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.

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.

Water scarcity continues to challenge arid regions such as Egypt, where growing population demands, climate change impacts, and increasing agricultural pressures intensify the need for sustainable water management. Treated wastewater has emerged as a viable alternative resource, provided that the effluent meets stringent quality standards for safe reuse. The purpose of this study was to develop a comprehensive model of the Khorshed Wastewater Treatment Plant (KWWTP) to depict the processes used for biological nutrient removal. Operational data was gathered and examined over a period of 18 months to describe the quality of wastewater discharged by the Advanced Sequencing Batch Reactor (ASBR) of the plant, using specific physicochemical parameters like TSS, COD, BOD₅, and N-NO₃⁻. A process flow diagram integrating the Activated Sludge Model No. 1 (ASM1) for biological nutrient removal was created using the GPS-X. The study determined the parameters influencing the nutrient removal efficiency by analyzing the responsiveness of kinetic and stoichiometric parameters. Variables related to denitrification, autotrophic growth, and yield for heterotrophic biomass were the main focus of the calibration modifications. The results showed that the Root Mean Square Error (RMSE) for the dynamic-state was COD (0.02), BOD₅ (0.07), N-NO₃⁻ (0.75), and TSS (0.82), and for the steady state was COD (0.04), BOD₅ (0.11), N-NO₃⁻ (0.67), and TSS (0.10). Since the model’s accuracy was deemed acceptable, it provides a validated foundation for future scenario analysis and operational decision support that produces a trustworthy model for predicting effluent data for the concentrations of TSS, COD, BOD₅, and N-NO₃⁻ in steady state conditions. Dynamic validation further confirmed model reliability, despite modest discrepancies in TSS and nitrate predictions; addressing this issue necessitates further research.