Articles | Volume 22, issue 7
https://doi.org/10.5194/acp-22-4853-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-4853-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Apr 2022
Research article |
| 12 Apr 2022
| 12 Apr 2022
Insights into the significant increase in ozone during COVID-19 in a typical urban city of China
Kun Zhang
School of Environmental and Chemical Engineering, Shanghai
University, Shanghai, 200444, China
Key Laboratory of Organic Compound Pollution Control Engineering,
Shanghai University, Shanghai, 200444, China
Zhiqiang Liu
School of Environmental and Chemical Engineering, Shanghai
University, Shanghai, 200444, China
Changzhou Institute of Environmental Science, Changzhou, Jiangsu,
213022, China
Xiaojuan Zhang
School of Environmental and Chemical Engineering, Shanghai
University, Shanghai, 200444, China
Changzhou Institute of Environmental Science, Changzhou, Jiangsu,
213022, China
Qing Li
School of Environmental and Chemical Engineering, Shanghai
University, Shanghai, 200444, China
Key Laboratory of Organic Compound Pollution Control Engineering,
Shanghai University, Shanghai, 200444, China
Andrew Jensen
Cooperative Institute for Research in Environmental Sciences,
University of Colorado Boulder, Boulder, Colorado 80309, USA
Department of Chemistry, University of Colorado Boulder, Boulder, Colorado
80309, USA
Wen Tan
Tofwerk AG, Thun, Switzerland
Ling Huang
School of Environmental and Chemical Engineering, Shanghai
University, Shanghai, 200444, China
Key Laboratory of Organic Compound Pollution Control Engineering,
Shanghai University, Shanghai, 200444, China
Yangjun Wang
School of Environmental and Chemical Engineering, Shanghai
University, Shanghai, 200444, China
Key Laboratory of Organic Compound Pollution Control Engineering,
Shanghai University, Shanghai, 200444, China
Joost de Gouw
Cooperative Institute for Research in Environmental Sciences,
University of Colorado Boulder, Boulder, Colorado 80309, USA
Department of Chemistry, University of Colorado Boulder, Boulder, Colorado
80309, USA
School of Environmental and Chemical Engineering, Shanghai
University, Shanghai, 200444, China
Key Laboratory of Organic Compound Pollution Control Engineering,
Shanghai University, Shanghai, 200444, China
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Cited
23 citations as recorded by crossref.
- Changes in first- and second-order sensitivities of ozone concentration to its precursors over the Yangtze River Delta region of China due to COVID-19 lockdown: Insights from CMAQ-HDDM modeling study E. Yaluk et al. 10.1016/j.atmosenv.2023.119931
- Research on ozone formation sensitivity based on observational methods: Development history, methodology, and application and prospects in China W. Chu et al. 10.1016/j.jes.2023.02.052
- Elucidating the mechanisms of rapid O3 increase in North China Plain during COVID-19 lockdown period R. Li et al. 10.1016/j.scitotenv.2023.167622
- Seasonal analysis of meteorological parameters and air pollutant concentrations in Kolkata: An evaluation of their relationship N. Maltare et al. 10.1016/j.jclepro.2023.140514
- Diagnosis of photochemical O3 production of urban plumes in summer via developing the real-field IRs of VOCs: A case study in Beijing of China S. Chen et al. 10.1016/j.envpol.2022.120836
- Analysis of COVID-19 Lockdown Effects on Urban Air Quality: A Case Study of Monterrey, Mexico B. Schiavo et al. 10.3390/su15010642
- Effects of significant emission changes on PM2.5 chemical composition and optical properties from 2019 to 2021 in a typical industrial city of eastern China X. Huang et al. 10.1016/j.atmosres.2024.107287
- The Relationship between the Transmission of Different SARS-CoV-2 Strains and Air Quality: A Case Study in China R. Ma et al. 10.3390/ijerph20031943
- Identify the key emission sources for mitigating ozone pollution: A case study of urban area in the Yangtze River Delta region, China X. Zhang et al. 10.1016/j.scitotenv.2023.164703
- Analyzing ozone formation sensitivity in a typical industrial city in China: Implications for effective source control in the chemical transition regime Y. Niu et al. 10.1016/j.scitotenv.2024.170559
- Drivers and impacts of decreasing concentrations of atmospheric volatile organic compounds (VOCs) in Beijing during 2016–2020 Y. Liu et al. 10.1016/j.scitotenv.2023.167847
- Composition and Reactivity of Volatile Organic Compounds and the Implications for Ozone Formation in the North China Plain S. Hao et al. 10.3390/atmos15020213
- Pollution characteristics, source appointment and environmental effect of oxygenated volatile organic compounds in Guangdong-Hong Kong-Macao Greater Bay Area: Implication for air quality management G. Liu et al. 10.1016/j.scitotenv.2024.170836
- VOCs sources and roles in O3 formation in the central Yangtze River Delta region of China Z. Liu et al. 10.1016/j.atmosenv.2023.119755
- Anomalies of O3, CO, C2H2, H2CO, and C2H6 detected with multiple ground-based Fourier-transform infrared spectrometers and assessed with model simulation in 2020: COVID-19 lockdowns versus natural variability I. Ortega et al. 10.1525/elementa.2023.00015
- Characteristics of volatile organic compounds in 11 cities along Taihang Mountain, an industrial region in Northern China J. Wu et al. 10.1016/j.apr.2023.101693
- LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations S. Zhu et al. 10.1038/s41597-023-02656-4
- Impacts of Omicron associated restrictions on vertical distributions of air pollution at a suburb site in Shanghai S. Zhang et al. 10.1016/j.atmosenv.2022.119461
- Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method J. Xue et al. 10.1007/s11783-023-1738-5
- Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China C. Lin et al. 10.1016/j.apr.2022.101549
- Ozone and its precursors at an urban site in the Yangtze River Delta since clean air action plan phase II in China H. Fang et al. 10.1016/j.envpol.2024.123769
- Machine learning model to predict vehicle electrification impacts on urban air quality and related human health effects V. Calatayud et al. 10.1016/j.envres.2023.115835
- Learning Surface Ozone From Satellite Columns (LESO): A Regional Daily Estimation Framework for Surface Ozone Monitoring in China S. Zhu et al. 10.1109/TGRS.2022.3184629
23 citations as recorded by crossref.
