Search Results (930)

Rapid urbanization in South Asian coastal cities is systematically dismantling natural cooling infrastructure, driving unprecedented urban heat island (UHI) intensification with severe consequences for human health, energy systems, and urban livability. Despite growing research attention, comprehensive frameworks that simultaneously capture temporal UHI dynamics, machine learning-based thermal projections, and community-grounded validation remain scarce, particularly for secondary coastal cities in tropical developing regions. This study addresses these gaps by investigating UHI dynamics in Chattogram City Corporation (CCC), Bangladesh, through three integrated methodological pillars: (1) multi-temporal remote sensing analysis using Landsat 5 and 8 imagery spanning 2005–2025; (2) comparative evaluation of five machine learning algorithms (LightGBM, Random Forest, XGBoost, SVM, and MLP) for land use/land cover (LULC) classification and land surface temperature (LST) regression, with iterative scenario projections for 2029, 2033, and 2037; and (3) a structured public perception survey of 384 residents validated through participatory mapping and focus group discussions. Landsat analysis revealed dramatic LULC transformations: built-up areas expanded 88% (12,649 to 23,719 acres), while waterbodies declined 53.1% and vegetation decreased 21.9%. Mean LST increased by 9.09 °C (from 30.94 °C to 40.03 °C), with mean UHI intensity rising from 19.59 to 33.88 standardized units over two decades. LightGBM achieved optimal LULC classification (F1-weighted: 0.765) while Random Forest best predicted LST (RMSE: 1.51, R²: 0.809). Projections indicate continued thermal escalation, with mean LST reaching 43.64 °C and UHI intensity exceeding 37.41 standardized units by 2037. Persistent thermal hotspots were identified in the southwestern coastal corridor, western industrial belt, and central business district. Community survey data corroborated satellite-derived patterns, with 73.44% of respondents observing environmental degradation, yet only 22% aware of formal heat mitigation policies, and 87% supporting vegetation-based cooling interventions. This integrated framework advances urban thermal monitoring in tropical coastal cities and provides spatially targeted, community-endorsed evidence for climate-responsive urban planning. 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

As a critical component of urban ecological infrastructure, urban forests play a pivotal role in regulating regional climate and mitigating the urban heat island (UHI) effect. However, existing studies have predominantly focused on single temporal snapshots or aggregate spatial scales, with limited attention to the seasonal dynamics of urban forest landscape patterns and a lack of systematic quantification of their nonlinear regulatory mechanisms. Empirical evidence from subtropical cities remains particularly scarce. In this study, Hangzhou was selected as the study area. Land Surface Temperature (LST) was retrieved using the Google Earth Engine (GEE) platform, and the Thermal Field Variance Index was employed to classify UHI intensity. Six representative forest landscape indices were selected to construct an evaluation framework. Pearson correlation analysis and the XGBoost model were further applied to quantify the relationships between landscape patterns and seasonal LST variations. The results reveal that: (1) LST in Hangzhou exhibits pronounced seasonal variability, following the order of summer > spring > autumn > winter. Areas without UHI effects dominate in spring, summer, and autumn, whereas the extent of strong UHI zones increases markedly in winter. (2) All landscape indices are significantly correlated with seasonal LST; forest ratio and forest largest patch index show negative correlations, while forest patch density, forest landscape shape index, number of patches, and landscape division index (DIVISION) are positively correlated. (3) The XGBoost model indicates that DIVISION consistently exhibits high contribution across all seasons, identifying it as a key determinant of LST variation. These findings provide a scientific basis for optimizing urban forest landscape configuration and developing effective UHI mitigation strategies. Full article

