As the demand for energy-efficient homes continues to rise, the importance of advanced mechanical ventilation systems in maintaining indoor air quality (IAQ) has become increasingly evident. However, challenges related to energy balance, IAQ, and occupant thermal comfort persist. This review examines the performance of mechanical ventilation systems in regulating indoor climate, improving air quality, and minimising energy consumption. The findings indicate that demand-controlled ventilation (DCV) can enhance energy efficiency by up to 88% while maintaining CO₂ concentrations below 1000 ppm during 76% of the occupancy period. Heat recovery systems achieve efficiencies of nearly 90%, leading to a reduction in heating energy consumption by approximately 19%. Studies also show that employing mechanical rather than natural ventilation in schools lowers CO₂ levels by 20–30%. Nevertheless, occupant misuse or poorly designed systems can result in CO₂ concentrations exceeding 1600 ppm in residential environments. Hybrid ventilation systems have demonstrated improved thermal comfort, with predicted mean vote (PMV) values ranging from –0.41 to 0.37 when radiant heating is utilized. Despite ongoing technological advancements, issues such as system durability, user acceptance, and adaptability across climate zones remain. Smart, personalized ventilation strategies supported by modern control algorithms and continuous monitoring are essential for the development of resilient and health-promoting buildings. Future research should prioritize the integration of renewable energy sources and adaptive ventilation controls to further optimise system performance.

Traditional water quality monitoring methods face significant limitations, including delayed data acquisition, high operational costs, and inadequate spatial and temporal resolution, which hinder timely responses to contamination events. This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (ML) algorithms. By synthesizing 1,032 studies (2011–2025), we demonstrate that AI-driven systems achieve 94% accuracy in prediction and reduce field sampling costs by 60% through Landsat 8 satellite integration. Our analysis reveals a 13-fold increase in AI adoption since 2011, with innovations such as adaptive neuro-fuzzy inference systems (ANFIS) and deep neural networks (DNNs) facilitating real-time anomaly detection and contamination forecasting. The novelty of this review lies in its dual focus—quantifying AI's scalability for global water security while critically addressing unresolved challenges in data standardization, model interpretability, and ethical governance. These findings offer policymakers actionable insights, advocating for hybrid frameworks that integrate AI with existing infrastructure to bridge urban-rural disparities in water management.

Harnessing the power of phase change materials (PCMs) in asphalt pavements proposes a sustainable solution for addressing temperature-related issues, affording more robust and energy-efficient infrastructure. PCMs hold enormous potential for reforming various industries due to their ability to store and release large amounts of thermal energy, offering noteworthy benefits in energy efficiency, thermal management, and sustainability. The integration of PCMs within pavements presents an increasingly exciting field of research. PCMs have the ability to efficiently manage the changes in and distribution of temperature in asphalt pavements via the release and absorption of latent heat that occurs during the phase shifts of PCMs. Asphalt pavements experience less severe temperatures and a slower rate of temperature fluctuation as a result of this, which in turn reduces the amount of stress caused by temperature. In addition, the function of temperature adjustment that PCMs provide is natural, intelligent, and in line with the direction in which the development of smart pavements is heading in the future. This study aims to explore the impact of organic, inorganic, and mixed organic–inorganic PCMs on diverse surface characteristics of asphalt. In addition, this review addresses current challenges associated with using PCMs in asphalt and explores potential advantages that could facilitate future research in addition to broadening the implementation of PCMs in construction.

This paper aims to analyze how AI can revolutionize dam and water resource management, the problem areas such as climate change, growing population rate, and deterioration of infrastructure; these AI technologies, in turn, drive predictive analytics, learning, real-time monitoring, decision-making, and resource management, thereby benefiting engineers and policymakers, among other stakeholders. Demand forecasting, flood management, and smart water quality monitoring enhance resource management, disaster prevention, and eco-conservation. On numerous occasions, AI models outcompete traditional hydrological approaches in terms of accurate water level and inflow predictions. In addition, the combination of AI with IoT sensors means that potential and actual conditions of dams and water quality are constantly monitored to optimize maintenance programs and avoid incidents. Problems arising from data quality and availability, interpretability of models, and the requirement of being a competent technical person hinder its widespread use. Similarly, ethical and legal considerations such as privacy and responsibility pose challenges to integrating AI into current systems. Addressing these challenges is very important if the impact of AI is to be enhanced. As highlighted in this analysis, there is a need for multi-disciplinary non-kingdom collaboration and specific expenditure to deal with these constraints. Each requires a greater research effort to improve our abilities to advance from non-parametric approaches to new paradigms of predictive modeling, big data, and real-time decision support and to become more responsible stewards of the Earth’s limited water supplies. The outcomes highlight AI’s potential to change water management and improve its effectiveness and sustainability. This research underscores the importance of ensuring a sustainable and secure water supply in the future to enable the provision of water supply as stipulated in the global sustainable environment and structures’ agenda.