- Changes in first- and second-order sensitivities of ozone concentration to its precursors over the Yangtze River Delta region of China due to COVID-19 lockdown: Insights from CMAQ-HDDM modeling study E. Yaluk et al. 10.1016/j.atmosenv.2023.119931
- Research on ozone formation sensitivity based on observational methods: Development history, methodology, and application and prospects in China W. Chu et al. 10.1016/j.jes.2023.02.052
- Elucidating the mechanisms of rapid O3 increase in North China Plain during COVID-19 lockdown period R. Li et al. 10.1016/j.scitotenv.2023.167622
- Seasonal analysis of meteorological parameters and air pollutant concentrations in Kolkata: An evaluation of their relationship N. Maltare et al. 10.1016/j.jclepro.2023.140514
- Diagnosis of photochemical O3 production of urban plumes in summer via developing the real-field IRs of VOCs: A case study in Beijing of China S. Chen et al. 10.1016/j.envpol.2022.120836
- Analysis of COVID-19 Lockdown Effects on Urban Air Quality: A Case Study of Monterrey, Mexico B. Schiavo et al. 10.3390/su15010642
- Effects of significant emission changes on PM2.5 chemical composition and optical properties from 2019 to 2021 in a typical industrial city of eastern China X. Huang et al. 10.1016/j.atmosres.2024.107287
- The Relationship between the Transmission of Different SARS-CoV-2 Strains and Air Quality: A Case Study in China R. Ma et al. 10.3390/ijerph20031943
- Identify the key emission sources for mitigating ozone pollution: A case study of urban area in the Yangtze River Delta region, China X. Zhang et al. 10.1016/j.scitotenv.2023.164703
- Analyzing ozone formation sensitivity in a typical industrial city in China: Implications for effective source control in the chemical transition regime Y. Niu et al. 10.1016/j.scitotenv.2024.170559
- Drivers and impacts of decreasing concentrations of atmospheric volatile organic compounds (VOCs) in Beijing during 2016–2020 Y. Liu et al. 10.1016/j.scitotenv.2023.167847
- Composition and Reactivity of Volatile Organic Compounds and the Implications for Ozone Formation in the North China Plain S. Hao et al. 10.3390/atmos15020213
- Pollution characteristics, source appointment and environmental effect of oxygenated volatile organic compounds in Guangdong-Hong Kong-Macao Greater Bay Area: Implication for air quality management G. Liu et al. 10.1016/j.scitotenv.2024.170836
- VOCs sources and roles in O3 formation in the central Yangtze River Delta region of China Z. Liu et al. 10.1016/j.atmosenv.2023.119755
- Anomalies of O3, CO, C2H2, H2CO, and C2H6 detected with multiple ground-based Fourier-transform infrared spectrometers and assessed with model simulation in 2020: COVID-19 lockdowns versus natural variability I. Ortega et al. 10.1525/elementa.2023.00015
- Characteristics of volatile organic compounds in 11 cities along Taihang Mountain, an industrial region in Northern China J. Wu et al. 10.1016/j.apr.2023.101693
- LESO: A ten-year ensemble of satellite-derived intercontinental hourly surface ozone concentrations S. Zhu et al. 10.1038/s41597-023-02656-4
- Impacts of Omicron associated restrictions on vertical distributions of air pollution at a suburb site in Shanghai S. Zhang et al. 10.1016/j.atmosenv.2022.119461
- Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method J. Xue et al. 10.1007/s11783-023-1738-5
- Risk tradeoffs between nitrogen dioxide and ozone pollution during the COVID-19 lockdowns in the Greater Bay area of China C. Lin et al. 10.1016/j.apr.2022.101549
- Ozone and its precursors at an urban site in the Yangtze River Delta since clean air action plan phase II in China H. Fang et al. 10.1016/j.envpol.2024.123769
- Machine learning model to predict vehicle electrification impacts on urban air quality and related human health effects V. Calatayud et al. 10.1016/j.envres.2023.115835
- Learning Surface Ozone From Satellite Columns (LESO): A Regional Daily Estimation Framework for Surface Ozone Monitoring in China S. Zhu et al. 10.1109/TGRS.2022.3184629
Latest update: 19 Apr 2024
Short summary
A significant increase in O3 concentrations was found during the lockdown period of COVID-19 in most areas of China. By field measurements coupled with machine learning, an observation-based model (OBM) and sensitivity analysis, we found the changes in the NOx / VOC ratio were a key reason for the significant rise in O3. To restrain O3 pollution, more efforts should be devoted to the control of anthropogenic OVOCs, alkenes and aromatics.
A significant increase in O3 concentrations was found during the lockdown period of COVID-19 in...
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