Articles | Volume 21, issue 6
https://doi.org/10.5194/acp-21-4599-2021
© Author(s) 2021. 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-21-4599-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Mar 2021
Research article |
| 25 Mar 2021
| 25 Mar 2021
Observed decreases in on-road CO2 concentrations in Beijing during COVID-19 restrictions
Di Liu
Laboratory of Numerical Modeling for Atmospheric Sciences & Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, China
Wanqi Sun
Meteorological Observation Centre, China Meteorological
Administration, Beijing, China
Ning Zeng
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Laboratory of Numerical Modeling for Atmospheric Sciences & Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, China
Bo Yao
CORRESPONDING AUTHOR
Meteorological Observation Centre, China Meteorological
Administration, Beijing, China
Zhiqiang Liu
Laboratory of Numerical Modeling for Atmospheric Sciences & Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, China
Pucai Wang
Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Ke Zheng
Laboratory of Numerical Modeling for Atmospheric Sciences & Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, China
Han Mei
Laboratory of Numerical Modeling for Atmospheric Sciences & Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, China
Qixiang Cai
Laboratory of Numerical Modeling for Atmospheric Sciences & Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing, China
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- The Spatio-Temporal Distribution Characteristics of Carbon Dioxide Derived from the Trajectory Mapping of Ground Observation Network Data in Shanxi Province, One of China’s Largest Emission Regions F. Zhang et al. https://doi.org/10.3390/atmos15010098
- A 30-month field evaluation of low-cost CO2 sensors using a reference instrument Q. Cai et al. https://doi.org/10.5194/amt-18-4871-2025
- Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment J. Lian et al. https://doi.org/10.1021/acs.est.1c04973
- Wood Vault: remove atmospheric CO2 with trees, store wood for carbon sequestration for now and as biomass, bioenergy and carbon reserve for the future N. Zeng & H. Hausmann https://doi.org/10.1186/s13021-022-00202-0
- Spatiotemporal variations in atmospheric CH4 concentrations and enhancements in northern China based on a comprehensive dataset: ground-based observations, TROPOMI data, inventory data, and inversions P. Han et al. https://doi.org/10.5194/acp-25-4965-2025
- Co-drivers of Air Pollutant and CO2 Emissions from On-Road Transportation in China 2010–2020 Z. Qi et al. https://doi.org/10.1021/acs.est.3c08035
- AI-based estimation of spatiotemporal variability of urban road CO2 concentration J. Lim & S. Jeong https://doi.org/10.1016/j.apr.2026.103211
- Near-Real-Time Carbon Emission Accounting Technology Toward Carbon Neutrality Z. Liu et al. https://doi.org/10.1016/j.eng.2021.12.019
- Zooming into Berlin: tracking street-scale CO2 emissions based on high-resolution traffic modeling using machine learning M. Anjos & F. Meier https://doi.org/10.3389/fenvs.2024.1461656
- Machine learning based estimation of urban on-road CO2 concentration in Seoul C. Park et al. https://doi.org/10.1016/j.envres.2023.116256
- Effects of Environmental Factors on the Performance of Ground-Based Low-Cost CO2 Sensors X. Ren et al. https://doi.org/10.3390/s25196114
- Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations on Hateruma and Yonaguni Islands, Japan Y. Tohjima et al. https://doi.org/10.1186/s40645-023-00542-6
- The Enhancement and Variation in the Carbon Dioxide Concentration in a Typical Industrial Park in the Northern China Q. Cai et al. https://doi.org/10.1007/s41810-025-00286-4
- Heating transformation in winter significantly will reduce GHGs (CO2, CH4, N2O) emissions: implication from multiple-year concentration observations in Beijing W. Huang et al. https://doi.org/10.1016/j.apr.2025.102861
- Atmospheric CO2 and CH4 observations in Hangzhou before, during, and after the 2023 Asian Games: Insights from vehicle-carried and fixed stations T. Wang et al. https://doi.org/10.1016/j.apr.2025.102499
- Evaluation of Low-Cost CO2 Sensors Using Reference Instruments and Standard Gases for Indoor Use Q. Cai et al. https://doi.org/10.3390/s24092680
