Inverse modeling of SO2 and NOx emissions over China using multi-sensor satellite data: 2. Downscaling techniques for air quality analysis and forecasts.

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 NO_x 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 NO_x 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 NO_x 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 NO_x 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 NO_x emissions from joint inverse modeling of both species is better than only using one (SO₂ or NO_x) 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]