Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China

Whether the aerosol optical thickness (AOT) products derived from MODIS data can be used as a reliable proxy of air pollutants measured near the surface depends on meteorological influence. This study attempts to assess the influence of four meteorological parameters (air pressure, temperature, relative humidity, and wind velocity) on predicting air pollution from MODIS AOT data for the city of Nanjing, China. It is found that PM10 (particulate matter with a diameter < 10 mu m) is linearly predictable from AOT at R-2 = 0.438. Seasonally, the prediction accuracy is much higher in summer (R-2 = 0.749, n = 24) and autumn (R-2 = 0.634, n = 45), but much lower in spring and winter (R-2 < 0.3). Scatterplots of the four meteorological parameters versus the residuals of PM10 estimates from AOT reveal no definite relationship between them. Thus, the seasonality of any parameters cannot account for the variation in the prediction accuracy. Stepwise regression analysis reveals that air pressure (R-2 = 0.268) is the most important factor affecting the residuals of Delta PM10 (Delta PM10) estimates in summer. In winter, the most important parameters are air pressure and air temperature (R-2 = 0.278). This accuracy rises to 0.510, similar to 0.409 for the summer season, if all four parameters are used in modeling PM10. However, no such models can be established for spring and autumn. It is concluded that meteorological parameters exert a mixed influence on the predictability of air pollution from AOT in the study area where air pollutants are dominated by suspended particulates.