Search Results (768)

Understanding how atmospheric pollutants interact with soil pollution is essential for assessing long-term environmental and human health risks. This study compares concentrations of polycyclic aromatic hydrocarbons (PAHs) and potentially toxic elements (PTEs) in PM₁₀ and surface soil near air quality monitoring stations in Zagreb, Croatia. While previous work identified primary emission sources affecting PM₁₀ composition in the area, this study extends the analysis to investigate potential pollutant transfer and accumulation in soils. Multivariate statistical tools, including correlation analysis and principal component analysis (PCA), were employed to gain a deeper understanding of the sources and behavior of pollutants. Results reveal significant correlations between air and soil concentrations for several PTEs and PAHs, particularly when air pollutant data are averaged over extended periods (up to 6 months), indicating cumulative deposition effects. Σ11PAH concentrations in soils ranged from 1.2 to 524 µg/g, while mean BaP in PM₁₀ was 2.2 ng/m³ at traffic-affected stations. Strong positive air–soil correlations were found for Pb and Cu, whereas PAH associations strengthened at longer averaging windows (3–6 months), especially at 10 cm depth. Seasonal variations were observed, with stronger associations in autumn, reflecting intensified emissions and atmospheric conditions that facilitate pollutant transfer. PCA identified similar pollutant groupings in both air and soil matrices, suggesting familiar sources such as traffic emissions, industrial activities, and residential heating. The integrated PCA approach, which jointly analyzed air and soil pollutants, showed coherent behaviour for heavier PAHs and several PTEs (e.g., Pb, Cu), as well as divergence in more volatile or mobile species (e.g., Flu, Zn). Spatial differences among monitoring sites show localized influences on pollutant accumulation. Furthermore, this work demonstrates the value of coordinated air–soil monitoring in urban environments and provides an understanding of pollutant distributions across different components of the environment. Full article

Microplastic (MP) pollution in urban areas is a growing global concern due to its health risks and environmental effects. This study investigates the sources, spatial distribution, and health risks of MPs in road dust across industrial, capital city, and peri-urban areas of Bangladesh. Street dust samples were collected from 15 heavily congested traffic sites across Dhaka and its surrounding areas. The samples were analyzed using fluorescence microscopy and Fourier Transform Infrared (FTIR) spectroscopy to identify MP types and their morphological characteristics. We have identified six types of polymers, including Polyvinyl alcohol (PVA), Polyethylene (PE), Polypropylene (PP), Polystyrene (PS), Low-Density Polyethylene (LDPE) and High-Density Polyethylene (HDPE), with industrial areas exhibiting the highest levels of MPs followed by capital city and peri-urban zones. PP was the most prevalent MP polymer, with the highest level in industrial areas (14.1 ± 1.7 MPs/g), followed by capital city (9.6 ± 1.92 MPs/g) and peri-urban areas (7.2 ± 1.56 MPs/g). Principal Component Analysis (PCA) identified traffic emissions, industrial activities, and mismanaged plastic waste as the primary sources of MPs. Health risk evaluations indicated that children are more susceptible to MP exposure through ingestion and inhalation, with industrial areas posing the highest carcinogenic risk. The findings underscore the pressing demand for better waste management systems and stricter regulatory measures to mitigate MP pollution and safeguard public health in urban environments. Addressing these challenges is essential to reduce the growing threat of MPs and their long-term effects on ecosystems and human well-being. Full article

