Search Results (713)

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

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

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

Uneven urbanization can lead to significant quantities of vacant and abandoned land while exacerbating urban heat island (UHI) effects and simultaneously adversely affecting socioeconomically disadvantaged communities. This study examines the correlation between land surface temperature (LST) and urban vacant and abandoned land in socially vulnerable neighborhoods in Houston, TX, USA, where extreme heat can present significant environmental and public health challenges. Six critical study locations exhibiting a social vulnerability index (SVI) over 0.7 and average land surface temperature (LST) values surpassing 82 °F (27.8 °C) are analyzed through spatial analytics and drone footage. Findings indicate that vegetated vacant spaces help mitigate urban heat by decreasing land surface temperature, but abandoned structures exacerbate temperatures due to heat retention from non-permeable surfaces. Findings suggest that elevated socioeconomic vulnerability correlates with increased land surface temperature, exacerbating heat-related hazards in at-risk communities. In this six-site sample, the abandonment rate exhibited a positive correlation with the site mean land surface temperature (exploratory linear fit: +2.42 °F [0.74, 4.11]/+1.35 °C [0.41, 2.28] per +1% increase in abandonment; to be interpreted as exploratory and potentially confounded). Results provide critical insights for climate resilience planning and urban heat reduction through high-resolution thermal and geographical analysis, highlighting the impact of vacant and abandoned land on LST. Such findings endorse certain urban cooling techniques, including land reutilization and green infrastructure, to enhance environmental equality and adaptation. Full article

Understanding vegetation-mediated mitigation of urban heat islands (UHI) is essential for sustainable urban adaptation strategies. Although vegetation responses to extreme heat events have been widely explored using satellite remote sensing and statistical methods, evidence remains limited regarding how these responses vary along urban–rural gradients, particularly in terms of resistance and recovery dynamics. This study focuses on the North Tianshan Slope Urban Agglomeration (TNSUA) in Xinjiang, China. Based on Enhanced Vegetation Index (EVI) data from 2000 to 2022, an urban–rural gradient was delineated using impervious surface fraction. Vegetation resistance and recovery during extreme heat events were quantified to reveal spatiotemporal response patterns. Generalized additive models (GAMs) and Random Forest (RF) models were applied to identify key driving factors and to evaluate their relative importance across multiple spatial scales. The results indicate that rural land cover along the gradient provides a strong cooling effect, particularly in areas with an urban development intensity (UDI) of 70–85%. Vegetation responses show pronounced seasonal differences, with urban vegetation generally exhibiting lower resistance and recovery than rural vegetation. At the county scale, local UHI intensity is the dominant driver of vegetation responses, whereas at the pixel scale, precipitation and vapor pressure deficit (VPD) play the most critical roles. Overall, this study improves the understanding of vegetation responses to extreme heat events in arid regions and provides scientific support for nature-based urban heat adaptation strategies. Full article

Rapid housing expansion exacerbates the urban heat island (UHI) effect, yet the influence of household-level awareness on sustainable housing decisions remains underexplored, particularly in tropical contexts. This study integrates the Theory of Planned Behavior (TPB) into a moderated-mediation model to examine how UHI awareness shapes the relationships among attitude, subjective norms, perceived behavioral control, socioeconomic factors, purchase intention, and willingness to pay (WTP) for heat-mitigating housing. Survey data from 441 homebuyers in Bandung City, Indonesia, were analyzed using partial least squares structural equation modeling (SEM). Results reveal that awareness fundamentally alters decision pathways: without awareness, subjective norms (β = 0.066, p-value = 0.007) and perceived behavioral control (β = 0.050, p-value = 0.005) significantly influence WTP via purchase intention; with high awareness, attitude becomes the sole significant predictor (β = 0.109, p-value = 0.035), while the effects of social pressure (β = −0.015, p-value = 0.130) and perceived control (β = −0.005, p-value = 0.376) diminish. The model explains 50.1% of the variance in purchase intention (R² = 0.501) but only 14.7% of the variance in WTP (R² = 0.147), reflecting the low-price premiums respondents are willing to pay (0–5%). These findings highlight that climate-specific awareness acts as a cognitive filter, guiding pro-environmental housing choices, and underscore the importance of awareness-driven interventions for promoting sustainable urban development in tropical cities. Full article

