Search Results (2,230)

Rapid urbanization in tropical Asia has fundamentally transformed land use–land cover while intensifying urban thermal stress, yet the relationship between land cover change and thermal conditions is frequently assumed to be spatially uniform. This study challenges that assumption by demonstrating that land cover–thermal relationships in Colombo District, Sri Lanka, are highly spatially and temporally heterogeneous, with statistically significant associations detected in only 17–47% of the study area in any given year, underscoring that context, not land cover type alone, governs thermal outcomes. Using multi-temporal Landsat satellite imagery, LULC maps were derived, and the urban heat island effect (UHIE) and urban thermal field variance index (UTFVI) were calculated for seven time periods (1989, 1996, 2002, 2009, 2014, 2019, 2024). Geographically weighted regression (GWR) was applied to model local relationships between LULC classes, namely wetland vegetation, water bodies, impervious surfaces, and other pervious surfaces, and thermal indices across a 500 m spatial grid, revealing a 74% loss in wetland vegetation and a 326% increase in impervious surfaces over the study period. Water bodies exhibited spatially variable cooling effects relative to wetland vegetation, most pronounced in eastern regions during earlier periods, while impervious surfaces showed consistent, spatially persistent warming effects concentrated in western and southern urban cores. By coupling GWR with a 35-year multi-sensor time series, this study provides a spatially explicit, longitudinal account of how land cover–thermal relationships evolve as tropical urbanization intensifies, offering an evidence base for spatially targeted rather than uniform climate adaptation planning in rapidly urbanizing tropical cities. Full article

Urban climate risks, particularly extreme heat and flooding, increasingly threaten public infrastructure in rapidly urbanizing regions. Public schools represent critical community assets, yet their spatial planning often overlooks the role of natural capital in mitigating environmental risks. This study examines the intersection of natural capital and urban design through a case study of public schools in the Mount Vernon District of Fairfax County, Virginia. Using cartographic modeling and spatial analysis, the study assesses school-site exposure to urban heat island effects and localized flood risks by integrating geospatial data on land cover, surface temperature, and hydrological conditions. Results indicate that all analyzed school sites exhibit notable vulnerability to both heat exposure and flooding. The findings highlight the importance of incorporating natural capital—such as expanded tree canopy, green infrastructure, and permeable surfaces—into school site planning to enhance climate resilience and environmental quality. Full article

Near-surface temperature inversions are prevalent during the cold months in Ulaanbaatar city, Mongolia, and significantly degrade urban air quality by trapping hazardous pollutants within a shallow atmospheric boundary layer. This study investigates spatiotemporal variability, physical mechanisms, and long-term evolution of near-surface temperature inversions over Ulaanbaatar by integrating 25 years (2000–2024) of ground-based meteorological and radiosonde observations, with high-resolution Weather Research and Forecasting (WRF) model simulations for 2012–2023. Our results demonstrate the four-dimensional data assimilation (FDDA) grid nudging effectively captures localized topographic influences in the WRF simulations, showing a strong agreement with radiosonde observations (R² = 0.783, p < 0.000). Near-surface temperature inversions are strongly controlled by the Siberian High, with the highest frequency occurring from December to February, when up to 67% of morning observations exhibit inversion conditions. A pronounced diurnal cycle was identified, with inversion intensity peaking at 5.6–6.8 °C during the early morning hours (02:00–08:00 LST) before reaching a minimum around 14:00 LST. Spatially, the strongest inversions occur along the low-lying Tuul River valley, where the planetary boundary layer is compressed to below 350 m and wind speeds decrease to less than 2.4 m·s⁻¹, creating persistent atmospheric stagnation. Despite these favorable conditions for inversion formation, long-term observations indicate that regional warming (+2.0 °C) and the urban heat island effects have reduced inversion frequency by 31%, inversion thickness by 170 m, and inversion intensity by 0.9 °C over the past 25 years. These findings demonstrate the strong coupling between regional complex terrain, and boundary layer thermodynamics, highlighting the need to incorporate urban ventilation corridors and topography-informed planning into climate adaptation and winter air-quality management strategies. Full article

