Search Results (6,255)

The rapid growth of urban populations and consumption patterns has significantly increased the volume and complexity of municipal waste streams, including recyclable, organic, hazardous, and electronic waste. Traditional waste management approaches such as manual sorting and fixed collection schedules struggle to handle the diversity and scale of modern waste systems efficiently. Consequently, recent research has explored artificial intelligence (AI), computer vision, and Internet of Things (IoT) technologies to automate waste classification and enable intelligent monitoring and management of waste infrastructures. However, the rapidly expanding literature in this domain is fragmented across different datasets, sensing platforms, and experimental settings, making it difficult to obtain a comprehensive understanding of current technological progress. To address this gap, this study conducts a systematic literature review using the PRISMA methodology, analyzing 50 rigorously selected peer-reviewed studies on automated waste classification and intelligent waste management systems. The protocol was registered in the Open Science Framework (OSF). The reviewed works are examined in terms of model architectures, sensing infrastructures, datasets, and system capabilities. The analysis shows that convolutional neural network architectures such as ResNet, MobileNet, and EfficientNet, together with object detection frameworks including YOLO and Faster R-CNN, dominate current visual waste classification research; however, emerging studies increasingly explore hybrid architectures combining convolutional networks with transformer-based models to improve contextual understanding and scalability. IoT-enabled smart bins equipped with ultrasonic sensors, load cells, and environmental sensors are widely used for real-time waste monitoring. The review also reveals a recent surge of research activity, particularly in Asia, with increasing contributions from major publishers such as Elsevier and MDPI. Despite promising experimental performance, most systems remain limited to small curated datasets and pilot-scale deployments, highlighting a gap between laboratory research and real-world implementation. These findings provide a consolidated overview of current research trends and identify key challenges for developing scalable and practical intelligent waste management systems. Full article

Climate change increases the need for urban stormwater management systems that can maintain hydrological performance under severe rainfall conditions. Although green and blue–green roofs are widely used as nature-based solutions for runoff management, their performance is commonly evaluated using average or cumulative runoff reduction, which may not capture differences that emerge during severe events. This study applied an event-based assessment to 31 rainfall events monitored on a conventional roof (Control), a Blue–Green Roof (BGR), and a Smart Blue–Green Roof (Smart BGR). Climate Adaptation Benefit (CAB) was calculated by integrating relative runoff reduction with rainfall severity, while the additional benefit of Smart BGR was evaluated using the difference in CAB (ΔCAB). The Severity-Weighted Runoff Vulnerability Index (FVI) and Severity-Weighted Runoff Vulnerability Reduction (FVR) were used to assess runoff-related vulnerability across four rainfall severity classes. Both systems provided substantial benefits under low rainfall severity, with relatively small differences. However, Smart BGR generally showed higher CAB and positive ΔCAB than BGR, while maintaining lower FVI and greater FVR under the observed higher-severity rainfall conditions. These findings suggest that the primary value of Smart BGR lies in maintaining greater climate adaptation benefits under severe rainfall conditions. The proposed event-based framework provides a complementary approach for evaluating rooftop nature-based solutions beyond average runoff reduction. Full article

Municipal wastewater reclamation is becoming increasingly important for alleviating water scarcity, reducing dependence on freshwater resources and improving the sustainability of urban water systems. This review examines the current status and major challenges of municipal wastewater reclamation and discusses its future development from the perspectives of water quality, energy use, resource recovery and greenhouse-gas mitigation. Future reclamation systems are expected to place greater emphasis on end-use-oriented treatment, energy efficiency, resource retention and carbon-conscious process design. Several emerging process configurations are evaluated, including aerobic and anaerobic membrane bioreactor systems coupled with reverse osmosis, algal–bacterial membrane processes, nutrient-retaining treatment for agricultural reuse and non-RO advanced treatment based on oxidation and biological filtration. Their applicability varies with the intended reuse because of differences in water quality control, energy demand, resource recovery and residual-stream generation. Future technology selection should therefore integrate treatment intensity with deployment scale. RO-based and non-RO-based pathways can be selected according to the dominant water quality constraints, while centralized, decentralized and hybrid systems should be matched to local infrastructure, reuse distance, water demand and resource recovery opportunities. The system-level evaluation of water quality, energy consumption, greenhouse-gas emissions, economic performance and residual-stream management will be essential for guiding future deployment. Municipal wastewater reclamation is expected to evolve toward adaptable, fit-for-purpose treatment portfolios that support water sustainability, resource circularity and the transition toward low-carbon urban water systems. Full article

