INTEGRATED AEROSOL OPTICAL THICKNESS, GASEOUS POLLUTANTS AND METEOROLOGICAL PARAMETERS TO ESTIMATE GROUND PM_(2.5) CONCENTRATION

摘要

It is difficult to estimate the surface-level particulate matter 2.5 (PM_(2.5)) using satellite-based aerosol optical thickness (AOT) because the relevant correlation is influenced by many factors, such as retrieval AOT algorithms, meteorological parameters and heterogeneous reactions. In this study, we analyzed the influences of four meteorological parameters (temperature, air pressure, relative humidity and wind speed) and four gaseous pollutants (SO_2, NO_2, O_3 and CO) on ground PM_(2.5) estimation. We obtained AOT by Moderate Resolution Imaging Spectrora-diometer (MODIS) and Landsat 8 in city of Beijing, China from April to November in 2013. By correlation analysis, PM_(2.5) significantly correlated with AOT (R = 0.8), SO_2 (R = 0.83) and relative humidity (R= 0.81), correlated somewhat with NO_2 and O_3, while only weakly correlated with the air pressure. The accuracy is too low to build a linear model to predict ground PM_(2.5) with AOT alone. A more accurate linear model was obtained when incorporating gaseous pollutants and meteorological data (R~2 increased from 0.64 to 0.93, RMSE decreased from 31.2 to 14.1). If meteorological data were not available, using AOT and SO_2 to estimate ground PM_(2.5) could also achieve good results (R~2 = 0.82, RMSE = 22.6). Comparing with linear models, using back propagation (BP) neutral network model to estimate ground PM_(2.5) was found to be more suitable as its RMSE was 6.6, being much smaller than that of the linear models.