Inverse modeling of SO2 and NOx emissions over China using multisensor...

Wang, J., J. Wang, M. Zhou, D. K. Henze, C. Ge, and W. Wang (2020), Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts, Atmos. Chem. Phys., 20, 6651-6670, doi:10.5194/acp-20-6651-2020.

Top-down emission estimates provide valuable up-to-date information on pollution sources; however, the computational effort and spatial resolution of satellite products involved with developing these emissions often require them to be estimated at resolutions that are much coarser than is necessary for regional air quality forecasting. This work thus introduces several approaches to downscaling coarseresolution (2◦ × 2.5◦ ) posterior SO2 and NOx emissions for improving air quality assessment and forecasts over China in October 2013. As in Part 1 of this study, these 2◦ × 2.5◦ posterior SO2 and NOx emission inventories are obtained from GEOS-Chem adjoint modeling with the constraints of OMPS SO2 and NO2 products retrieved at 50 km × 50 km at nadir and ∼ 190 km × 50 km at the edge of ground track. The prior emission inventory (MIX) and the posterior GEOSChem simulations of surface SO2 and NO2 concentrations at coarse resolution underestimate observed hot spots, which is called the coarse-grid smearing (CGS) effect. To mitigate the CGS effect, four methods are developed: (a) downscale 2◦ × 2.5◦ GEOS-Chem surface SO2 and NO2 concentrations to the resolution of 0.25◦ × 0.3125◦ through a dynamic downscaling concentration (MIX-DDC) approach, which assumes that the 0.25◦ × 0.3125◦ simulation using the prior MIX emissions has the correct spatial distribution of SO2 and NO2 concentrations but a systematic bias; (b) downscale surface NO2 simulations at 2◦ × 2.5◦ to 0.05◦ × 0.05◦ according to the spatial distribution of Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NL) observations (e.g., NL-DC approach) based on correlation between VIIRS NL intensity with TROPOspheric Monitoring Instrument (TROPOMI) NO2 observations; (c) downscale posterior emissions (DE) of SO2 and NOx to 0.25◦ × 0.3125◦ with the assumption that the prior fine-resolution MIX inventory has the correct spatial distribution (e.g., MIX-DE approach); and (d) downscale posterior NOx emissions using VIIRS NL observations (e.g., NL-DE approach). Numerical experiments reveal that (a) using the MIX-DDC approach, posterior SO2 and NO2 simulations improve on the corresponding MIX prior simulations with normalized centered root mean square error (NCRMSE) decreases of 63.7 % and 30.2 %, respectively; (b) the posterior NO2 simulation has an NCRMSE that is 17.9 % smaller than the prior when they are both downscaled through NL-DC, and NL-DC is able to better mitigate the CGS effect than MIX-DDC; (c) the simulation at 0.25◦ × 0.3125◦ using the MIX-DE approach has NCRMSEs that are 58.8 % and 14.7 % smaller than the prior 0.25◦ × 0.3125◦ MIX simulation for surface SO2 and NO2 concentrations, respectively, but the RMSE from the MIXDE posterior simulation is slightly larger than that from the MIX-DDC posterior simulation for both SO2 and NO2 ; (d) the NL-DE posterior NO2 simulation also improves on the prior MIX simulation at 0.25◦ × 0.3125◦ , but it is worse than

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Research Program: 
Atmospheric Composition
Atmospheric Composition Modeling and Analysis Program (ACMAP)
Radiation Science Program (RSP)
Tropospheric Composition Program (TCP)