Although conventionally quantified as an urban–rural thermal anomaly, urban heat islands are systematic expressions of development choices that shape unequal exposures and health risks across cities. This article develops an integrated urban development-health framework explaining how imperviousness, vegetation deficit, landscape configuration, urban morphology, thermally absorptive materials and nocturnal heat retention connect heat exposure with physical disease, mental well-being and climate-responsive planning. A critical integrative review reported using PRISMA 2020 and PRISMA-ScR principles, organised evidence from urban climate, public health, environmental epidemiology and planning. The synthesis covers the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Local Climate Zones (LCZs), sky-view factor (SVF), height-to-width ratio (H/W), land-surface temperature (LST), Universal Thermal Climate Index (UTCI), Physiological Equivalent Temperature (PET), wet-bulb globe temperature (WBGT) and nocturnal minimum temperature, together with cardiovascular, respiratory, psychiatric, sleep, mortality and well-being outcomes. Six recurrent amplification pathways were identified: imperviousness and low canopy cover; nocturnal heat retention; social vulnerability; blue-green and cool infrastructure; heat–pollution–humidity interaction; and sleep/mental-health disruption. The Urban Heat-Health Development Index (UHHDI) and Urban Heat-Health Amplification Pattern (UHHAP) are proposed as transparent, review-derived tools for urban diagnosis and policy prioritisation. A Spanish/Mediterranean climatic-zone transition analysis illustrates how future climatic severity can be translated into planning-relevant exposure potential. The findings support a shift from simply describing urban heat to diagnosing development-driven heat-health risk, with implications for urban regeneration, thermal justice, public-health adaptation and healthy-city governance. Full article

Rift Valley fever (RVF) is a climate-sensitive mosquito-borne zoonosis long regarded as a rural, pastoral disease, yet accelerating tropical urbanization and intensifying climate variability may be reshaping its epidemiology at the urban–peri-urban interface. This narrative review examines how global climate change and urban-specific climatic conditions jointly shape RVF virus (RVFV) vector habitats, transmission, and burden in tropical and subtropical cities, synthesizing 51 of 412 English-language records identified by a structured, non-systematic search of PubMed, Scopus, Web of Science, and Google Scholar (2009–2026) and selected for relevance to urban and peri-urban RVF. This research draws on human, livestock, and vector evidence from Sub-Saharan Africa, the Arabian Peninsula, and Indian Ocean islands across epidemic and inter-epidemic periods. The synthesis indicates that impervious surfaces, poor drainage, and open water storage can recreate the water-retaining function of rural dambos, sustaining a year-round larval habitat, and that Culex quinquefasciatus dominance together with peri-urban cattle may form an amplification bridge to humans. Direct evidence remains scarce, anchored by a single peri-urban serosurvey and limited urban slaughterhouse entomology. Critical gaps include urban primary-vector ecology, infection-rate data, urban-heat effects, city-specific exposure studies, and coupled climate–urban burden models. We conclude that RVF is a plausible emerging urban threat warranting proactive inter-epidemic surveillance and integration of RVF into urban planning and One Health systems. Full article

Urbanization can modify the near-surface environment for thunderstorm development, but how megacity surfaces affect the spatial distribution of cloud-to-ground (CG) lightning within urban areas remains unclear. Taking Beijing as a representative megacity, this study uses multi-source observations, including CG lightning flashes, automatic weather station (AWS) observations, radar data, reanalysis, urban land-use classification data, and DEM data, to examine the spatiotemporal characteristics of CG lightning and their relationships with the urban thermal–dynamic environment, synoptic background, and thunderstorm evolution. The results show that 304 thunderstorms passed over or affected the Beijing built-up area, producing 6.96 × 10⁴ CG flashes within the analysis domain. CG lightning exhibited clear interannual variability and reached its diurnal peak from late afternoon to early evening. However, high-density CG flash centers did not persistently occur over the urban core, but were more frequently located near the built-up edge and adjacent transition zones. Urban heat island intensity (UHII) partly corresponded to the diurnal variation in CG lightning, but its interannual and spatial relationships with CG activity were weak. Further analysis suggests that, under a weak background, higher pre-storm UHII and lower wind speed (WS) may help to maintain local convergence and upward motion, which may partly contribute to increased CG lightning within the urban core. In contrast, under the strong synoptic background, bifurcated thunderstorms were associated with lower pre-storm UHII, higher pre-storm WS, and CG lightning concentrated near the urban edge. These findings improve our understanding of how megacity surfaces may modulate thunderstorm-related CG lightning activity and provide observational context for spatially differentiated lightning monitoring within urban areas. Full article

