Abstract
Remote sensing technique constructs a regression model to describe the relations between Aerosol optical depth (AOD) and the ground PM 2.5 concentration. However, potential effects from meteorological and geographical factors on the regression models have never been carefully investigated. The manuscript selects three main meteorological variables and two major geographical variables, and investigates their impacts in the performance of geographical weighted regression (GWR) and ordinary least square (OLS) models for estimating ground PM 2.5 concentration. Preliminary results on the case of Yangtze River Delta show that meteorological factors have more significant influence on the estimation of PM 2.5 concentration than geographical factors. Moreover, the observations tell that the GWR is more preferably to estimate PM 2.5 concentration than the OLS.
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