초록 열기/닫기 버튼
Air quality forecasting system with data assimilation system using surface measurements in China and Korea was developed, and the performance of chemical transport model with data assimilation was validated and evaluated. The air quality forecasting modeling using data assimilation show better agreement with observations for PM10, PM2.5 and NO2 compared with model without data assimilation that was underestimated for PM10 and NO2 and overestimated for PM2.5 in Seoul metropolitan area, Korea. It is estimated that PM10 and NO2 emissions in the Seoul metropolitan area were underestimated in 2015. As a result of data assimilation using China's monitoring stations, different spatial distribution (with data assimilation - without data assimilation) of PM10 concentrations in the Korean Peninsula showed to be increased PM10 concentration in the West Sea because of long range transport from China to Korea. Therefore, it is suggest that data assimilation using ground observations in China and Korea could improve emissions for prediction or forecasting in upwind and local as well as performance of chemical transport model. The performance of air quality forecasting model with data assimilation showed that the forecasting index (Accuracy (A), Probability Of Detection (POD), False Alarm Rate (FAR)) was improved in most regions in Korea, compared to basic model without data assimilation during 2017 and 2018. Therefore, the developed air quality forecasting model with data assimilations was improved the forecasting performance of PM10 and proposed as a representative PM10 forecast model in South Korea.
