Articles | Volume 20, issue 22
https://doi.org/10.5194/acp-20-14347-2020
© Author(s) 2020. 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-20-14347-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Nov 2020
Research article |
| 25 Nov 2020
| 25 Nov 2020
Quantifying the emission changes and associated air quality impacts during the COVID-19 pandemic on the North China Plain: a response modeling study
Jia Xing
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control
of Air Pollution Complex, Beijing 100084, China
Siwei Li
CORRESPONDING AUTHOR
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Yueqi Jiang
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control
of Air Pollution Complex, Beijing 100084, China
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control
of Air Pollution Complex, Beijing 100084, China
Dian Ding
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control
of Air Pollution Complex, Beijing 100084, China
Zhaoxin Dong
State Key Joint Laboratory of Environmental Simulation and Pollution
Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control
of Air Pollution Complex, Beijing 100084, China
Yun Zhu
College of Environment and Energy, South China University of
Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, 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
Viewed
Total article views: 5,570 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 14 Jul 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,913 | 1,511 | 146 | 5,570 | 545 | 173 | 204 |
- HTML: 3,913
- PDF: 1,511
- XML: 146
- Total: 5,570
- Supplement: 545
- BibTeX: 173
- EndNote: 204
Total article views: 4,364 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 25 Nov 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,173 | 1,059 | 132 | 4,364 | 273 | 157 | 182 |
- HTML: 3,173
- PDF: 1,059
- XML: 132
- Total: 4,364
- Supplement: 273
- BibTeX: 157
- EndNote: 182
Total article views: 1,206 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 14 Jul 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 740 | 452 | 14 | 1,206 | 272 | 16 | 22 |
- HTML: 740
- PDF: 452
- XML: 14
- Total: 1,206
- Supplement: 272
- BibTeX: 16
- EndNote: 22
Viewed (geographical distribution)
Total article views: 5,570 (including HTML, PDF, and XML)
Thereof 5,536 with geography defined
and 34 with unknown origin.
Total article views: 4,364 (including HTML, PDF, and XML)
Thereof 4,364 with geography defined
and 0 with unknown origin.
Total article views: 1,206 (including HTML, PDF, and XML)
Thereof 1,151 with geography defined
and 55 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
67 citations as recorded by crossref.
- 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
- COVID-19 outbreak and air quality of Lahore, Pakistan: evidence from asymmetric causality analysis A. Tauqir & S. Kashif https://doi.org/10.1007/s40808-021-01210-8
- Monitoring of Ambient Air Quality Patterns and Assessment of Air Pollutants’ Correlation and Effects on Ambient Air Quality of Lahore, Pakistan W. Khan et al. https://doi.org/10.3390/atmos14081257
- City-Level Decision Model and Technology Pathways for Benefit-Oriented Synergistic Control of Air Pollutants and Carbon Dioxide Y. Zhang et al. https://doi.org/10.1021/acs.est.5c13715
- Air pollution control benefits in reducing inter-provincial trade-associated environmental inequality on PM2.5-related premature deaths in China J. Dong et al. https://doi.org/10.1016/j.jclepro.2022.131435
- Source contribution analysis of PM2.5 using Response Surface Model and Particulate Source Apportionment Technology over the PRD region, China Z. Li et al. https://doi.org/10.1016/j.scitotenv.2021.151757
- Provincial equity and enhanced health are key drivers for China's 2060 carbon neutrality J. Dong et al. https://doi.org/10.1016/j.jclepro.2024.143531
- Air Quality Changes during the COVID-19 Lockdown in an Industrial City in North China: Post-Pandemic Proposals for Air Quality Improvement H. Niu et al. https://doi.org/10.3390/su141811531
- Using the COVID-19 lockdown to identify atmospheric processes and meteorology influences on regional PM2.5 pollution episodes in the Beijing-Tianjin-Hebei, China I. Sulaymon et al. https://doi.org/10.1016/j.atmosres.2023.106940
- Satellite reveals a steep decline in China’s CO 2 emissions in early 2022 H. Li et al. https://doi.org/10.1126/sciadv.adg7429
- Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China M. Li et al. https://doi.org/10.1016/j.atmosenv.2024.120848
