Articles | Volume 22, issue 21
https://doi.org/10.5194/acp-22-14059-2022
https://doi.org/10.5194/acp-22-14059-2022
Research article
 | 
03 Nov 2022
Research article |  | 03 Nov 2022

Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis

Tai-Long He, Dylan B. A. Jones, Kazuyuki Miyazaki, Kevin W. Bowman, Zhe Jiang, Xiaokang Chen, Rui Li, Yuxiang Zhang, and Kunna Li

Related authors

High-resolution greenhouse gas flux inversions using a machine learning surrogate model for atmospheric transport
Nikhil Dadheech, Tai-Long He, and Alexander J. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2918,https://doi.org/10.5194/egusphere-2024-2918, 2024
Short summary
FootNet v1.0: Development of a machine learning emulator of atmospheric transport
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
EGUsphere, https://doi.org/10.31223/X5197G,https://doi.org/10.31223/X5197G, 2024
Short summary
A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China
Weichao Han, Tai-Long He, Zhaojun Tang, Min Wang, Dylan Jones, and Zhe Jiang
Geosci. Model Dev., 15, 4225–4237, https://doi.org/10.5194/gmd-15-4225-2022,https://doi.org/10.5194/gmd-15-4225-2022, 2022
Short summary
The Environment and Climate Change Canada Carbon Assimilation System (EC-CAS v1.0): demonstration with simulated CO observations
Vikram Khade, Saroja M. Polavarapu, Michael Neish, Pieter L. Houtekamer, Dylan B. A. Jones, Seung-Jong Baek, Tai-Long He, and Sylvie Gravel
Geosci. Model Dev., 14, 2525–2544, https://doi.org/10.5194/gmd-14-2525-2021,https://doi.org/10.5194/gmd-14-2525-2021, 2021
Short summary
Evaluation of MOPITT Version 7 joint TIR–NIR XCO retrievals with TCCON
Jacob K. Hedelius, Tai-Long He, Dylan B. A. Jones, Bianca C. Baier, Rebecca R. Buchholz, Martine De Mazière, Nicholas M. Deutscher, Manvendra K. Dubey, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Laura T. Iraci, Pascal Jeseck, Matthäus Kiel, Rigel Kivi, Cheng Liu, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Sébastien Roche, Coleen M. Roehl, Matthias Schneider, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Colm Sweeney, Yao Té, Osamu Uchino, Voltaire A. Velazco, Wei Wang, Thorsten Warneke, Paul O. Wennberg, Helen M. Worden, and Debra Wunch
Atmos. Meas. Tech., 12, 5547–5572, https://doi.org/10.5194/amt-12-5547-2019,https://doi.org/10.5194/amt-12-5547-2019, 2019
Short summary

Related subject area

Subject: Gases | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Assessing the relative impacts of satellite ozone and its precursor observations to improve global tropospheric ozone analysis using multiple chemical reanalysis systems
Takashi Sekiya, Emanuele Emili, Kazuyuki Miyazaki, Antje Inness, Zhen Qu, R. Bradley Pierce, Dylan Jones, Helen Worden, William Y. Y. Cheng, Vincent Huijnen, and Gerbrand Koren
Atmos. Chem. Phys., 25, 2243–2268, https://doi.org/10.5194/acp-25-2243-2025,https://doi.org/10.5194/acp-25-2243-2025, 2025
Short summary
Evaluating present-day and future impacts of agricultural ammonia emissions on atmospheric chemistry and climate
Maureen Beaudor, Didier Hauglustaine, Juliette Lathière, Martin Van Damme, Lieven Clarisse, and Nicolas Vuichard
Atmos. Chem. Phys., 25, 2017–2046, https://doi.org/10.5194/acp-25-2017-2025,https://doi.org/10.5194/acp-25-2017-2025, 2025
Short summary
Air-pollution-satellite-based CO2 emission inversion: system evaluation, sensitivity analysis, and future research direction
Hui Li, Jiaxin Qiu, and Bo Zheng
Atmos. Chem. Phys., 25, 1949–1963, https://doi.org/10.5194/acp-25-1949-2025,https://doi.org/10.5194/acp-25-1949-2025, 2025
Short summary
Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning
Yuqing Qiu, Xin Li, Wenxuan Chai, Yi Liu, Mengdi Song, Xudong Tian, Qiaoli Zou, Wenjun Lou, Wangyao Zhang, Juan Li, and Yuanhang Zhang
Atmos. Chem. Phys., 25, 1749–1763, https://doi.org/10.5194/acp-25-1749-2025,https://doi.org/10.5194/acp-25-1749-2025, 2025
Short summary
Quantification of regional net CO2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements
Jeongmin Yun, Junjie Liu, Brendan Byrne, Brad Weir, Lesley E. Ott, Kathryn McKain, Bianca C. Baier, Luciana V. Gatti, and Sebastien C. Biraud
Atmos. Chem. Phys., 25, 1725–1748, https://doi.org/10.5194/acp-25-1725-2025,https://doi.org/10.5194/acp-25-1725-2025, 2025
Short summary

Cited articles

Bauwens, M., Compernolle, S., Stavrakou, T., Müller, J.-F., van Gent, J., Eskes, H., Levelt, P. F., van der A, R., Veefkind, J. P., Vlietinck, J., Yu, H., and Zehner, C.: Impact of Coronavirus Outbreak on NO2 Pollution Assessed Using TROPOMI and OMI Observations, Geophys. Res. Lett., 47, e2020GL087978, https://doi.org/10.1029/2020GL087978, 2020. a
Beirle, S., Boersma, K. F., Platt, U., Lawrence, M. G., and Wagner, T.: Megacity Emissions and Lifetimes of Nitrogen Oxides Probed from Space, Science, 333, 1737–1739, https://doi.org/10.1126/science.1207824, 2011. a
Bertram, T. H., Heckel, A., Richter, A., Burrows, J. P., and Cohen, R. C.: Satellite measurements of daily variations in soil NOx emissions, Geophys. Res. Lett., 32, L24812, https://doi.org/10.1029/2005GL024640, 2005. a
Boersma, K. F., Jacob, D. J., Eskes, H. J., Pinder, R. W., Wang, J., and van der A, R. J.: Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: Observing the diurnal evolution of chemistry and emissions from space, J. Geophys. Res.-Atmos., 113, D16S26, https://doi.org/10.1029/2007JD008816, 2008. a
Boersma, K. F., Vinken, G. C. M., and Eskes, H. J.: Representativeness errors in comparing chemistry transport and chemistry climate models with satellite UV–Vis tropospheric column retrievals, Geosci. Model Dev., 9, 875–898, https://doi.org/10.5194/gmd-9-875-2016, 2016. a
Download
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.
Share
Altmetrics
Final-revised paper
Preprint