Empirical Estimation and Diurnal Patterns of Surface PM2.5 Concentration in Seoul Using GOCI AOD

The empirical/statistical models to estimate the ground Particulate Matter (PM2.5) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-PM2.5, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for PM2.5 concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and PM2.5 relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-PM2.5 model. when the MLR model is seasonally constructed, underestimation tendency in high PM2.5 cases for the whole year were improved. The monthly and diurnal patterns of observed PM2.5 and estimated PM2.5 were similar. The results of this study, which estimates surface PM2.5 concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.