Multi-modal image segmentation is a key task in various fields such as urban planning, infrastructure monitoring, and environmental analysis. However, it remains challenging due to complex scenes, varying object scales, and the integration of heterogeneous data sources (such as RGB, depth maps, and infrared). To address these challenges, we proposed a novel multi-modal segmentation framework, DyFuseNet, which features dynamic adaptive windows and cross-scale feature fusion capabilities. This framework consists of three key components: (1) Dynamic Window Module (DWM), which uses dynamic partitioning and continuous position bias to adaptively adjust window sizes, thereby improving the representation of irregular and fine-grained objects; (2) Scale Context Attention (SCA), a hierarchical mechanism that associates local details with global semantics in a coarse-to-fine manner, enhancing segmentation accuracy in low-texture or occluded regions; and (3) Hierarchical Adaptive Fusion Architecture (HAFA), which aligns and fuses features from multiple modalities through shallow synchronization and deep channel attention, effectively balancing complementarity and redundancy. Evaluated on benchmark datasets (such as ISPRS Vaihingen and Potsdam), DyFuseNet achieved state-of-the-art performance, with mean Intersection over Union (mIoU) scores of 80.40% and 80.85%, surpassing MFTransNet by 1.91% and 1.77%, respectively. The model also demonstrated strong robustness in challenging scenes (such as building edges and shadowed objects), achieving an average F1 score of 85% while maintaining high efficiency (26.19 GFLOPs, 30.09 FPS), making it suitable for real-time deployment. This work presents a practical, versatile, and computationally efficient solution for multi-modal image analysis, with potential applications beyond remote sensing, including smart monitoring, industrial inspection, and multi-source data fusion tasks. Full article

The paper contributes to the debate on the holistic sustainability assessment of real estate projects, integrating economic, financial, environmental, and social aspects. A methodological study is presented to support decision-making processes involving the preferability ranking of alternative investment scenarios: new building production vs. retrofitting the existing stock, in the context of urban transformation interventions. The study integrates life cycle approaches by introducing the social components besides the economic and environmental ones. Firstly, a composite unidimensional (monetary) indicator calculation is illustrated. The sustainability components are internalized in the NPV calculation through a Discounted Cash-Flow Analysis (DCFA). Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) are suggested to assess the economic and environmental impacts, and the Social Return on Investment (SROI) to assess the intervention’s extra-financial value. Secondly, a methodology based on multicriteria techniques is proposed. The Hierarchical Analytical Process (AHP) model is suggested to harmonize various performance indicators. Focus is placed on the criticalities emerging in both the methodological approaches, while highlighting the relevance of multidimensional approaches in decision-making processes and for supporting urban policies and urban resilience. Full article

The present investigation provides a comparative six-month analysis of atmospheric pollution by polycyclic aromatic hydrocarbons (PAHs) in the urban region of Opole, Poland. The study employs dual monitoring methods: traditional quartz filter-based active air sampling and active moss biomonitoring using Pleurozium schreberi, Sphagnum fallax, and Dicranum polysetum mosses. The experimental campaign took place from August 2021 to February 2022, spanning the autumn and winter seasons. PAH concentrations were measured using gas chromatography–mass spectrometry (GC-MS) following methodical sample extraction protocols. Filters documented transient air changes in PAHs, particularly high-molecular-weight (HMW) components such as benzo[a]pyrene (BaP), which exhibited considerable increases during the colder months due to heightened heating activities and less dispersion. The size of particles deposited on the filters varied from 0.16 to 73.6 μm, with an average size of 0.71 μm. Mosses exhibited cumulative uptake trends, with D. polysetum showing the greatest bioaccumulation efficiency, particularly for low- and medium-molecular-weight PAHs, followed by P. schreberi and S. fallax. Meteorological indices, including sun radiation and air temperature, demonstrated significant negative relationships with PAH buildup in mosses. Diagnostic ratio analysis verified primarily pyrogenic sources (e.g., fossil fuel burning), although petrogenic contributions were detected in D. polysetum, indicating its increased sensitivity to evaporative emissions. The study shows that the integration of moss biomonitoring with traditional filter samples provides a strong, complementary framework for assessing air quality, particularly in fluctuating meteorological settings. The results advocate for the integration of moss-based methodologies into environmental monitoring initiatives and provide significant insights into contaminant dynamics influenced by seasonal and meteorological factors. Full article

