature mortality from 1990 to 2019, and the driving factors behind decadal changes were evaluated. Since 2000, as a result of PM2.5 pollution, air quality in China has deteriorated substantially, especially in the fast-developing eastern and southern parts. In 2009, the nationwide population-weighted (PW) PM2.5 concentration peaked at 41.4 μg/m3 (95% confidence interval [CI], 36.7-46.2). Simultaneously, the GEMM results revealed that nationwide PM2.5-related deaths increased remarkably from 1089 (95% CI, 965-1210) thousand in 1990 to 1795 (1597-1986) thousand in 2009. The implementation of the toughest-ever Air Pollution Prevention and Control Action Plan (APPCAP) in 2013 effectively controlled PM2.5 pollution in China. By 2018, the nationwide PW PM2.5 concentration had decreased to 34.0 (29.2-38.9) μg/m3. Dynamic trend prediction revealed that, although the APPCAP achieved substantial health benefits, the policy did not result in further remarkable reductions in PM2.5-related deaths; in 2019, deaths peaked at 1932 (1716-2140) thousand. PM2.5-related deaths in 2030 were projected for each of four emission control scenarios. The results of the driving factor analysis and the future projections indicated that the health benefits from improving air quality are likely to be counterbalanced by changes in the population age structure. Because population ageing is becoming more and more rapid in China and the challenge of climate change is increasing, the results of this study imply that policymakers need to implement more stringent measures and set more ambitious emission control targets to reduce nationwide PM2.5-related premature mortality in the future.
Collapse
Affiliation(s)
- Shuai Yin
- Earth System Division, National Institute for Environmental Studies, Tsukuba 3058506, Japan.
| |
Collapse
|
36
|
Zhang F, Xing J, Ding D, Wang J, Zheng H, Zhao B, Qi L, Wang S. Role of black carbon in modulating aerosol direct effects driven by air pollution controls during 2013-2017 in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 832:154928. [PMID: 35367259 DOI: 10.1016/j.scitotenv.2022.154928] [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: 01/29/2022] [Revised: 03/21/2022] [Accepted: 03/26/2022] [Indexed: 06/14/2023]
Abstract
Aerosol direct effects (ADEs) can modulate shortwave radiation as well as atmospheric dynamics and air quality. As the key absorbing component of aerosol, the black carbon (BC) largely determines the aerosol optical properties. Therefore, it is expected that BC emission controls might gain co-benefits from the simultaneous reduction of ADEs. To demonstrate such synergy, here we quantified the ADEs changes and the role of BC controls in China during 2013-2017 using a regional two-way coupled meteorology chemistry transport model. Simulated results suggest that the control action effectively reduced the wintertime PM2.5 concentration (-26.0 μg m-3) and associated ADEs. In January, the influence of ADEs on surface shortwave radiation, 2-meter temperature, and planetary boundary layer height was weakened from -16.7 W m-2, -0.20 °C, and -15.4 m in 2013 to -11.3 W m-2, -0.06 °C, and -10.7 m in 2017, respectively. The enhancement of SO2, NO2, and PM2.5 concentrations due to ADEs was reduced from +3.1%, +5.2%, and +5.4% in 2013 to +2.6%, +4.5%, and +3.3% in 2017, respectively, demonstrating the extra benefit of air pollution controls for improving air quality by reducing ADEs. Meanwhile, the BC emission reduced by 12.5% simultaneously along with the effective controls on SO2 and NO2 emissions during 2013-2017, mainly from domestic combustion (-11.7%), resulting in 30.3% (-0.9 μg m-3) reduction of BC concentration. Such BC controls contributed 15.6-60.2% of such changes in the ADEs influence on meteorological variables, and 32.6-41.1% on air pollutants. More specially, the effectiveness of collaborative reduction of BC further reduced surface shortwave radiation in China by 3.6 W m-2 in January and 1.0 W m-2 in July, leading to a more weakened ADEs that bring extra benefits in reducing PM2.5 concentrations by 1.8 μg m-3 in January and 0.3 μg m-3 in July. Apparently, BC played an important role in modulating the ADEs and associated influences on meteorology and air quality, suggesting a wise control strategy by targeting absorbing component of PM2.5 reduction to address both air pollution and climate change in the future.
