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SO2 and NO2 observations from the Ozone Mapping and Profiler Suite (OMPS) sensor are used for the first time in conjunction with GEOS-Chem adjoint model to optimize both SO2 and NOx emission estimates over China for October 2013. OMPS SO2 and NO2 observations are first assimilated separately to optimize emissions of SO2 and NOx, respectively. Posterior emissions, compared to the prior, yield improvements in simulating columnar SO2 and NO2, in comparison to measurements from OMI and OMPS. The posterior SO2 and NOx emissions from separate inversions are 748 Gg S and 672 Gg N, which are 36 % and 6 % smaller than prior MIX emissions, respectively. In spite of the large reduction of SO2 emissions over the North China Plain, the simulated sulfate-nitrate-ammonium Aerosol Optical Depth (AOD) only decrease slightly, which can be attributed to (a) nitrate rather than sulfate as the dominant contributor to AOD and (b) replacement of ammonium sulfate with ammonium nitrate as SO2 emissions are reduced. Both data quality control and the weight given to SO2 relative to NO2 observations can affect the spatial distributions of the joint inversion results. When the latter is properly balanced, the posterior emissions from assimilating OMPS SO2 and NO2 jointly yield a difference of -3 % to 15 % with respect to the separate assimilations for total anthropogenic SO2 emissions and ± 2 % for total anthropogenic NOx emissions; but the differences can be up to 100 % for SO2 and 40 % for NO2 in some grid cells. Improvements on SO2 and NO2 simulations evaluated with OMPS and OMI measurements from the joint inversions are overall consistent with those from separate inversions. Moreover, the joint assimilations save ~ 50 % of the computational time than assimilating SO2 and NO2 separately when computational resources are limited to run one inversion at a time sequentially. The sensitivity analysis shows that a perturbation of NH3 to 50 % (20 %) of the prior emission inventory: (a) has negligible impact on the separate SO2 inversion, but can lead to decrease of posterior SO2 emissions over China by -2.4 % (-7.0 %) in total and up to -9.0 % (-27.7 %) in some grid cells in the joint inversion with NO2; (b) yield posterior NOx emissions over China decrease by -0.7 % (-2.8 %) for the separate NO2 inversion and by -2.7 % (-5.3 %) in total and up to -15.2 % (-29.4 %) in some grid cells for the joint inversion. The large reduction of SO2 between 2010 and 2013, however, only leads to ~ 10 % decrease of aerosol optical depth regionally; reducing surface aerosol concentration requires the reduction of emissions of NH3 as well. [ABSTRACT FROM AUTHOR]

Top-down emissions estimates provide valuable up-to-date information on pollution sources; however, the computational effort involved with developing these emissions often requires 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 coarse-resolution (2° × 2.5°) posterior SO2 and NOx emissions (derived through inverse modeling in Part I of this study) for improving air quality assessment and forecasts over China in October 2013. The SO2 and NOx emission inverse modeling was conducted at the 2° × 2.5° resolution in Part I to save computational time. The prior emission inventory (MIX) as well as the posterior GEOS-Chem simulations of surface SO2 and NO2 concentrations at this 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 observations (e.g., NL-DC approach) based on correlation between VIIRS NL intensity with 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 compared to the corresponding MIX prior simulations with normalized centered root mean square error (NCRMSE) decreases of 63.7 % and 30.2 %, respectively; (b) the NO2 simulation has an NCRMSE that is 17.9 % smaller than the prior NO2 simulation 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 MIX-DE 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 the MIX-DE posterior simulation; (e) in terms of evaluating the downscaled SO2 and NO2 simulations simultaneously, using the posterior SO2 and NOx emissions from joint inverse modeling of both species is better than only using one (SO2 or NOx) emissions from corresponding single-species inverse modeling and is similar to using the posterior emissions for both SO2 and inventories from single-species inverse modeling. Forecasts of surface concentrations for November 2013 using the posterior emissions obtained by applying the posterior MIX-DE emissions for October 2013 with the monthly variation information derived from the prior MIX emission inventory show (a) the improvements of forecasting surface SO2 concentrations through MIX-DE and MIX-DDC are comparable; (b) for NO2 forecast, MIX-DE show larger improvement than NL-DE and MIX-DDC; (c) NL-DC is able to better decrease the CGS effect than MIX-DE, but shows larger NCRMSE. Overall, for practical forecasting of air quality, it is recommended to use satellite-based observation already available from the last month to jointly constrain SO2 and NO2 emissions at coarser resolution and then downscale these posterior emissions at finer spatial resolution suitable for regional air quality model for the present month. [ABSTRACT FROM AUTHOR]