A Fast Method for Assessment of PM10 Concentration Using MODIS Images, A Case Study in Tehran

One of the most important pollutants that can be monitored in the atmosphere through remote sensing technology is particulate matter’s concentration (PM10). This study aimed to assess the MODIS sensor’s ability in monitoring of the PM10 in urban areas and producing daily maps of this pollutant. The MODIS Level 1B-1KM، MODIS Level 1B-250M images، as well as pollutants data collected from pollution surveying stations at different parts of Tehran were used in this study. The model was based on the concept of linear spectral unmixing of only two endmembers; surface reflectance without pollution and PM10 reflectance in the air. These two reflectances are extracted from MODIS images itself. In this respect، the surface reflectance was extracted from an image of a clear day and reflectance of aerosols was calculated from an image of highly polluted day when the surface was completely masked by the pollutants. Findings showed that there was an acceptable correlation between model prediction and insitu measurements. This correlation was found to be about 70% with a RMSE of about 132. 22 μg/m3 between model predicted and insitu measured for Tehran. Comparing RMSE with those of ground measurement of PM10، the relative error was high for low PM10 concentrations (about 200 μgr/m3); consequently model was exaggerated. This relative error was low for the higher values of the PM10. Existance of acceptable correlation between model prediction and insitu measurements were indicative of the ability of satellite images in monitoring of the air pollution due to particulate matter particularly over urban areas.