Urban areas in arid environments are increasingly affected by the urban heat island (UHI) effect, which intensifies thermal stress, disrupts ecological balance, and poses challenges for sustainable urban development. Understanding and predicting spatiotemporal variations in land surface temperature (LST) and land use dynamics is therefore critical for effective urban planning. This study develops a predictive framework for Riyadh, Saudi Arabia, using long-term Landsat time series data (1993–2023) and deep learning models to evaluate urban thermal patterns via the Urban Thermal Field Variation Index (UTFVI). Artificial Neural Networks (ANNs) with six hidden layers for LST and seven for UTFVI forecast future trends up to 2043. The results indicate that urban areas expanded by 521.62 km², increasing from 8.73% to 19.56% between 1993 and 2023, and are projected to reach 1509.40 km² (25.28%) by 2043, while vegetation coverage declined from 0.771% to 0.674%. The highest average summer LST increased from 56.73 °C in 1993 to 59.89 °C in 2023 and is predicted to rise to 60.79 °C by 2033 and 61.52 °C by 2043. Winter temperatures exhibited a comparable upward trend, rising from 30.75 °C to 32.33 °C in 2023 and projected to reach 34.48 °C by 2043. UTFVI analysis revealed a substantial expansion of weak thermal field zones, which covered 2778 km² in 2023 and are expected to reach 3018.44 km² (57%) by winter 2043, accompanied by a marked contraction of strong thermal field areas. The ANN models achieved a high predictive performance, with RMSE values of 0.759 (summer) and 0.789 (winter) for UTFVI and correlation coefficients of 0.91 and 0.89, respectively. Projections further indicate that, by 2043, approximately 39.31% of the study area will experience summer temperatures between 48 °C and 53 °C, compared to 5.59% in 2023. These findings highlight the accelerating interaction between urban growth and thermal intensification in arid cities. The proposed modeling framework provides a robust decision-support tool for urban planners and policymakers to mitigate UHI impacts and promote climate-resilient and sustainable urban development. Full article

Urban heat islands (UHIs) pose escalating threats to public health and thermal comfort in dense urban environments. However, physics-based evaluations of material-specific cooling interventions and their integration into operational digital twin platforms remain limited. This study develops an integrated framework connecting computational fluid dynamics (CFD) modeling with digital twin visualization to evaluate UHI mitigation strategies. The objectives are to quantify the thermal mitigation effects of surface emissivity optimization on land surface temperature (LST) and pedestrian-level air temperature ($T_{air}$) to establish a data preprocessing pipeline converting CFD outputs into platform-independent visualization datasets, and to comparatively evaluate 2D GIS-based and 3D voxelization visualization approaches. Four emissivity scenarios were simulated using STAR-CCM+ for a 4 km² residential area in Gwacheon City, Republic of Korea. Comprehensive optimization (Case D) reduced the mean LST from 46.6 °C to 42.0 °C and T_air from 35.7 °C to 35.3 °C. Concrete-only optimization achieved 90.5% of the total thermal reduction while decreasing spatial variability (σ) from 7.1 to 5.8 during peak hours. The voxel-based 3D visualization provided a superior representation of vertical thermal stratification compared to 2D mapping. These findings establish a scalable foundation for climate-responsive urban management. Full article

Since 2025, 45% of the world’s population of 8.2 billion people has lived in cities, and by 2050, that number is expected to increase to 66%. As the number of people living in cities increases, natural landscapes will be transformed into impervious surfaces, leading to serious challenges and resulting in a phenomenon named the urban heat island (UHI) effect. Although urban thermal variation has been studied globally, few studies have examined the impact of land use transitions on local surface temperatures. This study aims to address this gap by investigating the impact of LULC transitions on the land surface temperature (LST) and the urban thermal field variation index (UTFVI) in the five most populated cities in Saudi Arabia between 2000 and 2024: Riyadh, Jeddah, Makkah, Madinah, and Dammam. This study provides not only a comprehensive overview of the cities in Saudi Arabia but also a detailed analysis of each city using a novel approach that integrates thermal land use analysis. In this study, Landsat TM-5, OLI-TIRS-8, and OLI2-TIRS2-9 were used to process the LULC using random forest machine learning and thermal indices. Fifteen LULC maps were generated and assessed based on four classifications across the cities and time periods: urban area, barren land, vegetation, and water. The difference-in-difference (DiD) analytical approach was used to compute the thermal effect size and compare the specified changed pixels (barren-to-urban, vegetation-to-urban) with stable urban. Then, the relationship between the LST and the NDVI–NDBI were investigated. The results show that the overall accuracy of the 15 LULC classifications ranged from 89.00% to 97.00%. The urban area increased across all the cities, with the greatest changes being 448.84, 179.67, 177.96, 126.33, and 95.69 km² in Riyadh, Jeddah, Dammam, Madinah, and Makkah, respectively. Furthermore, the vegetation cover increased in most of the cities over time. The LST of the urban areas increased by 8.31 °C in Riyadh, 5.24 °C in Jeddah, and 1.41 °C in Makkah in 2024 compared to 2000, while those in Dammam and Madinah decreased by 2.67 °C and 0.60 °C, respectively. This study delivers robust insights into two decades of urban surface temperature dynamics across major Saudi Arabian cities, offering critical evidence to inform UHI mitigation strategies and support the long-term sustainability of urban environments. Full article