Collapse
Affiliation(s)
- Fenfen Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Dian Ding
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland
| | - Jiandong Wang
- School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Haotian Zheng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Ling Qi
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
37
|
Xing J, Li S, Zheng S, Liu C, Wang X, Huang L, Song G, He Y, Wang S, Sahu SK, Zhang J, Bian J, Zhu Y, Liu TY, Hao J. Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:9903-9914. [PMID: 35793558 DOI: 10.1021/acs.est.1c08337] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Accurate timely estimation of emissions of nitrogen oxides (NOx) is a prerequisite for designing an effective strategy for reducing O3 and PM2.5 pollution. The satellite-based top-down method can provide near-real-time constraints on emissions; however, its efficiency is largely limited by efforts in dealing with the complex emission-concentration response. Here, we propose a novel machine-learning-based method using a physically informed variational autoencoder (VAE) emission predictor to infer NOx emissions from satellite-retrieved surface NO2 concentrations. The computational burden can be significantly reduced with the help of a neural network trained with a chemical transport model, allowing the VAE emission predictor to provide a timely estimation of posterior emissions based on the satellite-retrieved surface NO2 concentration. The VAE emission predictor successfully corrected the underestimation of NOx emissions in rural areas and the overestimation in urban areas, resulting in smaller normalized mean biases (reduced from -0.8 to -0.4) and larger R2 values (increased from 0.4 to 0.7). The interpretability of the VAE emission predictor was investigated using sensitivity analysis by modulating each feature, indicating that NO2 concentration and planetary boundary layer (PBL) height are important for estimating NOx emissions, which is consistent with our common knowledge. The advantages of the VAE emission predictor in efficiency, flexibility, and accuracy demonstrate its great potential in estimating the latest emissions and evaluating the control effectiveness from observations.
Collapse
Affiliation(s)
- Jia Xing
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Siwei Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | | | - Chang Liu
- Microsoft Research Asia, Beijing 100080, China
| | - Xiaochun Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Lin Huang
- Microsoft Research Asia, Beijing 100080, China
| | - Ge Song
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
| | - Yihan He
- Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shovan Kumar Sahu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jia Zhang
- Microsoft Research Asia, Beijing 100080, China
| | - Jiang Bian
- Microsoft Research Asia, Beijing 100080, China
| | - Yun Zhu
- College of Environment and Energy, Guangzhou Higher Education Mega Center, South China University of Technology, Guangzhou 510006, China
| | - Tie-Yan Liu
- Microsoft Research Asia, Beijing 100080, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| |
Collapse
|
38
|
Impact of Climate-Driven Land-Use Change on O3 and PM Pollution by Driving BVOC Emissions in China in 2050. ATMOSPHERE 2022. [DOI: 10.3390/atmos13071086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This study predicted three future land-use type scenarios in 2050 (including the Shared Socioeconomic Pathway SSP126, SSP585, and carbon scenario) based on the Land-Use Harmonization (LUH2) project and the future evolution of land-use types considering China’s carbon neutrality background. The contribution of land-use changes to terrestrial natural source biogenic volatile organic compounds (BVOCs), as well as O3 and PM concentrations, were determined. Under the SSP126 pathway, meteorological changes would increase BVOC emissions in China by 1.0 TgC in 2050, compared with 2015, while land-use changes would increase them by 1.5–7.1 TgC. The impact of land-use changes on O3 and PM concentrations would be less than 3.6% in 2050 and greater in summer. Regional differences must be considered when calculating future environmental background concentrations of pollutants. Due to more afforestation measures under the SSP126 scenario, the impact of land-use change on pollutants was more obvious under the SSP126 pathway than under the SSP585 pathway. Under the carbon scenario, the increase in PM concentration caused by land-use changes would pose a risk to air quality compliance; thus, it is necessary to consider reducing or offsetting this potential risk through anthropogenic emission control measures.
Collapse
|
39
|
Effects of Meteorology Changes on Inter-Annual Variations of Aerosol Optical Depth and Surface PM2.5 in China—Implications for PM2.5 Remote Sensing. REMOTE SENSING 2022. [DOI: 10.3390/rs14122762] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PM2.5 retrieval from satellite-observed aerosol optical depth (AOD) is still challenging due to the strong impact of meteorology. We investigate influences of meteorology changes on the inter-annual variations of AOD and surface PM2.5 in China between 2006 and 2017 using a nested 3D chemical transport model, GEOS-Chem, by fixing emissions at the 2006 level. We then identify major meteorological elements controlling the inter-annual variations of AOD and surface PM2.5 using multiple linear regression. We find larger influences of meteorology changes on trends of AOD than that of surface PM2.5. On the seasonal scale, meteorology changes are beneficial to AOD and surface PM2.5 reduction in spring (1–50%) but show an adverse effect on aerosol reduction in summer. In addition, major meteorological elements influencing variations of AOD and PM2.5 are similar between spring and fall. In winter, meteorology changes are favorable to AOD reduction (−0.007 yr−1, −1.2% yr−1; p < 0.05) but enhanced surface PM2.5 between 2006 and 2017. The difference in winter is mainly attributed to the stable boundary layer that isolates surface PM2.5 from aloft. The significant decrease in AOD over the years is related to the increase in meridional wind speed at 850 hPa in NCP (p < 0.05). The increase of surface PM2.5 in NCP in winter is possibly related to the increased temperature inversion and more stable stratification in the boundary layer. This suggests that previous estimates of wintertime surface PM2.5 using satellite measurements of AOD corrected by meteorological elements should be used with caution. Our findings provide potential meteorological elements that might improve the retrieval of surface PM2.5 from satellite-observed AOD on the seasonal scale.