In evaluating energy consumption in building complexes, the influence of urban microclimate variations—primarily driven by the urban heat island (UHI) effect—is often overlooked, leading to modeling inaccuracies. This study develops a numerical simulation framework integrating Weather Research and Forecasting (WRF) and EnergyPlus to assess the energy consumption of university dormitories while accounting for regional microclimate conditions. This is because university dormitories serve as a key indicator for measuring the type of high-density residential buildings in China. The model incorporates dynamic microclimate variables, including ambient temperature, relative humidity, wind speed, solar radiation, and cloud cover, to simulate dormitory energy consumption profiles. Simulation results are validated against measured data, yielding an annual energy consumption error of −1.03%. Quantitative analysis indicates that ignoring the microclimate effect and directly using data from nearby meteorological stations or TMY data has a limited impact on the annual total energy consumption but has a significant impact on seasonal results. To improve the simulation accuracy of building complexes, more attention should be paid to temperature and relative humidity. Moreover, for areas with low occupant density and a high shape coefficient, energy consumption simulation should also consider the local microclimate factors. Full article

It is commonly accepted that vegetation plays an important role in climatic studies conducted at local, national, and international scales. The aim of this study is to examine the cooling effects of tree species in the cities and to reveal how they affect the microclimate in İzzetpaşa Neighborhood of Elazığ province of Turkiye. This study, which was conducted by purchasing ENVI-met 5.6.1 microclimate software, aimed to create the most appropriate microclimate scenarios in order to mitigate the urban heat island (UHI). Among the nine scenarios in which different tree species were used; the greatest cooling effect was obtained from the scenario where Acer platanoides L. was used. It was determined that the air temperature dropped by 0.8 °C compared to the base scenario and by 3.0 °C compared to the scenario in which a tree cover was not used. The lowest cooling effect was detected in the scenarios where Pinus sylvestris L. and Abies cilicica Carr. were used. In general, it was observed that there was no significant temperature decrease in the scenarios where coniferous trees were used. In scenarios where deciduous trees were used, more temperature decreases were detected compared to the coniferous trees. According to the winter simulation results of these scenarios, the daily average air temperature values vary between −0.6 and +0.1 °C compared to the base scenario. In the scenario where Acer platanoides L. was used, where the highest cooling effect was observed, the highest relative humidity rate and the lowest Tmrt value were determined. Evaluating the cooling effect of high vegetation on a species basis in reducing the UHI effect as a basis for planning in urban areas will constitute a key strategy in improving the UHI effect. It is envisaged that this study may provide a solution to help reduce the UHI in studies to be carried out in urban areas. Full article

Urban–climate interactions in a warming climate remain largely uncertain; therefore, it is crucial to realistically evaluate and project these feedbacks to establish effective adaptation strategies. This study investigates projected shifts in summertime urban–climate interactions over eastern North America by employing the GEM regional climate model coupled with the Town Energy Balance (TEB) scheme, driven by RCP4.5 and RCP8.5 scenarios for the 1981–2100 period. Evaluations for the current climate (1981–2010) demonstrate that the model simulates an urban-induced warming of 0.5–0.7 °C and a precipitation reduction of 0.2–0.4 mm/day with high fidelity. By the late 21st century (2071–2100), projections under the RCP8.5 scenario indicate a steady weakening of the summer mean Urban Heat Island (UHI) intensity by approximately 0.10 °C, with a more pronounced nighttime attenuation of 0.15 °C. Physically, this weakening is attributed to an enhanced urban-induced evaporative fraction, which limits solar radiation storage within the urban fabric during the day, thereby reducing the thermal energy available for post-sunset release. This UHI attenuation correlates strongly with localized increases in precipitation, particularly in coastal regions where urban-induced effects contribute 20–40% to the total precipitation rise. While this study intentionally utilizes static urban boundaries to isolate the specific sensitivities of current urban morphologies to global warming, these results emphasize that diverse climatological regions will undergo distinct urban–climate feedback changes, providing essential baseline data for resilient urban planning. Full article

Rapid urbanization and the decline of inner-city areas have led to a sharp increase in vacant houses in large cities. Cities are increasingly converting vacant land into green space to mitigate associated negative externalities. This study quantifies the urban heat island (UHI) mitigation effects of green infrastructure using meta-analysis and applies the derived relationships to predict both on-site and surrounding cooling effects for vacant land. First, we conducted a meta-analysis of published studies reporting the cooling effects of green infrastructure and derived regression equations relating green-space area to (i) cooling within the green space, (ii) cooling in the surrounding area, and (iii) the spatial extent of the cooling effect. Second, we applied these equations to two high-density areas in Sungui-dong, Nam-gu, Incheon, Republic of Korea. The results suggest that introducing a neighborhood park at Site A (7559.5 m²) would reduce air temperature by up to 2.751 °C within the park and by 1.507 °C up to 62 m beyond the park boundary. A pocket park at Site C (992.1 m²) would reduce air temperature by up to 2.269 °C within the park and by approximately 0.92 °C in the surrounding area. These findings provide quantitative evidence that green infrastructure can serve as an effective environmental intervention and support the adoption of climate-responsive urban regeneration policies. Full article

