Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multi-sensor satellite data: 1. formulation and sensitivity analysis

Abstract

Abstract. 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.

Inverse modeling of SO<sub>2</sub> and NO<sub><i>x</i></sub> emissions over China using multi-sensor satellite data: 2. Downscaling techniques for air quality analysis and forecasts

“…This paper, as the second of a two-part study, aims at using SO2 and NOx emissions constrained by OMPS SO2 and NO2 retrievals through 4D-Var (which is presented in part I, i.e. Wang et al (2019)) to improve air quality forecasts. Since the emission inventories in Part I are derived at the 2°x2.5° resolution to save computational resources, the focus here is to develop novel methods to downscale coarse-resolution emission inventories or simulation results to generate fine-resolution surface SO2 and NO2 concentrations and evaluate them from an air quality forecasting point of view.…”

“…In spite of variations among spatial resolutions of these emission inventories, they are regridded to 0.25°x0.25° to form the MIX emissions inventory (Li et al, 2017a). In our study, not only is MIX used in the posterior simulations and forecasts, but it also provides information for downscaling the posterior emission inventories from Part I (as in Wang et al (2019)).…”

“…Posterior SO2 and NOx emissions for October 2013 from Part I, i.e. Wang et al (2019), are used for October 2013 simulations and November 2013 forecasts at 2°x2.5° resolution, but need be downscaled for 0.25°x0.3125° simulations, as described in Sect. 3.3.…”

“…The prior emissions are MIX for October and November 2010. The posterior emissions are from separate inverse emission estimates in Part I (e.g., E-SO2 and E-NO2 experiments as described in Wang et al (2019)), unless it is specifically stated. The downscaling methods here should be distinguished from interpolation approaches to simply increasing spatial resolutions.…”

“…CC BY 4.0 License. between OMI VCDs and emission strength (Fioletov et al, 2016), and the approach was used to build a global SO2 emission inventory at 0.1°x0.1° (Liu et al, 2018). Advanced data assimilation approaches including the fourdimension variational data assimilation (4D-Var) (Qu et al, 2019a;Qu et al, 2019b;Qu et al, 2017;Wang et al, 2016b;Wang et al, 2019;Wang et al, 2012;Xu et al, 2013;Kurokawa et al, 2009) and the Ensemble Kalman 95 Filter (EnKF) approach (Miyazaki et al, 2012;Miyazaki et al, 2017) were developed to use satellite SO2 and NO2 columns densities and AOD retrievals to constrain emissions at low spatial resolutions (>50 km) as these approaches are computation-intensive. Some variations of the 4D-Var and Kalman filter approaches were developed to save computational time at the expense of accuracy or temporal resolution (Qu et al, 2017;Kong et al, 2019;Mijling and van der A, 2012;Ding et al, 2015).…”