Search Results (1,178)

Cities are artificial ecosystems that suffer most from environmental issues and climate change. Urban Heat Island (UHI) effects represent an increasing challenge, especially for compact Mediterranean cities characterized by high population density and extensive impervious surfaces. This study assessed localized microclimatic conditions within the small Maltese coastal town of Isla through a 15-day summer field monitoring campaign. Air temperature, relative humidity, and wind speed were measured across urban locations characterized by different levels of vegetation coverage and thermal vulnerability. The analysis combined descriptive statistics, Mann–Whitney U testing, and Multiple Linear Regression (MLR) models. In addition, site-specific Nature-based Solutions (NbS) scenarios were proposed as context-sensitive strategies to support urban heat mitigation and climate resilience. The results highlighted distinct microclimatic responses between the sites investigated. In particular, the MLR analysis suggested that non-vegetated areas were more sensitive to short-term atmospheric variability associated with wind speed and relative humidity fluctuations. These findings suggest that urban vegetation may contribute not only to localized cooling, but also to increased microclimatic stability within compact Mediterranean urban environments. Full article

During the winter, school-age children spend much of their time in two indoor environments, homes and classrooms, where ventilation is often restricted to conserve heat, favoring the accumulation of carbon dioxide (CO₂). This study evaluated CO₂ exposure in both environments in Santiago de Chile to characterize real conditions and their daily combinations. Continuous CO₂ monitoring was conducted using sensors in four dwellings with school-age children and four classrooms from different schools during August 2024. Hourly profiles, time over the operating threshold of 1250 ppm, and equivalent hours of exposure, standardized to a daily reference time, were analyzed. In classrooms, levels above the threshold were observed episodically. They were more concentrated during school hours, with marked differences between establishments, ranging from recurrent exposure to high levels to no exposure above the established level. In the bedrooms, the increases were concentrated during the night and early morning hours, consistent with reduced effective ventilation during prolonged stays. Overall, the bedroom-classroom combined exposure showed high variability across cases; together, it allows identifying priority scenarios and the orientation of winter ventilation strategies without neglecting thermal comfort. These results support the incorporation of winter ventilation operational criteria into schools and homes as input for implementing indoor environmental quality policies and standards in urban contexts. Full article

Urban destinations increasingly incorporate green–blue infrastructure, sensory-balanced public spaces, and microclimate-responsive design to mitigate visitor fatigue and support sustainable tourism experiences. To understand how insights from broader tourism environments, particularly nature-based contexts, can inform emerging urban well-being strategies, this study conducts a global bibliometric review (2005–2025) on psychological restoration, stress relief, and visitor well-being. Using Scopus and a Boolean search combining mental health constructs, tourism setting, and analytical approaches, 825 records were identified, and 149 articles were retained after applying eligibility criteria. Science mapping and performance analyses reveal accelerated post-2018 growth and three dominant knowledge clusters centered on restoration pathways, environmental determinants, and behavioral/hospitality components. Based on these patterns, this study introduces the RESTOR-URBAN model, integrating environmental moderators, psychological mechanisms, and behavioral interactions that jointly shape stress reduction and emotional well-being across urban tourism systems. The results show increasing relevance of micro-restorative experiences, thermal comfort management, and stress-aware service design, while highlighting persistent methodological heterogeneity and limited integration of environmental co-data (Universal Thermal Climate Index (UTCI), Physiological Equivalent Temperature (PET), and Discomfort Index (DI)). The findings suggest that restoration-based evidence from nature-based tourism can inform sustainable urban tourism planning, hospitality practice, and visitor experience design, and propose a research agenda emphasizing standardized well-being indicators, longitudinal and structural equation modeling (SEM)-based approaches, and environmental quality variables for resilient, health-oriented urban destinations. Full article