High-precision urban vegetation mapping is essential for assessing carbon sink capacities, mitigating the urban heat island effect, and supporting sustainable development. Although deep learning and high-resolution remote sensing have advanced automated vegetation monitoring, existing models still face challenges when a common segmentation architecture is evaluated under different imaging geometries. In this study, Cityscapes and ISPRS Vaihingen are treated as two independent benchmarks representing perspective street-level imagery and orthographic aerial imagery, rather than as simultaneous cross-view inputs. “Background dominance” caused by perspective distortion and the “gridding artifacts” inherent in orthographic textures severely constrain segmentation accuracy across varying vegetation scales, particularly for small targets. To address these limitations, we propose a Scale-Aware Mixture of Experts (SA-MoE) architecture for fine-grained vegetation segmentation under two distinct imaging views, together with a scene-specific optimization strategy. The core SA-MoE framework consists of two main components. First, the spatial gating network uses a temperature polarization mechanism with τ = 0.5 to adjust the initial logit maps, sharpening expert-weight differences while preserving stable gradient propagation. Second, we use a heterogeneous expert group with five parallel branches: a pixel-level expert, three spatial experts with different dilation rates, and a global average-pooling expert. A dynamic pixel-level weighted fusion mechanism is then applied, decoupling feature extraction from receptive-field allocation. Furthermore, to address the heterogeneity of “hard samples” and “label noise” across the two benchmark settings, we introduce a scene-specific optimization strategy. Our findings show that the Focal-Dice (FD) loss is more suitable for perspective scenes with severe target imbalance and hard-to-classify vegetation targets, whereas the Cross-Entropy (CE) loss is more robust to boundary jitter in orthographic imagery. Comparative experiments on the Cityscapes (perspective view) and ISPRS Vaihingen (orthographic view) datasets reveal that SA-MoE achieves a highly competitive balance between computational efficiency and fine-grained segmentation, particularly in micro-target recall. Notably, the recall for extra-small (XS) scale targets in the aerial dataset improved by 3.21 percentage points compared to the second-best model. For the street-level dataset, our model achieved competitive global performance in terms of Overall Accuracy (OA), Precision, and F1-Score. However, we also observed a performance trade-off, where Transformer-based models maintained an advantage in preserving fine boundary details for these extra-small targets. In the routing analysis, we observed a pattern that we refer to as “receptive field inversion”, in which the model assigns lower weights to large-dilation experts for large canopy regions in orthophotos. We interpret this pattern as a plausible routing hypothesis. Overall, SA-MoE offers an efficient and adaptive solution for urban vegetation mapping under two imaging views. Full article

Most studies have explored the impact of different types of local climate zones (LCZs) on energy consumption, but few have examined climate differences within intra-local climate zones (intra-LCZs) and the impact of cold waves (CW) on energy consumption. This study conducted a 150-day long-term observation of air temperature (Ta) and relative humidity (RH) in six LCZs in Shenyang, a severe cold region, to examine climate differences within intra-LCZs and the influence of CW and the urban heat island (UHI) on energy consumption. The results show that the temperatures and heating energy consumption in 5 LCZ4s exhibited a clear gradient from the urban core to the suburbs. During CW, the differences increased, and the nighttime differences were more pronounced. CW significantly increased residential heating energy consumption, whereas the urban heat island (UHI) reduced it in urban areas. Additionally, there was a clear positive correlation between UHI and the urban-suburban energy consumption difference. Under the combined effects of CW and UHI, the average daily cumulative heating energy consumption in urban areas (CW) was much higher than in suburban areas during non-cold-wave (NCW) periods, increasing by 69.2–85.9%. This study provides empirical evidence for energy conservation and strategies for responding to extreme weather events. Full article

Local climate zones (LCZs) provide a robust framework for understanding Urban Heat Island dynamics and for supporting climate-sensitive urban planning. Although widely adopted since their introduction in 2012, LCZ mapping remains constrained by urban morphology description and spectral separability among built-up classes. This study aims to strengthen the methodology to produce a high-resolution LCZ map by integrating multispectral (Sentinel-2, 10 m spatial resolution) and hyperspectral data (PRISMA, 30 m spatial resolution) with a suite of urban canopy parameters that describe the morphological and surface characteristics of the urban fabric, using a machine learning classification approach at finer spatial resolutions. The proposed approach is tested in the urban area of Bologna, Italy. The digitization of representative training and validation sites—which is one of the key challenges in accurate LCZ mapping, especially for spectrally heterogeneous classes—was conducted in a GIS environment by visual interpretation of high-resolution imagery with the aid of the Technical Map of the Municipality of Bologna. With the aim of strengthening the methodology, the present work tests different outlier-removal techniques on the training data and evaluates their impact on LCZ mapping performance. Finally, the Random Forest classifier was selected, and the workflow was implemented in a Python environment using the scikit-learn library. The results show that the classification achieved overall accuracy values of 0.79 using Sentinel-2 and 0.82 using PRISMA. Overall, the results show that urban morphology parameters are among the most important features. Training-sample refinement helped interpret the effect of sample heterogeneity, but LCZ classification performance was ultimately controlled by feature discriminative power, spatial resolution, and the intrinsic separability of each class. Full article