- Response of PM2.5 chemical composition to the emission reduction and meteorological variation during the COVID-19 lockdown Y. Gong et al. https://doi.org/10.1016/j.chemosphere.2024.142844
- Persistent high PM2.5 pollution driven by unfavorable meteorological conditions during the COVID-19 lockdown period in the Beijing-Tianjin-Hebei region, China I. Sulaymon et al. https://doi.org/10.1016/j.envres.2021.111186
- Improving PM2.5 predictions during COVID-19 lockdown by assimilating multi-source observations and adjusting emissions L. Chen et al. https://doi.org/10.1016/j.envpol.2021.118783
- DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite S. Li & J. Xing https://doi.org/10.59717/j.xinn-geo.2024.100061
- Assessment of Sectoral NOx Emission Reductions During COVID‐19 Lockdown Using Combined Satellite and Surface Observations and Source‐Oriented Model Simulations M. Kang et al. https://doi.org/10.1029/2021GL095339
- Impacts of the COVID-19 lockdown on atmospheric oxidizing capacity and secondary aerosol formation over the Beijing-Tianjin-Hebei region in Winter-Spring 2020 Y. Li et al. https://doi.org/10.1016/j.atmosenv.2022.119540
- Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020 B. Zheng et al. https://doi.org/10.5194/essd-13-2895-2021
- Unbalanced emission reductions and adverse meteorological conditions facilitate the formation of secondary pollutants during the COVID-19 lockdown in Beijing T. Ma et al. https://doi.org/10.1016/j.scitotenv.2022.155970
- Removing the effects of meteorological factors on changes in nitrogen dioxide and ozone concentrations in China from 2013 to 2020 C. Lin et al. https://doi.org/10.1016/j.scitotenv.2021.148575
- Exploring deep learning for air pollutant emission estimation L. Huang et al. https://doi.org/10.5194/gmd-14-4641-2021
- The effect of COVID-19 restrictions on atmospheric new particle formation in Beijing C. Yan et al. https://doi.org/10.5194/acp-22-12207-2022
- Intrinsic atmospheric circulation patterns associated with high PM2.5 concentration days in South Korea during the cold season Y. Jeong et al. https://doi.org/10.1016/j.scitotenv.2022.160878
- Non-negligible contributions to human health from increased household air pollution exposure during the COVID-19 lockdown in China Y. Zhang et al. https://doi.org/10.1016/j.envint.2021.106918
- Building a top-down method based on machine learning for evaluating energy intensity at a fine scale J. Guo et al. https://doi.org/10.1016/j.energy.2022.124505
- A novel machine learning method for evaluating the impact of emission sources on ozone formation Y. Cheng et al. https://doi.org/10.1016/j.envpol.2022.120685
- Accurate prediction of air quality response to emissions for effective control policy design M. Cao et al. https://doi.org/10.1016/j.jes.2022.02.009
- Highly Resolved Dynamic Emissions of Air Pollutants and Greenhouse Gas CO2 during COVID-19 Pandemic in East China C. Huang et al. https://doi.org/10.1021/acs.estlett.1c00600
- Changes in global air pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric modeling T. Doumbia et al. https://doi.org/10.5194/essd-13-4191-2021
- COVID-19 induced lower-tropospheric ozone changes M. Mertens et al. https://doi.org/10.1088/1748-9326/abf191
- A Holistic Clean Air Strategy Considering Integrated Health and Ecological Impacts Z. Dong et al. https://doi.org/10.1021/acs.est.5c08854
- Health-Oriented Emission Control Strategy of Energy Utilization and Its Co-CO2 Benefits: A Case Study of the Yangtze River Delta, China Z. Dong et al. https://doi.org/10.1021/acs.est.3c10693
- Radiative effects and feedbacks of anthropogenic aerosols on boundary layer meteorology and fine particulate matter during the COVID-19 lockdown over China M. Liang et al. https://doi.org/10.1016/j.scitotenv.2022.160767
- Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China W. Wu et al. https://doi.org/10.3390/ijerph20054199
- Improved O3 attribution and health impact assessment using RSM-based inverse emission inventories in the greater bay area S. Wang et al. https://doi.org/10.1016/j.envpol.2026.128313
- Response surface model based emission source contribution and meteorological pattern analysis in ozone polluted days Y. Chen et al. https://doi.org/10.1016/j.envpol.2022.119459
- The breathing human infrastructure: Integrating air quality, traffic, and social media indicators H. O'Leary et al. https://doi.org/10.1016/j.scitotenv.2022.154209
- Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions Y. Cheng et al. https://doi.org/10.1016/j.scitotenv.2024.172763
- Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning Y. Lyu et al. https://doi.org/10.1016/j.envpol.2024.125380
- Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19 H. Du et al. https://doi.org/10.1016/j.envpol.2021.117783
- East Asian climate response to COVID-19 lockdown measures in China S. Lee et al. https://doi.org/10.1038/s41598-021-96007-1
- Spatial and temporal changes of the ozone sensitivity in China based on satellite and ground-based observations W. Wang et al. https://doi.org/10.5194/acp-21-7253-2021
- Quantification of SO2 Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations J. Mo et al. https://doi.org/10.3390/atmos13030470
- COVID-19-Induced Lockdowns Indicate the Short-Term Control Effect of Air Pollutant Emission in 174 Cities in China D. Lu et al. https://doi.org/10.1021/acs.est.0c07170
- A forecasting tool for optimized emission control strategies to achieve short-term air quality attainment Z. Dong et al. https://doi.org/10.1016/j.jenvman.2024.123916
- Challenges in Observation of Ultrafine Particles: Addressing Estimation Miscalculations and the Necessity of Temporal Trends T. Lin et al. https://doi.org/10.1021/acs.est.4c07460
- Evaluating the impact of anthropogenic drivers and meteorological factors on air pollutants by explainable machine learning in Shandong Province, China Y. Yuan et al. https://doi.org/10.1016/j.apr.2025.102694
- Status of Ambient PM2.5 Pollution in the Seoul Megacity (2020) J. Uhm et al. https://doi.org/10.5572/ajae.2021.022
- Analysis of Changes and Statistical Characteristics in PM-2.5 based on the Seasonal Management Data H. Ahn et al. https://doi.org/10.4491/KSEE.2022.44.8.276
- New Insights into Unexpected Severe PM2.5 Pollution during the SARS and COVID-19 Pandemic Periods in Beijing P. Zuo et al. https://doi.org/10.1021/acs.est.1c05383
- Response Surface Model Based Emission Source Contribution and Meteorological Pattern Analysis in Ozone Polluted Days Y. Chen et al. https://doi.org/10.2139/ssrn.4045860
- COVID-19 lockdown closures of emissions sources in India: Lessons for air quality and climate policy K. Tibrewal & C. Venkataraman https://doi.org/10.1016/j.jenvman.2021.114079
- Variation of pollution sources and health effects on air pollution before and during COVID-19 pandemic in Linfen, Fenwei Plain W. Liu et al. https://doi.org/10.1016/j.envres.2022.113719
- Impact of COVID-19 Lockdown on NO2 Pollution and the Associated Health Burden in China: A Comparison of Different Approaches Z. Li https://doi.org/10.3390/toxics12080580
- Impact of the COVID-19 on the vertical distributions of major pollutants from a tower in the Pearl River Delta L. Li et al. https://doi.org/10.1016/j.atmosenv.2022.119068
- Disentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China F. Shen et al. https://doi.org/10.1038/s41612-022-00276-0
- The Characteristics of Heavy Ozone Pollution Episodes and Identification of the Primary Driving Factors Using a Generalized Additive Model (GAM) in an Industrial Megacity of Northern China L. Diao et al. https://doi.org/10.3390/atmos12111517
- Implications of COVID-19 Restriction Measures in Urban Air Quality of Thessaloniki, Greece: A Machine Learning Approach D. Akritidis et al. https://doi.org/10.3390/atmos12111500
- Ambient fine particulate matter and ozone pollution in China: synergy in anthropogenic emissions and atmospheric processes Y. Jiang et al. https://doi.org/10.1088/1748-9326/aca16a
- Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1) L. Zhang et al. https://doi.org/10.5194/gmd-15-5337-2022
- Temporal characteristics and spatial heterogeneity of air quality changes due to the COVID-19 lockdown in China J. Zeng & C. Wang https://doi.org/10.1016/j.resconrec.2022.106223
- Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation L. Kong et al. https://doi.org/10.5194/acp-23-6217-2023
- Mimicking atmospheric photochemical modeling with a deep neural network J. Xing et al. https://doi.org/10.1016/j.atmosres.2021.105919
- Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI) L. Kong et al. https://doi.org/10.5194/essd-16-4351-2024
- Data Assimilation of Ambient Concentrations of Multiple Air Pollutants Using an Emission-Concentration Response Modeling Framework J. Xing et al. https://doi.org/10.3390/atmos11121289
- Source impact and contribution analysis of ambient ozone using multi-modeling approaches over the Pearl River Delta region, China T. Fang et al. https://doi.org/10.1016/j.envpol.2021.117860
- Observed coupling between air mass history, secondary growth of nucleation mode particles and aerosol pollution levels in Beijing S. Hakala et al. https://doi.org/10.1039/D1EA00089F
67 citations as recorded by crossref.