The convergence development of urban–rural logistics is of great significance for narrowing the divide and promoting the exchange between urban and rural areas. The digital economy has emerged as an effective pathway to break down the industrial boundaries and drive high-quality convergence of urban–rural logistics. This study analyzes the driving mechanisms and indicator system for urban–rural logistics convergence in the digital economy context, and establishes a convergence development model, an entropy weight model, and a grey relational model. Based on statistical data from 2003 to 2022, it conducts an empirical study on the convergence level, evolutionary trends, and related factors of China’s urban–rural logistics, and explores convergence countermeasures from the composite system perspective. The findings indicate that, over the 20-year study period, China has achieved tangible progress in urban–rural logistics convergence, the subsystem’s orderly development levels alternately increased, and the composite system’s convergence level steadily improved. However, the coupling intensity of urban–rural logistics remained stagnant, and disparities in orderly development continued to exist. Among the factors, rural logistics infrastructure, urban logistics value creation, and urban logistics informatization level had relatively high relational degrees with convergence development. Urban–rural logistics convergence is a systematic project; the development of the digital economy should be taken as an opportunity, and countermeasures for convergence development should be explored from four aspects: the urban–rural logistics composite system, the urban logistics subsystem, the rural logistics subsystem, and the external environment. Full article

Guarded supervision may leave a learned adviser inactive, obscuring non-inferiority. We audited an eight-version yellowSimulation of Urban MObility (SUMO) program covering three networks and 3480 counterfactual pairs. A contrast model gained 16.38 percentage points of root-mean-square-error skill over zero effect; eight-step history added 0.82 points (95-percent interval −0.58 to 2.26). A 10-percent criterion was met in 10.5 percent of 20-cluster and 24.5 percent of 30-cluster subsamples: finite-pool diagnostics, not historical pass probabilities. A frozen estimator scored 13.99 percent on 1800 fresh pairs after an interim look. In version 3 (V3), 60,079 of 1,123,200 decisions were queried; budget-flagged non-queries confound attribution of gate inactivity. No calibrated V3 lower bound was positive; all 70 executed deviations required an emergency bonus. Under an empirical-Bernstein bound applied post hoc, a 0.10 harm ceiling was arithmetically unreachable below 88 active clusters. In a separate, registered five-network follow-up, a non-learned supervisor over an actuated incumbent reduced stopped time by 8.75 s per scheduled vehicle (12.58 percent; 95-percent interval 6.85 to 10.47 s), but arrivals fell on one network. The follow-up does not validate the audited programyellow. yellowWe recommend reporting baselines, activity denominators, and bound reachability; neither study establishes safety or general traffic benefit. Full article

The rapid growth of sensor networks, satellite platforms, Internet-of-Things (IoT) devices, and urban monitoring systems has produced vast volumes of spatiotemporal data. Modeling such data is central to applications ranging from air-quality forecasting and climate monitoring to traffic prediction and urban planning, yet it poses challenges, including high dimensionality, spatial autocorrelation, and complex temporal dependencies that classical statistical models struggle to capture jointly. This review provides a comprehensive survey of spatiotemporal machine learning (STML), synthesizing the literature published between 2020 and 2025. We present a taxonomy of STML methods spanning traditional machine learning, recurrent neural networks (LSTM and GRU), convolutional architectures (3D CNN and ConvLSTM), graph neural networks, transformer-based models, and hybrid or ensemble designs. We organize representative studies by application domain, climate and environmental monitoring, transportation and traffic forecasting, and urban planning within smart cities, and provide a comparative analysis of models, datasets, evaluation metrics, and open-source tools. Finally, we identify emerging research trends and open challenges, including real-time processing, interpretability and explainability, scalability and edge deployment, multimodal fusion, federated learning for privacy, self-supervised learning, and ethical concerns such as fairness. The review is intended to serve as a structured reference for researchers and practitioners developing STML systems. Full article

