Monitoring air quality is crucial for Middle East countries such as Iran, where dust and polluted aerosol sources heavily influence local air quality. The use of active satellite remote sensing techniques is therefore considered in monitoring air quality. This study presents an initial assessment of NASA’s Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol data over Tabriz and Mashhad cities in the north-western and north-eastern regions of Iran. We examined the Cloud and Aerosol Discrimination (CAD) score values, extinction coefficient, and the CALIOP Vertical Feature Mask (VFM) data product and Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Aerosol Optical Depth (DBOD) at wavelength of 0.55 μm. The ground-based PM10 measurements were analyzed for different time periods, seasons, and years from 2005 to 2016. We investigated the profiles of the particle backscatter and extinction coefficient, as well as information about the determined feature types (e.g., clouds or aerosols) and aerosol subtypes (e.g., dust, and smoke) from the VFM data product in 2 months of August 2009 and July 2013, which were statistically selected from 2009 to 2016. Evaluation of the comparison of the relative humidity, temperature, and their inversion shows that the performance of the CALIOP in the detection of aerosols in mid-troposphere (around 5.0 km) is better than cloud detection. Additionally, the correlations of the PM10 concentration, MODIS AOD, and MODIS DBOD were investigated for January 2005 to December 2014. The overall analyses show that monthly ground-based PM10 concentration measurements reveal better correlation (r = 0.65 and 0.67 for Tabriz and Mashhad, respectively) with monthly MODIS-DBOD than MODIS-AOD for different seasons. The observed differences in the investigation of the CALIPSO dataset with the actual measured values and the overall correlation results show that the cloud and aerosol discrimination algorithm should be modified and calibrated based on local measurements of relative humidity, temperature, and their inversions, MODIS-DBOD, and ground-based PM10 for the Iran region.

Keywords