Classification of aerosols over Saudi Arabia from 2004–2016

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, several of the parameter relationships misclassified the aerosol types over the study area, and only the FMF vs. AAE relationship was 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. aerosol relationship techniques such as FMF vs. UVAI etc. This enables classification and recognition of specific aerosol types over the Arabian Peninsula and similar desert regions.

Comparison of MODIS- and CALIPSO-Derived Temporal Aerosol Optical Depth over Yellow River Basin (China) from 2007 to 2015

“…Atmospheric aerosols are solid and liquid particles with a size between 0.01 and 100 nm, which have complex physical, chemical, and optical characteristics (Nichol et al 2020;Tian et al 2018). Aerosols have a considerable impact on climate, atmospheric environment quality, human health, and related matters (Ali et al 2020;Kaiser and Granmar 2005;Kaufman et al 2002;Kulmala et al 2013;Lelieveld et al 2015;Sun and Ariya 2006). Aerosols can scatter or absorb solar radiation, causing changes in the earth-air radiation budget and affecting the radiation balance of the earth system (Butt et al 2016;Dubovik et al 2012).…”

Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

“…In the case of sulfur oxides (SOx), SO 2 is the only one that can be measured by satellite observations, but it was excluded due to the greater uncertainty in satellite SO 2 column data [49] compared with that of column CO [50] and tropospheric NO 2 [51] data. The aerosol index is the parameter for identifying absorbing and non-absorbing aerosols, but it is not the only parameter useful for identifying dust aerosols [21]. The RF model was found to have sensitivity for distinguishing dust aerosols by training with satellite input variables (e.g., aerosol index, column amount of trace gases) and the AERONET-based aerosol type dataset [31].…”

“…Other studies introduced cluster analysis with inputs for aerosol properties such as refractive index, AE, mean radius, asymmetry factors, and single scattering albedo (SSA) [14][15][16]. In threshold-based aerosol classification, AOD, SSA, and aerosol particle size (AE and fine mode fraction; FMF) are commonly involved [17][18][19][20][21]. These threshold-based aerosol classification methods were applied to investigate aerosol types on a global scale [17], Southeast Asia [18], Beijing [19], Europe, the Middle East, North Africa, the Arabian Peninsula [20], and Saudi Arabia [21].…”

“…In threshold-based aerosol classification, AOD, SSA, and aerosol particle size (AE and fine mode fraction; FMF) are commonly involved [17][18][19][20][21]. These threshold-based aerosol classification methods were applied to investigate aerosol types on a global scale [17], Southeast Asia [18], Beijing [19], Europe, the Middle East, North Africa, the Arabian Peninsula [20], and Saudi Arabia [21]. The depolarization ratio, which is sensitive to aerosol particle shape, has recently been introduced in threshold-based aerosol classification [22].…”

Long-Term Variation Assessment of Aerosol Load and Dominant Types over Asia for Air Quality Studies Using Multi-Sources Aerosol Datasets

“…Aerosol type information can be obtained from aerosol identification models based on aerosol characteristics [35][36][37][38][39]. AERONET provides diverse, accurate, and high temporal resolution aerosol characteristics observations (AOD; Angstrom Exponent, AE; Single Scattering Albedo, SSA; Absorption Angstrom Exponent, AAE; Extinction Angstrom Exponent, EAE), and the observations are widely used to support aerosol identification work.…”