This study quantifies long-term surface urban heat island (SUHI) dynamics (2001–2016) in three climatically diverse South American metropolises—Buenos Aires (temperate), Bogotá (tropical highland), and Lima (arid coastal)—using MODIS data (0.05° resolution), comparing Streutker’s Gaussian model with a quantile-based approach (SUHI = 95th urban LST percentile—rural LST median). Method performance varied with surface heterogeneity: Gaussian modeling underestimated SUHI in coastal cities (nocturnal bias = +1.5 °C in Buenos Aires; R² ≤ 0.17 in Lima) due to non-Gaussian LST distributions driven by oceanic moisture advection, while quantile-based estimation proved robust in complex terrains (Bogotá agreement: day R² = 0.67, night R² = 0.53). Distinct SUHI signatures emerged: Buenos Aires exhibited stronger nocturnal SUHI (max 3.79 °C, linked to reduced rural evaporative cooling), Lima showed winter daytime Urban Cool Islands (−1.5 °C) from higher heat absorption in semi-desert surroundings, and Bogotá peaked in October (7.27 °C daytime SUHI) with altitudinal suppression of convective rainfall. Vegetation strongly modulated surface fluxes, with urban NDVI/EVI correlations (R² ≤ 0.29) confirming evaporative regulation’s role in halving thermal amplitudes (10 °C urban vs. 5 °C rural), while rural coupling varied with moisture availability (R² ≤ 0.65). Coastal proximity and elevation were key divergence drivers, disrupting surface energy partitioning. We recommend quantile methods for moisture-heterogeneous regions and advocate hydrometeorologically informed mitigation: optimizing green infrastructure for latent heat flux (Buenos Aires), leveraging nocturnal drainage flows (Bogotá), and integrating natural cool-island dynamics into water-sensitive design (Lima). Full article

This study assesses the long-term potential of European biorefineries toward 2050 by combining georeferenced bioresource data, regulatory eligibility, and technological performance across the EU-27. It links spatially explicit biomass availability with RED-compliant biofuel pathways, distinguishing between options that can be deployed immediately and those with greater long-term scaling potential. The results indicate that maritime biofuel production is strongly shaped by regional resource endowment. Used cooking oil supports only limited production of FAME and HEFA, with modest outputs concentrated in a few highly urbanized or industrialized areas. In contrast, lignocellulosic and wet biomass pathways offer much larger and more geographically distributed potentials. Methanol from gasification and synthesis, liquefied biomethane, liquefied biohydrogen, and microbial-oil-based routes emerge as the most scalable options, supported by regions rich in agricultural residues, forestry resources, municipal organic waste, and manure. Quantitatively, methanol shows the highest standalone potential, reaching about 19.17 Mtoe/y. Liquefied biomethane and liquefied biohydrogen can reach about 23.74 Mtoe/y and 21.37 Mtoe/y, respectively. By comparison, FAME and HEFA reach 18.57 Mtoe/y and 17.92 Mtoe/y, although only about 0.99 Mtoe/y and 0.96 Mtoe/y come directly from used cooking oil. The most promising hotspots are located in Spain, France, Germany, Poland, Romania, northern Italy, northern Finland, and parts of eastern Europe. Overall, the findings show that Europe’s maritime biofuel supply will depend on a strong match between regional feedstock availability, conversion pathway, and regulatory suitability, making a portfolio-based strategy essential. Full article

Urban transport is increasingly shifting from an efficiency-oriented approach toward accessibility-based and socio-spatial justice paradigms. This paper aims to synthesize current trends and emerging directions in transport and mobility, with a particular emphasis on sustainability and equity. While existing state-of-the-art reviews have extensively documented the technical evolution of smart mobility systems and decarbonization in transport infrastructures, this study introduces an integrated perspective that connects transport and mobility transitions with the concept of socio-spatial justice. The study employs a systematic literature review grounded in the Knowledge Development Process–Constructivist (ProKnow-C) model, and structured through thematic analysis around three axes: digitalization; integrated mobility platforms; and socio-spatial justice. The bibliographic database for this analysis is composed of 88 publications. The research suggests that the field is undergoing a gradual shift from a predominantly technical transport and mobility engineering toward a socio-technical approach increasingly shaped by sustainability and social equity. The transition toward low-carbon infrastructures and accessibility-driven mobility models, supported by digital technologies and Mobility-as-a-Service platforms, presents significant opportunities to fulfill global climate objectives (SDG 13), promote sustainable urban development (SDG 11) and strengthen socio-spatial justice (SDGs 10, 11). However, evidence also indicates that technological innovation alone cannot guarantee equitable opportunities in urban transport transitions. In this context, public participation becomes essential to prevent greenwashing and the so-called “transport mirage,” while ensuring that equity and justice are embedded in transport and mobility planning. Full article

