Estimation of PM2.5 Concentrations over Beijing with MODIS AODs Using an Artificial Neural Network

Three years of Aerosol Optical Depths (AODs) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and five meteorological parameters from the NCEP FNL reanalysis data, are used to generate an Artificial Neutral Network (ANN)-based nonlinear model for estimating the surface PM2.5 concentrations over Beijing. To increase the number of both the training and forecasting samples for better training results and to guarantee the continuity and representativeness of the samples, the MODIS AODs are gridded with seasonally dependent windows sizes. The past PM2.5 concentrations simulated by the ANN model are contrasted with the real observations for six years from 2008 to 2013. The results indicate that the ANN model can effectively simulate the surface PM2.5 concentrations, and the mean bias, correlation coefficient, and the root mean square error between these data are −16.10, 0.73, and 55.43, respectively. This study also demonstrates that the Planetary Boundary Layer Height (PBLH) is the most important meteorological factor in constructing the ANN model. Compared to the linear regression model using only AOD, the correlation coefficient can be increased from 0.68 to 0.76 with the ANN model by using both the AOD and the PBLH data.