This study focuses on the search and determination of organic pollutants in aerosol particles (PM_2.5) collected in Arkhangelsk, the largest urban agglomeration in the Arctic, during winter and summer periods. Thermal desorption gas chromatography coupled with high-resolution mass spectrometry was applied for non-targeted screening of atmospheric aerosols, enabling the detection of compounds at low concentrations ranging from 10 pg/m³ to several ng/m³. Representatives of various chemical classes were detected in samples from both seasons, including CHO compounds (with phthalates as the predominant subgroup), nitrogen-containing compounds (e.g., pyridines, quinolines, nicotine), phenols and monoaromatics, as well as polycyclic aromatic hydrocarbons and their oxygenated derivatives. In winter, PAHs and oxy-PAHs significantly predominated, likely due to increased combustion of fossil fuels and biomass for heating purposes. A total of approximately 300 compounds were identified via non-targeted screening, of which 32 were confirmed and quantified using authentic reference standards across six chemical classes. Seasonal variations in both the composition and concentration levels highlight the impact of local emission sources and atmospheric conditions on the organic aerosol profile in this arctic urban environment. Full article

Extreme heat has emerged as a pressing sustainability challenge in rapidly urbanizing metropolitan areas, where built environments intensify thermal exposure and its unequal distribution across socially vulnerable communities. Although previous studies have documented disparities in urban heat exposure, fewer have developed decision-oriented frameworks that can simultaneously quantify heat inequity, identify its dominant drivers, and evaluate mitigation strategies under an explicit equity objective. To address this gap, this study develops an interpretable machine-learning framework to support sustainable and equitable urban heat mitigation in Houston. Using 727 census tracts, we model summer daytime land surface temperature (LST) in 2022 as a function of tract-level natural and built-environment characteristics with XGBoost, interpret model behavior using SHAP, quantify inequity through a Concentration Index relative to social vulnerability, and compare targeted counterfactual intervention scenarios under a dual cooling–equity objective. The results show that heat exposure is disproportionately concentrated in more vulnerable communities, with mean LST increasing from 38.60 °C in low-vulnerability tracts to 39.10 °C in high-vulnerability tracts, alongside a positive and statistically significant Concentration Index. The model demonstrates solid predictive performance (R² = 0.774, RMSE = 0.793 °C), and SHAP results identify coastal distance, NDVI, building height, road density, and building coverage as the principal drivers of tract-level thermal variation. Under equity-targeted intervention scenarios, increasing NDVI and mean building height emerge as the clearest win–win strategies, reducing both average predicted LST and the unequal concentration of heat burden. Overall, this study provides a planning-relevant framework for identifying mitigation priorities that advance urban cooling, equity, and more just forms of climate adaptation. Full article

Rapid urbanization in tropical Southeast Asia is transforming pervious land into impervious surfaces, intensifying the surface urban heat island (SUHI) effect and increasing the need for consistent urban thermal monitoring. This study assesses how impervious surface area (ISA) expansion relates to the urban thermal environment across five tropical megacities (Bangkok, Jakarta, Manila, Kuala Lumpur, and Ho Chi Minh City). AlphaEarth geospatial foundation model embeddings were used to reduce observation gaps caused by persistent cloud-cover, while MODIS land surface temperature (LST) was used to quantify the thermal response. We compared AlphaEarth classification against conventional Sentinel-2/NDVI approaches and an additional fairer annual Sentinel-2 full-band-plus-index Random Forest baseline, quantified ISA expansion for 2017–2024, and related ISA fraction to dry-season LST at 1 km resolution. Repeated random-holdout tests based on Google Earth Engine samples showed AlphaEarth mean IoU = 0.866 (95% CI: 0.857–0.875), compared with 0.758 (0.749–0.767) for the annual Sentinel-2 full-band-plus-index baseline and 0.686 (0.674–0.698) for the best single-date 5-index baseline. Spatial-block holdout tests gave similar but slightly lower values (AlphaEarth IoU = 0.859; annual Sentinel-2 baseline = 0.747; best single-date baseline = 0.673). Ho Chi Minh City experienced the fastest ISA expansion (+11.0 percentage points; slope = 1.48 pp yr^-1, 95% CI: 1.06–1.91), whereas Bangkok reached the highest ISA fraction (65.1%). ISA fraction and LST were consistently and positively associated across cities and years (Pearson r = 0.748–0.900), and mean SUHI intensity during 2017–2024 ranged from 4.01 °C in Bangkok to 8.51 °C in Manila. These results indicate that foundation model embeddings can support cloud-resilient mapping of impervious surface change and thereby improve assessment of tropical urban thermal environments, while also highlighting the need for independent ground-truth validation. Full article

