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Li J, Yuan B, Yang S, Peng Y, Chen W, Xie Q, Wu Y, Huang Z, Zheng J, Wang X, Shao M. Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 932:173011. [PMID: 38719052 DOI: 10.1016/j.scitotenv.2024.173011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 05/14/2024]
Abstract
Ozone pollution presents a growing air quality threat in urban agglomerations in China. It remains challenge to distinguish the roles of emissions of precursors, chemical production and transportations in shaping the ground-level ozone trends, largely due to complicated interactions among these 3 major processes. This study elucidates the formation factors of ozone pollution and categorizes them into local emissions (anthropogenic and biogenic emissions), transport (precursor transport and direct transport from various regions), and meteorology. Particularly, we attribute meteorology, which affects biogenic emissions and chemical formation as well as transportation, to a perturbation term with fluctuating ranges. The Community Multiscale Air Quality (CMAQ) model was utilized to implement this framework, using the Pearl River Delta region as a case study, to simulate a severe ozone pollution episode in autumn 2019 that affected the entire country. Our findings demonstrate that the average impact of meteorological conditions changed consistently with the variation of ozone pollution levels, indicating that meteorological conditions can exert significant control over the degree of ozone pollution. As the maximum daily 8-hour average (MDA8) ozone concentrations increased from 20 % below to 30 % above the National Ambient Air Quality Standard II, contributions from emissions and precursor transport were enhanced. Concurrently, direct transport within Guangdong province rose from 13.8 % to 22.7 %, underscoring the importance of regional joint prevention and control measures under adverse weather conditions. Regarding biogenic emissions and precursor transport that cannot be directly controlled, we found that their contributions were generally greater in urban areas with high nitrogen oxides (NOx) levels, primarily due to the stronger atmospheric oxidation capacity facilitating ozone formation. Our results indicate that not only local anthropogenic emissions can be controlled in urban areas, but also the impacts of local biogenic emissions and precursor transport can be potentially regulated through reducing atmospheric oxidation capacity.
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Affiliation(s)
- Jin Li
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Bin Yuan
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China.
| | - Suxia Yang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Yuwen Peng
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Weihua Chen
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Qianqian Xie
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Yongkang Wu
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Zhijiong Huang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Junyu Zheng
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Xuemei Wang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Min Shao
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
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2
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Li P, Chen C, Liu D, Lian J, Li W, Fan C, Yan L, Gao Y, Wang M, Liu H, Pan X, Mao J. Characteristics and source apportionment of ambient volatile organic compounds and ozone generation sensitivity in urban Jiaozuo, China. J Environ Sci (China) 2024; 138:607-625. [PMID: 38135424 DOI: 10.1016/j.jes.2023.04.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 12/24/2023]
Abstract
In recent years, many cities have taken measures to reduce volatile organic compounds (VOCs), an important precursor of ozone (O3), to alleviate O3 pollution in China. 116 VOC species were measured by online and offline methods in the urban area of Jiaozuo from May to October in 2021 to analyze the compositional characteristics. VOC sources were analyzed by a positive matrix factorization (PMF) model, and the sensitivity of ozone generation was determined by ozone isopleth plotting research (OZIPR) simulation. The results showed that the average volume concentration of total VOCs was 30.54 ppbv and showed a bimodal feature due to the rush-hour traffic in the morning and at nightfall. The most dominant VOC groups were oxygenated VOCs (OVOCs, 29.3%) and alkanes (26.7%), and the most abundant VOC species were acetone and acetylene. However, based on the maximum incremental reactivity (MIR) method, the major VOC groups in terms of ozone formation potential (OFP) contribution were OVOCs (68.09 µg/m3, 31.5%), aromatics (62.90 µg/m3, 29.1%) and alkene/alkynes (54.90 µg/m3, 25.4%). This indicates that the control of OVOCs, aromatics and alkene/alkynes should take priority. Five sources of VOCs were quantified by PMF, including fixed sources of fossil fuel combustion (27.8%), industrial processes (25.9%), vehicle exhaust (19.7%), natural and secondary formation (13.9%) and solvent usage (12.7%). The empirical kinetic modeling approach (EKMA) curve obtained by OZIPR on O3 exceedance days indicated that the O3 sensitivity varied in different months. The results provide theoretical support for O3 pollution prevention and control in Jiaozuo.
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Affiliation(s)
- Pengzhao Li
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Chun Chen
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China; Henan Key Laboratory of Environmental Monitoring Technology, Henan Ecological Environment Monitoring and Safety Center, Zhengzhou 450046, China
| | - Dan Liu
- Henan Key Laboratory of Environmental Monitoring Technology, Henan Ecological Environment Monitoring and Safety Center, Zhengzhou 450046, China
| | - Jie Lian
- Jiaozuo Ecological Environment Monitoring Center of Henan Province, Jiaozuo 454003, China
| | - Wei Li
- Jiaozuo Ecological Environment Monitoring Center of Henan Province, Jiaozuo 454003, China
| | - Chuanyi Fan
- Jiaozuo Ecological Environment Monitoring Center of Henan Province, Jiaozuo 454003, China
| | - Liangyu Yan
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Yue Gao
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Miao Wang
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Hang Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Xiaole Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Jing Mao
- State Centre for International Cooperation on Designer Low-Carbon and Environmental Materials, School of Materials Science and Engineering, Zhengzhou University, Zhengzhou 450001, China.
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3
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Wu W, Fu TM, Arnold SR, Spracklen DV, Zhang A, Tao W, Wang X, Hou Y, Mo J, Chen J, Li Y, Feng X, Lin H, Huang Z, Zheng J, Shen H, Zhu L, Wang C, Ye J, Yang X. Temperature-Dependent Evaporative Anthropogenic VOC Emissions Significantly Exacerbate Regional Ozone Pollution. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5430-5441. [PMID: 38471097 DOI: 10.1021/acs.est.3c09122] [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: 03/14/2024]
Abstract
The evaporative emissions of anthropogenic volatile organic compounds (AVOCs) are sensitive to ambient temperature. This sensitivity forms an air pollution-meteorology connection that has not been assessed on a regional scale. We parametrized the temperature dependence of evaporative AVOC fluxes in a regional air quality model and evaluated the impacts on surface ozone in the Beijing-Tianjin-Hebei (BTH) area of China during the summer of 2017. The temperature dependency of AVOC emissions drove an enhanced simulated ozone-temperature sensitivity of 1.0 to 1.8 μg m-3 K-1, comparable to the simulated ozone-temperature sensitivity driven by the temperature dependency of biogenic VOC emissions (1.7 to 2.4 μg m-3 K-1). Ozone enhancements driven by temperature-induced AVOC increases were localized to their point of emission and were relatively more important in urban areas than in rural regions. The inclusion of the temperature-dependent AVOC emissions in our model improved the simulated ozone-temperature sensitivities on days of ozone exceedance. Our results demonstrated the importance of temperature-dependent AVOC emissions on surface ozone pollution and its heretofore unrepresented role in air pollution-meteorology interactions.
