Articles | Volume 22, issue 21
https://doi.org/10.5194/acp-22-14059-2022
© 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-22-14059-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Nov 2022
Research article |
| 03 Nov 2022
| 03 Nov 2022
Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis
Department of Physics, University of Toronto, Toronto, ON, Canada
now at: Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA
Dylan B. A. Jones
Department of Physics, University of Toronto, Toronto, ON, Canada
Kazuyuki Miyazaki
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Kevin W. Bowman
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Zhe Jiang
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, China
Xiaokang Chen
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, China
Rui Li
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, China
Yuxiang Zhang
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, China
Kunna Li
Department of Physics, University of Toronto, Toronto, ON, Canada
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Cited
26 citations as recorded by crossref.
- Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method Y. Zhang et al.
- Daily seamless dataset of HCHO concentrations: Vertical relationship between surface and column HCHO in China in 2019–2022 M. Wang et al.
- 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.
- Ozone in the Desert Southwest of the United States: A Synthesis of Past Work and Steps Ahead A. Sorooshian et al.
- Localized pollutant emission increases in China due to COVID-19 lockdowns F. Zhao et al.
- Satellite-Based Emission Inversion for Air Pollutants and Greenhouse Gases: A Review Z. Jiang et al.
- Multiple sources emission inventory closely integrated with atmospheric environment management: A case study of Guangdong, China M. Li et al.
- Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources A. Penenko et al.
- TROPOMI NO2 Shows a Fast Recovery of China’s Economy in the First Quarter of 2023 H. Li & B. Zheng
- Tracking daily NOx emissions from an urban agglomeration based on TROPOMI NO2 and a local ensemble transform Kalman filter Y. Kong et al.
- FootNet v1.0: development of a machine learning emulator of atmospheric transport T. He et al.
- Retrieval and Evaluation of NOX Emissions Based on a Machine Learning Model in Shandong T. Liu et al.
- Top-down estimates of anthropogenic NOx emissions over China through a new zone-stratified RF machine learning model Y. Huang et al.
- Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning K. Miyazaki et al.
- The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe W. Han et al.
- Spatiotemporal estimates of anthropogenic NOx emissions across China during 2015–2022 using a deep learning model Y. Lou et al.
- Inversion of hourly NOx emissions through air quality monitoring data and deep learning response surface modeling Z. Liu et al.
- Increased methane emissions from oil and gas following the Soviet Union’s collapse T. He et al.
- Satellite-based Air Quality Monitoring Using Artificial Intelligence: Research Trends and Future Perspectives H. Choi et al.
- A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx D. Wu et al.
- Long-term variations and trends of tropospheric and ground-level NO2 over typical coastal areas X. Tian et al.
- Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method Z. Yang et al.
- Deep learning for air pollutant forecasting: opportunities, challenges, and future directions C. Tao et al.
- Key Challenges and Future Trends of Spectral Inversion Technology in Environmental Monitoring Applications 琳. 乔
- Huge challenges of improving ozone pollution in China: High regional background ozone concentrations calculated from observational data Y. Liu et al.
- Next Generation Air Quality Models: Dynamical Mesh, New Insights into Mechanism, Datasets and Applications J. Li et al.
26 citations as recorded by crossref.
- Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method Y. Zhang et al.
- Daily seamless dataset of HCHO concentrations: Vertical relationship between surface and column HCHO in China in 2019–2022 M. Wang et al.
- 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.
- Ozone in the Desert Southwest of the United States: A Synthesis of Past Work and Steps Ahead A. Sorooshian et al.
- Localized pollutant emission increases in China due to COVID-19 lockdowns F. Zhao et al.
- Satellite-Based Emission Inversion for Air Pollutants and Greenhouse Gases: A Review Z. Jiang et al.
- Multiple sources emission inventory closely integrated with atmospheric environment management: A case study of Guangdong, China M. Li et al.
- Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources A. Penenko et al.
- TROPOMI NO2 Shows a Fast Recovery of China’s Economy in the First Quarter of 2023 H. Li & B. Zheng
- Tracking daily NOx emissions from an urban agglomeration based on TROPOMI NO2 and a local ensemble transform Kalman filter Y. Kong et al.
- FootNet v1.0: development of a machine learning emulator of atmospheric transport T. He et al.
- Retrieval and Evaluation of NOX Emissions Based on a Machine Learning Model in Shandong T. Liu et al.
- Top-down estimates of anthropogenic NOx emissions over China through a new zone-stratified RF machine learning model Y. Huang et al.
- Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning K. Miyazaki et al.
- The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe W. Han et al.
- Spatiotemporal estimates of anthropogenic NOx emissions across China during 2015–2022 using a deep learning model Y. Lou et al.
- Inversion of hourly NOx emissions through air quality monitoring data and deep learning response surface modeling Z. Liu et al.
- Increased methane emissions from oil and gas following the Soviet Union’s collapse T. He et al.
- Satellite-based Air Quality Monitoring Using Artificial Intelligence: Research Trends and Future Perspectives H. Choi et al.
- A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx D. Wu et al.
- Long-term variations and trends of tropospheric and ground-level NO2 over typical coastal areas X. Tian et al.
- Downscaling NOx emission into 1 km resolution over a typical mega-city based on POI with machine learning method Z. Yang et al.
- Deep learning for air pollutant forecasting: opportunities, challenges, and future directions C. Tao et al.
- Key Challenges and Future Trends of Spectral Inversion Technology in Environmental Monitoring Applications 琳. 乔
- Huge challenges of improving ozone pollution in China: High regional background ozone concentrations calculated from observational data Y. Liu et al.
- Next Generation Air Quality Models: Dynamical Mesh, New Insights into Mechanism, Datasets and Applications J. Li et al.
Saved (final revised paper)
Latest update: 06 May 2026
Short summary
We use a deep-learning (DL) model to estimate Chinese NOx emissions by combining satellite analysis and in situ measurements. Our results are consistent with conventional analyses of Chinese NOx emissions. Comparison with mobility data shows that the DL model has a better capability to capture changes in NOx. We analyse Chinese NOx emissions during the COVID-19 pandemic lockdown period. Our results illustrate the potential use of DL as a complementary tool for conventional air quality studies.
We use a deep-learning (DL) model to estimate Chinese NOx emissions by combining satellite...
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