Urban fire risk in high-density cities is characterized by complex spatial heterogeneity and nonlinear relationships with the built environment, population distribution, and climatic conditions. However, most existing studies rely on linear assumptions and offer limited interpretability. To address this gap, we developed an interpretable analytical framework that integrates the CatBoost model with SHAP (SHapley Additive exPlanations), using Futian District in Shenzhen as a case study. We constructed a fire risk surface from historical fire incident data using kernel density estimation (KDE) and incorporated multiple urban environmental factors—including points of interest (POIs), road networks, and meteorological variables—as explanatory variables. The CatBoost model captured nonlinear relationships, while SHAP quantified feature importance and revealed interaction effects. The results show that urban fire risk is strongly associated with the spatial agglomeration of population-related facilities, especially high-density commercial and residential areas, as well as thermal conditions. Several variables exhibit clear nonlinear threshold effects, with their influence on fire risk varying markedly across different intensity ranges. Interaction analysis further indicates that combinations of built-environment characteristics and climatic factors jointly shape the spatial pattern of fire risk. These findings provide empirical insights into the spatial mechanisms underlying urban fire risk and highlight the value of interpretable machine learning in urban safety research. The proposed framework offers a practical tool for developing more targeted, evidence-based fire risk management strategies in high-density urban areas. Full article

Artificial intelligence (AI) is rapidly redefining the management of urban energy systems by coupling predictive analytics with closed-loop control across buildings, power grids, and mobility networks, positioning cities as critical leverage points in global decarbonization efforts. Contemporary urban environments generate vast, heterogeneous datasets that enable advanced machine learning applications; however, limitations remain, including interpretability–fairness trade-offs, fragmented data governance, interoperability gaps, cybersecurity risks, and insufficient long-term validation across diverse climatic and socio-economic contexts. This review evaluates AI-driven strategies for energy-efficient urban systems and identifies the technical and governance conditions required for scalable impact. The evidence synthesized indicates that supervised and ensemble learning models achieve high predictive accuracy for electricity demand and chiller performance, with models such as Random Forest Regression achieving R² values up to 0.9835 in electricity consumption forecasting, while unsupervised approaches detect latent inefficiencies in HVAC systems, delivering measurable savings typically around 6% under controlled benchmarking conditions. Deep learning architectures improve multi-building forecasting and real-time control, with hybrid CNN–LSTM models achieving prediction accuracies up to 97% and outperforming traditional statistical approaches in weekly energy demand forecasting achieving higher prediction accuracy and significant energy savings in complex urban subsystems with reported reductions of approximately 21–23% in residential and educational buildings and up to 37% in office HVAC systems. Hybrid and physics-informed AI models embed thermodynamic principles into data-driven frameworks, improving robustness, interpretability, and generalization. IoT sensor networks and edge-computing architectures support adaptive HVAC, demand–response, and smart-grid management, while integrated building–grid–mobility systems enhance load balancing, storage use, and carbon reduction. AI-enhanced strategies offer a credible pathway toward measurable reductions in urban energy use and emissions with deep reinforcement learning in digital twin environments reducing HVAC energy demand by 10–35% while maintaining thermal comfort within ±0.5 °C in line with ASHRAE standards, and overall energy savings reaching up to 44% in optimized systems when supported by interoperable infrastructures, secure digital-twin architectures, and standardized measurement and verification protocols. Full article

In urban environments, large buildings influence air quality in their surroundings by altering natural wind patterns, obstructing airflow or creating high-velocity wind tunnels, often resulting in stagnant zones that trap pollutants. Furthermore, the extensive shadows cast by these structures reduce ground-level temperatures. For urban planners, accounting for these aerodynamic, thermal and air quality effects is important to fostering healthier, more livable cities. In this work, measurements assessing how shadow and micrometeorological conditions—driven by the proximity of large buildings—influence PM_2.5 levels were conducted in an urban commune of Santiago, Chile, during the winter and spring seasons. This commune is characterized by a mixture of one-story houses and high-rise buildings. PM_2.5 and meteorological parameters were measured outside three pairs of houses in winter of 2021, one of which received shadow from a nearby building and the other was under the sun. In one pair of houses, PM_2.5 concentrations were elevated in the shaded site exclusively during the winter months. This was attributed to shadow-induced temperature reductions, which likely increased local atmospheric stability and inhibited pollutant dispersion. However, this effect was limited to periods of low wind speed; during the spring, the transition to a higher wind speed regime facilitated sufficient mechanical mixing to neutralize the thermal influence of the shadow, resulting in no detectable difference between the sites. In another pair of houses, the result was attributed to the difference in wind speed in one of the houses, because the building acts as a windbreak, no shading effect were observed. Regarding the third pair of houses, no significant impact on PM_2.5 concentrations was observed in the whole period. This lack of variation is likely attributable to the absence of substantial micrometeorological differences between the two sites. Full article