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Affiliation(s)
- Wenlu Wu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, U.K
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Steve R Arnold
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, U.K
| | - Dominick V Spracklen
- Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, U.K
| | - Aoxing Zhang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Wei Tao
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Xiaolin Wang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Yue Hou
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Jiajia Mo
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Jiongkai Chen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Yumin Li
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Xu Feng
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Haipeng Lin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Zhijiong Huang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou, Guangdong 511443, China
| | - Junyu Zheng
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong 511453, China
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Lei Zhu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Chen Wang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Jianhuai Ye
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Xin Yang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
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4
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Mao YH, Shang Y, Liao H, Cao H, Qu Z, Henze DK. Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 925:171585. [PMID: 38462008 DOI: 10.1016/j.scitotenv.2024.171585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 03/06/2024] [Accepted: 03/06/2024] [Indexed: 03/12/2024]
Abstract
Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS-Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25° × 0.3125° nested grids are adopted in the model. By using the T-mode principal component analysis (T-PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O3 concentrations ≥160 μg m-3) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 μg m-3 and 0.96 μg m-3, respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %-74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %-49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.
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Affiliation(s)
- Yu-Hao Mao
- 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 (NUIST), Nanjing 210044, China; Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/International Joint Research Laboratory on Climate and Environment Change (ILCEC), NUIST, Nanjing 210044, China.
| | - Yongjie Shang
- 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 (NUIST), Nanjing 210044, 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 (NUIST), Nanjing 210044, China; Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/International Joint Research Laboratory on Climate and Environment Change (ILCEC), NUIST, Nanjing 210044, China
| | - Hansen Cao
- Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
| | - Zhen Qu
- Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
| | - Daven K Henze
- Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
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5
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Zheng L, Adalibieke W, Zhou F, He P, Chen Y, Guo P, He J, Zhang Y, Xu P, Wang C, Ye J, Zhu L, Shen G, Fu TM, Yang X, Zhao S, Hakami A, Russell AG, Tao S, Meng J, Shen H. Health burden from food systems is highly unequal across income groups. NATURE FOOD 2024; 5:251-261. [PMID: 38486126 DOI: 10.1038/s43016-024-00946-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 02/21/2024] [Indexed: 03/27/2024]
Abstract
Food consumption contributes to the degradation of air quality in regions where food is produced, creating a contrast between the health burden caused by a specific population through its food consumption and that faced by this same population as a consequence of food production activities. Here we explore this inequality within China's food system by linking air-pollution-related health burden from production to consumption, at high levels of spatial and sectorial granularity. We find that low-income groups bear a 70% higher air-pollution-related health burden from food production than from food consumption, while high-income groups benefit from a 29% lower health burden relative to their food consumption. This discrepancy largely stems from a concentration of low-income residents in food production areas, exposed to higher emissions from agriculture. Comprehensive interventions targeting both production and consumption sides can effectively reduce health damages and concurrently mitigate associated inequalities, while singular interventions exhibit limited efficacy.
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Affiliation(s)
- Lianming Zheng
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Wulahati Adalibieke
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Feng Zhou
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China.
- College of Geography and Remote Sensing, Hohai University, Nanjing, China.
| | - Pan He
- School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK.
| | - Yilin Chen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
- School of Urban Planning and Design, Peking University, Shenzhen Graduate School, Shenzhen, China
| | - Peng Guo
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Jinling He
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Yuanzheng Zhang
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Peng Xu
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, China
| | - Chen Wang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Jianhuai Ye
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Lei Zhu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Guofeng Shen
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Xin Yang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
| | - Shunliu Zhao
- Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada
| | - Amir Hakami
- Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shu Tao
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China
- Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Jing Meng
- The Bartlett School of Sustainable Construction, University College London, London, UK.
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China.
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6
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Hu W, Zhao Y, Lu N, Wang X, Zheng B, Henze DK, Zhang L, Fu TM, Zhai S. Changing Responses of PM 2.5 and Ozone to Source Emissions in the Yangtze River Delta Using the Adjoint Model. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:628-638. [PMID: 38153406 DOI: 10.1021/acs.est.3c05049] [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: 12/29/2023]
Abstract
China's industrial restructuring and pollution controls have altered the contributions of individual sources to varying air quality over the past decade. We used the GEOS-Chem adjoint model and investigated the changing sensitivities of PM2.5 and ozone (O3) to multiple species and sources from 2010 to 2020 in the central Yangtze River Delta (YRDC), the largest economic region in China. Controlling primary particles and SO2 from industrial and residential sectors dominated PM2.5 decline, and reducing CO from multiple sources and ≥C3 alkenes from vehicles restrained O3. The chemical regime of O3 formation became less VOC-limited, attributable to continuous NOX abatement for specific sources, including power plants, industrial combustion, cement production, and off-road traffic. Regional transport was found to be increasingly influential on PM2.5. To further improve air quality, management of agricultural activities to reduce NH3 is essential for alleviating PM2.5 pollution, while controlling aromatics, alkenes, and alkanes from industry and gasoline vehicles is effective for O3. Reducing the level of NOX from nearby industrial combustion and transportation is helpful for both species. Our findings reveal the complexity of coordinating control of PM2.5 and O3 pollution in a fast-developing region and support science-based policymaking for other regions with similar air pollution problems.
