The scarcity of drinkable water supplies is becoming a serious and troubling challenge to long-term development. This issue emphasises the urgency of implementing rapid water desalination options. However, desalination is a capital-intensive process that requires a significant amount of energy, and since it is now mostly powered by fossil fuels across the globe, it has the potential to leave a significant carbon footprint. In this light, transitioning to green energy sources for desalination is a necessity in today’s society. Humidification–dehumidification (HD) is one of the most effective desalination procedures to consider for distant places with modest fresh water demands for household and agricultural demands. This is mostly because it requires minimal operational and maintenance concerns. The current article examines the many kinds of HD desalination systems driven by solar energy sources and their key components. The current study established that the seawater HD system is viable for producing fresh water, especially for greenhouses and dispersed demand. HD is an effective technique for eluting fresh water from concentrated brines, boosting production, and minimising harmful environmental impact, benefitting ecosystems and human services. The packed bed humidifier is the most commonly used kind of humidifier in the HD water desalination system. Additionally, as compared to an indirect dehumidifier, the direct dehumidifier improves the performance of the HD desalination system. According to the findings of this research, hybrid energy systems have the greatest gain output ratio (GOR). Solar water heating significantly increases system productivity as compared to solar air heating. When combined with a heat pump, the HD delivers optimal system productivity and GOR at the lowest cost per litre. The conclusion is that, even though humidification–dehumidification is a promising method for decentralised small-scale fresh water production applications, it needs additional improvement to optimise system performance in terms of economy and gain output ratio.

We conducted surveys of Mediterranean beaches in Egypt, Morocco, and Tunisia including 37 macro-litter (> 25 mm) and 41 meso-litter (5–25 mm) assessments. Our study identified key litter items and assessed pollution sources on urban, semi-urban, tourist, and semi-rural beaches. Macro-litter concentration averaged 5032 ± 4919 pieces per 100 m or 1.71 ± 2.28 pieces/m², with higher values observed on urban (mean 2.63 pieces/m² ± 3.03) and tourist (mean 1.23 pieces/m² ± 0.91) beaches. Similarly, urban (mean 9.91 pieces/m² ± 12.70) and tourist beaches (mean 5.32 pieces/m² ± 4.48) revealed elevated levels of meso-litter contamination, particularly in the upper third of the beach, which contained the highest quantities both in terms of number (51%) and weight (50%). 55% of the macro-litter and 35% of the meso-litter originated from human shoreline activities and poor waste management. Given the width of some beaches and their high levels of pollution, the standard 100 m macro-litter approach was impractical. To enable cost-effective, long-term monitoring, we adapted it to a faster 10 m transect approach, which provided reliable data on the top 25 litter items, accounting for 82% of beach pollution. Our Sand Rake method effectively quantified pollution on both cleaned and uncleaned beaches, addressing the often neglected meso-litter size fraction. The high pollution levels, top litter items, and identified sources indicate that beach cleaning alone will not solve the pollution problem. Efforts to raise environmental awareness, enhanced waste management, and law enforcement are needed to improve the situation in a sustainable way.

Obtaining precise solar radiation data is the first stage in determining the availability of solar energy. It is also regarded as one of the major inputs for a variety of solar applications. Due to the scarcity of solar radiation measurement data for many locations throughout the world, many solar radiation models are utilized to predict global solar radiation. Indeed, the most widely used AI technique is artificial neural networks (ANNs). Hitherto, while ANNs have been utilized in various studies to estimate global solar radiation (GSR), limited attention has been given to the architecture of ANN. Thus, this study aimed to: first, optimize the design of one of the faster and most used machine-learning (ML) algorithms, the ANN, to forecast GSR more accurately while saving computation power; second, optimize the number of neurons in the hidden layer to obtain the most significant ANN model for accurate GSR estimation, since it is still lacking; in addition to investigating the impact of varying the number of neurons in the hidden layer on the proficiency of the ANN-based model to predict GSR with high accuracy; and, finally, conduct a comparative study between the ANN and empirical techniques for estimating GSR. The results showed that the best ANN model and the empirical model provided an excellent estimation for the GSR, with a Coefficient of Determination $R^2$ greater than 0.98%. Additionally, ANN architectures with a smaller number of neurons in the single hidden layer (1–3 neurons) provided the best performance, with $R^2$ > 0.98%. Furthermore, the performance of the developed ANN models remained approximately stable and excellent when the number of hidden layer’s neurons was less than ten neurons ($R^2$ > 0.97%), as their performance was very close to each other. However, the ANN models experienced performance instability when the number of hidden layer’s neurons exceeded nine neurons. Furthermore, the performance comparison between the best ANN-based model and the empirical one revealed that both models performed well ($R^2$ > 0.98%). Moreover, while the relative error for the best ANN model slightly exceeded the range, ±10% in November and December, it remained within the range for the empirical model even in the winter months. Additionally, the obtained results of the best ANN model in this work were compared with the recent related work. While it had a good RMSE value of 0.8361 MJ/m² day⁻¹ within the ranges of previous work, its correlation coefficient (r) was the best one. Therefore, the developed models in this study can be utilized for accurate GSR forecasting. The accurate and efficient estimation of global solar radiation using both models can be valuable in designing and performance evaluation for different solar applications.