The urban heat island (UHI) refers to higher air temperatures (T_air) in urban areas than in surrounding rural environments, while the surface urban heat island (SUHI) describes analogous differences in land surface temperature (LST). This study presents a long-term assessment of UHI and SUHI in Modena, Italy, combining meteorological T_air observations with Landsat-derived LST from 188 daytime and 19 nighttime summer scenes (1985–2023). Four indicators—magnitude and range 1 of overall thermal variability and magnitude and range 2 of urban–rural thermal excess—were applied in parallel to LST and T_air to characterize the intensity and spatial variability of thermal conditions within a consistent daytime/nighttime framework. Results indicate significant long-term increases in summer LST, with daytime warming rates of 0.26 °C yr⁻¹ (urban) and 0.27 °C yr⁻¹ (rural). Daytime urban–rural LST differences ranged from 4 to 6 °C; nighttime differences were smaller (1–3 °C). Daytime T_air urban–rural differences were weak and not statistically significant, whereas nighttime T_air showed a clearer urban warming signal. Nighttime LST correlated more closely with T_air (r = 0.49–0.52 across indicators) than daytime LST, and nighttime LST showed strong correlations with T_air in both urban and rural areas (r = 0.96–0.98). Daytime imagery better captures SUHI spatial intensity, whereas nighttime observations provide a more consistent surface-to-atmosphere thermal link, highlighting the value of integrating satellite LST with in situ T_air for integrated UHI and SUHI assessment in medium-sized cities. Full article

As climate change intensifies and urbanization continues, Fuzhou faces growing public health risks from the urban heat island effect. This study develops a Heat Risk Index (HRI) by integrating the Local Climate Zone (LCZ) classification with the Crichton Risk Triangle framework. Hazard was characterized using quality-controlled Landsat LST and the proportion of valid MYD11A2 composites exceeding a citywide P90 LST threshold. Exposure was represented by normalized population density; ln(P + 1) was used only for cartographic classification. Vulnerability incorporated older population, nighttime light, NDVI, and MNDWI. The continuous HRI was calculated as H × E × V, and ln(HRI) was used only for Jenks five-class map presentation. The results show clear spatial differences among LCZ types and counties, with higher risks concentrated in densely built and populated areas of the five urban districts, Changle, and localized county centers. LCZ differences in LST and HRI were statistically significant (both p < 0.001), and HRI showed significant positive spatial autocorrelation. These findings provide a basis for LCZ-specific heat-risk management while acknowledging uncertainty associated with data-year mismatch, spatial resampling, remotely sensed surface temperature, and the absence of independent health-outcome validation. Full article

Extreme heatwaves intensify urban heat islands and pose increasing risks to urban sustainability and human health. However, how urban morphology is associated with daytime and nighttime land surface temperature (LST) across urban functional zones (UFZs), particularly under heatwave conditions, remains insufficiently understood. To address this gap, this study integrates daytime and nighttime LST data derived from SDGSAT-1, multi-dimensional urban morphology indicators, and two interpretable ensemble models (XGBoost and GWRF) to investigate overall sample-level nonlinear model-based associations between urban morphology and LST and to explore spatial variation in local predictor importance within Beijing and Nanjing, China. Because the daytime and nighttime scenes were not always paired within the same heatwave episode, the analysis focuses on selected heatwave-condition observations. The results show marked contrasts between the selected daytime and nighttime observations in UFZ-level thermal patterns. Industrial zones generally exhibited the highest daytime LST, whereas residential zones showed the highest nighttime LST. Building density was identified as the primary model-based predictor of daytime LST in both cities, although its association with LST was nonlinear and varied across density ranges. In contrast, nighttime LST was characterized by more heterogeneous predictor associations, involving vegetation structure, sky openness, building form, anthropogenic indicators, and material-related variables, with their relative importance differing across cities and UFZ types. Local predictor-importance patterns also varied across neighborhoods, cities, and observation times, indicating that model-identified locally important predictors were not spatially uniform within each city. These findings highlight the potential of SDGSAT-1 daytime and nighttime thermal observations and interpretable machine learning for screening candidate local thermal priority areas and key morphology-related factors under heatwave conditions. Full article

