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Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation. [ABSTRACT FROM AUTHOR]

The empirical/statistical models to estimate the surface Particulate Matter (PM2.5) concentration in Seoul from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed for the period of 2015. Two vertical correction methods, vertical ratio of aerosol (VRA) calculated from LIDAR vertical profile of backscattering coefficients and AOD below boundary layer extracted from planetary boundary layer height (PAOD), were effective to increase the correlation between the satellite-derived AOD and ground-observed PM2.5. In order to improve the accuracy in PM2.5 estimation, additional meteorological factors (wind speed, visibility, and air temperature, relative humidity) affecting AOD and PM2.5 relationships were considered as predictor variables in the multiple linear regression (MLR) models. Stable atmospheric state with low wind speed and high relative humidity conditions are closely associated with high PM2.5 concentrations in the morning. The estimates show a similar variation in time (day and season), and a high correlation (R=0.85) compared to observed PM2.5 concentration. The models developed in this study are applicable to the surface PM2.5 estimation using AOD retrieved from the future GK-2A and GK-2B. [ABSTRACT FROM AUTHOR]