The use of wind energy worldwide has overgrown in recent years to reduce greenhouse gas emissions. Wind power is free, but the installation and maintenance of wind turbines remain very costly. The size of the installation of the wind turbine is not only determined by wind statistics at a given location, but also by turbine infrastructure and maintenance costs. The payback time of the turbine is dependent on turbine energy costs. This study estimates the wind power generation capacity of Northern and Southern Oman and discusses the selection of the most economical, efficient and reliable wind turbines in Oman. HOMER Pro Software was used in this paper to evaluate the wind energy data in the north and south of Oman and to provide well-informed guidance on the most suitable turbines for the power needs of each area. Six different standard wind turbines were measured and compared in terms of the cost of energy and performance. The simulation analysis reveals that the DW54 turbine is the best possible turbine to generate electricity in northern Oman at $0.119/kW. Due to the difference in the wind regime between the north and the south of Oman, the simulation showed that the Hummer H25.0–200 kW turbine is the best option for south Oman with power generation at $0.070/kW. The northern wind turbine plant can efficiently contribute to decarbonization of the energy sector in Oman, with a potential reduction of CO₂ emission approximately 19,000 tons/year in comparison to natural gas and 28,000 tons/year in comparison to diesel. In the Southern Power Plant, carbon emissions are reduced by 18,000 and 12,000 tons/year compared to diesel and natural gas.

In the chemical industry and in the manufacturing sector, the adsorption properties of porous materials have been proven to be of great interest for the removal of impurities from liquid and gas media. While it is acknowledged that significant progress and literature production have been developed in this field, there have been adsorption studies that failed to further advance our knowledge in generating a better understanding of the prevailing sorption types and dominant adsorption processes. Therefore, this review study has focused on porous materials, their sorption types and their adsorption properties, further investigating the adsorption properties of porous materials at either solid–gas and solid–liquid interfaces, underscoring both the properties of the materials, the characterization and the correlation between the porosity and the adsorption capacity, as well as the emergent interactions between the adsorbent and adsorbate molecules, including the adsorption mechanisms, the types of sorption and the kinetic and thermodynamic information conveyed.

Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial seas with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the wind power density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and capacity factor by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani peak electricity demand during the summer. Thus, offshore wind turbines have great advantages over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment and production of offshore wind energy in Oman. A thorough study using bankable wind data along with various logistical considerations would still be required to turn offshore wind potential into real wind farms in Oman.

This paper presents an in-depth characterization of a raw industrial sludge (IS-R) and its KOH-activated biochar pyrolyzed at 750 °C (IS-KOH-B) followed by their application to remove a cationic dye from aqueous solution. Materials characterization shows that compared to the IS-R, the IS-KOH-B has improved structural, textural, and surface chemical properties. In particular, the IS-KOH-B’s BET surface area and total pore volume are about 78 and 6 times higher than those found for the IS-R, respectively. The activated biochar efficiently retained the cationic dye under wide experimental conditions. Indeed, for an initial dye concentration of 50 mg L⁻¹, removal yields were assessed to be more than 92.5%, 93.5%, and 97.8% for a large pH range (4–10), in the presence of high contents of competing cations (3000 mg L⁻¹ of Ca²⁺, Mg²⁺, Na⁺, and K⁺), and a low used adsorbent dose (1 g L⁻¹), respectively. The Langmuir’s adsorption capacities were 48.5 and 65.9 mg g⁻¹ for of IS-R and IS-KOH-B, respectively, which are higher than those reported for various adsorbents in the literature. The dye removal was found to be monolayer, spontaneous, and endothermic for both the adsorbents. Moreover, this removal process seems to be controlled by chemical reactions for IS-KOH-B whereas by both physico–chemical reactions for IS-R. This study demonstrates that the raw industrial sludge and especially its KOH-activated derived biochar could be considered as promising adsorbents for the removal of dyes from aqueous solutions.

Local, bivariate relationships between coronavirus 2019 (COVID-19) infection rates and a set of demographic and socioeconomic variables were explored at the district level in Oman. To limit multicollinearity a principal component analysis was conducted, the results of which showed that three components together could explain 65% of the total variance that were therefore subjected to further study. Comparison of a generalized linear model (GLM) and geographically weighted regression (GWR) indicated an improvement in model performance using GWR (goodness of fit=93%) compared to GLM (goodness of fit=86%). The local coefficient of determination (R2) showed a significant influence of specific demographic and socioeconomic factors on COVID-19, including percentages of Omani and non-Omani population at various age levels; spatial interaction; population density; number of hospital beds; total number of households; purchasing power; and purchasing power per km2. No direct correlation between COVID- 19 rates and health facilities distribution or tobacco usage. This study suggests that Poisson regression using GWR and GLM can address unobserved spatial non-stationary relationships. Findings of this study can promote current understanding of the demographic and socioeconomic variables impacting the spatial patterns of COVID-19 in Oman, allowing local and national authorities to adopt more appropriate strategies to cope with this pandemic in the future and also to allocate more effective prevention resources.