Urbanisation and climate change have intensified the Urban Heat Island (UHI) effect in European cities, increasing thermal stress and health risks, especially for vulnerable people. Nature-based Solutions (NbS), such as green infrastructures (GI), offer effective mitigation strategies. A notable example is the Horizon 2020 URBAN GreenUP project in Valladolid, Spain, where a green shade shelter infrastructure was installed and monitored between 2019 and 2022 to evaluate its cooling performance and its effects on temperature-based urban heat indicators through the use of Key Performance Indicators (KPIs). Local thermal conditions were monitored across two adjacent narrow streets—one with the green shade shelter and one as a control. The installation of the green shade shelter in Valladolid produced measurable thermal benefits. During summer peaks, it reduced ambient temperatures by approximately 0.9 °C, with daily maximum temperatures decreasing by 1.0–3.0 °C. In winter, daily minimum temperatures dropped by around 1.0 °C, while autumn saw an increase of 0.7 °C. Summer minimum temperatures varied, ranging from a 0.2 °C decrease in early summer to a 0.7 °C increase in late summer, with a 0.3 °C rise during peak summer in mid-July. The intervention also reduced the frequency and delayed the onset of days exceeding 35 °C and nights above 20 °C, although the delay in peak tropical nights was limited to seven days. These findings indicate that green shade shelters can contribute to improving street-level thermal conditions and reducing temperature-based heat exposure indicators in dense urban environments. Full article

To mitigate the health and environmental risks of urbanisation and the urban heat island effect, urban planning is shifting towards promoting active mobility. The success of these efforts largely depends on understanding people’s thermal comfort on the move, driving research towards the investigation of the complex interdependencies between microclimatic conditions and the real-time experiences of pedestrians and cyclists. This literature review aims to present the state of the art of research at the neighbourhood and street levels by analysing the methodological frameworks employed and the outcomes achieved. The paper adopts a thematic clustering approach, grouping the articles on the basis of their research objectives, methodologies and the relationships between active mobility and comfort. The literature review highlights a shift from static to dynamic comfort assessments and a focus on active travel as a continuous experience rather than the sum of stationary moments. The primary findings include the consolidation of the thermal walk methodology and the emergence of cumulative stress indices. Evidence from heat stress contexts suggests that pedestrians prefer thermal diversity, which causes thermal alliesthesia, over monotonous conditions. The research highlights the role of heat stress as a barrier to walkability in hot and temperate climates, causing route deviations and lower walking speeds, with the opposite pattern in severely cold settings. This review identifies several research gaps, including a lack of standardised dynamic assessment methods, low generalisability and transferability of the results, and insufficient investigation of cyclists’ dynamic comfort compared with that of pedestrians. Full article

Urban areas face increasing challenges related to heat stress and urban heat islands, necessitating effective strategies for microclimate regulation and thermal comfort enhancement. This study investigates the cooling effect of three tree species, Ficus retusa, Ficus benjamina and Fraxinus spp. (Ash trees), on urban microclimates and thermal comfort in downtown Annaba, Algeria, during the summer. The methodology comprises two complementary methods. First, field measurements of air temperature (Ta), relative humidity (RH), and wind speed (Ws) were recorded hourly from 9 a.m. to 9 p.m. Second, collected data was inputted into the ENVI-met microclimate model to simulate various urban canopy scenarios. Microclimate variations, including air temperature (Ta), mean radiant temperature (T_mrt), relative humidity, wind speed, and the Physiological Equivalent Temperature (PET) index, were computed over a 13 h period, from 9 a.m. to 9 p.m., to examine the cooling effect of each species and their impact on outdoor thermal comfort. The results demonstrate the notable cooling effect of Ficus species and Fraxinus spp. with Ficus species generally exhibiting larger reductions in air temperature (0.9 °C to 2 °C) and PET index (7.3 °C to 7.7 °C), and higher increases in relative humidity (5.6% to 6.8%) compared to Ash trees. Full article