Urban morphology strongly affects spatial quality and residents’ quality of life, yet many assessments rely on aggregated indicators and overlook causal links between street-scale attributes. This study proposes a multicriteria framework that combines Landscape Units (LUs) with Fuzzy DEMATEL to evaluate urban morphology and spatial quality. It integrates visual, spatial, and qualitative dimensions through three descriptor families—Green Areas System, Urban Furniture, and Urban Systems—and 12 descriptors. Five LUs in Maringá (southern Brazil) were selected to represent distinct urban fabrics and development stages. Expert judgments were used to weight families and descriptors and to score each LU. Urban Systems emerged as the most influential family, while Buildings, Road System, and General Tree Planting were identified as key drivers of spatial quality. Clear differences appeared across LUs: LU2, aligned with Garden City principles, achieved the highest score, whereas LU5, a peripheral neighborhood with fragmented vegetation and narrow streets, scored the lowest. Overall, higher spatial quality depends on integrating robust urban systems, well-structured green infrastructure, and adequate public-space amenities. The framework is transparent and replicable, supporting morphology-sensitive urban planning and decision-making. Full article

This study investigates the potential of Autonomous Electric Wagons (AEWs) as an emerging technology for sustainable urban logistics. Because AEWs are still at the pilot-development stage, the objective is to develop a conceptual framework for assessing how such vehicles could be integrated into a metropolitan freight system and to explore its application to Rome. The proposed framework combines a peripheral Urban Consolidation Center (UCC), autonomous electric rail transport, inner-urban Urban Distribution Centers (UDCs), and low-emission last-mile delivery. The Rome application uses parcel-demand estimation, AEW capacity and fleet dimensioning, railway-cycle analysis, and scenario analysis to examine the potential scale and operating requirements of the proposed system. Under the reference assumptions, approximately 40,000 parcels/day are on average estimated for Amazon’s Rome market. A four-AEW platoon completing four daily cycles provides a theoretical capacity of approximately 54,000 parcels/day, indicating that the reference configuration could accommodate the estimated demand with a substantial margin. A second scenario with a UCC managed by a third-party logistics (3PL) operator and a shorter cycle time, allows a four AEW platoon six daily cycles for a theoretical transport capacity of 81,000 parcels/day. The analysis also identifies railway capacity, coupling and terminal operations, signaling and interoperability, and the availability of reliable freight-demand data as critical conditions requiring further investigation. The study therefore provides a conceptual and quantitative basis for evaluating AEWs in urban logistics and identifies the technical and operational issues that must be addressed before pilot-scale implementation can be assessed. Full article

Emerging and re-emerging infectious diseases (EIDs and REIDs) continue to pose a major threat to human and animal health globally. The close interactions among wildlife, livestock, and humans especially in areas where natural habitats are disturbed create ideal conditions for diseases to spill over from animals to humans. Changes in land use, deforestation, climate change, urban expansion, and global travel have contributed both to the rise in new infections and the return of old ones. Outbreaks of Nipah virus in Malaysia, Ebola in West Africa, and COVID-19 highlight the large-scale epidemics caused by human–wildlife contact. Ecological pressure and increasing movement of people and goods have facilitated the cross-species transmission of pathogens. In India, outbreaks of diseases like Lumpy Skin Disease, avian influenza, and Nipah virus have affected both public health and the livestock sector, causing significant economic losses. The One Health approach—linking human, animal, and environmental health—is now essential for controlling disease risk. Strategies like improved surveillance, better diagnostics, vaccine development, responsible use of antibiotics, and restoration of ecological buffer zones are essential in mitigating the risk of disease. This review emphasizes the need for coordinated action across disciplines to reduce future outbreaks and improve preparedness for zoonotic and environmentally driven infectious diseases. Full article

Urban air pollution remains the single largest environmental health risk factor for premature mortality in the European Union (EU) and a central concern of the European Green Deal and the Zero Pollution Action Plan. Numerical dispersion and chemistry-transport models have become increasingly sophisticated, yet the communities who live with polluted air rarely interact with raw concentration fields. Instead, they interact with interpretive, user-facing representations such as colour-coded indices, source–receptor diagrams, heuristic risk judgements, and conceptual policy frameworks including the Drivers–Pressures–State–Impact–Response (DPSIR) model. This paper critically examines five families of interpretive approaches, including Explainable AI (XAI) as an emerging entrant. Drawing on recent reviews, the Directive (EU) 2024/2881 on Ambient Air Quality and Cleaner Air for Europe (hereafter the recast Directive), and the 2026 WHO roadmap on air quality indexes, the paper argues that these approaches are vital tools for translating and governing what numbers alone cannot communicate. Strengths, limitations, and trade-offs are synthesised into three comparative tables, and the implications are illustrated through a conceptual discussion of Malta. The nation represents a small, densely populated island state, where limited monitoring networks, street-canyon geometries, and maritime emissions make interpretable frameworks particularly valuable. The review concludes that the five families are complementary rather than interchangeable, and that in Malta, and plausibly similar jurisdictions, this translation layer is essential for converting stringent requirements into practical public-health and policy action. Full article

