Island Effect Research Articles

Investigating the influence of urban green spaces on urban heat island mitigation – taking four districts in Shijiazhuang as an example

The primary objective of this scholarly investigation is to elucidate the intricate interplay between the urban heat island (UHI) effect and municipal green spaces. The geographical focus includes the four areas with the highest urbanization rate in Shijiazhuang, China. To conduct this survey, ECOSTRESS remote sensing imagery was acquired during distinct temporal intervals–morning, midday, and evening. The data were collected using the equal-scale city blocks performed by the OpenStreetMap urban network and ECOSTRESS remote sensing images at different times (morning, noon and evening). Surface temperature inversion of satellite images was performed using ArcGIS 10.7 software to obtain surface temperature. The overarching aim was to discern the nuanced impact of urban parks on the surface temperatures of their proximate environs during the summer season. The findings of this investigation revealed that, in order to effectively ameliorate the discernible heat island effect (SUH), rejuvenation initiatives ought to be directed toward sites maintaining a distance from green spaces within the range of 160 to 370 meters. Furthermore, augmentation of green space configurations is recommended in vicinities characterized by building densities falling within the range of 0.2 to 0.3. Notably, in locales marked by high building density, park layouts should adhere to a more regularized design during the renovation process. Additionally, it is advisable to ensure that the spatial separation between distinct urban parks exceeds 900 meters. These empirical insights are poised to enhance the comprehension of urban planners regarding the intricate dynamics through which urban parks exert influence on municipal surface temperatures. Furthermore, the discerned patterns furnish pragmatic guidance for mitigating the heat island effect, thereby offering invaluable recommendations for urban planning endeavors.

Urban growth dynamics and its influence on land surface temperature in Bhubaneswar metropolitan city: a 1990–2021 analysis

This study investigates the impact of urbanization on land use and land cover (LULC) changes and their consequent effects on land surface temperature (LST) in Bhubaneswar, India, over a 30-year period from 1990 to 2021. It uses Landsat satellite imagery from Thematic Mapper (TM) and Operational Land Imager (OLI) sensors, along with correlation analysis, to assess the spatial and temporal dynamics of LULC and their influence on LST. The analysis reveals a significant transition in LULC, with a 60.91% overall change observed during the study period. Built-up areas increase substantially, from 18.83% to 43.17%, while vegetation cover and agricultural land, decline from 28.34% to 12.03% and 34.18% to 21.60%, respectively. Results indicate a direct correlation between increased built-up land and elevated LST, while vegetation cover shows a negative correlation with LST, as measured by the Normalized Difference Vegetation Index (NDVI). Conversely, the Normalized Difference Built-up Index (NDBI) exhibits a positive correlation with LST, highlighting the thermal impact of urban expansion. These findings underscore the critical role of sustainable urban planning in mitigating the adverse effects of urban heat islands and guiding future development policies in Bhubaneswar. The study’s outcomes provide a scientific basis for urban planners to design strategies that balance urban growth with environmental sustainability, aiming to reduce the city’s rising temperatures and promote a resilient urban ecosystem.

Dataset of microscale atmospheric flow and pollutant concentration large-eddy simulations for varying mesoscale meteorological forcing in an idealized urban environment.

By 2050, two-thirds of the world's population will live in urban areas under climate change, exacerbating the environmental and public health risks associated with poor air quality and urban heat island effects. Assessing these risks requires the development of microscale meteorological models that quickly and accurately predict wind velocity and pollutant concentration with high resolution, as the heterogeneity of urban environments leads to complex wind patterns and strong pollutant concentration gradients. Computational Fluid Dynamics (CFD) has emerged as a powerful tool to address this challenge by providing obstacle-resolved flow and dispersion predictions. However, CFD models are very expensive and require intensive computing resources, which can hinder their systematic use in practical engineering applications. They are also subject to significant uncertainties, particularly those arising from the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, some of which are aleatory and thereby irreducible. Given these issues, the construction of CFD datasets that account for uncertainty would be an interesting avenue of research for microscale atmospheric science. In this context, we present the PPMLES (Perturbed-Parameter ensemble of MUST Large-Eddy Simulations) dataset, which consists of 200 large-eddy simulations (LES) characterizing the complex interactions between the turbulent airflow, the tracer dispersion, and an idealized urban environment. These simulations reproduce the canonical MUST dispersion field campaign while perturbing the model's mesoscale meteorological forcing parameters. PPMLES includes time series at human height within the built environment to track wind velocity and pollutant release and dispersion over time. PPMLES also includes complete 3-D fields of first- and second-order temporal statistics of the wind velocity and pollutant concentration, with a sub-metric resolution. The uncertainty of the fields induced by the internal variability of the atmospheric boundary layer is also provided. The computation of PPMLES required significant resources, consuming 6 million CPU core hours, equivalent to the emission of approximately 10 tCO2eq of greenhouse gases. This significant computational effort and associated carbon footprint motivates the sharing of the data generated. The added value of the PPMLES dataset is twofold. First, the perturbed-parameter ensemble of LES enables to quantify and understand the effects of the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, which has been identified as a major challenge in predicting atmospheric flow and pollutant dispersion in urban environments. Secondly, PPMLES reference data can be used to benchmark models of different levels of complexity, and to extract key information about the physical processes involved to inform more operational modeling approaches, for example through learning surrogate models.