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Affiliation(s)
- Weiyang Hu
- State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment, Nanjing University, 163 Xianlin Road, Nanjing, Jiangsu 210023, China
| | - Yu Zhao
- State Key Laboratory of Pollution Control and Resource Reuse and School of the Environment, Nanjing University, 163 Xianlin Road, Nanjing, Jiangsu 210023, China
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Jiangsu 210044, China
| | - Ni Lu
- Laboratory for Climate and Ocean-Atmosphere Sciences, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Xiaolin Wang
- Laboratory for Climate and Ocean-Atmosphere Sciences, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, China
| | - Daven K Henze
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Sciences, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Tzung-May Fu
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Shixian Zhai
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
- Division of Environment and Sustainability, HKUST Jockey Club Institute for Advanced Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China
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Wang X, Zhang S, Yan H, Ma Z, Zhang Y, Luo H, Yang X. Association of exposure to ozone and fine particulate matter with ovarian reserve among women with infertility. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 340:122845. [PMID: 37926414 DOI: 10.1016/j.envpol.2023.122845] [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: 04/11/2023] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/07/2023]
Abstract
Evidence linking diminished ovarian reserve, a significant cause of female infertility, and exposure to particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5) or O3 exposure remains a critical knowledge gap in female fertility. This study investigated the association between ambient PM2.5, O3 pollution, and anti-Müllerian hormone (AMH), a sensitive marker of ovarian reserve, in reproductive-aged Chinese women. We enrolled 2212 women with spontaneous menstrual cycles who underwent AMH measurements at a reproductive medicine center between 2018 and 2021. The daily mean concentrations of outdoor PM2.5 and O3 were estimated using a validated spatiotemporal model, followed by matching the participants' residential addresses. Three exposure periods were designed according to AMH expression patterns during follicle development. A generalized linear model was used to investigate changes in AMH associated with air pollution. The results showed a mean AMH level of 3.47 ± 2.61 ng/mL. During the six months from primary to early antral follicle stage (Period 1), each 10 μg/m3 increase in PM2.5 and O3 exposure was associated with AMH changes of -0.21 (95% confidence interval [CI]: -0.48, 0.06) ng/mL and -0.31 (95% CI: -0.50, -0.12) ng/mL, respectively. Further analyses indicated that the reduced ovarian reserve measured by AMH level was only significantly associated with PM2.5 exposure during follicle development from the primary to preantral follicle stage (Period 2) but was significantly associated with O3 exposure during Periods 1, 2, and 3. These observations were robust in the dual-pollutant model considering co-exposure to PM2.5 and O3. The results indicated an inverse association between ovarian reserve and ambient O3 exposure and suggested distinct susceptibility windows for O3 and PM2.5 for reduced ovarian reserve. These findings highlight the need to control ambient air pollution to reduce invisible risks to women's fertility, especially at high O3 concentrations.
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Affiliation(s)
- Xinyan Wang
- Center for Reproductive Medicine, Tianjin Central Hospital of Obstetrics and Gynecology, Maternal Hospital of Nankai University, Tianjin Key Laboratory of Human Development and Reproductive Regulation, No. 156 Nankai Third Road, Tianjin 300100, China
| | - Shuai Zhang
- Center for Reproductive Medicine, Tianjin Central Hospital of Obstetrics and Gynecology, Maternal Hospital of Nankai University, Tianjin Key Laboratory of Human Development and Reproductive Regulation, No. 156 Nankai Third Road, Tianjin 300100, China
| | - Huihui Yan
- Center for Reproductive Medicine, Tianjin Central Hospital of Obstetrics and Gynecology, Maternal Hospital of Nankai University, Tianjin Key Laboratory of Human Development and Reproductive Regulation, No. 156 Nankai Third Road, Tianjin 300100, China
| | - Zhao Ma
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
| | - Yunshan Zhang
- Center for Reproductive Medicine, Tianjin Central Hospital of Obstetrics and Gynecology, Maternal Hospital of Nankai University, Tianjin Key Laboratory of Human Development and Reproductive Regulation, No. 156 Nankai Third Road, Tianjin 300100, China
| | - Haining Luo
- Center for Reproductive Medicine, Tianjin Central Hospital of Obstetrics and Gynecology, Maternal Hospital of Nankai University, Tianjin Key Laboratory of Human Development and Reproductive Regulation, No. 156 Nankai Third Road, Tianjin 300100, China.
| | - Xueli Yang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, China
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Wang Z, Zhao H, Xu H, Li J, Ma T, Zhang L, Feng Y, Shi G. Strategies for the coordinated control of particulate matter and carbon dioxide under multiple combined pollution conditions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165679. [PMID: 37481086 DOI: 10.1016/j.scitotenv.2023.165679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/29/2023] [Accepted: 07/18/2023] [Indexed: 07/24/2023]
Abstract
Air pollutants represented by fine particulate matter (PM2.5) and the greenhouse effect caused by carbon dioxide (CO2), are both urgent threats to public health. Tackling the synergistic reduction of PM2.5 and CO2 is critical to achieving improvements in clean air worldwide. A persistent issue is the identification of their common sources and integrated impacts under different environmental conditions. In this study, we investigated the characteristics of the pollution types captured by combined analysis through a comprehensive observational dataset for 2017-2020, and applied machine learning algorithms to quantify the effects of drivers on air pollutants and CO2 formation. More importantly, detailed conclusions were drawn for the joint control of PM2.5-CO2 in multiple pollution types by using ensemble traceability technique. We demonstrated that reducing coal combustion emissions was an effective measure to maximize the benefits of PM2.5-CO2 in weather with low CO2 levels and no PM2.5 pollution. Correspondingly, on days with severe PM2.5 episodes, prioritizing control of vehicle emissions can simultaneously mitigate PM2.5 and CO2. Similar conclusions were found at high CO2 levels, accompanied by a more extensive role of vehicle emissions. Furthermore, a comparison of the differences in source impacts between PM2.5-CO2 and individual species suggests that focusing only on the sources that contribute significantly to one species may result in an underestimation or overestimation of PM2.5-CO2 source impacts. One such implication, as evidenced by our findings, is that synergistic controlling common sources of pollutants should be efficient. Thereby, common source management targeting PM2.5-CO2 under multiple pollution types is a more workable solution to alleviate environmental pollution.
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Affiliation(s)
- Zhenyu Wang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Huan Zhao
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Han Xu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Jie Li
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Tong Ma
- Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Linlin Zhang
- China National Environmental Monitoring Centre, Beijing 100012, China.
