Evaluation Of The Simulated Pm2.5 Concentrations Using Air Quality Forecasting System According To Emission Inventories - Focused On China And South Korea

Emission inventory is the essential component for improving the performance of air quality forecasting system. This study evaluated the simulated daily mean PM2.5 concentrations in South Korea and China for 1-year period (Sept. 2016 similar to Aug. 2017) using air quality forecasting system which was applied by the emission inventory of E2015 (predicted CAPSS 2015 for South Korea and KORUS 2015 v1 for the other regions). To identify the impacts of emissions on the simulated PM2.5, the emission inventory replaced by E2010 (CAPSS 2010 and MIX 2010) were also applied under the same forecasting conditions. These results showed that simulated daily mean PM2.5 concentrations had generally suitable performance with both emission data-sets for China (IOA>0.87, R>0.87) and South Korea (IOA>0.84, R>0.76). The impacts of the changes in emission inventories on simulated daily mean PM2.5 concentrations were quantitatively estimated. In China, normalized mean bias (NMB) showed 5.5% and 26.8% under E2010 and E2015, respectively. The tendency of overestimated concentrations was larger in North Central and Southeast China than other regions under both E2010 and E2015. Seasonal differences of NMB were higher in non-winter season (28.3% (E2010)similar to 39.3% (E2015)) than winter season (-0.5% (E2010)similar to 8.0% (E2015)). In South Korea, NMB showed -5.4% and 2.8% for all days, but -15.2% and -11.2% for days below 40 mu g/m(3) to minimize the impacts of long-range transport under E2010 and E2015, respectively. For all days, simulated PM2.5 concentrations were overestimated in Seoul, Incheon, Southern part of Gyeonggi and Daejeon, and underestimated in other regions such as Jeonbuk, Ulsan, Busan and Gyeongnam, regardless of what emission inventories were applied. Our results suggest that the updated emission inventory, which reflects current status of emission amounts and spatio-temporal allocations, is needed for improving the performance of air quality forecasting.