Blue-Green space seasonal influence on land surface temperatures across different urban functional zones: Integrating Random Forest and geographically weighted regression.

As climate change and urbanization progress, the urban heat island issue will affect more people. Urban blue-green spaces can effectively mitigate the urban heat island effect, and their structure and morphology significantly impact the degree of mitigation. To identify the most effective blue-green space distribution for mitigating the heat island effect across different urban function zones (UFZ), we selected 14 landscape metrics of blue-green spaces in the main urban area of Nanjing. Using the Random Forest model, we identified the four metrics with the most significant contribution, and then applied the Geographically Weighted Regression (GWR) model to obtain explicit spatial-related implications. We found that GWR model outperforms others (R2Mar.=0.574, R2Aug.=0.482, R2Sept.=0.618, R2Oct.=0.567, R2Dec.=0.460). in March the Landscape Shape Index of green spaces has the greatest impact (Feature Importance (FI)=0.350), in August, the average size of green spaces is most influential (FI=0.206). Patch Density of green spaces plays the most significant role in September (FI=0.251) and October (FI=0.253). Industrial areas are most impacted by green space structure (coefLSI3=0.49, coefAREA-MN8=-0.53, coefPD9=0.45). The influence of water bodies on Land Surface Temperature (LST) is weaker in winter, with minimal differences across different functional zones. This study introduced an effective method for reducing the number of independent variables in linear modeling, and elucidated that the optimization of blue-green space design should be flexibly adjusted according to urban functional zones.

Quantifying heat-related risks from urban heat island effects: A global urban expansion perspective

Quantifying heat-related risks from urban heat island effects: A global urban expansion perspective

Unveiling differential impacts of multidimensional urban morphology on heat island effect across local climate zones: Interpretable CatBoost-SHAP machine learning model

Unveiling differential impacts of multidimensional urban morphology on heat island effect across local climate zones: Interpretable CatBoost-SHAP machine learning model

Passive Dissipation of Canopy Urban Heat Through Double Skin Façades

In the face of global warming, mitigating the urban heat island effect has become an important concern worldwide. This study applies the principle of buoyancy ventilation formed by sunlight in double skin façades (DSFs) to improve the thermal environment outside buildings by discharging heat through temperature and pressure differences. The study subject is a 15 × 30 × 40 m residential concrete building situated in a subtropical climate. The lower opening of the DSF faces the outdoor environment; heat is absorbed through this opening from the ground environment and then evacuated up to above the urban canopy layer heat island in order to cool pedestrian environments on the ground. We used numerical simulation to analyze the cooling potential of this DSF in summer daytime conditions. The results show that the DSF can successfully transport heat energy and discharge it above the urban canopy layer. Significant cooling effects were observed in both the horizontal and vertical spaces on the leeward side of the building DSF through the passage of surface heat, thereby reducing the load of indoor air conditioning.

Noise pollution as a major disturbance of avian predation in Amsterdam

Trophic interactions play a key role in maintaining ecological balance. In urban environments, avian predation has been demonstrated to be particularly important due to its effects on community structure, pest control, and nutrient cycling. As humanity relies upon ecosystem services for sustenance, and with 70% of the global population projected to reside in urban areas by 2050, understanding the impact of urbanization on avian predation is becoming increasingly important. This study investigates the impacts of urban microclimates, shaped by impervious surfaces and green/blue infrastructure, and human‐induced disturbances, on avian predation in urban areas, with the aim of testing the increased disturbance hypothesis. To assess the avian predation rate, plasticine caterpillars were placed in Quercus robur trees in the city of Amsterdam for a period of two months. The analyses evaluated the impact of artificial lighting at night, human population density, the urban heat island effect, impervious surfaces, vegetation, noise pollution, and water bodies on predation rates. The results indicated a substantial increase in predation during the second month, which was likely caused by an increase in naïve fledglings or elevated ambient temperatures. Noise pollution was identified as the most frequent and robust predictor of predation, consistently leading to a reduction in predation rates, possibly due to avoidance behavior. Other predictors exhibited substantial temporal and spatial variability. The variables related to urbanization increased predation in the initial month, suggesting that insectivorous birds prey on areas with higher illumination and temperature. However, the effect diminished in the subsequent month, potentially due to the increased daylight hours or a reduction in heating effects. During the second month, all predictors exhibited a negative effect on predation, thereby supporting the increasing disturbance hypothesis. These findings underscore the complex relationship between urban factors and avian predation, emphasizing the necessity for mitigation efforts in urban planning.

