Preprints
https://doi.org/10.5194/acp-2022-251
https://doi.org/10.5194/acp-2022-251
 
12 May 2022
12 May 2022
Status: this preprint is currently under review for the journal ACP.

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

Tai-Long He1, Dylan B. A. Jones1, Kazuyuki Miyazaki2, Kevin W. Bowman2, Zhe Jiang3, Xiaokang Chen3, Rui Li3, Yuxiang Zhang3, and Kunna Li1 Tai-Long He et al.
  • 1Department of Physics, University of Toronto, Toronto, Ontario, Canada
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 3School of Earth and Space Sciences, University of Science and Technology of China

Abstract. Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverage due to cloud cover and by their reduced sensitivity toward the surface. Combining the information from satellites with surface observations of NO2 will provide greater constraints on NOx emission estimates. We have developed a deep learning (DL) model to integrate satellite data and in situ observations of surface NO2 to estimate NOx emissions in China. A prior information for the DL model was obtained from satellite-derived emissions from the Tropospheric Chemistry Reanalysis (TCR-2). A two-stage training strategy was used to integrate in situ measurements from the China Ministry of Ecology and Environment (MEE) observation network with the TCR-2 data. The DL model is trained from 2005 to 2018, and is evaluated for 2019 and 2020. The DL model estimated a source of 19.4 Tg NO for total Chinese NOx emissions in 2019, which is consistent with the TCR-2 estimate of 18.5±3.9 Tg NO and the 20.9 Tg NO suggested by the Multi-resolution Emission Inventory for China (MEIC). Combining the MEE data with TCR-2, the DL model suggested higher NOx emissions in some of the less densely populated provinces, such as Shaanxi and Sichuan, where the MEE data indicated higher surface NO2 concentrations than TCR-2. The DL model also suggested a faster recovery of NOx emissions than TCR-2 after the Chinese New Year (CNY) holiday in 2019, with a recovery time scale that is consistent with Baidu “Qianxi” mobility data. In 2020, the DL-based analysis estimated about a 30 % reduction in NOx emissions in eastern China during the COVID-19 lockdown period, relative to pre-lockdown levels. In particular, the maximum emission reductions were 42 % and 30 % for the Jing-Jin-Ji and the Yangtze River Delta megaregions, respectively. Our results illustrate the potential utility of the DL model as a complementary tool for conventional data assimilation approaches for air quality applications.

Tai-Long He et al.

Status: open (until 23 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Tai-Long He et al.

Tai-Long He et al.

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Short summary
We use a deep learning (DL) model to estimate Chinese NOx emissions by combining satellite analysis and in situ measuremements. 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 analyze Chinese NOx emissions during the COVID-19 pandemic period. Our results illustrate the potential utility of DL as a complementary tool for conventional air quality studies.
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