Collapse
|
40
|
Conibear L, Reddington CL, Silver BJ, Chen Y, Arnold SR, Spracklen DV. Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020. GEOHEALTH 2022; 6:e2021GH000567. [PMID: 35765413 PMCID: PMC9207900 DOI: 10.1029/2021gh000567] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/22/2022] [Accepted: 03/31/2022] [Indexed: 06/15/2023]
Abstract
Anthropogenic emissions and ambient fine particulate matter (PM2.5) concentrations have declined in recent years across China. However, PM2.5 exposure remains high, ozone (O3) exposure is increasing, and the public health impacts are substantial. We used emulators to explore how emission changes (averaged per sector over all species) have contributed to changes in air quality and public health in China over 2010-2020. We show that PM2.5 exposure peaked in 2012 at 52.8 μg m-3, with contributions of 31% from industry and 22% from residential emissions. In 2020, PM2.5 exposure declined by 36% to 33.5 μg m-3, where the contributions from industry and residential sources reduced to 15% and 17%, respectively. The PM2.5 disease burden decreased by only 9% over 2012 where the contributions from industry and residential sources reduced to 15% and 17%, respectively 2020, partly due to an aging population with greater susceptibility to air pollution. Most of the reduction in PM2.5 exposure and associated public health benefits occurred due to reductions in industrial (58%) and residential (29%) emissions. Reducing national PM2.5 exposure below the World Health Organization Interim Target 2 (25 μg m-3) would require a further 80% reduction in residential and industrial emissions, highlighting the challenges that remain to improve air quality in China.
Collapse
Affiliation(s)
- Luke Conibear
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Carly L. Reddington
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Ben J. Silver
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Ying Chen
- College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK
| | - Stephen R. Arnold
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| | - Dominick V. Spracklen
- Institute for Climate and Atmospheric ScienceSchool of Earth and EnvironmentUniversity of LeedsLeedsUK
| |
Collapse
|
41
|
Shen J, Zhao Q, Ying Q, Cheng Z, Xu J, Zhang H, Fu Q. An explainable integrated optimization methodology for source apportionment of ambient particulate matter components. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 310:114789. [PMID: 35220094 DOI: 10.1016/j.jenvman.2022.114789] [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: 04/05/2021] [Revised: 01/11/2022] [Accepted: 02/20/2022] [Indexed: 06/14/2023]
Abstract
Source apportionment of fine particulate matter (PM2.5) components is crucial for air pollution control. Prediction accuracies by the chemical transport model (CTM) significantly affect source apportionment results. Many efforts have been made to improve source apportionment results based on the CTM using mathematical algorithms, but the reasons for uncertainties in source apportionment results are less concerned. Here, an integrated optimization methodology is developed to quantify deviations from emission inventory and chemical mechanism in the model for improving prediction and source apportionment accuracies. Emission deviations of primary aerosols and gaseous pollutants are firstly calculated by an optimization algorithm with observation and receptor model constraints. Emission inventory is then adjusted for a new CTM simulation. Deviations from chemical mechanism for secondary conversions are evaluated by biases between observations and new predictions. Source apportionment results are adjusted according to both emission and chemical mechanism deviations. A winter month in 2016 at the Qingpu supersite in eastern China is selected as a case study. Results show that our integrated optimization methodology can successfully adjust emissions to pull original predictions towards observations. Total deviations of emissions for elemental carbon, organic carbon, primary sulfate, primary nitrate, primary ammonium, sulfur dioxide (SO2), nitrogen oxides (NOx) and ammonia (NH3) are estimated +59.6%, +95.9%, +72.9%, +82.2%, +75.9%, -6.4%, +67.6% and -17.6%, respectively. Also, major directions of deviations from chemical mechanisms can be captured. Deviations from SO2 to secondary sulfate, nitrogen dioxide (NO2) to secondary nitrate and NH3 to secondary ammonium conversions are estimated -77.3%, +27.1% and -38.8%, respectively. Consequently, source apportionment results are significantly improved. This developed methodology provides an efficient way to quantify deviations from emissions and chemical mechanisms to improve source apportionment for air pollution management.
