Commercial non-steroidal anti-inflammatory drugs (NSAIDs) are considered as toxic to the environment since they induce side effects when consumed by humans or aquatic life. Ibuprofen is a member of the NSAID family and is widely used as an anti-inflammatory and painkiller agent. Photolysis is a potentially important method of degradation for several emerging contaminants, and individual compounds can undergo photolysis to various degrees, depending on their chemical structure. The efficiency oftitanium dioxide (TiO₂) and photocatalysis was investigated for the removal of ibuprofen from the aquatic environment, and the performance of these different processes was evaluated. In heterogeneous photocatalysis, two experiments were carried out using TiO₂ as (i) dispersed powder, and (ii) TiO₂ immobilized on the active surface of commercial coated glass. The kinetics of each photoreaction was determined, and the identification of the photoproducts was carried out by liquid chromatography coupled with Fourier-transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). The overall results suggest that the TiO₂ active thin layer immobilized on the glass substrate can avoid recovery problems related to the use of TiO₂ powder in heterogeneous photocatalysis and may be a promising tool toward protecting the environment from emerging contaminants such as ibuprofen and its derivatives.

Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site.

Urban forests play a critical role in improving the quality of life in cities, but in arid environments, little is known about the potential benefits and growth conditions of different tree species. Our study aimed to fill this gap by investigating the relationships between tree dimensions, above-ground biomass carbon storage, and shading potential in three common urban trees in the arid city of Jericho, Palestine, (i.e., Ficus nitida, Delonix regia, and Phoenix dactylifera). The trees were chosen according to their distribution in urban locations and tree vitality, with ages ranging from 20 to 90 years. Based on the results from tree structure measurements, the carbon storage and shading potential were calculated using the City Tree model. The results indicate a moderate to strong relationship between tree height, crown diameter, and crown volume for F. nitida and D. regia (R² = 0.28–0.66), but no relationship for P. dactylifera (R² = 0.03–0.06). The findings suggest that the analyzed tree species can considerably contribute to the potential benefits of trees in improving the climate of an arid city: D. regia shows a higher median of above-ground biomass carbon storage of 155 kg C tree⁻¹, while P. dactylifera 91 kg C and F. nitida 76 Kg C. D. regia and F. nitida have a higher median of shading potential, (31 m²–41 m²), respectively. Information on the ecosystem services from urban trees and their relationships in terms of species, age, and tree planting urban location are very important for city planners, in relation to sustainable urban green spaces in arid cities.

Organic solid waste represents 50% of the Municipal Solid Waste (MSW) composition in Palestine. MSW in the West Bank (WB) are managed by municipalities, Local Governmental Units (LGUs), and Joint Service Councils (JSCs). MSW are collected and transferred to the existing four landfills along the WB, there are no waste separation or recycling in Palestine except small projects and enterprises. The collected MSW reaches the landfills as a mixed wastes with the composition of metal, paper, glass, plastic, and organic wastes. The current organic waste treatment in Palestinian territories is represented by composting and biogas generation, the small pilot projects of composting that have been implemented facing problems related to quality, competition, and financing. Biogas production from organic solid wastes is also limited and concentrated in producing biogas from animals manure. New proposals have been discussed regarding solid waste incineration in the field of Waste To Energy (WTE) projects, except the composting and bioenergy there are no another treatment for organic MSW in the WB. This study ensures about the importance of the concept of waste separation at source, and to adapt the concept of WTE operations. Due to the high moisture content and high organic proportion in the MSW; bio-drying processes are valuable as a pretreatment stage for organic waste treatment. This study also highlights on the Refuse Derive Fuel (RDF) as a product of bio-drying processes that could help in organic MSW treatment.