Inverse modeling of SO2 and NOx emissions over China using multisensor satellite data – Part 2: Downscaling techniques for air quality analysis and forecasts
Authors: Wang Yi , Wang Jun , Zhou Meng , Henze Daven K. , Ge Cui , Wang Wei 
Published: June 2020

Abstract. 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 coarse-resolution
(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 ∼190km×50km at the edge of ground track. The prior emission inventory (MIX) and the posterior GEOS-Chem 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 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) emission from corresponding
single-species inverse modeling and is similar to using the posterior
emissions of SO2 and NOx emission inventories respectively 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 that (a) the improvements of forecasting surface SO2
concentrations through MIX-DE and MIX-DDC are comparable; (b) for the NO2
forecast, MIX-DE shows 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; (d) the forecast of surface O3 concentrations is improved by
MIX-DE downscaled posterior NOx emissions. 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 modeling for the present month.