Collapse
Affiliation(s)
- Juanyong Shen
- Shanghai Environmental Protection Key Lab of Environmental Big Data and Intelligent Decision-making, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qianbiao Zhao
- Shanghai Environmental Monitoring Center, Shanghai, 200030, China; Academy of Environmental Planning & Design, Co., Ltd., Nanjing University, Nanjing, 210093, China
| | - Qi Ying
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Zhen Cheng
- Shanghai Environmental Protection Key Lab of Environmental Big Data and Intelligent Decision-making, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Junzhe Xu
- Shanghai Environmental Protection Key Lab of Environmental Big Data and Intelligent Decision-making, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hairui Zhang
- Shanghai Environmental Protection Key Lab of Environmental Big Data and Intelligent Decision-making, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qingyan Fu
- Shanghai Environmental Monitoring Center, Shanghai, 200030, China
| |
Collapse
|
42
|
Shi G, Lu X, Zhang H, Zheng H, Zhang Z, Chen S, Xing J, Wang S. Air pollutant emissions induced by rural-to-urban migration during China's urbanization (2005-2015). ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2022; 10:100166. [PMID: 36159731 PMCID: PMC9488084 DOI: 10.1016/j.ese.2022.100166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/01/2022] [Accepted: 03/01/2022] [Indexed: 06/16/2023]
Abstract
As the world's most populous country, China has witnessed rapid urbanization in recent decades, with population migration from rural to urban (RU) regions as the major driving force. Due to the large gap between rural and urban consumption and investment level, large-scale RU migration impacts air pollutant emissions and creates extra uncertainties for air quality improvement. Here, we integrated population migration assessment, an environmentally extended input-output model and structural decomposition analysis to evaluate the NOx, SO2 and primary PM2.5 emissions induced by RU migration during China's urbanization from 2005 to 2015. The results show that RU migration increased air pollutant emissions, while the increases in NOx and SO2 emissions peaked in approximately 2010 at 2.4 Mt and 2.2 Mt, accounting for 9.2% and 8.7% of the national emissions, respectively. The primary PM2.5 emissions induced by RU migration also peaked in approximately 2012 at 0.3 Mt, accounting for 2.8% of the national emissions. The indirect emissions embodied in consumption and investment increased, while household direct emissions decreased. The widening gap between urban and rural investment and consumption exerted a major increasing effect on migration-induced emissions; in contrast, the falling emission intensity contributed the most to the decreasing effect benefitting from end-of-pipe control technology applications as well as improving energy efficiency. The peak of air pollutant emissions induced by RU migration indicates that although urbanization currently creates extra environmental pressure in China, it is possible to reconcile urbanization and air quality improvement in the future with updating urbanization and air pollution control policies.
Collapse
Affiliation(s)
- Guang Shi
- State Key Joint Laboratory of Environment Simulation and Pollution Control and School of Environment, Tsinghua University, Beijing, 100084, PR China
| | - Xi Lu
- State Key Joint Laboratory of Environment Simulation and Pollution Control and School of Environment, Tsinghua University, Beijing, 100084, PR China
- Institute for Carbon Neutrality, Tsinghua University, Beijing, 100084, PR China
- Beijing Laboratory of Environmental Frontier Technologies, Tsinghua University, Beijing, 100084, PR China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, PR China
| | - Hongxia Zhang
- School of Applied Economics, Renmin University of China, Beijing, 100872, PR China
| | - Haotian Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control and School of Environment, Tsinghua University, Beijing, 100084, PR China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, PR China
| | - Zhonghua Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control and School of Environment, Tsinghua University, Beijing, 100084, PR China
| | - Shi Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control and School of Environment, Tsinghua University, Beijing, 100084, PR China
| | - Jia Xing
- State Key Joint Laboratory of Environment Simulation and Pollution Control and School of Environment, Tsinghua University, Beijing, 100084, PR China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, PR China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control and School of Environment, Tsinghua University, Beijing, 100084, PR China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, 100084, PR China
| |
Collapse
|
43
|
Li J, Li J, Wang G, Ho KF, Han J, Dai W, Wu C, Cao C, Liu L. In-vitro oxidative potential and inflammatory response of ambient PM 2.5 in a rural region of Northwest China: Association with chemical compositions and source contribution. ENVIRONMENTAL RESEARCH 2022; 205:112466. [PMID: 34863982 DOI: 10.1016/j.envres.2021.112466] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 11/15/2021] [Accepted: 11/27/2021] [Indexed: 06/13/2023]
Abstract
Overproduction of reactive oxygen species (ROS) induced by atmospheric particles and subsequent inflammatory responses are considered as one of the most important pathological mechanisms with regard to the adverse effects of air pollution exposure. In this study, fine particulate matter (PM2.5) samples were collected at a rural site in Guanzhong Basin, Northwest China, in both summer (August 3-23, 2016) and winter (January 5-February 1, 2017). Then, human bronchial epithelial BEAS-2B cells were exposed to the PM2.5, cultured for 24 h, and then assayed for particle-induced ROS and three inflammatory factors (tumor necrosis-α (TNF-α), interleukin-6 (IL-6), and interferon-γ (IFN-γ)) in vitro. The oxidative potential (OP) induced by winter PM2.5 samples was higher than that induced by summertime samples, whereas inflammatory values showed contrasting seasonal variations. Both OP and inflammatory factors were significantly correlated with most chemical compounds in winter, but not in summer, which was thought to be related mainly to the higher contribution from secondary aerosols formed during the warm season. Source apportionment results showed secondary aerosols formation have significant contribution to OP of PM2.5 in both seasons, but the dominant oxidation processes is different. Secondary nitrates-related process was the major contributors regulating the OP in winter; however, secondary sulfates formation were mainly responsible for the ROS responses in summer. For primary emission, biomass burning, rather than coal emission or vehicle exhaust, was the significant source for OP of PM2.5 in winter. In most cases, the source contribution of each inflammatory factor was similar to that of the ROS. Our results highlighted the significant health risk of atmospheric aerosols from biomass burning in the rural regions of Guanzhong Basin, Northwest China.