- 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
- COVID-19 outbreak and air quality of Lahore, Pakistan: evidence from asymmetric causality analysis A. Tauqir & S. Kashif https://doi.org/10.1007/s40808-021-01210-8
- Monitoring of Ambient Air Quality Patterns and Assessment of Air Pollutants’ Correlation and Effects on Ambient Air Quality of Lahore, Pakistan W. Khan et al. https://doi.org/10.3390/atmos14081257
- City-Level Decision Model and Technology Pathways for Benefit-Oriented Synergistic Control of Air Pollutants and Carbon Dioxide Y. Zhang et al. https://doi.org/10.1021/acs.est.5c13715
- Air pollution control benefits in reducing inter-provincial trade-associated environmental inequality on PM2.5-related premature deaths in China J. Dong et al. https://doi.org/10.1016/j.jclepro.2022.131435
- Source contribution analysis of PM2.5 using Response Surface Model and Particulate Source Apportionment Technology over the PRD region, China Z. Li et al. https://doi.org/10.1016/j.scitotenv.2021.151757
- Provincial equity and enhanced health are key drivers for China's 2060 carbon neutrality J. Dong et al. https://doi.org/10.1016/j.jclepro.2024.143531
- Air Quality Changes during the COVID-19 Lockdown in an Industrial City in North China: Post-Pandemic Proposals for Air Quality Improvement H. Niu et al. https://doi.org/10.3390/su141811531
- Using the COVID-19 lockdown to identify atmospheric processes and meteorology influences on regional PM2.5 pollution episodes in the Beijing-Tianjin-Hebei, China I. Sulaymon et al. https://doi.org/10.1016/j.atmosres.2023.106940
- Satellite reveals a steep decline in China’s CO 2 emissions in early 2022 H. Li et al. https://doi.org/10.1126/sciadv.adg7429
- Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China M. Li et al. https://doi.org/10.1016/j.atmosenv.2024.120848
- Response of PM2.5 chemical composition to the emission reduction and meteorological variation during the COVID-19 lockdown Y. Gong et al. https://doi.org/10.1016/j.chemosphere.2024.142844
- Persistent high PM2.5 pollution driven by unfavorable meteorological conditions during the COVID-19 lockdown period in the Beijing-Tianjin-Hebei region, China I. Sulaymon et al. https://doi.org/10.1016/j.envres.2021.111186
- Improving PM2.5 predictions during COVID-19 lockdown by assimilating multi-source observations and adjusting emissions L. Chen et al. https://doi.org/10.1016/j.envpol.2021.118783
- DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite S. Li & J. Xing https://doi.org/10.59717/j.xinn-geo.2024.100061
- Assessment of Sectoral NOx Emission Reductions During COVID‐19 Lockdown Using Combined Satellite and Surface Observations and Source‐Oriented Model Simulations M. Kang et al. https://doi.org/10.1029/2021GL095339
- Impacts of the COVID-19 lockdown on atmospheric oxidizing capacity and secondary aerosol formation over the Beijing-Tianjin-Hebei region in Winter-Spring 2020 Y. Li et al. https://doi.org/10.1016/j.atmosenv.2022.119540
- Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020 B. Zheng et al. https://doi.org/10.5194/essd-13-2895-2021
- Unbalanced emission reductions and adverse meteorological conditions facilitate the formation of secondary pollutants during the COVID-19 lockdown in Beijing T. Ma et al. https://doi.org/10.1016/j.scitotenv.2022.155970
- Removing the effects of meteorological factors on changes in nitrogen dioxide and ozone concentrations in China from 2013 to 2020 C. Lin et al. https://doi.org/10.1016/j.scitotenv.2021.148575
- Exploring deep learning for air pollutant emission estimation L. Huang et al. https://doi.org/10.5194/gmd-14-4641-2021
- The effect of COVID-19 restrictions on atmospheric new particle formation in Beijing C. Yan et al. https://doi.org/10.5194/acp-22-12207-2022
- Intrinsic atmospheric circulation patterns associated with high PM2.5 concentration days in South Korea during the cold season Y. Jeong et al. https://doi.org/10.1016/j.scitotenv.2022.160878
