Ponente
Descripción
Top-down atmospheric inversion uses spatially distributed observations of atmospheric compositions to provide estimates of surface-atmosphere fluxes. In this study, we constructed a Regional multi-Air Pollutant Assimilation System (RAPASv1.0) based on the Weather Research and Forecasting/Community Multiscale Air Quality Modeling System (WRF/CMAQ) model, the three-dimensional variational (3DVAR) algorithm and the ensemble square root filter (EnSRF) algorithm, which simultaneously assimilates spatially distributed hourly in-situ measurements of CO, SO2, NO2, PM2.5 and PM10 concentrations to quantitatively optimize gridded emissions of CO, SO2, NOx, primary PM2.5 (PPM2.5) and coarse PM10 (PMC) on regional scale. A “two-step” inversion scheme is adopted in each data assimilation (DA) window, in which the emission is inferred in the first step, and then, it is input into the CMAQ model to simulate the initial field of the next window, meanwhile, it is also transferred to the next window as the prior emission. In this way, the original emission inventory is only used in the first DA window, and the 3D-Var algorithm used to optimizing the chemical IC is also run only in the first window. Besides, a “super-observation” approach is implemented based on optimal estimation theory to decrease the computational costs and observation error correlations and reduce the influence of representativeness errors. Based on this system, the emissions of CO, SO2, NOx, PPM2.5 and PMC in December 2016 are inferred using the corresponding nationwide observations over China. The 2016 Multi-resolution Emission Inventory for China (MEIC 2016) is used as the prior emission. The results showed that, compared to the prior emission (MEIC 2016), the posterior emissions increased by 129%, 20%, 5%, and 95% for CO, SO2, NOx and PPM2.5, respectively, in December, indicating that there was significant underestimation in the MEIC inventory.