Collapse
Affiliation(s)
- Jianjun Li
- Key Lab of Aerosol Chemistry & Physics, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China; CAS Center for Excellence in Quaternary Science and Global Change, Xi'an, 710061, China.
| | - Jin Li
- Key Lab of Aerosol Chemistry & Physics, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China
| | - Gehui Wang
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai, 200241, China; Institute of Eco-Chongming, 3663 N. Zhongshan Rd., Shanghai, 200062, China.
| | - Kin Fai Ho
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China; Shenzhen Municipal Key Laboratory for Health Risk Analysis, Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
| | - Jing Han
- College of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, 710061, China
| | - Wenting Dai
- Key Lab of Aerosol Chemistry & Physics, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China
| | - Can Wu
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai, 200241, China; Institute of Eco-Chongming, 3663 N. Zhongshan Rd., Shanghai, 200062, China
| | - Cong Cao
- Key Lab of Aerosol Chemistry & Physics, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China
| | - Lang Liu
- Key Lab of Aerosol Chemistry & Physics, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China
| |
Collapse
|
44
|
Khan MN, Aziz G, Khan MS. The Impact of Sustainable Growth and Sustainable Environment on Public Health: A Study of GCC Countries. Front Public Health 2022; 10:887680. [PMID: 35433611 PMCID: PMC9008305 DOI: 10.3389/fpubh.2022.887680] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
The current study investigates the impact of economic growth, carbon emission, temperature, and environmental technology on public health in GCC countries. Panel data from 1990 to 2020 is used, and the panel unit root test is used to check the stationarity of the data. After cointegration analysis, the ARDL estimation technique checks the long-run and short-run association between variables. The results have proved that economic growth enhances exposure to PM2.5 and mortality but helps in increasing life expectancy. Likewise, carbon emission also enhances exposure to PM2.5 and mortality but improves life expectancy. As far as temperature is concerned, although it increases the exposure to PM2.5, it also increases life expectancy. It is also found that environmental technology enhances exposure to PM2.5. For policy implication, the study reports that investment in research and development and modifications the energy mix are key measures to enhance the public health in GCC countries.
Collapse
Affiliation(s)
- Mohd Naved Khan
- Department of Business Administration, College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh, Saudi Arabia
- *Correspondence: Mohd Naved Khan
| | - Ghazala Aziz
- Department of Business Administration, College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Mohd Saeed Khan
- Finance and Economics Department, College of Business, University of Jeddah, Jeddah, Saudi Arabia
| |
Collapse
|
45
|
Cao Y, Wang Q, Zhou D. Does air pollution inhibit manufacturing productivity in Yangtze River Delta, China? Moderating effects of temperature. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 306:114492. [PMID: 35033887 DOI: 10.1016/j.jenvman.2022.114492] [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: 11/02/2021] [Revised: 12/20/2021] [Accepted: 01/10/2022] [Indexed: 06/14/2023]
Abstract
China has been experiencing serious and recurrent incidences of air pollution in recent years. The frequency and timespans of such incidences are uncertain because of variable urban weather conditions, especially temperature, that inhibit the productivity of manufacturing companies. Matching data about listed manufacturing companies in China's Yangtze River Delta urban cluster from 2003 to 2018 with data on urban air pollution and weather, we explored the effects of air pollution on corporate productivity and the moderating role of temperature. We found that air pollution significantly inhibited the productivity of these companies, which decreased by about 0.1% for 1% increase in the concentration of PM2.5. Regarding industry heterogeneity, high-energy-consumption and low-technology manufacturing industries were more sensitive to the negative effects of air pollution. Furthermore, we concluded that low temperatures played an important role in causing significant increases in the negative effects of air pollution. In the fall and winter (October to January), the lower the temperatures resulted in stronger inhibitory effects of air pollution on corporate productivity. When the average daily temperature is 0°C-3°C, the moderating effects of temperature are even more obvious. To minimize the inhibitory effects of air pollution on productivity, governments and companies should implement positive adaptions to simultaneously confront air pollution and temperature change.
