Abstract
Knowledge of aerosol size and composition is very important for investigating the radiative forcing impacts of aerosols, distinguishing aerosol sources, and identifying harmful particulate types in air quality monitoring. The ability to identify aerosol type synoptically would greatly contribute to the knowledge of aerosol type distribution at both regional and global scales, especially where there are no data on chemical composition. In this study, aerosol classification techniques were based on aerosol optical properties from remotely-observed data from the Ozone Monitoring Instrument (OMI) and Aerosol Robotic Network (AERONET) over Saudi Arabia for the period 2004–2016 and validated using data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). For this purpose, the OMI-based Aerosol Absorption Optical Depth (AAOD) and Ultra-Violet Aerosol Index (UVAI), and AERONET-based AAOD, Ångström Exponent (AE), Absorption Ångström Exponent (AAE), Fine Mode Fraction (FMF), and Single Scattering Albedo (SSA) were obtained. Spatial analysis of the satellite-based OMI-AAOD showed the dominance of absorbing aerosols over the study area, but with high seasonal variability. The study found significant underestimation by OMI AAOD suggesting that the OMAERUV product may need improvement over bright desert surfaces such as the study area. Aerosols were classified into (i) Dust, (ii) Black Carbon (BC), and (iii) Mixed (BC and Dust) based on the relationships technique, between the aerosol absorption properties (AAE, SSA, and UVAI) and size parameters (AE and FMF). Additionally, the AE vs. UVAI and FMF vs. UVAI relationships misclassified the aerosol types over the study area, and the FMF vs. AE, FMF vs. AAE and FMF vs. SSA relationships were found to be robust. As expected, the dust aerosol type was dominant both annually and seasonally due to frequent dust storm events. Also, fine particulates such as BC and Mixed (BC and Dust) were observed, likely due to industrial activities (cement, petrochemical, fertilizer), water desalination plants, and electric energy generation. This is the first study to classify aerosol types over Saudi Arabia using several different aerosol property relationships, as well as over more than one site, and using data over a much longer time-period than previous studies. This enables classification and recognition of specific aerosol types over the Arabian Peninsula and similar desert regions.
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•AERONET, OMI, and CALIPSO datasets were used for classifying aerosols.•OMI AAOD shows the dominance of absorbing aerosols with high seasonal variability.•Dust, then mixed black carbon and dust dominated over the study area, Saudi Arabia.•Mixed aerosol types suggest increasing fossil fuel and biogenic emissions.•FMF vs. (AE, AAE, and SSA) are the best techniques for classifying aerosols.