- Non-negligible contributions to human health from increased household air pollution exposure during the COVID-19 lockdown in China Y. Zhang et al. https://doi.org/10.1016/j.envint.2021.106918
- Building a top-down method based on machine learning for evaluating energy intensity at a fine scale J. Guo et al. https://doi.org/10.1016/j.energy.2022.124505
- A novel machine learning method for evaluating the impact of emission sources on ozone formation Y. Cheng et al. https://doi.org/10.1016/j.envpol.2022.120685
- Accurate prediction of air quality response to emissions for effective control policy design M. Cao et al. https://doi.org/10.1016/j.jes.2022.02.009
- Highly Resolved Dynamic Emissions of Air Pollutants and Greenhouse Gas CO2 during COVID-19 Pandemic in East China C. Huang et al. https://doi.org/10.1021/acs.estlett.1c00600
- Changes in global air pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric modeling T. Doumbia et al. https://doi.org/10.5194/essd-13-4191-2021
- COVID-19 induced lower-tropospheric ozone changes M. Mertens et al. https://doi.org/10.1088/1748-9326/abf191
- A Holistic Clean Air Strategy Considering Integrated Health and Ecological Impacts Z. Dong et al. https://doi.org/10.1021/acs.est.5c08854
- Health-Oriented Emission Control Strategy of Energy Utilization and Its Co-CO2 Benefits: A Case Study of the Yangtze River Delta, China Z. Dong et al. https://doi.org/10.1021/acs.est.3c10693
- Radiative effects and feedbacks of anthropogenic aerosols on boundary layer meteorology and fine particulate matter during the COVID-19 lockdown over China M. Liang et al. https://doi.org/10.1016/j.scitotenv.2022.160767
- Analysis of Factors Influencing Air Quality in Different Periods during COVID-19: A Case Study of Tangshan, China W. Wu et al. https://doi.org/10.3390/ijerph20054199
- Improved O3 attribution and health impact assessment using RSM-based inverse emission inventories in the greater bay area S. Wang et al. https://doi.org/10.1016/j.envpol.2026.128313
- Response surface model based emission source contribution and meteorological pattern analysis in ozone polluted days Y. Chen et al. https://doi.org/10.1016/j.envpol.2022.119459
- The breathing human infrastructure: Integrating air quality, traffic, and social media indicators H. O'Leary et al. https://doi.org/10.1016/j.scitotenv.2022.154209
- Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions Y. Cheng et al. https://doi.org/10.1016/j.scitotenv.2024.172763
- Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning Y. Lyu et al. https://doi.org/10.1016/j.envpol.2024.125380
- Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19 H. Du et al. https://doi.org/10.1016/j.envpol.2021.117783
- East Asian climate response to COVID-19 lockdown measures in China S. Lee et al. https://doi.org/10.1038/s41598-021-96007-1
- Spatial and temporal changes of the ozone sensitivity in China based on satellite and ground-based observations W. Wang et al. https://doi.org/10.5194/acp-21-7253-2021
- Quantification of SO2 Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations J. Mo et al. https://doi.org/10.3390/atmos13030470
- COVID-19-Induced Lockdowns Indicate the Short-Term Control Effect of Air Pollutant Emission in 174 Cities in China D. Lu et al. https://doi.org/10.1021/acs.est.0c07170
- A forecasting tool for optimized emission control strategies to achieve short-term air quality attainment Z. Dong et al. https://doi.org/10.1016/j.jenvman.2024.123916
- Challenges in Observation of Ultrafine Particles: Addressing Estimation Miscalculations and the Necessity of Temporal Trends T. Lin et al. https://doi.org/10.1021/acs.est.4c07460
- Evaluating the impact of anthropogenic drivers and meteorological factors on air pollutants by explainable machine learning in Shandong Province, China Y. Yuan et al. https://doi.org/10.1016/j.apr.2025.102694