This study investigates the impact of the Urban Heat Island (UHI) effect on building energy performance and the optimization of passive design strategies in six Moroccan climate zones: Agadir, Tangier, Fez, Ifrane, Marrakech, and Errachidia. A computer simulation approach combined with multi-objective optimization using the NSGA-II algorithm was employed to improve energy efficiency while maintaining thermal comfort for a single-family house. The optimum solutions include several passive design parameters, such as insulation materials and thickness, glazing types, window-to-wall ratio (WWR), ventilation rates, shading devices, building orientation, and heating and cooling set point temperatures. The analysis was studied under both standard climate data and UHI scenarios to evaluate the impact of increased urban temperatures on building performance. The results show that under standard climate conditions, the optimal design can achieve up to 76% energy savings throughout all the climate zones, while Marrakech can save 67% and Errachidia 64%; however, under UHI scenarios, these energy savings dropped by 8–30% depending on the climate zone. For example, Agadir drops from 76% to 49% under a 5°C UHI scenario, and Marrakech drops from 67% to 56% under a 3.5 °C UHI scenario, highlighting the significant impact of urban overheating on buildings. These findings emphasize that integrating the UHI effect is essential for accurately assessing passive design performance and for ensuring that selected design solutions truly minimize energy consumption under realistic urban conditions, while also underscoring the importance of integrating passive design strategies into residential buildings. These strategies promote sustainable building practices in Morocco by reducing energy consumption and improving occupant thermal comfort. Full article

Climate change exerts growing pressure on the built environment, intensifying urban heat stress, altering microclimatic conditions, and increasing energy demand and health risks. Urban areas, characterized by dense construction and extensive soil sealing, are particularly susceptible to thermal anomalies such as Urban Heat Islands (UHIs), making thermal assessment a crucial element in adaptation and mitigation strategies. This research provides an updated and critical review of methodologies for the thermal evaluation of the built environment, with a focus on remote sensing as an emerging and integrative measurement paradigm. The study presents a comprehensive framework of detection systems, including satellite and aerial remote sensing, ground-based monitoring, and hybrid approaches, complemented by analytical and modeling techniques that combine physical and data-driven methods. A comparative assessment of open-access satellite sensors is carried out, analyzing spatial, spectral, and temporal resolutions and their relevance to urban-scale applications. The integration of remote sensing data with artificial intelligence, machine learning, and cloud-based processing is highlighted as a key advancement for improving interpretative, predictive, and decision-support capabilities. The findings indicate that such integration represents a new frontier for multiscale thermal analysis, supporting resilient urban planning, enhanced energy efficiency, and effective climate change mitigation policies. Full article

With global warming and accelerated urbanization, building air-conditioning (AC) releases more heat into the environment, exacerbating the urban heat island (UHI) effects and increasing building cooling energy consumption. Existing research has limited quantification of the impact of air-conditioning anthropogenic heat (ACAH) on the cooling energy consumption of different types. This study aims to explore the distribution characteristics of ACAH and its impact on residential building energy consumption. Firstly, typical residential buildings in the Pearl River Delta region were selected as a case study. Field experiments were conducted to measure temperature and humidity at 0.5 m, 1 m, 2 m, and 3 m from the outdoor unit, alongside ambient temperature and wind speed. Three grid densities were applied to verify the CFD model, with a prediction error of less than 0.3 °C at 0.5 m under a medium grid. The simulated temperature at 1 m from the outdoor unit under calm wind conditions was compared with field measurements to reveal the horizontal and vertical distribution characteristics of ACAH. Secondly, the effects of different building shapes, ambient temperatures, and wind speeds on the spatial distribution of ACAH were investigated. Finally, EnergyPlus (V23.1.0) was employed as the building energy simulation software, with its microclimate coupling interface implemented via Python scripts to quantify cooling energy consumption variations across different building floors under ACAH influence. Results indicated that ACAH exhibits significant horizontal non-uniformity, exerting the greatest impact within a 0.5 m radius (affected air temperature 4.3 °C higher than ambient). Vertically, localized heat accumulation occurs in the building’s central area, with air temperature 3.5 °C higher than at the bottom. Furthermore, compared to fixed meteorological conditions, the cooling energy consumption difference across floors considering ACAH reaches approximately 7.8%. This study provides accurate meteorological boundary conditions for building energy assessment and supports microclimate management in residential areas. Full article

