Articles | Volume 24, issue 16
https://doi.org/10.5194/acp-24-9645-2024
https://doi.org/10.5194/acp-24-9645-2024
Research article
 | 
30 Aug 2024
Research article |  | 30 Aug 2024

Estimation of ground-level NO2 and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model

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

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Cited articles

Ahmad, N., Lin, C., Lau, A. K. H., Kim, J., Li, C., Qin, K., Zhao, C., Lin, J., Fung, J. C. H., and Li, Y.: Effects of meteorological conditions on the mixing height of Nitrogen dioxide in China using new-generation geostationary satellite measurements and machine learning, Chemosphere, 346, 140615, https://doi.org/10.1016/j.chemosphere.2023.140615, 2024. 
Akther, T., Rappenglueck, B., Osibanjo, O., Retama, A., and Rivera-Hernández, O.: Ozone precursors and boundary layer meteorology before and during a severe ozone episode in Mexico city, Chemosphere, 318, 137978, https://doi.org/10.1016/j.chemosphere.2023.137978, 2023. 
Bhattarai, H., Tripathee, L., Kang, S., Sharma, C. M., Chen, P., Guo, J., and Ghimire, P. S.: Concentration, sources and wet deposition of dissolved nitrogen and organic carbon in the Northern Indo-Gangetic Plain during monsoon, J. Environ. Sci.-China, 102, 37–52, https://doi.org/10.1016/j.jes.2020.09.011, 2021. 
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. 
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016, Association for Computing Machinery, 785–794, https://doi.org/10.1145/2939672.293978, 2016. 
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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.
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