Against the growing prominence of global climate change and environmental governance challenges, synergistic pollution and carbon reduction (SPCR) has emerged as a core pathway for regional sustainable-development strategies. Focusing on the refined evaluation and dynamic prediction of intra-urban SPCR, this study selects 20 county-level administrative units in Chengdu, western China, and establishes an SPCR analytical framework based on a spatiotemporal adaptive ensemble model. First, the TOPSIS–gray relational analysis (GRA) approach is adopted to systematically measure the spatiotemporal evolution pattern of county-scale SPCR during 2001–2022. The overall SPCR level exhibits gradual improvement following a U-shaped trend. Complex-network theory is further applied to reveal the synergistic characteristics of its “core-periphery” structure. Second, the XGBoost–SHAP model identifies that the increased proportion of the tertiary industry and the control of total energy consumption constitute the most prominent positive and inhibiting driving factors for SPCR, respectively. On this basis, a Spatiotemporal Heterogeneity Ensemble Learning Model (STE-ML) is constructed. By dynamically integrating geographical proximity and socio-economic functional relevance, this model adaptively synthesizes the strengths of base learners, including the geographically and temporally weighted regression (GTWR) model, geographical optimal similarity (GOS) model, and Random Forest (RF), and achieves high-accuracy simulation of county-level SPCR (R² = 0.806). Finally, four scenarios, namely the Business-as-Usual Scenario (BAUS), Structural Optimization Scenario (SOS), Technology Leadership Scenario (TLS), and Collaborative Governance Scenario (CGS), were established to simulate the evolutionary pathways of SPCR from 2023 to 2035. The results indicate that the BAUS, which continues the existing development pattern, will trigger further divergence in regional synergistic levels. By contrast, the CGS, which incorporates in-depth adjustment of industrial structure, low-carbon technological innovation, and cross-regional collaborative governance, can most effectively elevate the overall SPCR level and facilitate balanced development among county-level jurisdictions. In terms of robustness, a single-factor sensitivity analysis with ±10% variation in scenario parameters and a 95% prediction-interval test were conducted. The results show that the absolute value of the sensitivity index |S| does not exceed 0.0113, and the gap (1.44%) between the CGS and BAUS substantially exceeds the maximum effect of any single parameter. These findings verify that the forecasting conclusions are robust and that scenario discrepancies are not driven by a single hypothetical parameter. This study provides methodological support and empirical evidence for the implementation of differentiated and refined collaborative governance of pollution and carbon reduction within cities. Full article

Urban water systems face acute disruptions, including drinking-water advisories and wildfire-related contamination, alongside chronic pressures from climate variability, ageing infrastructure, and limited institutional capacity. Decentralized treatment components may complement centralized infrastructure at point-of-use, point-of-entry, building, and district scales. Within these systems, compact adsorbent and catalytic modules could provide targeted polishing or emergency-treatment functions; however, many candidate materials are still evaluated as powders in batch tests. Three-dimensional (3D) printing can instead shape them into recoverable monoliths with tunable, interconnected flow channels. This review evaluates 3D-printed metal–organic framework (MOF), porous carbon, and MOF-derived carbon monoliths for resilient urban water treatment. We assess material selection, fabrication, and performance across four linked scales: active material, engineered monolith, treatment device, and urban water system. The evidence shows that additive manufacturing can improve material recovery, control macroscopic flow, expose active phases, and enable regenerable modules. Nevertheless, most studies remain laboratory-scale demonstrations based on model contaminants, batch operation, simplified matrices, or short cycling. Long-term continuous-flow performance, pressure drop, active-material release, transformation products, potable-water safety, device-specific life-cycle impacts, and field economics remain poorly characterized. Progress requires geometry-controlled comparisons, realistic-water breakthrough tests, service-relevant regeneration, leachables assessment, and accountable maintenance and end-of-life strategies. Resilience therefore depends on integrating material chemistry, hydraulic design, treatment-train compatibility, environmental performance, and equitable service delivery. Full article