Phenol and its derivatives, commonly detected not only in industrial effluents but also in various wastewater treatment systems, pose significant environmental and health hazards due to their toxicity to humans and aquatic life. This review focuses on the recent advancements in hydrogel-based nanocomposites for sustainable phenol remediation. Various types of hydrogels, nanoparticles, and their integrated hydrogel–nanoparticle composites are discussed in terms of synthesis techniques and structural properties. This study also examines different degradation mechanisms, including adsorption, photocatalysis, and advanced oxidation processes, highlighting their effectiveness in phenol removal. Key influencing factors such as initial pH, catalyst type, and reactant concentrations are critically analysed for their roles in enhancing degradation efficiency. In addition, comprehensive characterisation techniques like high-resolution transmission electron microscopy, X-ray diffraction, and field emission scanning electron microscopy are reviewed to evaluate the morphology and crystallinity of nanoparticles. Reported degradation efficiencies of phenol using various nanomaterials are summarised to establish comparative insights. This review aims to provide a consolidated understanding of nanocomposite hydrogel-based systems for effective phenol degradation, contributing to the development of sustainable wastewater treatment technologies.
Groundwater depletion in arid regions requires reliable approaches for prioritising wells under competing hydrogeological, environmental, and economic constraints. This study develops a Technique for Order Preference by Similarity to Ideal Solution-based multi-criteria decision making framework to prioritise five existing observation wells for potential conversion into operational pumping wells in the water-stressed Yazd-Ardakan Plain, Iran. Nine criteria representing aquifer properties, groundwater conditions, well construction, spatial interactions, and economic factors were integrated, including hydraulic conductivity (K), groundwater level, aquifer sediment texture, and geological factors such as distance and density of neighbouring wells, drilling cost, and borehole screen quality, in addition to hydraulic gradient and bedrock slope to better represent regional hydrogeological and hydro-geomorphological conditions. Five candidate wells were evaluated using expert-derived weights and field-based data. The resulting ranking was Wells No. 5, 1, 2, 4, and 3, with Well No. 5 achieving the highest relative closeness coefficient (0.616). This result indicates the highest relative preference among the evaluated alternatives rather than independently validated pumping performance. By integrating hydrogeological, spatial, and economic criteria, the proposed model provides a transparent and practical decision-support tool for groundwater resource planning in arid regions, facilitating cost-effective and sustainable well placement.
More than half of the world’s population lives in urban areas, a share projected to reach 68% by 2050. As cities concentrate emissions, infrastructure investment and inequality, smart city models increasingly use digital technologies and data-driven management to support sustainable urban development. However, comparable evaluation across environmental, social and governance (ESG) dimensions remains unresolved. This study examines how ESG is operationalised in smart city research and proposes an integrated assessment framework. Following PRISMA 2020, searches of Web of Science and Scopus identified 84 records published between 2000 and 2026; 26 peer-reviewed studies were retained, complemented by 23 institutional documents and 10 academic book chapters. Bibliometric and thematic analyses show uneven pillar development: governance appears in 100% of studies, environmental issues in 96% and social issues in 77%. Keyword mapping yielded four clusters, and content synthesis yielded six; neither produced an autonomous social-pillar cluster. China accounts for 23% of publications, while only 27% involve international co-authorship. The study proposes a three-layer framework linking ESG pillars, governance functions and urban performance outcomes, aligned with ISO 37120, U4SSC, CITYkeys and World Economic Forum benchmarks.
Projecting mobile phone-based lithium-ion battery (MLIB) waste in developing countries such as India is challenging due to diverse and informal sources of device entry into the market. Unmanaged MLIB waste presents a significant threat to the sustainability of energy storage systems, as it increases reliance on virgin raw materials and weakens the principles of a circular economy. In India, informal systems contribute nearly 70% of mobile phone circulation, yet these dominant flows are often overlooked in conventional waste assessment and forecasting approaches. Conventional sales data-based estimation methods capture only formal supply channels and therefore fail to account for grey market imports and refurbished devices. As a result, current approaches do not accurately reflect the actual volume of MLIB waste generated. Mobile phone user database (UDB) is an important way for the assessment of such waste. The current study highlights the critical role of informal waste handlers in the quantification of MLIBs. Two primary parameters were considered: (1) mobile phone SIM UDB on single- and dual-SIM mobile phone usage and (2) stakeholder surveys conducted within a representative model area. Integrating stakeholder insights with SIM-based UDB enables a more accurate estimation of MLIB waste at both national and global scales.
The rising need for lightweight and eco-friendly automotive components has spurred the evolution of aluminium matrix composites with enhanced performance and lower environmental impact. This study examines the development of titanium carbide (TiC)-reinforced Al6063–Si composite materials combined with life-cycle assessment (LCA) and defect inspection using artificial intelligence to create a sustainable steering knuckle manufacturing process. Al6063 composites with 10 wt.% silicon (Si) and 3–5 wt.% titanium carbide were produced using the stir casting method with less energy consumption and subjected to T6 heat treatment to further enhance their mechanical properties. Optimum titanium carbide contents were determined using mechanical, fatigue, and microstructural characterisation, and a geometry-aware ResNet-50 convolutional neural network was designed for automatic defect detection. A titanium carbide content of 4 wt.% composite had higher strength and even particle distribution and better interfacial bonding, but higher titanium carbide content tended to promote agglomeration and porosity. Compared to traditional manufacturing, ISO 14040/14044 cradle-to-gate LCA showed that there was less material loss, less energy usage, and less greenhouse gas emissions. The proposed integrated framework allows the production of automotive components with a low consumption of resources and high sustainability.