For the environment and the daily life of urban settlements, in the context of contemporary challenges, severe meteorological events rank second worldwide. Therefore, these events tend to become a real threat to human society and to specific economic activities. The main objective of this study is to analyze the spatio-temporal evolution of severe meteorological events in urban environments and to assess their relationship with atmospheric circulation regimes and urban thermal conditions. The analysis focuses on five types of severe events (significant atmospheric precipitation, hail, strong winds, tornadic structures, and cloud-to-ground lightning) recorded in 11 cities located in the historical region of Muntenia, Romania, over the period 2014–2024. The methodological framework is based on three complementary components. First, a new database was developed by integrating information from multiple sources, including the National Meteorological Administration (ANM), the European Severe Storms Laboratory (ESSL), international databases, and validated media reports, with spatio-temporal filtering and aggregation into synoptic episodes. Second, atmospheric circulation regimes were identified using ECMWF ERA5 reanalysis data, based on geopotential height anomalies at the 500 hPa level, allowing the classification of large-scale synoptic patterns. Third, urban thermal conditions were assessed using the ECMWF CERRA regional reanalysis dataset, which provides high-resolution air temperature data, enabling the analysis of urban–peri-urban thermal contrasts and the estimation of the urban heat island effect. The results highlight a total of 997 severe meteorological events, of which 253 (25.6%) were recorded in the analyzed urban areas, 85 (15.9%) in other towns, and 583 (58.5%) in rural areas. The analysis reveals pronounced interannual and intraseasonal variability, as well as distinct spatial clustering patterns, particularly in urban and peri-urban zones. Among the circulation regimes, the Zonal Regime exhibits the highest event rate, suggesting increased favorability for severe weather occurrence, while other regimes show weaker or even inhibitory effects. In addition, most severe events were associated with positive urban–peri-urban temperature contrasts, indicating an active contribution of the urban heat island effect. By combining observational data, synoptic-scale analysis, and urban-scale thermal assessment, this study provides an integrated regional perspective on severe meteorological events and contributes to the enrichment of data sources in the region, while improving the understanding of their dynamics in urban environments affected by data limitations. Full article

This research examines the complex relationship between climate change, rapid population growth, and building stock expansion in Egypt, as well as their combined impact on energy demand and urban sustainability, to address the rapidly increasing electricity demand. This study uses a mixed-methods approach, including quantitative analysis to examine climatic data (1970–2100), demographic trends, and building energy consumption patterns, quantifying their synergistic effects; a qualitative evaluation of policy frameworks and urban planning strategies; and building energy performance simulation using Design Builder to utilize climate-responsive design techniques for energy reduction. Finally, this study proposes energy-efficient design guidance. The research findings reveal that Egypt’s unique hot–arid climate, projected to warm by 4 °C by 2100, combined with a population set to reach 160 million by 2050, has driven the near-doubling of building stock since 1986, with residential buildings accounting for 70–83% of structures and 60% of national electricity use. The research results highlight the importance of implementing climate-responsive design strategies (optimized building-envelope thermal insulation and energy-efficient HVAC systems) in Egypt’s built environment to reduce electricity consumption by up to 40%, thereby aligning urban growth with sustainability objectives. These insights are scalable to other arid, rapidly urbanizing regions globally. Full article