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Guoliang Shi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
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9
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Feng X, Ma Y, Lin H, Fu TM, Zhang Y, Wang X, Zhang A, Yuan Y, Han Z, Mao J, Wang D, Zhu L, Wu Y, Li Y, Yang X. Impacts of Ship Emissions on Air Quality in Southern China: Opportunistic Insights from the Abrupt Emission Changes in Early 2020. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:16999-17010. [PMID: 37856868 DOI: 10.1021/acs.est.3c04155] [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: 10/21/2023]
Abstract
In early 2020, two unique events perturbed ship emissions of pollutants around Southern China, proffering insights into the impacts of ship emissions on regional air quality: the decline of ship activities due to COVID-19 and the global enforcement of low-sulfur (<0.5%) fuel oil for ships. In January and February 2020, estimated ship emissions of NOx, SO2, and primary PM2.5 over Southern China dropped by 19, 71, and 58%, respectively, relative to the same period in 2019. The decline of ship NOx emissions was mostly over the coastal waters and inland waterways of Southern China due to reduced ship activities. The decline of ship SO2 and primary PM2.5 emissions was most pronounced outside the Chinese Domestic Emission Control Area due to the switch to low-sulfur fuel oil there. Ship emission reductions in early 2020 drove 16 to 18% decreases in surface NO2 levels but 3.8 to 4.9% increases in surface ozone over Southern China. We estimated that ship emissions contributed 40% of surface NO2 concentrations over Guangdong in winter. Our results indicated that future abatements of ship emissions should be implemented synergistically with reductions of land-borne anthropogenic emissions of nonmethane volatile organic compounds to effectively alleviate regional ozone pollution.
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Affiliation(s)
- Xu Feng
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Yaping Ma
- National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China
| | - Haipeng Lin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Shenzhen National Center for Applied Mathematics, Shenzhen 518055, Guangdong, China
- Center for Oceanic and Atmospheric Science at SUSTech (COAST), Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Yan Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Xiaolin Wang
- Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
| | - Aoxing Zhang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Yupeng Yuan
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Zimin Han
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jingbo Mao
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Dakang Wang
- School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, Guangdong, China
| | - Lei Zhu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Yujie Wu
- School of Public and International Affairs, Princeton University, Princeton, New Jersey 08544, United States
| | - Ying Li
- Center for Oceanic and Atmospheric Science at SUSTech (COAST), Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Department of Ocean Sciences and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
| | - Xin Yang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
- Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
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10
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Wen Y, Liu M, Zhang S, Wu X, Wu Y, Hao J. Updating On-Road Vehicle Emissions for China: Spatial Patterns, Temporal Trends, and Mitigation Drivers. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:14299-14309. [PMID: 37706680 DOI: 10.1021/acs.est.3c04909] [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: 09/15/2023]
Abstract
Vehicle emissions in China have been decoupled from rapid motorization owing to comprehensive control strategies. China's increasingly ambitious goals for better air quality are calling for deep emission mitigation, posing a need to develop an up-to-date emission inventory that can reflect the fast-developing policies on vehicle emission control. Herein, large-sample vehicle emission measurements were collected to update the vehicle emission inventory. For instance, ambient temperature correction modules were developed to depict the remarkable regional and seasonal emission variations, showing that the monthly emission disparities for total hydrocarbon (THC) and nitrogen oxide (NOX) in January and July could be up to 1.7 times in northern China. Thus, the emission ratios of THC and NOX can vary dramatically among various seasons and provinces, which have not been considered well by previous simulations regarding the nonlinear atmospheric chemistry of ozone (O3) and fine particulate matter (PM2.5) formation. The new emission results indicate that vehicular carbon monoxide (CO), THC, and PM2.5 emissions decreased by 69, 51, and 61%, respectively, during 2010-2019. However, the controls of NOX and ammonia (NH3) emissions were not as efficient as other pollutants. Under the most likely future scenario (PC [1]), CO, THC, NOX, PM2.5, and NH3 emissions were anticipated to reduce by 35, 36, 35, 45, and 4%, respectively, from 2019 to 2025. These reductions will be expedited with expected decreases of 56, 58, 74, 53, and 51% from 2025 to 2035, which are substantially promoted by the massive deployment of new energy vehicles and more stringent emission standards. The updated vehicle emission inventory can serve as an important tool to develop season- and location-specific mitigation strategies of vehicular emission precursors to alleviate haze and O3 problems.
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Affiliation(s)
- Yifan Wen
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - Min Liu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China
| | - 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, Beijing 100084, China
| | - Xiaomeng Wu
- School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, 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, Beijing 100084, China
| | - Jiming Hao
- 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, Beijing 100084, China
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11
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Tan W, Wang H, Su J, Sun R, He C, Lu X, Lin J, Xue C, Wang H, Liu Y, Liu L, Zhang L, Wu D, Mu Y, Fan S. Soil Emissions of Reactive Nitrogen Accelerate Summertime Surface Ozone Increases in the North China Plain. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:12782-12793. [PMID: 37596963 DOI: 10.1021/acs.est.3c01823] [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: 08/21/2023]
Abstract
Summertime surface ozone in China has been increasing since 2013 despite the policy-driven reduction in fuel combustion emissions of nitrogen oxides (NOx). Here we examine the role of soil reactive nitrogen (Nr, including NOx and nitrous acid (HONO)) emissions in the 2013-2019 ozone increase over the North China Plain (NCP), using GEOS-Chem chemical transport model simulations. We update soil NOx emissions and add soil HONO emissions in GEOS-Chem based on observation-constrained parametrization schemes. The model estimates significant daily maximum 8 h average (MDA8) ozone enhancement from soil Nr emissions of 8.0 ppbv over the NCP and 5.5 ppbv over China in June-July 2019. We identify a strong competing effect between combustion and soil Nr sources on ozone production in the NCP region. We find that soil Nr emissions accelerate the 2013-2019 June-July ozone increase over the NCP by 3.0 ppbv. The increase in soil Nr ozone contribution, however, is not primarily driven by weather-induced increases in soil Nr emissions, but by the concurrent decreases in fuel combustion NOx emissions, which enhance ozone production efficiency from soil by pushing ozone production toward a more NOx-sensitive regime. Our results reveal an important indirect effect from fuel combustion NOx emission reduction on ozone trends by increasing ozone production from soil Nr emissions, highlighting the necessity to consider the interaction between anthropogenic and biogenic sources in ozone mitigation in the North China Plain.