Collapse
Affiliation(s)
- Yaru Cao
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Research Centre for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Qunwei Wang
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Research Centre for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
| | - Dequn Zhou
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China; Research Centre for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| |
Collapse
|
46
|
Li C, Hammer MS, Zheng B, Cohen RC. Accelerated reduction of air pollutants in China, 2017-2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 803:150011. [PMID: 34525772 DOI: 10.1016/j.scitotenv.2021.150011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/22/2021] [Accepted: 08/25/2021] [Indexed: 06/13/2023]
Abstract
Emission regulations of the power and industry sectors have been identified as the major driver of PM2.5 mitigation over China during 2013-2017. In this study, we use ground-based observations of four air pollutants (CO, NO2, SO2, and PM2.5) to show that additional stringent emission policies on the industrial, transportation, and residential sectors during the new 3-year protection plan (2018-2020) have accelerated the improvement of China's air quality. Based on regional (North and South China) trends of annual mean measurements, significant reductions are observed for all four pollutants during 2017-2020. These decreasing trends are found to be >30% stronger than 2015-2017 for NO2, CO, and PM2.5. For CO and PM2.5, the acceleration is the strongest in winter and North China, when and where the residential clean-heating actions were implemented. While for NO2, the accelerations are pronounced regardless of region or season, reflecting nationwide measures to reduce NOx emissions from industrial and transportation activities. SO2 concentration reductions that were already substantial before 2017 are maintained but not accelerated, consistent with the dominance of end-of-pipe measures rather than a structural change of energy fuels. Our investigation highlights the value of multi-pollutant analysis to relate emission policies with air quality changes.
Collapse
Affiliation(s)
- Chi Li
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, USA.
| | - Melanie S Hammer
- Department of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Ronald C Cohen
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, USA; Department of Earth and Planetary Science, University of California, Berkeley, Berkeley, CA, USA.
| |
Collapse
|
47
|
Zhou G, Wu J, Yang M, Sun P, Gong Y, Chai J, Zhang J, Afrim FK, Dong W, Sun R, Wang Y, Li Q, Zhou D, Yu F, Yan X, Zhang Y, Jiang L, Ba Y. Prenatal exposure to air pollution and the risk of preterm birth in rural population of Henan Province. CHEMOSPHERE 2022; 286:131833. [PMID: 34426128 DOI: 10.1016/j.chemosphere.2021.131833] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/29/2021] [Accepted: 08/05/2021] [Indexed: 06/13/2023]
Abstract
Due to the poor living and healthcare conditions, preterm birth (PTB) in rural population is a pressing health issue. However, PTB studies in rural population are rare. To explore the effects of air pollutants on PTB in rural population, we collected 697,316 medical records during 2014-2016 based on the National Free Preconception Health Examination Project. Logistic regression models were used to estimate the association between air pollutants and PTB and the modifying effects of demographic characteristics. Relative contribution and principal component analysis-generalized linear model (PCA-GLM) analysis were used to explore the most significant air pollutant and gestational period. Our results demonstrated that PTB risk is positively associated with exposure to air pollutants including PM10, PM2.5, SO2, NO2, and CO, while negatively associated with O3 exposure (P < 0.05). In addition, we found that NO2 was the largest contributor to the risk of PTB caused by air pollutants (26.5%). The third trimester of pregnancy was the most sensitive exposure window. PCA-GLM analysis showed that the first component (a combination of PM, SO2, NO2, and CO) increased the risk of PTB. Moreover, we found that rural women who are younger, had higher educated, multi-parity, or smoke appeared to be more sensitive to the association between air pollutants exposure and PTB (P-interaction<0.05). Our findings suggested that increased air pollutants except O3 were associated with elevated PTB risk, especially among vulnerable mothers. Therefore, the effects of air pollutants exposure on PTB should be mitigated by restricting emission sources of NO2 and SO2 in rural population, especially during the third trimester.