- Dynamics of urban carbon emissions: A comparative analysis of pre- and post-lockdown through eddy covariance measurements L. Zhao et al. https://doi.org/10.1016/j.jes.2025.01.023
- Quantifying the Impact of COVID‐19 Pandemic on the Spatiotemporal Changes of CO2 Concentrations in the Yangtze River Delta, China Y. Wang et al. https://doi.org/10.1029/2023JD038512
- Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change J. Laughner et al. https://doi.org/10.1073/pnas.2109481118
- Changing weekend effects of air pollutants in Beijing under 2020 COVID-19 lockdown controls L. Wu et al. https://doi.org/10.1038/s42949-022-00070-0
- Quantifying the Impact of the COVID-19 Pandemic Restrictions on CO, CO2, and CH4 in Downtown Toronto Using Open-Path Fourier Transform Spectroscopy Y. You et al. https://doi.org/10.3390/atmos12070848
- Reductions in California's Urban Fossil Fuel CO2 Emissions During the COVID‐19 Pandemic C. Yañez et al. https://doi.org/10.1029/2022AV000732
- Carbon Sequestration Potential of Biomass Production along Highways in China X. Ge et al. https://doi.org/10.1021/acs.est.3c06267
- Unveiling the changes in urban atmospheric CO2 in the time of COVID-19 pandemic: A case study of Florence (Italy) S. Venturi et al. https://doi.org/10.1016/j.scitotenv.2021.148877
- Toward Establishing a Low-cost UAV Coordinated Carbon Observation Network (LUCCN): First Integrated Campaign in China D. Yang et al. https://doi.org/10.1007/s00376-023-3107-5
- Tracking and Quantifying Fossil Fuel CO2 Emissions by Radiocarbon (14C): A Review S. Cui et al. https://doi.org/10.3390/atmos17040363
- Mobile Observations of Intracity Variations in Atmospheric CO2 and CH4 J. Wang et al. https://doi.org/10.1007/s00376-025-5059-4
- On the Detection of COVID‐Driven Changes in Atmospheric Carbon Dioxide N. Lovenduski et al. https://doi.org/10.1029/2021GL095396
- On the Large Variation in Atmospheric CO2 Concentration at Shangdianzi GAW Station during Two Dust Storm Events in March 2021 X. Li et al. https://doi.org/10.3390/atmos14091348
- Portraying on-road CO2 concentrations using street view panoramas and ensemble learning Y. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.174326
- Peak patterns and drivers of city-level daily CO2 emissions in China Y. Huang et al. https://doi.org/10.1016/j.jclepro.2024.143206
- Short-term effect of COVID-19 lockdowns on atmospheric CO2, CH4 and PM2.5 concentrations in urban environment E. Gulyaev et al. https://doi.org/10.1007/s13762-022-04314-5
32 citations as recorded by crossref.
- The Spatio-Temporal Distribution Characteristics of Carbon Dioxide Derived from the Trajectory Mapping of Ground Observation Network Data in Shanxi Province, One of China’s Largest Emission Regions F. Zhang et al. https://doi.org/10.3390/atmos15010098
- A 30-month field evaluation of low-cost CO2 sensors using a reference instrument Q. Cai et al. https://doi.org/10.5194/amt-18-4871-2025
- Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment J. Lian et al. https://doi.org/10.1021/acs.est.1c04973
- Wood Vault: remove atmospheric CO2 with trees, store wood for carbon sequestration for now and as biomass, bioenergy and carbon reserve for the future N. Zeng & H. Hausmann https://doi.org/10.1186/s13021-022-00202-0
- Spatiotemporal variations in atmospheric CH4 concentrations and enhancements in northern China based on a comprehensive dataset: ground-based observations, TROPOMI data, inventory data, and inversions P. Han et al. https://doi.org/10.5194/acp-25-4965-2025
- Co-drivers of Air Pollutant and CO2 Emissions from On-Road Transportation in China 2010–2020 Z. Qi et al. https://doi.org/10.1021/acs.est.3c08035
- AI-based estimation of spatiotemporal variability of urban road CO2 concentration J. Lim & S. Jeong https://doi.org/10.1016/j.apr.2026.103211
- Near-Real-Time Carbon Emission Accounting Technology Toward Carbon Neutrality Z. Liu et al. https://doi.org/10.1016/j.eng.2021.12.019
- Zooming into Berlin: tracking street-scale CO2 emissions based on high-resolution traffic modeling using machine learning M. Anjos & F. Meier https://doi.org/10.3389/fenvs.2024.1461656
- Machine learning based estimation of urban on-road CO2 concentration in Seoul C. Park et al. https://doi.org/10.1016/j.envres.2023.116256
