Articles | Volume 21, issue 6
https://doi.org/10.5194/acp-21-4357-2021
© 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-21-4357-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Mar 2021
Research article |
| 22 Mar 2021
| 22 Mar 2021
Revealing the sulfur dioxide emission reductions in China by assimilating surface observations in WRF-Chem
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Yueming Cheng
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Daisuke Goto
National Institute for Environmental Studies, Tsukuba, Japan
Yingruo Li
Environmental Meteorology Forecast Center of Beijing–Tianjin–Hebei, China Meteorological Administration, Beijing, China
Xiao Tang
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Guangyu Shi
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
Teruyuki Nakajima
National Institute for Environmental Studies, Tsukuba, Japan
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21 citations as recorded by crossref.
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- The Impact of Firework Ban Relaxation on Variations in SO2 Emissions in China During the 2023 Chinese New Year X. He et al. https://doi.org/10.3390/rs16224191
- Constrained Estimates of Anthropogenic NOx Emissions in China (2014–2021) from Surface Observations Y. Shen et al. https://doi.org/10.3390/atmos17010051
- Characteristics of atmospheric reduced-sulfur compounds at a suburban site of Shanghai K. Deng et al. https://doi.org/10.1016/j.jes.2024.06.030
- Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy J. Lin et al. https://doi.org/10.5194/gmd-18-2231-2025
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- CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations Z. Peng et al. https://doi.org/10.1029/2022JD037154
- The optimization of SO2 emissions by the 4DVAR and EnKF methods and its application in WRF-Chem Y. Hu et al. https://doi.org/10.1016/j.scitotenv.2023.163796
- Carbonyl Compounds Regulate Atmospheric Oxidation Capacity and Particulate Sulfur Chemistry in the Coastal Atmosphere M. Zhao et al. https://doi.org/10.1021/acs.est.4c03947
- Improving Clear-Sky Solar Power Prediction over China by Assimilating Himawari-8 Aerosol Optical Depth with WRF-Chem-Solar S. Wang et al. https://doi.org/10.3390/rs14194990
- Improving aerosol forecasting through visibility data assimilation in WRF-chem: Methodology and evaluation S. Mei et al. https://doi.org/10.1016/j.atmosres.2025.108383
- Adjustment of the Power of Model Emissions of Anthropogenic Atmospheric Pollution Sources Based on Measurement Data and Adjoint Problem Methods P. Antokhin et al. https://doi.org/10.1134/S1024856025700319
- Future control pathways of PM2.5 constituents and emission sources under different SSPs from a health perspective G. Chen et al. https://doi.org/10.1016/j.envint.2025.109698
- 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
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. https://doi.org/10.3390/rs14010220
- A Decadal Change in Atmospheric Nitrogen Deposition at a Rural Site in Southern China K. Ren et al. https://doi.org/10.3390/atmos15050583
- Improvement and Uncertainties of Global Simulation of Sulfate Concentration and Radiative Forcing in CESM2 W. Ge et al. https://doi.org/10.1029/2022JD037623
- Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China Y. Hu et al. https://doi.org/10.5194/acp-22-13183-2022
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- Investigating the Changes in Air Pollutant Emissions over the Beijing-Tianjin-Hebei Region in February from 2014 to 2019 through an Inverse Emission Method X. Luo et al. https://doi.org/10.1007/s00376-022-2039-9
- Hybrid IFDMB/4D-Var inverse modeling to constrain the spatiotemporal distribution of CO and NO2 emissions using the CMAQ adjoint model J. Moon et al. https://doi.org/10.1016/j.atmosenv.2024.120490
21 citations as recorded by crossref.
- A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region X. Cheng et al. https://doi.org/10.5194/acp-21-13747-2021
- The Impact of Firework Ban Relaxation on Variations in SO2 Emissions in China During the 2023 Chinese New Year X. He et al. https://doi.org/10.3390/rs16224191
- Constrained Estimates of Anthropogenic NOx Emissions in China (2014–2021) from Surface Observations Y. Shen et al. https://doi.org/10.3390/atmos17010051
- Characteristics of atmospheric reduced-sulfur compounds at a suburban site of Shanghai K. Deng et al. https://doi.org/10.1016/j.jes.2024.06.030
- Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy J. Lin et al. https://doi.org/10.5194/gmd-18-2231-2025
- CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model D. Pendergrass et al. https://doi.org/10.5194/gmd-16-4793-2023
- CO2 Flux Inversion With a Regional Joint Data Assimilation System Based on CMAQ, EnKS, and Surface Observations Z. Peng et al. https://doi.org/10.1029/2022JD037154
- The optimization of SO2 emissions by the 4DVAR and EnKF methods and its application in WRF-Chem Y. Hu et al. https://doi.org/10.1016/j.scitotenv.2023.163796
- Carbonyl Compounds Regulate Atmospheric Oxidation Capacity and Particulate Sulfur Chemistry in the Coastal Atmosphere M. Zhao et al. https://doi.org/10.1021/acs.est.4c03947
- Improving Clear-Sky Solar Power Prediction over China by Assimilating Himawari-8 Aerosol Optical Depth with WRF-Chem-Solar S. Wang et al. https://doi.org/10.3390/rs14194990
- Improving aerosol forecasting through visibility data assimilation in WRF-chem: Methodology and evaluation S. Mei et al. https://doi.org/10.1016/j.atmosres.2025.108383
- Adjustment of the Power of Model Emissions of Anthropogenic Atmospheric Pollution Sources Based on Measurement Data and Adjoint Problem Methods P. Antokhin et al. https://doi.org/10.1134/S1024856025700319
- Future control pathways of PM2.5 constituents and emission sources under different SSPs from a health perspective G. Chen et al. https://doi.org/10.1016/j.envint.2025.109698
- 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
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. https://doi.org/10.3390/rs14010220
- A Decadal Change in Atmospheric Nitrogen Deposition at a Rural Site in Southern China K. Ren et al. https://doi.org/10.3390/atmos15050583
- Improvement and Uncertainties of Global Simulation of Sulfate Concentration and Radiative Forcing in CESM2 W. Ge et al. https://doi.org/10.1029/2022JD037623
- Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China Y. Hu et al. https://doi.org/10.5194/acp-22-13183-2022
- Assessing the assimilation of Himawari-8 observations on aerosol forecasts and radiative effects during pollution transport from South Asia to the Tibetan Plateau M. Zhao et al. https://doi.org/10.5194/acp-24-235-2024
- Investigating the Changes in Air Pollutant Emissions over the Beijing-Tianjin-Hebei Region in February from 2014 to 2019 through an Inverse Emission Method X. Luo et al. https://doi.org/10.1007/s00376-022-2039-9
- Hybrid IFDMB/4D-Var inverse modeling to constrain the spatiotemporal distribution of CO and NO2 emissions using the CMAQ adjoint model J. Moon et al. https://doi.org/10.1016/j.atmosenv.2024.120490
Saved (final revised paper)
Latest update: 03 Jun 2026
Short summary
The anthropogenic emission of sulfur dioxide (SO2) over China has significantly declined as a consequence of the clean air actions. We have developed a new emission inversion system to dynamically update the SO2 emission grid by grid over China by assimilating ground-based SO2 observations. The inverted SO2 emission over China in November 2016 on average had declined by 49.4 % since 2010, which is well in agreement with the bottom-up estimation of 48.0 %.
The anthropogenic emission of sulfur dioxide (SO2) over China has significantly declined as a...
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