Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. 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 for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA 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. This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies.

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Sustainable Development Goal 13 (SDG 13) is about climate action and is one of 17 SDGs established by the United Nations General Assembly in 2015. The official mission statement of this goal is to "Take urgent action to combat climate change and its impacts and focusing on tackling climate change. According to the most recent report on Sustainable Development Goal 13, rising greenhouse gas concentrations, more frequent and extreme weather events, and rising sea levels have caused global temperatures to rise by 1.5°C above pre-industrial levels. To reduce emissions and prepare for climate change, immediate action is necessary. Comprehensive risk assessment and management require complete information. However, it is not always possible to gather information using a probabilistic or quantitative risk assessment (QRA). This study concentrated on the quantitative assessment of the risks that might result from a vinyl chloride monomer (VCM) release accidentally as a result of various activities during production and handling operations in petrochemical industries. ALOHA (Areal Locations of Hazardous Atmospheres) is employed to calculate the rate of release and total amount of Vinyl Chloride Monomer released from various potential leaking expected sources through a 1.0-inch orifice from a polymerization reactor in the PVC production and its effect on human health.