- Status of Ambient PM2.5 Pollution in the Seoul Megacity (2020) J. Uhm et al. https://doi.org/10.5572/ajae.2021.022
- Analysis of Changes and Statistical Characteristics in PM-2.5 based on the Seasonal Management Data H. Ahn et al. https://doi.org/10.4491/KSEE.2022.44.8.276
- New Insights into Unexpected Severe PM2.5 Pollution during the SARS and COVID-19 Pandemic Periods in Beijing P. Zuo et al. https://doi.org/10.1021/acs.est.1c05383
- Response Surface Model Based Emission Source Contribution and Meteorological Pattern Analysis in Ozone Polluted Days Y. Chen et al. https://doi.org/10.2139/ssrn.4045860
- COVID-19 lockdown closures of emissions sources in India: Lessons for air quality and climate policy K. Tibrewal & C. Venkataraman https://doi.org/10.1016/j.jenvman.2021.114079
- Variation of pollution sources and health effects on air pollution before and during COVID-19 pandemic in Linfen, Fenwei Plain W. Liu et al. https://doi.org/10.1016/j.envres.2022.113719
- Impact of COVID-19 Lockdown on NO2 Pollution and the Associated Health Burden in China: A Comparison of Different Approaches Z. Li https://doi.org/10.3390/toxics12080580
- Impact of the COVID-19 on the vertical distributions of major pollutants from a tower in the Pearl River Delta L. Li et al. https://doi.org/10.1016/j.atmosenv.2022.119068
- Disentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China F. Shen et al. https://doi.org/10.1038/s41612-022-00276-0
- The Characteristics of Heavy Ozone Pollution Episodes and Identification of the Primary Driving Factors Using a Generalized Additive Model (GAM) in an Industrial Megacity of Northern China L. Diao et al. https://doi.org/10.3390/atmos12111517
- Implications of COVID-19 Restriction Measures in Urban Air Quality of Thessaloniki, Greece: A Machine Learning Approach D. Akritidis et al. https://doi.org/10.3390/atmos12111500
- Ambient fine particulate matter and ozone pollution in China: synergy in anthropogenic emissions and atmospheric processes Y. Jiang et al. https://doi.org/10.1088/1748-9326/aca16a
- Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1) L. Zhang et al. https://doi.org/10.5194/gmd-15-5337-2022
- Temporal characteristics and spatial heterogeneity of air quality changes due to the COVID-19 lockdown in China J. Zeng & C. Wang https://doi.org/10.1016/j.resconrec.2022.106223
- Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation L. Kong et al. https://doi.org/10.5194/acp-23-6217-2023
- Mimicking atmospheric photochemical modeling with a deep neural network J. Xing et al. https://doi.org/10.1016/j.atmosres.2021.105919
- Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI) L. Kong et al. https://doi.org/10.5194/essd-16-4351-2024
- Data Assimilation of Ambient Concentrations of Multiple Air Pollutants Using an Emission-Concentration Response Modeling Framework J. Xing et al. https://doi.org/10.3390/atmos11121289
- Source impact and contribution analysis of ambient ozone using multi-modeling approaches over the Pearl River Delta region, China T. Fang et al. https://doi.org/10.1016/j.envpol.2021.117860
- Observed coupling between air mass history, secondary growth of nucleation mode particles and aerosol pollution levels in Beijing S. Hakala et al. https://doi.org/10.1039/D1EA00089F
Saved (final revised paper)
Latest update: 05 Sep 2026
Short summary
Quantifying emission changes is a prerequisite for assessment of control effectiveness in improving air quality. However, traditional bottom-up methods usually take months to perform and limit timely assessments. A novel method was developed by using a response model that provides real-time estimation of emission changes based on air quality observations. It was successfully applied to quantify emission changes on the North China Plain due to the COVID-19 pandemic shutdown.
Quantifying emission changes is a prerequisite for assessment of control effectiveness in...
Altmetrics
Final-revised paper
Preprint