Collapse
Affiliation(s)
- Guoyu Zhou
- Department of Environmental Health & Environment and Health Innovation Team, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China; Yellow River Institute for Ecological Protection & Regional Coordinated Development, Zhengzhou University, Zhengzhou, Henan, 450001, PR China
| | - Jingjing Wu
- Department of Environmental Health & Environment and Health Innovation Team, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China
| | - Meng Yang
- Department of Environmental Health & Environment and Health Innovation Team, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China
| | - Panpan Sun
- National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Henan Institute of Reproduction Health Science and Technology, Zhengzhou, Henan, 450002, PR China
| | - Yongxiang Gong
- Department of Environmental Health & Environment and Health Innovation Team, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China
| | - Jian Chai
- National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Henan Institute of Reproduction Health Science and Technology, Zhengzhou, Henan, 450002, PR China
| | - Junxi Zhang
- National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Henan Institute of Reproduction Health Science and Technology, Zhengzhou, Henan, 450002, PR China
| | - Francis-Kojo Afrim
- Department of Environmental Health & Environment and Health Innovation Team, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China
| | - Wei Dong
- National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Henan Institute of Reproduction Health Science and Technology, Zhengzhou, Henan, 450002, PR China
| | - Renjie Sun
- Department of Environmental Health & Environment and Health Innovation Team, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China
| | - Yuhong Wang
- National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Henan Institute of Reproduction Health Science and Technology, Zhengzhou, Henan, 450002, PR China
| | - Qinyang Li
- Department of Environmental Health & Environment and Health Innovation Team, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China
| | - Dezhuan Zhou
- National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Henan Institute of Reproduction Health Science and Technology, Zhengzhou, Henan, 450002, PR China
| | - Fangfang Yu
- Department of Environmental Health & Environment and Health Innovation Team, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China
| | - Xi Yan
- Department of Neurology, Henan Provincial People's Hospital; Zhengzhou University People's Hospital; Henan University People's Hospital, Zhengzhou, Henan, 450001, PR China
| | - Yawei Zhang
- Department of Environment Health Science, Yale University School of Public Health, New Haven, CT, USA
| | - Lifang Jiang
- National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Henan Institute of Reproduction Health Science and Technology, Zhengzhou, Henan, 450002, PR China
| | - Yue Ba
- Department of Environmental Health & Environment and Health Innovation Team, School of Public Health, Zhengzhou University, Zhengzhou, Henan, 450001, PR China; Yellow River Institute for Ecological Protection & Regional Coordinated Development, Zhengzhou University, Zhengzhou, Henan, 450001, PR China.
| |
Collapse
|
48
|
Zhang S, An K, Li J, Weng Y, Zhang S, Wang S, Cai W, Wang C, Gong P. Incorporating health co-benefits into technology pathways to achieve China's 2060 carbon neutrality goal: a modelling study. Lancet Planet Health 2021; 5:e808-e817. [PMID: 34758346 DOI: 10.1016/s2542-5196(21)00252-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND The announcement of China's 2060 carbon neutrality goal has drawn the world's attention to the specific technology pathway needed to achieve this pledge. We aimed to evaluate the health co-benefits of carbon neutrality under different technology pathways, which could help China to achieve the carbon neutrality goal, air quality goal, and Healthy China goal in a synergetic manner that includes health in the decision-making process. METHODS In this modelling study, we used Shared Socioeconomic Pathway 2 with no climate policy as the reference scenario, and two representative carbon neutrality scenarios with identical emission trajectories and different technology pathways-one was led by renewable energies and the other was led by negative emission technologies. We had three modules to analyse health co-benefits and mitigation costs for each policy scenario. First, we used a computable general equilibrium model that captures the operation of the whole economic system to investigate the carbon mitigation costs and air pollutant emission pathways of different technology portfolios. Second, we used a reduced complexity air quality model to estimate the concentrations of particulate matter in the atmosphere from the air pollutant emission pathways. Finally, we used a health impact evaluation model to estimate premature deaths, morbidity, and the resulting loss of life expectancy, then these health impacts were monetised according to value of a statistical life and cost of illness. We compared the monetised health co-benefits against the corresponding mitigation costs to explore the cost-effectiveness of different technology portfolios. A series of uncertainties embodied in carbon neutrality pathways and models were considered. FINDINGS In our models, sole dependence on improving end-of-pipe air pollution control measures is not sufficient for all Chinese provinces to meet the 2005 WHO PM2·5 standards (10 μg/m3) by 2060. Only a combination of strong climate and air pollution control policies can lead to substantial improvement of air quality across China. If the carbon neutrality pathway led by developing renewable energies was followed, the air quality of all provinces could meet the WHO guideline by 2060. With the realisation of carbon neutrality goals, the total discounted mitigation costs (discount rate 5%) from 2020-60 would range from 40-125 trillion Chinese yuan (CNY), and 22-50 million cumulative premature