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Wang M, Chen X, Jiang Z, He TL, Jones D, Liu J, Shen Y. Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015-2021. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 906:167763. [PMID: 37832678 DOI: 10.1016/j.scitotenv.2023.167763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/16/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023]
Abstract
Surface ozone (O3) concentrations in China have increased largely in the past decade. An accurate understanding of O3 pollution evolution is critical for making effective regulatory policies. Here we integrate data- and process-based models to explore the drivers of the observed summertime surface O3 change in the North China Plain (NCP) over 2015-2021. The data-based model by the deep learning (DL) suggests the reverse of meteorological contributions to the observed O3 change, i.e., 0.14 ppb/y in 2015-2019 and -1.74 ppb/y in 2019-2021. This is mainly resulted from the reversed changes in meteorological variables in surface air temperature and relative humidity. The simulations from a global chemical transport model, GEOS-Chem, also support those results, i.e., the meteorological contribution to O3 changes are 0.26 ppb/y in 2015-2019 and -0.74 ppb/y in 2019-2021. Furthermore, our analysis exhibits possible weakened anthropogenic contributions to surface O3 rise, for example, 1.53 and 0.54 ppb/y by DL in 2015-2019 and 2019-2021, respectively. Similarly, GEOS-Chem simulations suggest an accelerated decrease in surface O3 concentrations driven by the decline in nitrogen dioxide (NO2) concentrations, i.e., approximately 0.4 and 1.2 ppb in 2015-2019 and 2019-2021, respectively. The combined effects of meteorological and anthropogenic contributions led to a significant decrease in surface O3 concentrations by -1.20 ppb/y in 2019-2021. The findings in this work offer valuable insights to mitigate O3 pollution in China.
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Affiliation(s)
- Min Wang
- School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Xiaokang Chen
- School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Zhe Jiang
- School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.
| | - Tai-Long He
- Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada; Department of Atmospheric Sciences, University of Washington, Seattle, WA 98195, USA.
| | - Dylan Jones
- Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada
| | - Jane Liu
- School of Geographical Sciences, Fujian Normal University, Fuzhou, Fujian 350007, China; Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
| | - Yanan Shen
- School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
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Ren Y, Guan X, Zhang Q, Li L, Tao C, Ren S, Wang Q, Wang W. A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 884:163190. [PMID: 37061051 PMCID: PMC10102532 DOI: 10.1016/j.scitotenv.2023.163190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/25/2023] [Accepted: 03/27/2023] [Indexed: 05/07/2023]
Abstract
Large-scale restrictions on anthropogenic activities in China in 2020 due to the Corona Virus Disease 2019 (COVID-19) indirectly led to improvements in air quality. Previous studies have paid little attention to the changes in nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3) concentrations at different levels of anthropogenic activity limitation and their interactions. In this study, machine learning models were used to simulate the concentrations of three pollutants during periods of different levels of lockdown, and compare them with observations during the same period. The results show that the difference between the simulated and observed values of NO2 concentrations varies at different stages of the lockdown. Variation between simulated and observed O3 and PM2.5 concentrations were less distinct at different stages of lockdowns. During the most severe period of the lockdowns, NO2 concentrations decreased significantly with a maximum decrease of 65.28 %, and O3 concentrations increased with a maximum increase of 75.69 %. During the first two weeks of the lockdown, the titration reaction in the atmosphere was disrupted due to the rapid decrease in NO2 concentrations, leading to the redistribution of Ox (NO2 + O3) in the atmosphere and eventually to the production of O3 and secondary PM2.5. The effect of traffic restrictions on the reduction of NO2 concentrations is significant. However, it is also important to consider the increase in O3 due to the constant volatile organic compounds (VOCs) and the decrease in NOx (NO+NO2). Traffic restrictions had a limited effect on improving PM2.5 pollution, so other beneficial measures were needed to sustainably reduce particulate matter pollution. Research on COVID-19 could provide new insights into future clean air action.
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Affiliation(s)
- Yuchao Ren
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Xu Guan
- Shandong Academy for Environmental Planning, Jinan 250101, PR China.
| | - Qingzhu Zhang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China.
| | - Lei Li
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Chenliang Tao
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Shilong Ren
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Qiao Wang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Wenxing Wang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
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Sun J, Zhou T, Wang D. Relationships between urban form and air quality: A reconsideration based on evidence from China's five urban agglomerations during the COVID-19 pandemic. LAND USE POLICY 2022; 118:106155. [PMID: 35450142 PMCID: PMC9010237 DOI: 10.1016/j.landusepol.2022.106155] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 02/28/2022] [Accepted: 04/11/2022] [Indexed: 05/19/2023]
Abstract
The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form-air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O3, NO2, PM2.5, PM10, SO2). The results showed that urban fragmentation is the most important factor affecting air quality and the concentration of the six pollutants. Additionally, the relationship between urban form and air quality varies in different urban agglomerations. By analyzing the extremely important indicators affecting air pollution, the urban form-air quality relationship in Beijing-Tianjin-Hebei is rather complex. In the Chengdu-Chongqing and the Pearl River Delta, urban sprawl and urban compactness are extremely important indicators for some air pollutants, respectively. Furthermore, urban shape ranks first for some air pollutants both in the Triangle of Central China and the Yangtze River Delta. Based on the robustness test, the performance of the random forest model is better than that of the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model.