This study investigates microclimatic variation across four environmentally and socially vulnerable neighborhoods in Philadelphia, utilizing ground-based measurements to assess urban heat (UH) and heat stress (HS). HS metrics, specifically Wet-Bulb Globe Temperature (WBGT) and heat index (HI), were calculated from UH measurements, including dry bulb and globe temperature, relative humidity, and wind speed. The methodology incorporates statistical modeling to identify significant predictors of HS, with street orientation (north–south and east–west) emerging as a key determinant, while categorical shade conditions were not statistically significant. Notably, Kingsessing exhibited lower HS and a unique humidity profile, whereas temperatures in Point Breeze and Grays Ferry and Hunting Park were consistently elevated. The research demonstrates that neighborhood-scale measurements can reveal critical spatial differences in UH and HS that are helpful in customizing mitigation strategies to specific communities. The approach is adaptable for integration with public health and emergency response initiatives, supporting data-driven decision-making for local governments and community-based organizations. Although assessment of physiological metrics and sampling during peak heat periods were not possible, overall, the study provides a practical framework for addressing urban heat vulnerability and underscores the importance of context-specific, community-engaged solutions to protect at-risk populations from extreme heat impacts. Full article

Utilizing MODIS LST data from 2003 to 2024, in conjunction with multi-source remote sensing data including DEM, land use, NDVI, and nighttime lights, this study conducts a remote sensing quantitative assessment and spatiotemporal characteristic analysis of the urban heat island (UHI) effect in Yinchuan City. An improved urban-rural dichotomy approach was adopted to select rural background areas, and elevation correction of land surface temperature was performed based on the zonal ordinary least squares (OLS) regression to eliminate systematic errors caused by topographic differences. The results show that: (1) From 2003 to 2024, the overall intensity of the UHI in Yinchuan City showed a slight downward trend, while the UHI area continued to expand, presenting the characteristics of “decreasing intensity and expanding scope”; (2) The UHI exhibited concentrated and contiguous distribution in summer, and the cold island phenomenon was significant in winter, reflecting the typical seasonal contrast between summer and winter; (3) The global Moran’s I value increased from 0.39 to 0.82, indicating a significant enhancement in the spatial agglomeration of the UHI; (4) The standard deviation ellipse analysis revealed that the centroid of the UHI migrated toward the westward as a whole, which was consistent with the main axis of urban construction. The research results reveal the long-term evolution law and spatial pattern characteristics of the UHI effect in Yinchuan City, and provide a scientific reference for ecological planning and thermal environment regulation of cities in arid regions. These findings enhance the understanding of long-term urban thermal environment dynamics and provide important scientific support for sustainable urban planning, climate adaptation, and ecological management in arid regions. The study contributes to the quantitative monitoring of urban environmental sustainability and supports sustainable development goals related to climate action and sustainable cities. Full article