deaths could be avoided. China has the potential to increase the associated life expectancy by 0·88-2·80 years per person in 2060 versus the reference scenario. The health benefits are higher in the renewable energies-led scenarios, whereas the mitigation costs are smaller in the negative emission technologies-led scenarios. If the value of a statistical life is set higher than 12·5 million CNY (39% of the Organisation for Economic Co-operation and Development value), the health co-benefits will be higher than mitigation costs, even when considering all included uncertainties, implying the cost-effectiveness of China's carbon neutrality goal. INTERPRETATION The life expectancy increase from the realisation of China's 2060 carbon neutrality goal could be equivalent to the past 5-10 years of life expectancy growth in China. Choosing an appropriate carbon neutrality pathway affects the health of China's population both today and in the future. Our findings suggest that, if China incorporates health co-benefits into climate policy making and puts a high value on people's health, it should choose a carbon neutrality pathway that relies more on developing renewable energies and avoid over-reliance on negative emission technologies. FUNDING National Key R&D Program of China, National Natural Science Foundation of China, Tsinghua-Toyota Joint Research Fund, Tsinghua-Rio Tinto Joint Research Centre for Resources, and Global Energy Interconnection Group. TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
Collapse
Affiliation(s)
- Shihui Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China; State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), and School of Environment, Tsinghua University, Beijing, China; Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Laboratory for Low Carbon Energy, Tsinghua University, Beijing, China
| | - Kangxin An
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), and School of Environment, Tsinghua University, Beijing, China
| | - Jin Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), and School of Environment, Tsinghua University, Beijing, China
| | - Yuwei Weng
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
| | - Shaohui Zhang
- School of Economics and Management, Beihang University, Beijing, China; Pollution Management Research Group, Energy, Climate, and Environment Program International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), and School of Environment, Tsinghua University, Beijing, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China
| | - Wenjia Cai
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China; Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Laboratory for Low Carbon Energy, Tsinghua University, Beijing, China.
| | - Can Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), and School of Environment, Tsinghua University, Beijing, China; Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Laboratory for Low Carbon Energy, Tsinghua University, Beijing, China
| | - Peng Gong
- Department of Earth Sciences and Department of Geography, The University of Hong Kong, Hong Kong Special Administrative Region, China
| |
Collapse
|
49
|
Zhang S, Sarwar G, Xing J, Chu B, Xue C, Sarav A, Ding D, Zheng H, Mu Y, Duan F, Ma T, He H. Improving the representation of HONO chemistry in CMAQ and examining its impact on haze over China. ATMOSPHERIC CHEMISTRY AND PHYSICS 2021; 21:15809-15826. [PMID: 34804135 PMCID: PMC8597575 DOI: 10.5194/acp-21-15809-2021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We compare Community Multiscale Air Quality (CMAQ) model predictions with measured nitrous acid (HONO) concentrations in Beijing, China for December 2015. The model with the existing HONO chemistry in CMAQ severely under-estimates the observed HONO concentrations with a normalized mean bias of -97%. We revise the HONO chemistry in the model by implementing six additional heterogeneous reactions in the model: reaction of nitrogen dioxide (NO2) on ground surfaces, reaction of NO2 on aerosol surfaces, reaction of NO2 on soot surfaces, photolysis of aerosol nitrate, nitric acid displacement reaction, and hydrochloric acid displacement reaction. The model with the revised chemistry substantially increases HONO predictions and improves the comparison with observed data with a normalized mean bias of -5%. The photolysis of HONO enhances day-time hydroxyl radical by almost a factor of two. The enhanced hydroxyl radical concentrations compare favourably with observed data and produce additional sulfate via the reaction with sulfur dioxide, aerosol nitrate via the reaction with nitrogen dioxide, and secondary organic aerosols via the reactions with volatile organic compounds. The additional sulfate stemming from revised HONO chemistry improves the comparison with observed concentration; however, it does not close the gap between model prediction and the observation during polluted days.
Collapse
Affiliation(s)
- Shuping Zhang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Golam Sarwar
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC, 27711, USA
| | - Jia Xing
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Biwu Chu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Chaoyang Xue
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Arunachalam Sarav
- Institute for the Environment, The University of North Carolina at Chapel Hill, 100 Eurpoa Drive, Chapel Hill, NC 27514, USA
| | - Dian Ding
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Haotian Zheng
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yujing Mu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Tao Ma
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Hong He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| |
Collapse
|
50
|
Dong Z, Xing J, Ding D, Liu X, Wang S. Response of fine particulate matter and ozone to precursors emission reduction in the Yangtze River Delta andits policy implications. CHINESE SCIENCE BULLETIN-CHINESE 2021. [DOI: 10.1360/tb-2021-0615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|