HYDROMETEOROLOGICAL DRIVERS OF PARTICULATE MATTER USING BAYESIAN MODEL AVERAGING

Abstract

In this study, we tried to find out the relationships between Particulate Matter (PM) and several hydrometeorological factors from satellite and reanalysis data, also setting a goal to figure out the way to overcome limitations that ground-based air pollution measurements have. We used particulate matter data from Air Korea, which is being managed by the Korean ministry of environment, Aerosol Optical Depth (AOD) from satellite Terra and Aqua and 9 hydrometeorological variables from Global Land Data Assimilation System (GLDAS). The relationships were produced through Bayesian Model Averaging (BMA) method. The variables affected PM10 the most, were AOD, net shortwave radiation, specific humidity and precipitation and for PM2.5, they were AOD, wind speed, net shortwave radiation and precipitation. It is expected if the accumulation and analysis of the subsequent data is progressively carried out, real-time monitoring of particulate matter with higher accuracy will be possible, totally without ground-based measurements.