Search Results (1,313)

Urban green infrastructure (UGI) serves as a core pillar of sustainable urban development, delivering multiple benefits including urban heat island mitigation, stormwater regulation, and health promotion. However, allergenic pollen released by urban vegetation represents a typical ecosystem disservice, creating a prominent sustainability trade-off between greening benefits and residents’ allergy risk. For a long time, research in this field has evolved along two largely independent lines: vegetation allergenic hazard assessment and atmospheric pollen exposure analysis. Marked disconnections persist in indicator systems, spatial scales, and outcome translation, and an integrated spatial assessment framework remains lacking. Based on a systematic review of 127 publications from the Web of Science Core Collection (2000~2025) using framework synthesis methods, this study constructs a hazard–exposure–risk–planning (HERP) framework for the spatial assessment of allergenic pollen risk in UGI. This paper systematically synthesizes the core indicators, technical approaches and inherent limitations of each framework layer and identifies five key barriers: inconsistent indicator definitions, lack of cross-validation between surface vegetation and atmospheric pollen data, missing population vulnerability layer, insufficient multi-scale coupling, and weak translation of research into planning practice. The proposed framework provides a potential comparable and reproducible standardized pathway for cross-regional allergenic risk assessment and offers methodological support for healthy urban planning that balances ecosystem services and residents’ respiratory health. Full article

Urban heat islands (UHI) intensify as cities expand, exposing residents to elevated thermal stress and complicating urban climate adaptation planning. Existing satellite-based approaches to detecting surface urban heat islands (SUHI) typically rely on a single class of data and narrow temporal windows, limiting their ability to capture the full range of processes driving surface overheating. This study develops and evaluates a random forest model for SUHI detection in Warsaw, Poland, integrating two classical spectral indices (NDVI, NDBI) derived from Landsat 8/9 Collection 2 imagery with three land-cover probability layers (built-up, tree, water) from the Dynamic World deep-learning product, processed in Google Earth Engine. Both a multi-year summer median composite (2020–2025) and individual annual summer composites were used, the latter enabling a leave-one-year-out temporal validation. Heat island pixels were defined as those whose land surface temperature anomaly exceeded +3 °C relative to the study area mean, a local criterion rather than a city-versus-rural contrast. The model achieved high and stable performance (accuracy = 0.831, AUC = 0.910 on the test set; AUC = 0.907 ± 0.004 in five-fold cross-validation and 0.905 ± 0.018 in leave-one-year-out validation). An ablation analysis showed that combining the probability layers with the spectral indices clearly outperformed the indices alone (AUC = 0.852 vs. 0.905), whereas the additional gain over the Dynamic World layers alone remained within uncertainty. Vegetation-related predictors (NDVI and tree probability) contributed more to classification than built-up indicators. These results indicate that vegetation deficit, rather than built-up presence alone, is the primary driver of surface overheating in Warsaw and that the proposed open-data workflow offers municipalities a low-cost screening tool for identifying priority areas for climate adaptation and, thanks to its reliance solely on open data, can be adapted to other cities, subject to further validation. Full article

Warsaw experiences significant urban heat island (UHI) effects driven by low-albedo surfaces and urban geometry, which pose ongoing challenges for public health and climate adaptation. This study evaluates daytime SUHI intensity at satellite acquisition time across the entire city to provide a comparison of two acquisition dates during heatwaves. Utilizing Landsat 7 and Landsat 9 satellite imagery from July 2015 and July 2022, the research compares six distinct SUHII calculation methods, including spectral indices, statistical normalizations, and area-based temperature differences, as minimum SUHII values differed significantly between the two observations (shifting from approximately −13.8 °C to −7.7 °C). This indicates that suburban areas can become thermally similar to the city due to rapid land conversion and decreased evaporative cooling of vegetation during severe heat. Average intensities calculated via the SUHII 4 method reached 5.80 °C in 2015 and 2.07 °C in 2022. Under the criteria considered in this case study—interpretability, explicit physical units, treatment of water bodies, data requirements, and spatial consistency—SUHII 4 was the most suitable of the six tested formulations for the Warsaw analysis. Conversely, dimensionless spectral indices and purely statistical approaches are not recommended due to interpretative limitations. Full article

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

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

Rapid urbanization is reshaping land surface conditions and local thermal environments in fast-growing coastal cities. This study examines how Land Use Land Cover (LULC) transformation influenced seasonal land surface temperature (LST) dynamics in Chattogram City Corporation (CCC), Bangladesh, over 2004–2024. Multi-temporal Landsat imagery was analyzed using a Random Forest classifier, and spectral indices (NDVI, NDBI, NDBaI, MNDWI) were derived to characterize surface biophysical conditions. Built-up land expanded by 27.71 km², largely replacing agricultural and vegetated areas. Summer mean LST rose from 36.08 °C to 36.50 °C, while winter LST rose from 25.25 °C to 26.97 °C. Only the winter warming trend is statistically significant; the summer change falls within the ±1–2 °C retrieval uncertainty of Landsat-derived LST. The summer–winter thermal gap consequently narrowed from 10.83 °C to 9.53 °C, indicating that urbanization-driven warming in this tropical coastal city is disproportionately concentrated in the cool dry season. Partial correlation and multiple regression analyses confirm that built-up intensity (NDBI) is the dominant driver of surface warming, while vegetation (NDVI) exerts a consistent cooling influence. Water bodies showed contrasting seasonal trends, with winter extent declining alongside a slight summer increase. These findings highlight the critical role of vegetation and water bodies in moderating urban heat and provide data-driven insights for climate-responsive planning in rapidly urbanizing coastal cities. Full article

Urban Heat Island (UHI) effects are increasingly acknowledged as a critical urban climate challenge with far-reaching consequences for human health and overall well-being. However, the conceptual structure, temporal evolution, and intellectual landscape of studies examining the relationships between UHI and human well-being remain fragmented, thereby constraining the development of integrated knowledge frameworks needed to guide future research, urban adaptation strategies, and evidence-based policy interventions. To this end, a total of 4857 studies were retrieved from the Scopus and Web of Science databases and screened following the PRISMA guidelines. These studies were then analyzed using Bibliometrix and VOSviewer. The results reveal a rapid and exponential growth in scientific output, particularly after 2010, with the output reaching its highest level in recent years. These outputs were shaped mostly in China and the United States with a well-established international collaboration network, while the Global South remain significantly underrepresented in scientific productions. We also found that studies are primarily structured around four dominant research clusters: urban heat island, thermal comfort, land surface temperature, and climate change. Furthermore, early studies predominantly focused on urban surface properties and built-environment characteristics, while recent research has increasingly shifted toward human health impacts, thermal stress, heat vulnerability, and well-being. Emerging research directions further highlight growing interest in nature-based solutions for mitigating UHI effects, alongside the application of advanced technologies such as machine learning and remote sensing for high-resolution urban climate assessment. Overall, our findings indicate a transition toward a more integrated urban climate–health–well-being research framework, while simultaneously revealing persistent geographical and conceptual gaps, particularly across the Global South. We therefore advocate for increased empirical research, stronger international collaboration, and context-specific urban adaptation strategies to better safeguard human well-being under intensifying urban heat conditions. Full article