Driven by environmental concerns and aligned with the principles of the circular economy, urban plastic waste—including packaging materials, disposable items, non-functional objects, and industrial scrap—is increasingly being collected, recycled, and marketed as a potential substitute for virgin polymers. However, the use of recycled polymers introduces uncertainties that can significantly affect both the durability and the further recyclability of the resulting products. This paper demonstrates how spectroscopic analysis in the mid-infrared (MIR) and near-infrared (NIR) regions can be applied well beyond the basic identification of the main polymeric component, typically performed during the sorting stage of recycling processes. A detailed interpretation of spectral data, based on well-established correlations between spectroscopic response and material structure, enables the classification of recycled polymers according to specific physicochemical properties, such as chemical composition, molecular architecture, and morphology. In this context, infrared spectroscopy not only provides a reliable comparison with the corresponding virgin polymer references but also proves particularly effective in assessing the homogeneity of recycled materials and the reproducibility of their properties—factors not inherently guaranteed due to the variability of input sources. As a case study, we present a robust protocol for determining the polypropylene content in recycled polyethylene samples. Full article

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

Against the backdrop of an intensifying global climate change and energy crisis, energy system decarbonization constitutes a primary sector for carbon mitigation. Integrated Energy Systems (IES) of district heating systems (DHS), a critical component of district energy networks (DEN), enable energy cascade utilization and enhance renewable energy integration efficiency when coupled with incremental distribution networks (IDN). However, retrofitting coupled systems necessitates significant capital investment and sustained operational expenditures. To evaluate the economic and environmental benefits of system retrofitting and assess cross-sector coordinated optimization potential, this study develops a multi-objective optimization framework for IES transition planning of DHS. Using an operational DHS energy station as a case study, we establish multi-scenario retrofitting strategies and operational protocols with comprehensive feasibility assessments, incorporating sensitivity analysis of cross-sector optimization potential while evaluating how varying electricity-to-heat load ratios affect optimization performance. Results demonstrate that intelligent operation optimization is essential for coordinating multi-equipment operations and maximizing energy conservation. Significant long-term economic and carbon mitigation potential remains untapped in ground source heat pumps and combined cooling, heating, and power (CCHP) systems. Coordinated optimization with campus incremental distribution networks further enhances energy cascade utilization in urban energy systems. Full article

Urban agriculture in space-constrained cities requires compact, reproducible propagation systems. Therefore, the aim of this Technical Note is to design, implement, and functionally validate a low-cost, modular hydroponic chamber (SSHG) for early-stage vegetative propagation. This system couples DHT11-based temperature/RH monitoring with rule-based actuation—irrigation 4×/day and temperature-triggered ventilation—under the control of an Arduino Uno microcontroller; LED lighting was not controlled nor analyzed. Two 15-day trials with basil (Ocimum basilicum L.) yielded rooting rates of 61.7% (37/60) and 43.3% (26/60) under a deliberate minimal-input configuration without nutrient solutions or rooting hormones. Environmental summaries and spatial survival maps revealed edge-effect patterns and RH variability that inform irrigation layout improvements. The chamber, bill of materials, and protocol are documented to support replication and iteration. Thus, the SSHG provides a transferable baseline for educators and researchers to audit, reproduce, and improve small-footprint, controlled-environment propagation. Beyond its technical feasibility, the SSHG contributes to sustainability by leveraging low-cost, readily available components, enabling decentralized seedling production in space-constrained settings, and operating under a minimal-input configuration. In line with widely reported hydroponic efficiencies (e.g., lower water use relative to soil-based propagation), this open and replicable platform aligns with SDGs 2, 11, 12, and 13. Full article

Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF cables experience faults caused by insulation degradation, thermal expansion, and environmental stressors, which, due to their subtle and gradual nature, complicate incipient fault detection and subsequent fault localization. This study presents a novel, proactive, and retrofit-friendly predictive condition monitoring method. It leverages distributed accelerometer sensors non-intrusively mounted on the HPFF steel pipe within existing manholes to continuously monitor vibration signals in real time. A physics-enhanced convolutional neural network–long short-term memory (CNN–LSTM) deep learning architecture analyzes these signals to detect incipient faults before they evolve into critical failures. The CNN–LSTM model captures temporal dependencies in acoustic data streams, applying time-series analysis techniques tailored for the predictive condition monitoring of HPFF cables. Experimental validation uses vibration data from a scaled-down HPFF laboratory test setup, comparing normal operation to incipient fault events. The model reliably identifies subtle changes in sequential acoustic patterns indicative of incipient faults. Laboratory experimental results demonstrate a high accuracy of the physics-enhanced CNN–LSTM architecture for incipient fault detection with effective data feature extraction. This approach aims to support enhanced operational resilience and faster response times without intrusive infrastructure modifications, facilitating early intervention to mitigate service disruptions. Full article

As cities confront intensifying environmental challenges and increasing expectations for sustainable governance, extending Environmental, Social, and Governance (ESG) evaluation frameworks to the urban scale has become a pressing need. However, existing ESG systems are typically designed for corporate contexts, lacking city-specific indicators, integrated data representations, and reliable ESG information with high spatial and temporal resolution for informed decision-making. This study proposes a comprehensive ESG evaluation framework tailored to green cities, which consists of three core components: (1) The construction of a green-oriented ESG indicator system with an expert-informed weighting system; (2) the design of a GIS-BIM-IoT integrated ontology that semantically aligns spatial, infrastructure, and observational data with ESG dimensions; and (3) the implementation of a web-based data integration and visualization platform that dynamically aggregates and visualizes ESG insights. A case study involving a primary school and an air quality monitoring station in Hong Kong demonstrates the system’s capability to infer material recycling rates and pollution concentration scores using ontology-driven reasoning and RDF-based knowledge graphs. The results are rendered in an interactive 3D urban interface, supporting real-time, multi-scale ESG evaluation. This framework transforms ESG assessment from a static reporting tool into a strategic asset for transparent, adaptive, and evidence-based urban sustainability governance. Full article

Parks, as essential elements of urban green public spaces, play a crucial role in sustainable urban development. Their features have features confirmed to significantly affect park vitality. Prior research has indirectly indicated that park features might impact park vitality via interaction and synergy; however, such effects have been neglected in park vitality studies. The study area is South China, with data collected from 20 urban comprehensive parks in 8 representative cities. This study constructs linear mixed models of principal component main effects, interaction effects, and synergy effects to empirically examine the interaction of internal element features and the synergy of external environmental features affecting park vitality. The findings indicate: (1) Structural interaction effects exist among internal element features that impact park vitality. The structures of “medium plaza + small plaza” and “primary park road + open grassland” significantly enhance vitality. Conversely, the structure of “aquatic plant coverage area + lake + dense woodland” has a negative influence. Single structure features are ineffective in significantly influencing park vitality. (2) The inclusion of interaction terms between internal feature structures enhances the significance of their effects on vitality. The interaction of “medium plaza + small plaza” × “primary park road + open grassland” shows the strongest effect. (3) There is a synergy between internal and external features: with external features like accessibility and disposable income, certain internal interaction structures positively contribute to vitality. Additionally, the “service capacity” external environmental feature exhibits a negative synergy with internal element features. These findings provide theoretical and practical insights for sustainable urban park design, planning, and refined management in cities with similar socioeconomic and spatial contexts. Full article

