Preprints
https://doi.org/10.5194/egusphere-2024-558
https://doi.org/10.5194/egusphere-2024-558
13 Mar 2024
 | 13 Mar 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Improving Ground-Level NO2 Estimation in China Using GEMS Measurements and a Nested Machine Learning Model

Naveed Ahmad, Changqing Lin, Alexis K. H. Lau, Jhoon Kim, Fangqun Yu, Chengcai Li, Ying Li, Jimmy C. H. Fung, and Xiang Qian Lao

Abstract. The major bridge linking satellite-derived vertical column densities (VCDs) of nitrogen dioxide (NO2) with ground-level concentration is theoretically the NO2 mixing height (NMH). Various meteorological parameters have been used as a proxy of NMH in existing studies. This study developed a nested machine learning model to convert VCDs of NO2 into ground-level NO2 concentrations across China using Geostationary Environmental Monitoring Spectrometer (GEMS) measurements. This nested model was designed to directly incorporate NMH into the methodological framework and explore its impact on performance. The inner machine learning model predicted the NMH from the meteorological parameters, which were then input into the main machine learning model to predict the ground-level NO2 concentrations from its VCDs. The inclusion of NMH significantly enhanced the accuracy of estimating ground-level NO2 concentration, reducing bias and improving R² values to 0.93 in 10-fold cross-validation and 0.99 in the fully-trained model. Furthermore, NMH was identified as the second most important predictor variable, following the VCDs of NO2. Subsequently, satellite-derived ground-level NO2 data were analyzed across subregions with varying geolocations and urbanization levels. Highly populated areas typically experienced peak NO2 concentrations during early morning rush hours, whereas areas categorized as lightly populated observed a slight increase in NO2 levels one or two hours later, likely due to regional pollutant dispersion from urban sources. This study underscores the importance of incorporating NMH in estimating ground-level NO2 from satellite column measurements and highlights the significant advantages of geostationary satellites in providing detailed air pollution information at an hourly resolution.

Naveed Ahmad, Changqing Lin, Alexis K. H. Lau, Jhoon Kim, Fangqun Yu, Chengcai Li, Ying Li, Jimmy C. H. Fung, and Xiang Qian Lao

Status: open (until 24 Apr 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-558', Anonymous Referee #1, 05 Apr 2024 reply
  • RC2: 'Comment on egusphere-2024-558', Anonymous Referee #2, 10 Apr 2024 reply
Naveed Ahmad, Changqing Lin, Alexis K. H. Lau, Jhoon Kim, Fangqun Yu, Chengcai Li, Ying Li, Jimmy C. H. Fung, and Xiang Qian Lao
Naveed Ahmad, Changqing Lin, Alexis K. H. Lau, Jhoon Kim, Fangqun Yu, Chengcai Li, Ying Li, Jimmy C. H. Fung, and Xiang Qian Lao

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Short summary
This study developed a nested machine learning model to convert the GEMS NO2 column measurements into ground-level concentrations across China. The model directly incorporates the NO2 mixing height (NMH) into the methodological framework. The study underscores the importance of considering NMH when estimating ground-level NO2 from satellite column measurements and highlights the significant advantages of new-generation geostationary satellites in air quality monitoring.