The Urban Heat Island (UHI) effect represents a major barrier to sustainable urban development, amplifying energy demand, public health risks, and climate vulnerability. This study provides an advanced geospatial assessment of UHI dynamics in Southampton, UK, using Landsat 8 and 9 imagery (2017–2023) to evaluate seasonal and interannual variations relevant to climate-resilient urban planning. This study integrates spatial techniques, including Land Surface Temperature estimation, NDVI-based emissivity modelling, hotspot analysis, and urban–rural gradient profiling, to identify persistent UHI hotspots concentrated in high-density commercial and industrial zones, with intensities reaching 2–3 °C above the citywide mean. It combines seasonal UHI mapping, hotspot analysis, and urban–rural gradient profiling to provide a comprehensive assessment of Southampton’s thermal landscape. The findings reveal persistent UHI hotspots in the city centre and industrial zones, with intensity peaks of 2–3 °C above the mean. Temporal analysis reveals winter-intensified UHI patterns, consistent with climate-sensitive processes observed in temperate coastal environments. Green spaces demonstrate measurable cooling benefits (up to ~1 °C), underscoring their role as sustainable nature-based mitigation strategies. By delivering a replicable, data-driven framework for continuous environmental monitoring, the research directly supports sustainable urban design, targeted greening interventions, and climate-adaptation policies. The findings provide practical tools for reducing heat stress, enhancing energy efficiency, and strengthening long-term urban resilience in medium-sized coastal cities. Full article

Rapid urbanization has significantly exacerbated the Urban Heat Island (UHI) effect in high-density megacities, driven by the intensifying competition between built-up morphology and natural cooling infrastructure. Current research, however, often fails to accurately predict land surface temperatures (LST) because traditional models frequently overlook the complex spatial dependencies and neighborhood spillover effects inherent in urban environments. Existing studies often ignore the spatial dependence of heat transfer. This study proposes an explainable machine learning framework incorporating spatial lag variables to capture the thermal spillover from adjacent neighborhood context—such as green space cooling diffusion or built-up heat accumulation—which is frequently treated as noise in traditional models. Taking Shenzhen as a case study, we integrated multi-source data (Landsat 8, building vectors, DEM) and developed an XGBoost regression model (R² = 0.806) augmented with SHAP (Shapley Additive exPlanations) to quantify the contributions of local and contextual features. The results revealed that: (1) Non-linear Thresholds: Vegetation cooling exhibits a saturation effect, with the highest marginal benefit observed in the NDVI range of 0.2–0.4, while building warming effects converge at extremely high densities due to mutual shading; (2) Neighborhood Spillovers: Spatial interaction analysis confirms significant cool island synergy (where clustered green spaces provide amplified cooling) and heat island agglomeration effects—e.g., green spaces surrounded by high ecological backgrounds provide amplified cooling benefits; (3) Spatial Antagonism: A novel Interaction Balance Index (IBI) based on game-theoretic SHAP contributions was constructed to map the source-sink competition patterns, identifying distinct heat-dominated (West) and cool-dominated (East) zones. Unlike traditional area-weighted source-sink landscape metrics, IBI enables a pixel-level additive decomposition of warming and cooling factors, quantifying the net thermal outcome of local morphology and neighborhood spillover. By explicitly encoding spatial context into non-linear modeling, this study provides a more mechanistically robust understanding of urban thermal environments. The identified thresholds and dominant driver maps offer precise, spatially differentiated guidance for urban climate-adaptive planning and ecological restoration. Full article