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Affiliation(s)
- Wanshan Tan
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Haolin Wang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Jiayin Su
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Ruize Sun
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Cheng He
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Xiao Lu
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Jintai Lin
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, People's Republic of China
| | - Chaoyang Xue
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (LPC2E), CNRS-Université Orléans-CNES, CEDEX 2 Orléans 45071, France
| | - Haichao Wang
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Yiming Liu
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
| | - Lei Liu
- College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, People's Republic of China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, People's Republic of China
| | - Dianming Wu
- Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, People's Republic of China
- Institute of Eco-Chongming (IEC), Shanghai 202162, People's Republic of China
| | - Yujing Mu
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Shaojia Fan
- School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong 519082, People's Republic of China
- Guangdong Provincial Observation and Research Station for Climate Environment and Air Quality Change in the Pearl River Estuary, Zhuhai, Guangdong 519082, People's Republic of China
- Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai, China, Zhuhai, Guangdong 519082, People's Republic of China
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12
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Zhang H, Wang X, Shen X, Li X, Wu B, Li G, Bai H, Cao X, Hao X, Zhou Q, Yao Z. Chemical characterization of volatile organic compounds (VOCs) emitted from multiple cooking cuisines and purification efficiency assessments. J Environ Sci (China) 2023; 130:163-173. [PMID: 37032033 DOI: 10.1016/j.jes.2022.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 07/25/2022] [Accepted: 08/02/2022] [Indexed: 06/19/2023]
Abstract
Cooking process can produce abundant volatile organic compounds (VOCs), which are harmful to environment and human health. Therefore, we conducted a comprehensive analysis in which VOCs emissions from multiple cuisines have been sampled based on the simulation and acquisition platform, involving concentration characteristics, ozone formation potential (OFP) and purification efficiency assessments. VOCs emissions varied from 1828.5 to 14,355.1 µg/m3, with the maximum and minimum values from Barbecue and Family cuisine, respectively. Alkanes and alcohol had higher contributions to VOCs from Sichuan and Hunan cuisine (64.1%), Family cuisine (66.3%), Shandong cuisine (69.1%) and Cantonese cuisine (69.8%), with the dominant VOCs species of ethanol, isobutane and n-butane. In comparison, alcohols (79.5%) were abundant for Huaiyang cuisine, while alkanes (19.7%), alkenes (35.9%) and haloalkanes (22.9%) accounted for higher proportions from Barbecue. Specially, carbon tetrachloride, n-hexylene and 1-butene were the most abundant VOCs species for Barbecue, ranging from 8.8% to 14.6%. The highest OFP occurred in Barbecue. The sensitive species of OFP for Huaiyang cuisine were alcohols, while other cuisines were alkenes. Purification efficiency assessments shed light on the removal differences of individual and synergistic control technologies. VOCs emissions exhibited a strong dependence on the photocatalytic oxidation, with the removal efficiencies of 29.0%-54.4%. However, the high voltage electrostatic, wet purification and mechanical separation techniques played a mediocre or even counterproductive role in the VOCs reduction, meanwhile collaborative control technologies could not significantly improve the removal efficiency. Our results identified more effective control technologies, which were conductive to alleviating air pollution from cooking emissions.
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Affiliation(s)
- Hanyu Zhang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xuejun Wang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xianbao Shen
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xin Li
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Bobo Wu
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Guohao Li
- Beijing Municipal Research Institute of Environmental Protection, Beijing Key Laboratory of Urban Atmospheric Volatile Organic Compounds Pollution Control and Application, National Urban Environmental Pollution Control Engineering Research Center, Beijing 100037, China
| | - Huahua Bai
- Beijing Municipal Research Institute of Environmental Protection, Beijing Key Laboratory of Urban Atmospheric Volatile Organic Compounds Pollution Control and Application, National Urban Environmental Pollution Control Engineering Research Center, Beijing 100037, China
| | - Xinyue Cao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Xuewei Hao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Qi Zhou
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
| | - Zhiliang Yao
- School of Ecology and Environment, Beijing Technology and Business University, Beijing 100048, China; State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China.
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13
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Wang Y, Bastien L, Jin L, Harley RA. Location-Specific Control of Precursor Emissions to Mitigate Photochemical Air Pollution. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37329338 DOI: 10.1021/acs.est.3c01934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The effects of precursor emission controls on air quality can vary greatly depending on where emission reductions occur. We use the adjoint of the Community Multiscale Air Quality (CMAQ) model to evaluate impacts of spatially targeted NOx emission reductions on odd oxygen (Ox = O3 + NO2). The air quality responses studied here include one population-weighted regionwide and three city-level receptors in Central California. We map high-priority locations for NOx control and their changes over decadal time scales. The desirability of NOx-focused emission control programs has increased between 2000 and 2022. We find for present-day conditions that reducing NOx emissions by 28% from targeted high-priority locations can achieve 60% of the air quality benefits of uniform NOx reductions at all locations. High-priority source locations are found to differ for individual city-level versus regionwide receptors of interest. While high-impact emission hotspots for improving city-level metrics are found within the city itself or closely adjacent, the spatial pattern of emission hotspots for improving regionwide air quality is more complex and requires comprehensive consideration of upwind sources. Results of this study can help to inform strategic decision-making at local and regional levels about where to prioritize emission control efforts.
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Affiliation(s)
- Yuhan Wang
- Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Lucas Bastien
- Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Ling Jin
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Robert A Harley
- Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
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14
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Wang Y, Jiang S, Huang L, Lu G, Kasemsan M, Yaluk EA, Liu H, Liao J, Bian J, Zhang K, Chen H, Li L. Differences between VOCs and NOx transport contributions, their impacts on O 3, and implications for O 3 pollution mitigation based on CMAQ simulation over the Yangtze River Delta, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 872:162118. [PMID: 36791851 DOI: 10.1016/j.scitotenv.2023.162118] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/04/2023] [Accepted: 02/04/2023] [Indexed: 06/18/2023]
Abstract
The relationship between O3 and its precursors during urban polluted episodes remains unclear. In this study, the simultaneous source apportionment of VOCs, NOx, and O3 over the Yangtze River Delta (YRD) region during the O3 polluted episode on July 24-30, 2018, was performed based on the Integrated Source Apportionment Method (ISAM) embedded in the Community Multiscale Air Quality Modeling System (CMAQ). The results of the ISAM were compared with those of the Brute Force Method (BFM) and Positive Matrix Factorization (PMF). Furthermore, the differences between the transport contributions of VOCs and NOx, and their impacts on O3 were analyzed. The results indicate that observations of VOCs species can be well captured by simulated VOCs, and the ISAM has a significant advantage in the source apportionment of VOCs, especially for sources emitting highly reactive species. In the clean and polluted periods, the local contribution percentages of VOCs in urban sites ranged from 60 % to 77 %, much higher than those of NOx (31 %-43 %) and O3 (16 %-33 %). NOx and O3 have strong transport abilities with high and close contribution percentages, which are highly correlated, mainly because oxygen atoms produced by the photolysis of NO2 in the aged air mass combined rapidly with O2 to form O3 during transport. The VOCs chemical loss caused by the oxidation of OH radicals during transport makes the ability of VOCs for long-distance transport much weaker than that of NOx. Furthermore, owing to the sufficient aging of VOCs, those contributed by long-distance transport have little effect on O3. To a certain extent, controlling one's NOx emissions can help other cities more, while controlling one's VOCs emissions can help itself more. Therefore, it is recommended to attach enough importance to joint prevention and control of NOx among cities and even long-distance areas to alleviate regional O3 pollution.
