الفهرس 
Chapter 1Only 14 pages are availabe for public view
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Abstract
Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region and Sub-Saharan Africa. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries and (NO2, CO, CH4, and HCHO) in Sub-Saharan countries for the first time. The correlation analysis among the aforementioned indicators shows the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries and eleven Sub-Saharan countries are utilized here to study the aerosol optical depth (AOD) and Ångström exponent (AE) with other available observations. Additionally, a total of 65 different machine learning models of four categories, namely: linear regression, ensemble, decision tree, and deep neural network (DNN), were built from multiple data sources (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data. DNN validates well against AERONET data and proves to be the best model to generate optimized aerosol products when the ground observations are insufficient.