Articles | Volume 20, issue 11
https://doi.org/10.5194/acp-20-6651-2020
© 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-20-6651-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jun 2020
Research article |
| 05 Jun 2020
| 05 Jun 2020
Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
Yi Wang
CORRESPONDING AUTHOR
Interdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA 52242, USA
Interdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA 52242, USA
Department of Chemical and Biochemical Engineering, and Center for
Global and Regional Environmental Research, The University of Iowa, Iowa
City, IA 52242, USA
Meng Zhou
Interdisciplinary Graduate Program in Informatics, The University of Iowa, Iowa City, IA 52242, USA
Daven K. Henze
Department of Mechanical Engineering, University of Colorado, Boulder, CO 80309, USA
Cui Ge
Department of Chemical and Biochemical Engineering, and Center for
Global and Regional Environmental Research, The University of Iowa, Iowa
City, IA 52242, USA
South Coast Air Quality Management District, Diamond Bar, CA 91765,
USA
Wei Wang
China National Environmental Monitoring Center, Beijing 100012, China
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Cited
16 citations as recorded by crossref.
- Estimating NOx Emissions in China via Multisource Satellite Data and Deep Learning Model K. Cai et al. 10.3390/rs17071231
- Application of satellite data and GIS services for studying air pollutants in Lithuania (case study: Kaunas city) A. Soleimany et al. 10.1007/s11869-020-00946-z
- Improved modelling of soil NO x emissions in a high temperature agricultural region: role of background emissions on NO2 trend over the US Y. Wang et al. 10.1088/1748-9326/ac16a3
- Improved spatial representation of a highly resolved emission inventory in China: evidence from TROPOMI measurements N. Wu et al. 10.1088/1748-9326/ac175f
- Aggravated surface O3 pollution primarily driven by meteorological variations in China during the 2020 COVID-19 pandemic lockdown period Z. Lu et al. 10.5194/acp-24-7793-2024
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
- Near-Surface NO2 Concentration Estimation by Random Forest Modeling and Sentinel-5P and Ancillary Data M. Li et al. 10.3390/rs14153612
- Direct Retrieval of NO 2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network C. Li et al. 10.34133/2022/9817134
- Is the efficacy of satellite-based inversion of SO2 emission model dependent? N. Li et al. 10.1088/1748-9326/abe829
- Ground-Level NO2Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence J. Wei et al. 10.1021/acs.est.2c03834
- Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations M. Yu & Q. Liu 10.1016/j.scitotenv.2021.145145
- Air Quality Forecasting with Inversely Updated Emissions for China H. Wu et al. 10.1021/acs.estlett.3c00266
- Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes F. Cifuentes et al. 10.5194/gmd-18-621-2025
- Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 1: Formulation and sensitivity analysis Y. Wang et al. 10.5194/acp-20-6631-2020
- High-resolution anthropogenic emission inventories with deep learning in northern South America F. Antezana Lopez et al. 10.1016/j.rse.2025.114761
- First lunar-light mapping of nighttime dust season oceanic aerosol optical depth over North Atlantic from space M. Zhou et al. 10.1016/j.rse.2024.114315
16 citations as recorded by crossref.
- Estimating NOx Emissions in China via Multisource Satellite Data and Deep Learning Model K. Cai et al. 10.3390/rs17071231
- Application of satellite data and GIS services for studying air pollutants in Lithuania (case study: Kaunas city) A. Soleimany et al. 10.1007/s11869-020-00946-z
- Improved modelling of soil NO x emissions in a high temperature agricultural region: role of background emissions on NO2 trend over the US Y. Wang et al. 10.1088/1748-9326/ac16a3
- Improved spatial representation of a highly resolved emission inventory in China: evidence from TROPOMI measurements N. Wu et al. 10.1088/1748-9326/ac175f
- Aggravated surface O3 pollution primarily driven by meteorological variations in China during the 2020 COVID-19 pandemic lockdown period Z. Lu et al. 10.5194/acp-24-7793-2024
- Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation Y. Hu et al. 10.3390/rs14010220
- Near-Surface NO2 Concentration Estimation by Random Forest Modeling and Sentinel-5P and Ancillary Data M. Li et al. 10.3390/rs14153612
- Direct Retrieval of NO 2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network C. Li et al. 10.34133/2022/9817134
- Is the efficacy of satellite-based inversion of SO2 emission model dependent? N. Li et al. 10.1088/1748-9326/abe829
- Ground-Level NO2Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence J. Wei et al. 10.1021/acs.est.2c03834
- Deep learning-based downscaling of tropospheric nitrogen dioxide using ground-level and satellite observations M. Yu & Q. Liu 10.1016/j.scitotenv.2021.145145
- Air Quality Forecasting with Inversely Updated Emissions for China H. Wu et al. 10.1021/acs.estlett.3c00266
- Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes F. Cifuentes et al. 10.5194/gmd-18-621-2025
- Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 1: Formulation and sensitivity analysis Y. Wang et al. 10.5194/acp-20-6631-2020
- High-resolution anthropogenic emission inventories with deep learning in northern South America F. Antezana Lopez et al. 10.1016/j.rse.2025.114761
- First lunar-light mapping of nighttime dust season oceanic aerosol optical depth over North Atlantic from space M. Zhou et al. 10.1016/j.rse.2024.114315
Latest update: 16 Jul 2025
Short summary
We developed four different methods to downscale SO2 and NO2 emissions derived from OMPS satellite observations (in Part 1) for regional air quality modeling at a spatial resolution that is finer than satellite observations. The VIIRS (city lights), TROPOMI, and OMI satellite data as well as surface data are used to evaluate the model. The method of using the top-down emissions from the past month for the air quality forecast in the present month is also shown to have practical merit.
We developed four different methods to downscale SO2 and NO2 emissions derived from OMPS...
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