- Effects of Environmental Factors on the Performance of Ground-Based Low-Cost CO2 Sensors X. Ren et al. https://doi.org/10.3390/s25196114
- Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations on Hateruma and Yonaguni Islands, Japan Y. Tohjima et al. https://doi.org/10.1186/s40645-023-00542-6
- The Enhancement and Variation in the Carbon Dioxide Concentration in a Typical Industrial Park in the Northern China Q. Cai et al. https://doi.org/10.1007/s41810-025-00286-4
- Heating transformation in winter significantly will reduce GHGs (CO2, CH4, N2O) emissions: implication from multiple-year concentration observations in Beijing W. Huang et al. https://doi.org/10.1016/j.apr.2025.102861
- Atmospheric CO2 and CH4 observations in Hangzhou before, during, and after the 2023 Asian Games: Insights from vehicle-carried and fixed stations T. Wang et al. https://doi.org/10.1016/j.apr.2025.102499
- Evaluation of Low-Cost CO2 Sensors Using Reference Instruments and Standard Gases for Indoor Use Q. Cai et al. https://doi.org/10.3390/s24092680
- Dynamics of urban carbon emissions: A comparative analysis of pre- and post-lockdown through eddy covariance measurements L. Zhao et al. https://doi.org/10.1016/j.jes.2025.01.023
- Quantifying the Impact of COVID‐19 Pandemic on the Spatiotemporal Changes of CO2 Concentrations in the Yangtze River Delta, China Y. Wang et al. https://doi.org/10.1029/2023JD038512
- Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change J. Laughner et al. https://doi.org/10.1073/pnas.2109481118
- Changing weekend effects of air pollutants in Beijing under 2020 COVID-19 lockdown controls L. Wu et al. https://doi.org/10.1038/s42949-022-00070-0
- Quantifying the Impact of the COVID-19 Pandemic Restrictions on CO, CO2, and CH4 in Downtown Toronto Using Open-Path Fourier Transform Spectroscopy Y. You et al. https://doi.org/10.3390/atmos12070848
- Reductions in California's Urban Fossil Fuel CO2 Emissions During the COVID‐19 Pandemic C. Yañez et al. https://doi.org/10.1029/2022AV000732
- Carbon Sequestration Potential of Biomass Production along Highways in China X. Ge et al. https://doi.org/10.1021/acs.est.3c06267
- Unveiling the changes in urban atmospheric CO2 in the time of COVID-19 pandemic: A case study of Florence (Italy) S. Venturi et al. https://doi.org/10.1016/j.scitotenv.2021.148877
- Toward Establishing a Low-cost UAV Coordinated Carbon Observation Network (LUCCN): First Integrated Campaign in China D. Yang et al. https://doi.org/10.1007/s00376-023-3107-5
- Tracking and Quantifying Fossil Fuel CO2 Emissions by Radiocarbon (14C): A Review S. Cui et al. https://doi.org/10.3390/atmos17040363
- Mobile Observations of Intracity Variations in Atmospheric CO2 and CH4 J. Wang et al. https://doi.org/10.1007/s00376-025-5059-4
- On the Detection of COVID‐Driven Changes in Atmospheric Carbon Dioxide N. Lovenduski et al. https://doi.org/10.1029/2021GL095396
- On the Large Variation in Atmospheric CO2 Concentration at Shangdianzi GAW Station during Two Dust Storm Events in March 2021 X. Li et al. https://doi.org/10.3390/atmos14091348
- Portraying on-road CO2 concentrations using street view panoramas and ensemble learning Y. Zhang et al. https://doi.org/10.1016/j.scitotenv.2024.174326
- Peak patterns and drivers of city-level daily CO2 emissions in China Y. Huang et al. https://doi.org/10.1016/j.jclepro.2024.143206
- Short-term effect of COVID-19 lockdowns on atmospheric CO2, CH4 and PM2.5 concentrations in urban environment E. Gulyaev et al. https://doi.org/10.1007/s13762-022-04314-5
Saved (final revised paper)
Latest update: 09 Oct 2026
Short summary
It is difficult to directly observe the COVID-19 signals in CO2 due to the strong weather induced variations. Here, we determined the on-road CO2 concentration declines in Beijing using mobile observatory data before (BC), during (DC) and after COVID-19 (AC). We chose trips with the most similar weather and calculated the enhancement, the difference between on-road and the city “background”. We showed a clear on-road CO2 decrease in DC, which is consistent with the emissions reductions in DC.
It is difficult to directly observe the COVID-19 signals in CO2 due to the strong weather...
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