Historical water infrastructures represent an overlooked cultural heritage of extraordinary importance, encompassing centuries of technical knowledge deeply intertwined with the landscape and social life. Matera stands out as a case study of international relevance, where the morphology of the historic urban fabric of the Sassi has been shaped by the Grabiglioni, or Fossi, streams that today lie hidden and compromised, deprived of the recognition they deserve. This study presents an integrated analysis that combines history, morphology, hydrology, and infrastructure to uncover the origin, evolution and cultural value of the entire context. Thus, the environmental and identity-related potential of these historical infrastructures emerges, along with the critical issues they pose, partly as a consequence of urban expansions. Reintegrating the Grabiglioni into urban development policies is not merely a matter of preservation; it represents a strategic opportunity to transform this heritage into a resource for safety, sustainability, and urban regeneration. The multidisciplinary approach proposed here can serve as a guide for similar studies on historical water infrastructures, restoring life and memory to legacies that narrate a timeless engineering intelligence and a careful understanding of the various territorial components (morphology, climate, works, and transformations). This article is a revised and expanded version of Altamura D. et al., Interdisciplinary investigation approach to analyze historical water infrastructures and urban transformations: the case study of the Grabiglioni in the Sassi of Matera, Italy, presented at CEES—International Conference on Construction, Energy, Environment and Sustainability in Bari (2025). Full article

Engine oil condition critically affects vehicle performance, fuel efficiency, and engine durability. While conventional oil change strategies are based on fixed intervals or mileage thresholds, they often neglect real operating conditions and the actual state of lubricant degradation. This study investigates nine used engine oil samples collected from passenger vehicles operating in diverse environments, including city traffic, highway routes, hybrid systems, and diesel engines. The oils were assessed using kinematic viscosity measurements and Fourier transform infrared (FTIR) spectroscopy to monitor key degradation indicators—oxidation, nitration, sulfonation, fuel dilution, soot contamination, and additive depletion. Each case is fully documented with detailed operational histories, facilitating a nuanced, real-world understanding of oil aging. The results demonstrate that degradation levels vary considerably, even under similar mileage ranges, highlighting the influence of urban usage patterns and engine design. In several cases, premature or delayed oil changes were observed, confirming that standard service intervals may be suboptimal. FTIR proved effective in detecting subtle chemical transformations, particularly in samples affected by biofuel components or prolonged thermal stress. These findings emphasize the value of integrating laboratory diagnostics into oil change decision-making and support more tailored maintenance strategies. Such an approach can reduce unnecessary oil replacement, limit waste generation, and extend engine lifespan, contributing to both environmental and economic sustainability. This study supports the implementation of condition-based oil change strategies to minimize lubricant waste and promote maintenance practices aligned with sustainability principles. Full article

The characterization and mapping of urban landscape spatial form are critical for advancing sustainable planning and informed environmental management. From a morphometric perspective, this study introduces a novel, data-driven framework for typo-morphological analysis. First, morphological cells (MCs) are defined as objectively and universally applicable spatial units for morphometric investigation. Second, by integrating a multi-dimensional cognition of full-scale morphological and associated landscape elements, we construct a set of 48 spatial form indicators and attach them to morphological cells, enabling a precise description of each unit. Third, a Gaussian mixture model (GMM) is employed to cluster the metrical information within the spatially lagged context derived from the topological structure of the morphological cells, resulting in the delineation of distinct typo-morphological zones (TMZs). We then adopt Ward’s algorithm to establish a hierarchical relationship among identified urban landscape types. Using Wuxi City, China, as a case study, our results demonstrate the effectiveness of the proposed framework in capturing the heterogeneity and underlying connotation of urban landscape spatial characteristics. Building upon the unsupervised clustering results, we further apply the classification and regression tree (CART) to provide a supervised interpretation of the key spatial form conditions driving typological decisions. It facilitates the systematic identification of the components and formative mechanisms of spatial form. The findings contribute a scalable, reproducible, and interpretable typo-morphometric approach for analyzing urban landscape spatial characteristics, thereby providing a robust quantitative foundation for integrated decision-making in landscape planning, socio-ecological assessment, and urban design practices. More broadly, the study carries both applied and theoretical significance for advancing refined urban governance and fostering interdisciplinary research related to urban sustainable development. Full article