Urban areas in subtropical regions are increasingly exposed to heat stress as climate change intensifies extreme heat events. In high-density cities, urban renewal is widely implemented to upgrade aging building stock, yet its potential role as a passive heat adaptation strategy remains insufficiently understood, particularly for projects below environmental impact assessment thresholds. This study examines how urban renewal influences neighborhood-scale microclimates through a comparative analysis of six residential renewal cases using computational fluid dynamics (CFD) simulations. Pre- and post-renewal scenarios are evaluated to assess changes in wind environment and thermal conditions, with a particular focus on the spatial extent and distance–decay characteristics of renewal-induced effects. The results reveal a consistent distance–decay pattern of microclimate responses across all cases. The influence of urban renewal is strongest within 0–50 m, remains detectable up to approximately 100 m, and diminishes substantially beyond 100–150 m, indicating a clear neighborhood-scale impact radius. Ventilation performance improves systematically following renewal, while thermal responses are more heterogeneous. Localized cooling of up to 1.5 °C is observed in selected cases, whereas others exhibit negligible temperature change despite enhanced airflow. These findings demonstrate that improved ventilation alone does not guarantee thermal mitigation. Instead, thermal outcomes depend on the interaction between airflow, solar exposure, and surface thermal properties. Urban renewal can therefore function as a form of passive heat adaptation when morphological changes are coordinated with shading and surface design strategies. By quantifying the spatial limits of renewal-induced microclimate effects, this study provides empirical evidence for integrating microclimate considerations into neighborhood-scale planning. The identified influence radius offers a practical reference for climate-responsive urban renewal, particularly in high-density subtropical cities where incremental redevelopment plays a dominant role. Full article

With the progressive decline of tailpipe emissions, non-exhaust sources such as brake wear are becoming an increasingly important contributor to traffic-related particulate matter in urban environments. In this context, improving real-world characterization of brake wear particles is essential for air-pollution assessment, source apportionment, and the development of cleaner and more sustainable road transport systems. Here, we investigated the emissions levels, particle size distribution and elemental composition of particles released during harsh real-world braking events by a single light-duty vehicle braking system equipped with an original manufacturer (OEM) non-asbestos organic (NAO) pad formulation. Using a direct on-vehicle sampling system combined with real-time particle sizing and high-resolution microscopy, we observed that particle emissions remained close to background levels at speeds up to 100 km/h, but rose sharply at 120 km/h, reaching 3.7 × 10⁷ #/cm³ in the 8–10 nm size range. This increase suggests that higher speeds are associated with elevated particle emissions, likely due to the higher braking temperatures reached at increased vehicle speeds. The emitted particles were mainly spherical agglomerates rich in iron, titanium, barium, zirconium, and sulphur, consistent with NAO pad formulations. Our results show that the investigated NAO pad system can deteriorate under thermal stress, potentially leading to higher levels of nanoparticle emissions compared to low-metallic or semi-metallic pads investigated under similar conditions. These findings provide real-world evidence relevant to urban air quality research, support the refinement of non-exhaust emissions inventories, and highlight the importance of thermally resilient friction-material formulations for mitigating residual particulate emissions in increasingly cleaner transport systems. Full article

Accurately quantifying the driving mechanisms of land surface temperature (LST) is fundamental to developing climate-resilient urban strategies. However, traditional linear models often fail to capture the complex nonlinear interactions and spatial non-stationarity inherent in urban thermal environments, especially when hindered by multicollinearity among morphological indicators. This study proposes a multi-scale spatial explainability attribution framework by integrating an XGBoost machine learning model with SHAP (SHapley Additive Explanations) to decipher the thermal dynamics of Changchun, a representative cold-region city in China. Utilizing a 500 m grid-based dataset, we incorporated 3D urban morphology (BVD), land cover (NDVI, NDWI), and socioeconomic factors. The results indicate that the XGBoost model achieves superior predictive performance (R² = 0.694) compared to traditional OLS models. SHAP global attribution identified Building Volume Density (BVD) as the primary warming driver, as its three-dimensional volume creates “thermal traps” through radiation trapping and reduced ventilation. Notably, NDVI exhibits a significant nonlinear “cooling threshold effect” at 0.3, beyond which its mitigation efficiency stagnates or even reverses due to vegetation fragmentation and heat-induced physiological stress. Furthermore, spatial mapping reveals a distinct “sign reversal” in NDWI’s impact, reflecting the dualistic thermal regulation of water bodies across different urban–rural gradients. These findings suggest that urban thermal management strategies should shift from merely restricting 2D surface occupancy (e.g., Building Density) to a more sophisticated approach focused on precisely controlling 3D volume intensity (BVD). This study provides a “point-to-area” diagnostic tool supporting a transition to spatially targeted urban planning interventions. Full article

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities. Full article