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Affiliation(s)
- Yangjun Wang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Sen Jiang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Ling Huang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Guibin Lu
- School of economics, Shanghai University, Shanghai 200444, China
| | - Manomaiphiboon Kasemsan
- The Joint Graduate School of Energy and Environment, King Mongkut's University of Technology, Thonburi, Bangkok 10140, Thailand; Center of Excellence on Energy Technology and Environment, Ministry of Higher Education, Science, Research and Innovation, Bangkok 10140, Thailand
| | - Elly Arukulem Yaluk
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Hanqing Liu
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Jiaqiang Liao
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Jinting Bian
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Kun Zhang
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Hui Chen
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China
| | - Li Li
- School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.
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15
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Ye X, Wang X, Zhang L. Diagnosing the Model Bias in Simulating Daily Surface Ozone Variability Using a Machine Learning Method: The Effects of Dry Deposition and Cloud Optical Depth. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:16665-16675. [PMID: 36437714 DOI: 10.1021/acs.est.2c05712] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Machine learning methods are increasingly used in air quality studies to predict air pollution levels, while few applied them to diagnose and improve the underlying mechanisms controlling air pollution represented in chemical transport models (CTMs). Here, we use the random forest (RF) method to diagnose high biases of surface daily maximum 8 h average (MDA8) ozone concentrations in the GEOS-Chem CTM evaluated against measurements from the nationwide monitoring network in summer 2018 over China. The feature importance results show that cloud optical depth (COD), relative humidity, and precipitation are the top three factors affecting CTM high biases. Such results indicate that the high ozone biases in summer over China mainly occur on wet/cloudy days (∼40% biased high), while biases on dry/clear days are small (within 5%). We link the important features with model parameterizations and variables, identifying model underestimates in the dry deposition velocity and COD on wet/cloudy days. By accounting for the enhanced dry deposition on wet plant cuticles and using satellite observation constrained COD, we find that CTM high ozone biases can be halved with an improved agreement in the temporal variability, highlighting the effects of dry deposition and COD on ozone, as suggested by the RF outcomes.
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Affiliation(s)
- Xingpei Ye
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing100871, China
| | - Xiaolin Wang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing100871, China
| | - Lin Zhang
- Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing100871, China
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16
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Wang L, Zhang J, Wei J, Zong J, Lu C, Du Y, Wang Q. Association of ambient air pollution exposure and its variability with subjective sleep quality in China: A multilevel modeling analysis. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 312:120020. [PMID: 36028077 DOI: 10.1016/j.envpol.2022.120020] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 08/14/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM2.5) and ≤10 μm (PM10) led to 0.4% (95% confidence interval (CI): 1.002-1.006) and 0.3% (95% CI: 1.001-1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM2.5 and PM10 were 2.2% (95% CI: 1.012-1.032) and 2.2% (95% CI: 1.012-1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.
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Affiliation(s)
- Lingli Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jingxuan Zhang
- Shandong Provincial Mental Health Center, Jinan City, Shandong, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, USA
| | - Jingru Zong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chunyu Lu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yajie Du
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qing Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; National Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China.
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17
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Zhou M, Li Y, Zhang F. Spatiotemporal Variation in Ground Level Ozone and Its Driving Factors: A Comparative Study of Coastal and Inland Cities in Eastern China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19159687. [PMID: 35955043 PMCID: PMC9367812 DOI: 10.3390/ijerph19159687] [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: 07/06/2022] [Revised: 07/29/2022] [Accepted: 08/03/2022] [Indexed: 05/24/2023]
Abstract
Variations in marine and terrestrial geographical environments can cause considerable differences in meteorological conditions, economic features, and population density (PD) levels between coastal and inland cities, which in turn can affect the urban air quality. In this study, a five-year (2016-2020) dataset encompassing air monitoring (from the China National Environmental Monitoring Centre), socioeconomic statistical (from the Shandong Province Bureau of Statistics) and meteorological data (from the U.S. National Centers for Environmental Information, National Oceanic and Atmospheric Administration) was employed to investigate the spatiotemporal distribution characteristics and underlying drivers of urban ozone (O3) in Shandong Province, a region with both land and sea environments in eastern China. The main research methods included the multiscale geographically weighted regression (MGWR) model and wavelet analysis. From 2016 to 2019, the O3 concentration increased year by year in most cities, but in 2020, the O3 concentration in all cities decreased. O3 concentration exhibited obvious regional differences, with higher levels in inland areas and lower levels in eastern coastal areas. The MGWR analysis results indicated the relationship between PD, urbanization rate (UR), and O3 was greater in coastal cities than that in the inland cities. Furthermore, the wavelet coherence (WTC) analysis results indicated that the daily maximum temperature was the most important factor influencing the O3 concentration. Compared with NO, NO2, and NOx (NOx ≡ NO + NO2), the ratio of NO2/NO was more coherent with O3. In addition, the temperature, the wind speed, nitrogen oxides, and fine particulate matter (PM2.5) exerted a greater impact on O3 in coastal cities than that in inland cities. In summary, the effects of the various abovementioned factors on O3 differed between coastal cities and inland cities. The present study could provide a scientific basis for targeted O3 pollution control in coastal and inland cities.