Solar radiation is considered the main renewable energy source which reshapes the global sustainability plan for future development. Due to the lack of solar radiation measurements, this work investigates the performance of several temperature-based hybrid solar radiation models combining the parametric, statistical and satellite data approaches to estimate the global solar radiation on a horizontal surface. Over 35 years of meteorological data in the new location, Arar City, KSA (Latitude 30°96′ N and longitude 41°05′ E) are employed to establish and validate the models. These models are validated using two datasets with different averaging time spans to investigate the accuracy and reliability of different models as forecasting tools for the solar radiation. The mostly common statistical indicators are calculated to identify the most accurate model. The results show that Model (1) has the best performance among all models with high reliability as a solar radiation forecasting tool in this new location. This model is also validated against the widely-used datasets, namely NASA, On-Site measurements and PVGIS-SARAH data. The model shows excellent values for statistical indicators with high values of coefficient of determination, R² > 0.955, presenting the best performance regardless of the time span of the validation datasets.

Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation methods have been developed to forecast global solar radiation (GSR). With this consideration, this study aims to develop temperature-based GSR models using a commonly utilized approach in machine learning techniques, ANNs, to predict GSR using just temperature data. It also compares the performance of these models to the commonly used empirical technique. Additionally, it develops precise GSR models for five new sites and the entire region, which currently lacks AI-based models despite the presence of proposed solar energy plants in the area. The study also examines the impact of varying lengths of validation datasets on solar radiation models’ prediction and accuracy, which has received little attention. Furthermore, it investigates different ANN architectures for GSR estimation and introduces a comprehensive comparative study. The findings indicate that the most advanced models of both methods accurately predict GSR, with coefficient of determination, R², values ranging from 96% to 98%. Moreover, the local and general formulas of the empirical model exhibit comparable performance at non-coastal sites. Conversely, the local and general ANN-based models perform almost identically, with a high ability to forecast GSR in any location, even during the winter months. Additionally, ANN architectures with fewer neurons in their single hidden layer generally outperform those with more. Furthermore, the efficacy and precision of the models, particularly ANN-based ones, are minimally impacted by the size of the validation data sets. This study also reveals that the performance of the empirical models was significantly influenced by weather conditions such as clouds and rain, especially at coastal sites. In contrast, the ANN-based models were less impacted by such weather variations, with a performance that was approximately 7% better than the empirical ones at coastal sites. The best-developed models, particularly the ANN-based models, are thus highly recommended. They enable the precise and rapid forecast of GSR, which is useful in the design and performance evaluation of various solar applications, with the temperature data continuously and easily recorded for various purposes.

The world is facing climate change, biodiversity loss, and pollution, significantly impacting lower-middle-income countries like Egypt, Morocco, and Tunisia, which depend heavily on tourism. Poor waste management, unclear responsibilities, and weak policies contribute to environmental degradation. Tourism, a key economic driver, also increases the problem by high plastic use and waste generation during peak seasons. This study evaluates current waste management practices in Alexandria (Egypt), Essaouira (Morocco), and Hammam Sousse (Tunisia) and proposes improvements using a newly developed “Extended Sector Responsibility” (ESR) model, which introduces an innovative organizational approach to waste management in touristic destinations. Using a combination of desk research, questionnaires, waste sorting analyses, and expert interviews, our research identifies systemic deficiencies. None of the studied locations have formal source separation systems, and waste management heavily depends on the informal sector. Hotels exhibit limited capacity for effective waste practices due to the lack of municipal infrastructure for separate collection. Economic analysis of the ESR model, which involves the establishment of a new waste recovery facility, demonstrates that while such facilities can generate revenue exceeding operational costs under specific scenarios, their long-term viability hinges on additional funding, possibly through Extended Producer Responsibility (EPR) mechanisms. Although Egypt and Tunisia have EPR legislation, implementation remains inadequate, and Morocco lacks such frameworks. The study emphasizes the critical need for investments in municipal waste management infrastructure, including logistics, sorting, and recycling systems. It also highlights actionable opportunities for the tourism sector to reduce waste by minimizing single-use plastics and food waste. By adopting the ESR model, the tourism sector can play a pivotal role in transitioning to a circular economy, ultimately mitigating environmental impacts and enhancing sustainability in the region.