Articles | Volume 20, issue 11
Atmos. Chem. Phys., 20, 6651–6670, 2020
https://doi.org/10.5194/acp-20-6651-2020
Atmos. Chem. Phys., 20, 6651–6670, 2020
https://doi.org/10.5194/acp-20-6651-2020
Research article
05 Jun 2020
Research article | 05 Jun 2020

Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts

Yi Wang et al.

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Cited articles

Bechle, M. J., Millet, D. B., and Marshall, J. D.: Remote sensing of exposure to NO2: Satellite versus ground-based measurement in a large urban area, Atmos. Environ., 69, 345–353, https://doi.org/10.1016/j.atmosenv.2012.11.046, 2013. 
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Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., and Ghosh, T.: VIIRS night-time lights, Int. J. Remote Sens., 38, 5860–5879, https://doi.org/10.1080/01431161.2017.1342050, 2017a. 
Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., and Ghosh, T.: VIIRS night-time lights, available: https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html (last access: 29 May 2020), 2017b. 
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Short summary
We developed four different methods to downscale SO2 and NO2 emissions derived from OMPS satellite observations (in Part 1) for regional air quality modeling at a spatial resolution that is finer than satellite observations. The VIIRS (city lights), TROPOMI, and OMI satellite data as well as surface data are used to evaluate the model. The method of using the top-down emissions from the past month for the air quality forecast in the present month is also shown to have practical merit.
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