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Affiliation(s)
- Mengge Zhou
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yonghua Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Fengying Zhang
- China National Environmental Monitoring Centre, Beijing 100012, China
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18
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Wang C, An X, Zhao D, Sun Z, Jiang L, Li J, Hou Q. Development of GRAPES-CUACE adjoint model version 2.0 and its application in sensitivity analysis of ozone pollution in north China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 826:153879. [PMID: 35182623 DOI: 10.1016/j.scitotenv.2022.153879] [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: 10/30/2021] [Revised: 02/10/2022] [Accepted: 02/10/2022] [Indexed: 06/14/2023]
Abstract
We presented the development of the gaseous chemistry adjoint module of the meteorological-chemical model system GRAPES-CUACE (Global/Regional Assimilation and PrEdiction System coupled with CMA Unified Atmospheric Chemistry Environmental Forecasting System) on the basis of the previously constructed aerosol adjoint module. The latest version of the GRAPES-CUACE adjoint model mainly includes the adjoint of the physical and chemical processes, the adjoint of the transport processes, and the adjoint of interface programs, of both gas and aerosol. The adjoint implementation was validated for the full model, and adjoint results showed good agreement with brute force sensitivities. We also applied the newly developed adjoint model to the sensitivity analysis of an ozone episode occurred in Beijing on July 2, 2017, as well as the design of emission-reduction strategies for this episode. The relationships between the ozone concentration and precursor emissions were well captured by the adjoint model. It is indicated that for a case used here, the Beijing peak ozone concentration was influenced mostly by local emissions (6.2-24.3%), as well as by surrounding emissions, including Hebei (4.4-16.8%), Tianjin (1.8-6.6%), Shandong (1.8-2.6%), and Shanxi (<1%). In addition, reduction of NOx, VOCs, and CO emissions in these regions would effectively decrease the Beijing peak ozone concentration. This study highlights the capability of GRAPES-CUACE adjoint model in quantifying "emission-concentration" relationship and in providing guidance for environmental control policy.
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Affiliation(s)
- Chao Wang
- Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China; State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Xingqin An
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
| | - Defeng Zhao
- Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China.
| | - Zhaobin Sun
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
| | - Linsen Jiang
- Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
| | - Jiangtao Li
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
| | - Qing Hou
- State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
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19
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Wang Y, Bastien L, Jin L, Harley RA. Responses of Photochemical Air Pollution in California's San Joaquin Valley to Spatially and Temporally Resolved Changes in Precursor Emissions. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7074-7082. [PMID: 35467865 DOI: 10.1021/acs.est.1c07011] [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: 06/14/2023]
Abstract
Ground-level ozone adversely affects human health and ecosystems. The effectiveness of control programs depends on which precursor(s) are controlled, by how much, and where and when emission reductions occur. We use the adjoint of the Community Multiscale Air Quality model to investigate odd oxygen (Ox ≡ O3 + NO2) sensitivities in California's San Joaquin Valley (SJV) to precursor emissions from local and upwind sources. Sensitivities are mapped and disaggregated by hour and day. Taken together, impacts of precursor emissions in the San Francisco Bay area and Sacramento Valley are similar in magnitude to impacts of local SJV emissions. Same-day emission sensitivities are mostly attributable to local sources, with the most influential anthropogenic emissions of VOCs (volatile organic compounds) and NOx (nitrogen oxides) occurring in the morning (9-11 am) and early afternoon hours (1-3 pm), respectively. For the northernmost SJV receptor, the influence from Sacramento Valley emissions peaks 5-6 h later than Bay area emissions; this difference diminishes for SJV receptors located further downwind. Results show a shift toward more NOx-sensitive conditions in the afternoon with all but the southernmost receptor shifting from VOC- to NOx-sensitive conditions. We also evaluate opportunities to control pollution through shifts in precursor emission location and timing.
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Affiliation(s)
- Yuhan Wang
- Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
| | - Lucas Bastien
- Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Ling Jin
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Robert A Harley
- Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
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20
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Mao J, Yan F, Zheng L, You Y, Wang W, Jia S, Liao W, Wang X, Chen W. Ozone control strategies for local formation- and regional transport-dominant scenarios in a manufacturing city in southern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 813:151883. [PMID: 34826481 DOI: 10.1016/j.scitotenv.2021.151883] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/23/2021] [Accepted: 11/18/2021] [Indexed: 06/13/2023]
Abstract
Given the leveling off of fine particulate matter (PM2.5), ground-level ozone (O3) pollution has become one of the most significant atmospheric pollution issues in the Pearl River Delta (PRD) region in China, especially in the manufacturing city of Dongguan, which faces more severe O3 pollution. The development of strategies to control O3 precursor emissions, including volatile organic compounds (VOCs) and nitrogen oxide (NOx), depends to a large extent on the source region of the O3 pollution. In this study, by combining the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), the Empirical Kinetic Modeling Approach (EKMA), and the Flexible Particle model (FLEXPART), more effective strategies of controlling O3 precursor emissions were identified under two typical types of O3 pollution episodes: local formation (LF)-dominant (8-12 September 2019) and regional transport (RT)-dominant (23-27 October 2017) episodes, distinguished by the WRF-FLEXPART model. During the LF-dominant episode, the EKMA revealed that the O3 formation in Dongguan was in a transitional regime, and the abatement of solvent use-VOCs emissions in the key area of Dongguan was more effective in reducing O3 levels, with an emission reduction benefit 1.7 times that of total VOCs emission sources throughout Dongguan. With respect to the RT-dominant episode, the reduction in VOCs emissions in the local region did not effectively curb O3 pollution, although the photochemical regime of the O3 formation in Dongguan was VOCs-limited. A 50% reduction in NOx emissions in the upwind regions (parts of Guangzhou and Huizhou) effectively decreased the O3 concentration in Dongguan by 17%. The results of this study emphasize the importance of the source region of O3 pollution in the implementation of effective O3 control strategies and provide valuable insights for region-specific precursor emission policy formulation, not only in Dongguan, but also in other regions facing severe O3 pollution.
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Affiliation(s)
- Jingying Mao
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Fenghua Yan
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Lianming Zheng
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Yingchang You
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Weiwen Wang
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Shiguo Jia
- School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou 510275, China
| | - Wenhui Liao
- Guangdong University of Finance, Guangzhou 510521, PR China
| | - Xuemei Wang
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China
| | - Weihua Chen
- Guangdong-Hong Kong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou 510632, PR China.
