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Aerosol Types Identification over the Arabian Peninsula Using AERONET Products: Evaluation with Multisource Datasets

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Abstract

Knowing aerosol size and composition is essential for studying aerosol radiative forcing (ARF) and human health impacts, identifying their sources, and improving satellite aerosol estimation methods. An aerosol classification technique (T1) is proposed here using K-mean clustering analysis based on Aerosol Optical Depth (AOD500 nm) vs Angstrom Exponent (AE440–870 nm) data (1999–2018) from Aerosol Robotic Network (AERONET) in the Arabian Peninsula. The proposed classified aerosol types are: (1) Clean background/clean maritime (CB/CM): AOD < 0.20 & AE < 1.35; (2) Clean Continental (CC): AOD < 0.20 & AE > 1.35; (3) Desert Dust (DD): AOD > 0.20 & AE < 0.6; (4) Dusty Mixture (DM): AOD > 0.20 & 0.60 < AE < 1.0; (5) Urban Industrial (UI): AOD > 0.20 & 1.0 < AE < 1.35; and (6) Biomass Burning (BB): AOD > 0.20 & AE > 1.35. Further verification of our results was conducted with respect to AERONET depolarization ratio (δ), surface observations (e.g., wind speed, relative humidity, and visibility), and dust RGB products from Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG) satellite. The T1 reveals that DD was prevalent at the Solar Village in Riyadh (51%), and the KAUST Campus (47.65%) and Hada-El-Sham (37.39%) in Jeddah. Additionally, urban industrial and biomass burning aerosol types were observed due to an increase in industrial activities and biofuel emissions. The evaluation study showed that other previously published techniques (e.g., T2, T3, and T4) misclassified aerosol types, whereas T1 significantly classified aerosol types. Moreover, back trajectory analysis based on the NOAA HYSPLIT model demonstrated that the predominant DD- and other-aerosols originated and transported from both local and external sources of the Arabian Peninsula. Finally, the study examined ARF from AERONET and SBDART (the Santa Barbara DISTORT Atmospheric Radiative Transfer) models, which found that scattering radiation at the top and bottom of the atmosphere cooled the atmosphere, while absorbing radiation within the atmosphere heated the atmosphere. As a result of the present study, specific aerosol types are now classified and recognized as well as they may have a direct impact on local and regional climate change and radiation budget over the Arabian Peninsula.

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Acknowledgements

The authors are grateful to NASA for providing AERONET data (https://aeronet.gsfc.nasa.gov/). We would like to give special thanks to EUMETSAT and Wyoming University for making the MSG dust-related and surface observation data available.

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  1. The Regional Center for Climate Change at National Center for Meteorology, 21431, Jeddah, Saudi Arabia

    Mazen E. Assiri

  2. Department of Meteorology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

    Mazen E. Assiri

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Assiri, M.E. Aerosol Types Identification over the Arabian Peninsula Using AERONET Products: Evaluation with Multisource Datasets. Earth Syst Environ 8, 483–499 (2024). https://doi.org/10.1007/s41748-024-00389-x

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