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Affiliation(s)
- Jianing Sun
- School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China
| | - Tao Zhou
- School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China
- Research Center for Construction Economy and Management, Chongqing University, Chongqing 400044, China
| | - Di Wang
- School of Management Science and Real Estate, Chongqing University, Chongqing 400044, China
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Variations of Urban NO2 Pollution during the COVID-19 Outbreak and Post-Epidemic Era in China: A Synthesis of Remote Sensing and In Situ Measurements. REMOTE SENSING 2022. [DOI: 10.3390/rs14020419] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Since the COVID-19 outbreak in 2020, China’s air pollution has been significantly affected by control measures on industrial production and human activities. In this study, we analyzed the temporal variations of NO2 concentrations during the COVID-19 lockdown and post-epidemic era in 11 Chinese megacities by using satellite and ground-based remote sensing as well as in situ measurements. The average satellite tropospheric vertical column density (TVCD) of NO2 by TROPOMI decreased by 39.2–71.93% during the 15 days after Chinese New Year when the lockdown was at its most rigorous compared to that of 2019, while the in situ NO2 concentration measured by China National Environmental Monitoring Centre (CNEMC) decreased by 42.53–69.81% for these cities. Such differences between both measurements were further investigated by using ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) remote sensing of NO2 vertical profiles. For instance, in Beijing, MAX-DOAS NO2 showed a decrease of 14.19% (versus 18.63% by in situ) at the ground surface, and 36.24% (versus 36.25% by satellite) for the total tropospheric column. Thus, vertical discrepancies of atmospheric NO2 can largely explain the differences between satellite and in situ NO2 variations. In the post-epidemic era of 2021, satellite NO2 TVCD and in situ NO2 concentrations decreased by 10.42–64.96% and 1.05–34.99% compared to 2019, respectively, possibly related to the reduction of the transportation industry. This study reveals the changes of China’s urban NO2 pollution in the post-epidemic era and indicates that COVID-19 had a profound impact on human social activities and industrial production.
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Huang XF, Cao LM, Tian XD, Zhu Q, Saikawa E, Lin LL, Cheng Y, He LY, Hu M, Zhang YH, Lu KD, Liu YH, Daellenbach K, Slowik JG, Tang Q, Zou QL, Sun X, Xu BY, Jiang L, Shen YM, Ng NL, Prévôt ASH. Critical Role of Simultaneous Reduction of Atmospheric Odd Oxygen for Winter Haze Mitigation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:11557-11567. [PMID: 34431667 DOI: 10.1021/acs.est.1c03421] [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: 06/13/2023]
Abstract
The lockdown due to COVID-19 created a rare opportunity to examine the nonlinear responses of secondary aerosols, which are formed through atmospheric oxidation of gaseous precursors, to intensive precursor emission reductions. Based on unique observational data sets from six supersites in eastern China during 2019-2021, we found that the lockdown caused considerable decreases (32-61%) in different secondary aerosol components in the study region because of similar-degree precursor reductions. However, due to insufficient combustion-related volatile organic compound (VOC) reduction, odd oxygen (Ox = O3 + NO2) concentration, an indicator of the extent of photochemical processing, showed little change and did not promote more decreases in secondary aerosols. We also found that the Chinese provinces and international cities that experienced reduced Ox during the lockdown usually gained a greater simultaneous PM2.5 decrease than other provinces and cities with an increased Ox. Therefore, we argue that strict VOC control in winter, which has been largely ignored so far, is critical in future policies to mitigate winter haze more efficiently by reducing Ox simultaneously.
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Affiliation(s)
- Xiao-Feng Huang
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Li-Ming Cao
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Xu-Dong Tian
- Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control of Zhejiang, Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Qiao Zhu
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Department of Environmental Sciences, Emory University, Atlanta, Georgia 30322, United States
| | - Eri Saikawa
- Department of Environmental Sciences, Emory University, Atlanta, Georgia 30322, United States
| | - Li-Liang Lin
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Yong Cheng
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Ling-Yan He
- Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Min Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yuan-Hang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Ke-Ding Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yu-Han Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Kaspar Daellenbach
- Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), Villigen 5232, Switzerland
| | - Jay G Slowik
- Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), Villigen 5232, Switzerland
| | - Qian Tang
- Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control of Zhejiang, Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Qiao-Li Zou
- Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control of Zhejiang, Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Xin Sun
- Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control of Zhejiang, Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Bing-Ye Xu
- Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control of Zhejiang, Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Lan Jiang
- Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control of Zhejiang, Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Ye-Min Shen
- Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control of Zhejiang, Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China
| | - Nga Lee Ng
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - André S H Prévôt
- Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), Villigen 5232, Switzerland
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