Accelerated urbanization, population growth, and industrial revolution have resulted in significant environmental degradation and health issues for residents due to municipal solid waste. The effective management of solid waste is a significant issue, especially in developing economies, where improper waste disposal leads to economic losses and adverse impacts on environmental and public health. This study evaluates existing waste management procedures in Tabuk City in northern Saudi Arabia, finds significant challenges, and suggests possible solutions. For this purpose, this study administered a survey to assess public awareness regarding municipal solid waste recycling and management. Therefore, a two-stage analytic investigation was conducted that included a total of 228 respondents from Tabuk City, Saudi Arabia. Among them, 62.28% were male, and 37.72% were female, with the largest age group represented was 22–35 years, accounting for 42.10% of participants. The questionnaire responses were evaluated using SPSS V27, and relative scores were analyzed using SHapley Additive exPlanations and Partial Dependence Plots. Based on the study results, most respondents are knowledgeable and worried about the adverse environmental effects of solid waste. The Pearson correlation highlighted that lack of awareness with responsible authorities exhibited the highest positive correlations with recycling behavior improvement, with respective correlation values of + 0.55. The results also revealed an absence of recycling systems since the majority of respondents were uninformed of existing practices and expressed a readiness to engage in recycling programs if established. Further, parametric modeling illustrates that most of the participants believe that lack of awareness along with authority responsibility is the most influential factor impacting poor waste management practice. The study concluded by highlighting the necessity for an organized municipal solid waste recycling system, enhanced community education, and more stringent governmental restrictions to mitigate environmental damage and foster sustainable waste management in Tabuk.

Sustainable design in large-scale infrastructure projects, such as airports, is crucial for minimizing environmental impacts while ensuring long-term financial feasibility. This study focuses on selecting the most sustainable pavement solution for airport construction, using Tabuk Airport in Saudi Arabia as a case study. The purpose of this study is to evaluate four pavement alternatives using a multi-criteria decision-making approach to identify the optimal solution in terms of sustainability, cost-effectiveness, and feasibility. The alternatives were assessed based on nine key criteria, including environmental impact, durability, cost, and maintenance. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method ranks the alternatives, while the Fuzzy Analytic Network Process (FANP) calculates the criteria weights, addressing uncertainties and interdependencies. Geographic Information System (GIS) is integrated to incorporate spatial factors affecting pavement sustainability. The results show that the alternative using recycled materials (A4) is the most suitable, offering the best balance of sustainability and cost. A4 achieved the highest ranking in the evaluation, making it the recommended choice for the upcoming Tabuk Airport project. This study demonstrates the effective application of decision-making tools, such as TOPSIS, FANP, and GIS, in guiding sustainable infrastructure development and providing a replicable framework for similar projects worldwide.

Wastewater membrane bioreactors (MBRs) have become an important advanced treatment technology due to their ability to produce high-quality effluent suitable for discharge and water reuse. However, their broader and more sustainable application remains constrained by membrane fouling, elevated energy demand, and the operational complexity of coupled biological and membrane separation processes. This comprehensive review critically evaluates the growing application of machine learning (ML), explainable artificial intelligence (XAI), and digital twin (DT) technologies in MBR systems. Published studies on fouling prediction, energy optimization, effluent quality estimation, and intelligent operational support are critically evaluated, with explicit attention to model performance, dataset limitations, and generalizability. The reviewed literature shows that ML models, particularly ensemble methods, support vector machines, and deep learning approaches, have demonstrated strong potential for predicting major MBR performance indicators, including transmembrane pressure, permeate flux, fouling resistance, and selected effluent-quality variables. In parallel, XAI methods such as SHAP, LIME, and Anchors are increasingly being used to enhance model transparency and to reveal the dominant factors controlling process performance. Digital twin frameworks further extend this potential by enabling the integration of mechanistic understanding, online sensor data, data-driven prediction, and interpretable decision support within real-time operational platforms. Nevertheless, several barriers continue to hinder practical implementation, including the limited number of full-scale studies, the scarcity of openly accessible and standardized datasets, insufficient consideration of uncertainty and model drift, and the early-stage maturity of DT deployment in operational plants. The evidence reviewed suggests that integrating ML, XAI, and DT can substantially improve the reliability, interpretability, and operational efficiency of MBR systems. Future research should therefore focus on full-scale validation, the development of benchmark datasets, uncertainty-aware modeling, and practical deployment strategies for interpretable intelligent MBR management.

Municipal management involves making decisions on various technical issues, and one such crucial aspect is the multicriteria decision-making process. When choosing suitable locations for wastewater treatment plants, it becomes necessary to consider a range of factors such as technical feasibility, economic viability, environmental impact, ecological aspects, and management requirements. However, evaluating these criteria and dealing with uncertainties can be complex. To address this challenge in the Tabuk region, a combination of two powerful analytical methods, the fuzzy analytical hierarchy process (FAHP) and geographical information system (GIS), were employed. The FAHP methodology allows for considering uncertainties and subjective judgements, while GIS provides spatial analysis capabilities. By combining FAHP and GIS, a thorough evaluation of potential wastewater treatment plant locations was conducted by determining the relative weights for each geospatial parameter. These weights were then used to generate a suitability map, visually representing the most favourable areas for site selection. The FAHP analysis resulted in higher importance given to the treatment plant’s distance to urban areas, followed by the distance to roads among the seven investigated parameters. The integrated FAHP-GIS model results show that the western parts of the region are most suitable for constructing wastewater treatment plants. These findings are valuable in facilitating multicriteria decision-making for identifying the optimum site in the area. In summary, integrating FAHP and GIS in the assessment process enables decision-makers to consider various technical, economic, environmental, ecological, and management aspects, thereby providing a comprehensive framework for site selection that can be replicated in other regions with different conditions. This approach enhances the decision-making process in municipal management and promotes more informed and effective planning in the Tabuk region.