Urban Heat Islands (UHIs) are widely analyzed using Land Surface Temperature (LST), yet most studies remain limited to single cities, rely on a single machine-learning model, analyze LST alone, and use inconsistent Surface Urban Heat Island Intensity (SUHII) definitions, which restrict cross-city comparability and broader generalization. This study introduces an explainable artificial intelligence (XAI) framework implemented in Google Earth Engine (GEE) to analyze census-tract summer surface heat (2018–2024) across eight climatically contrasting U.S. cities. The main novelty is a standardized tract-scale cross-city framework that jointly models LST and SUHII using a consistent SUHII definition, a common physical predictor set, city-held-out nested cross-validation, and SHAP-based interpretation, allowing absolute surface heat to be distinguished from relative within-city heat anomaly; this combination is rarely implemented within a single urban heat study. Multiple machine-learning models were evaluated, with ensemble trees performing best: Extreme Gradient Boosting (XGBoost) best predicted SUHII (R² = 0.879; RMSE = 0.213), while Extra Trees best predicted LST (R² = 0.908; RMSE = 0.745 °C). SHapley Additive exPlanations (SHAP) indicate that SUHII is driven primarily by impervious surface fraction and surface moisture availability, whereas LST is structured by latitude and mean summer air temperature. Overall, the framework provides interpretable multi-city attribution of urban surface heat drivers with demonstrated cross-city generalization. Full article

High-density cities face dual challenges of aging populations and climate change, driving widespread renewal of aging residential communities. Current practices, however, often treat sustainability goals (e.g., energy efficiency, carbon reduction) and age-friendly design objectives (e.g., accessibility, social inclusion), often guided by frameworks like the World Health Organization’s (WHO) age-friendly cities initiative, as separate or conflicting agendas, leading to fragmented policies and suboptimal outcomes. This study addresses this gap by proposing and testing a framework for “Sustainable-Age-friendly Coordinated Renewal” (SACR). Through a mixed-methods case study of a typical old community in the humid subtropical city of Guangzhou, China, we investigate how green infrastructure and low-carbon interventions can be synergistically designed to enhance both environmental performance and the well-being of elderly residents. A “Coordinated Renewal Strategy Package” was developed, incorporating ecological shading, sponge city facilities, energy retrofits, and accessible slow-traffic systems. Post-intervention simulation and evaluation indicated significant improvements in microclimate (e.g., reduced mean radiant temperature and Physiological Equivalent Temperature (PET)) and marked increases in outdoor activity duration and social interaction frequency among elderly residents. This study concludes that a human-centric, needs-based design approach is key to unlocking synergistic benefits. The proposed SACR framework and evaluation matrix offer a practical tool for urban planners, architects, and policymakers to holistically assess and implement community renewal projects, contributing to more resilient, inclusive, and sustainable urban futures by addressing localized challenges like the Urban Heat Island (UHI) effect. Full article

This study examines land surface temperature (LST) variability over Malta, a small island in the central Mediterranean, using satellite observations compared with in situ near-surface air temperature (NSAT) measurements. The analysis focuses on the comparison between satellite-derived LST and local atmospheric thermal conditions for urban and rural land cover types. LST data from Landsat-8, MODIS (Terra and Aqua), and Sentinel-3A and 3B were analysed over a six-month period (September 2024 to February 2025). Monthly morning and evening field campaigns were conducted at 19 monitoring sites distributed across the island, during which NSAT, relative humidity, wind speed, and wind direction were recorded. Morning comparisons showed strong correlations between satellite-derived LST and in situ NSAT, i.e., Pearson’s correlation coefficient, r, in the range of 0.82–0.85. Landsat-8 exhibited a slight positive bias (+1.04 °C), while MODIS and Sentinel-3 Level-2 products showed negative biases (−3.82 °C and −1.89 °C, respectively). Nighttime comparisons revealed larger negative biases for MODIS (−6.91 °C) and Sentinel-3 (−6.89 °C). After empirical-based harmonisation, these discrepancies were reduced to near-zero mean bias, maintaining strong correlations. Spatial analysis indicated a persistent nocturnal urban heat island (UHI) effect, with urban areas retaining more heat than rural zones. Morning patterns showed seasonal modulation: during late summer and early autumn, rural areas exhibited higher surface temperatures due to sparse vegetation and exposed soils, whereas during cooler months the urban signal became more pronounced as vegetation recovery enhanced rural cooling. Overall, the results demonstrate the usefulness of multi-sensor satellite observations, interpreted alongside ground-based measurements for characterising thermal behaviour in small island environments. Full article