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21
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Bhatti UA, Zeeshan Z, Nizamani MM, Bazai S, Yu Z, Yuan L. Assessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19. CHEMOSPHERE 2022; 288:132569. [PMID: 34655644 PMCID: PMC8514250 DOI: 10.1016/j.chemosphere.2021.132569] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/01/2021] [Accepted: 10/12/2021] [Indexed: 05/21/2023]
Abstract
Following the outbreak of the novel coronavirus in early 2020, to effectively prevent the spread of the disease, major cities across China suspended work and production. While the rest of the world struggles to control COVID-19, China has managed to control the pandemic rapidly and effectively with strong lockdown policies. This study investigates the change in air pollution (focusing on the air quality index (AQI), six ambient air pollutants nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter with aerodynamic diameters ≤10 μm (PM10) and ≤2.5 μm (PM2.5)) patterns for three periods: pre-COVID (from 1 January to May 30, 2019), active COVID (from 1 January to May 30, 2020) and post-COVID (from 1 January to May 30, 2021) in the Jiangsu province of China. Our findings reveal that the change in air pollution from pre-COVID to active COVID was greater than in previous years due to the government's lockdown policies. Post-COVID, air pollutant concentration is increasing. Mean change PM2.5 from pre-COVID to active COVID decreased by 18%; post-COVID it has only decreased by 2%. PM10 decreased by 19% from pre-COVID to active COVID, but post-COVID pollutant concentration has seen a 23% increase. Air pollutants show a positive correlation with COVID-19 cases among which PM2.5, PM10 and NO2 show a strong correlation during active COVID-19 cases. Metrological factors such as minimum temperature, average temperature and humidity show a positive correlation with COVID-19 cases while maximum temperature, wind speed and air pressure show no strong positive correlation. Although the COVID-19 pandemic had numerous negative effects on human health and the global economy, the reduction in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits; the government must implement policies to control post-COVID environmental issues.
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Affiliation(s)
- Uzair Aslam Bhatti
- School of Geography, Nanjing Normal University, Nanjing, 210023, China; Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, No. 1 Wenyuan Road, Nanjing, China
| | | | - Mir Muhammad Nizamani
- Key Laboratory of Tropical Biological Resources of Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan University, Haikou, 570228, China
| | - Sibghatullah Bazai
- School of Natural and Computational Sciences, Massey University, Auckland, 0632, New Zealand; Department of Computer Engineering, BUITEMS, Quetta 87300, Pakistan
| | - Zhaoyuan Yu
- School of Geography, Nanjing Normal University, Nanjing, 210023, China; Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, No. 1 Wenyuan Road, Nanjing, China
| | - Linwang Yuan
- School of Geography, Nanjing Normal University, Nanjing, 210023, China; Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, No. 1 Wenyuan Road, Nanjing, China.
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22
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Ding D, Xing J, Wang S, Dong Z, Zhang F, Liu S, Hao J. Optimization of a NO x and VOC Cooperative Control Strategy Based on Clean Air Benefits. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:739-749. [PMID: 34962805 DOI: 10.1021/acs.est.1c04201] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Serious ambient PM2.5 and O3 pollution is one of the most important environmental challenges of China, necessitating an urgent cost-effective cocontrol strategy. Herein, we introduced a novel integrated assessment system to optimize a NOx and volatile organic compound (VOC) control strategy for the synergistic reduction of ambient PM2.5 and O3 pollution. Focusing on the Beijing-Tianjin-Hebei cities and their surrounding regions, which are experiencing the most serious PM2.5 and O3 pollution in China, we found that NOx emission reduction (64-81%) is essential to attain the air quality standard no matter how much VOC emission is reduced. However, the synergistic VOC control is strongly recommended considering its substantially human health and crop production benefits, which are estimated up to 163 (PM2.5-related) and 101 (O3-related) billion CHY during the reduction of considerable emissions. Notably, such benefits will be greatly reduced if the synergistic VOC reduction is delayed. This study also highlights the necessity of simultaneous VOC and NOx emission control in winter while enhancing the NOx control in the summer, which is contrary to the current control strategy adopted in China. These findings point out the right pathways for future policy making on comitigating PM2.5 and O3 pollution in China and other countries.
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Affiliation(s)
- 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
| | - 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
| | - Zhaoxin Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Fenfen Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Shuchang Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, 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
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23
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Liu C, Shi K. A review on methodology in O 3-NOx-VOC sensitivity study. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 291:118249. [PMID: 34600066 DOI: 10.1016/j.envpol.2021.118249] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/26/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
Gaining insight into the response of surface ozone (O3) formation to its precursors plays an important role in the policy-making of O3 pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O3 and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O3 and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O3 prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O3 and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O3 concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O3 formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O3 formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O3 and its precursors could be used to test and improve the simulation performance of modern models.
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Affiliation(s)
- Chunqiong Liu
- College of Environmental Sciences and Engineering, China West Normal University, Nanchong, Sichuan, China; College of Biology and Environmental Sciences, Jishou University, Jishou, Hunan, China
| | - Kai Shi
- College of Environmental Sciences and Engineering, China West Normal University, Nanchong, Sichuan, China; College of Biology and Environmental Sciences, Jishou University, Jishou, Hunan, China.
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Trends and Variability of Ozone Pollution over the Mountain-Basin Areas in Sichuan Province during 2013–2020: Synoptic Impacts and Formation Regimes. ATMOSPHERE 2021. [DOI: 10.3390/atmos12121557] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Sichuan Province, the most industrialized and populated region in southwestern China, has been experiencing severe ozone pollution in the boreal warm season (April–September). With a surface ozone monitoring network and reanalysis dataset, we find that nearly all cities in Sichuan Province showed positive increasing trends in the warm-season ozone levels. The warm-season daily maximum 8-h average (MDA8) ozone levels increased by 2.0 ppb (4.8%) year−1 as a whole, with slightly larger trends in some sites such as a site in Zigong (5.2 ppb year−1). Seasonally, the monthly ozone level in Sichuan peaks from May to August (varies with year). The predominant warm-season synoptic patterns were objectively identified based on concurrent hourly meteorological fields from ERA5. High-pressure systems promote ozone production and result in high ozone concentrations, due to strong solar radiation as well as hot and dry atmospheric conditions. The increased occurrence of high-pressure patterns probably drives the ozone increase in Sichuan. When ozone pollution is relatively weak (with MDA8 ozone around 170 μg m−3), the air quality standard could be achieved in the short term by a 25% reduction of NOx and VOCs emissions. Strengthened emission control is needed when ozone pollution is more severe. Our study provides implications for effective emission control of ozone pollution in Sichuan.
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