Effect of Blue–Green Infrastructure in Mitigating Microenvironmental Heat Islands: Field- and Simulation-Based Insights

Urban heat island (UHI) effects, intensified by urbanization and environmental changes, are critical challenges to urban thermal comfort and sustainability. This study investigates the combined role of water bodies and vegetation in mitigating microclimatic heat island effects within a campus area, focusing on their cooling impacts and interactions. Using field measurements and numerical simulations, the research evaluates the cooling effects and wind flow modifications induced by greenery and water bodies in the surrounding environment. The findings demonstrate that both vegetation and water bodies provide a significant cooling effect, reducing temperatures by 0–6 K in downstream regions, with the impact being more pronounced closer to the blue–green spaces. Furthermore, the combined application of water bodies and vegetation offers enhanced cooling effects; however, their influence on the thermal environment exhibits a nonlinear relationship. These results underscore the importance of strategic blue–green infrastructure planning in mitigating UHI effects and optimizing urban thermal environments.

Impact of introducing electric vehicles on ground-level O3 and PM2.5 in the Greater Tokyo Area: yearly trends and the importance of changes in the urban heat island effect

Abstract. Battery electric vehicles (BEVs) are considered a solution for global warming and air pollution, and several countries have announced they will shift to BEVs in the 2030s. Even though previous studies have shown the effects of reducing vehicular emissions on the formation of tropospheric ozone (O3), no studies have evaluated the effect of decreasing anthropogenic heat, which is expected to mitigate urban heat island (UHI) effect, on air quality issues. We used a numerical weather prediction to estimate changes in the UHI effect in the Greater Tokyo Area (GTA) of Japan by introducing BEVs. The results indicated that the introduction of BEVs would lead to a maximum local temperature decrease of 0.25 °C in the GTA. The effects of introducing BEVs on O3 and fine particulate matter (PM2.5) were estimated using a regional chemical transport model. The results indicated that mitigating the UHI effect would lead to a reduction in ground-level O3 formation. This is due to the increased NO titration effect caused by the lowered planetary boundary layer height and due to the degradation of photochemistry related to O3 formation caused by a decrease in temperature and biogenic volatile organic compounds (BVOCs). The mitigation of UHI would result in enhanced particle coagulation, with an increase in ground-level PM2.5. Furthermore, a decrease in BVOC emissions would result in increased PM2.5 owing to enhancement of the OH + SO2 reaction. A total of 175 and 77 annual premature deaths would be prevented from changes in O3 and PM2.5, respectively.

Urban Street Greening and Resident Comfort: An Integrated Approach Based on High-Precision Shadow Distribution and Facade Visual Assessment

With the acceleration of global climate change and urbanization, the urban heat island effect has significantly impacted the quality of life of urban residents. Although numerous studies have focused on macro-scale factors such as air temperature, surface albedo, and green space coverage, relatively little attention has been paid to micro-scale factors, such as shading provided by building facades and tree canopy coverage. However, these micro-scale factors play a significant role in enhancing pedestrian thermal comfort. This study focuses on a city community in China, aiming to assess the thermal comfort of urban streets during the summer. Utilizing high-resolution 3D geographic data and street view images extracted from drone data, this study comprehensively considers the mechanisms affecting the urban street thermal environment and the human comfort requirements for shading and greening. By proposing quantitative indicators from multiple scales and dimensions, this study thoroughly quantifies the impact of the surrounding environment, greening, shading effects, buildings, and road design on the thermal comfort of summer streets. The results show that increasing tree canopy coverage by 10 m can significantly reduce the surrounding temperature, and a building layout extending 200 m can regulate temperature. The distribution of shadows at different times significantly affects thermal comfort, while the sky view factor negatively correlates with thermal comfort. Environments with a high green view index enhance visual comfort. This study reveals the specific contributions of different environmental characteristics to street thermal comfort and identifies factors that significantly impact thermal comfort. This provides a scientific basis for urban green space planning and thermal comfort improvement, holding substantial practical significance.

Exploring the impact of urban spatial morphology on land surface temperature: A case study in Linyi City, China.

The increasing population density and impervious surface area have exacerbated the urban heat island effect, posing significant challenges to urban environments and sustainable development. Urban spatial morphology is crucial in mitigating the urban heat island effect. This study investigated the impact of urban spatial morphology on land surface temperature (LST) at the township scale. We proposed a six-dimensional factor system to describe urban spatial morphology, comprising Atmospheric Quality, Remote Sensing Indicators, Terrain, Land Use/Land Cover, Building Scale, and Socioeconomic Factors. Spatial autocorrelation and spatial regression methods were used to analyze the impact. To this end, the township-scale data of Linyi City from 2013 to 2022 were collected. The results showed that LST are significantly influenced by urban spatial morphology, with the strongest correlations found in the factors of land use types, landscape metrics, and remote sensing indices. The global Moran's I value of LST exceeds 0.7, indicating a strong positive spatial correlation. The High-High LISA values are distributed in the central and western areas, and the Low-Low LISA values are found in the northern regions and some scattered counties. The Geographically Weighted Regression (GWR) model outperforms the Spatial Error Model (SEM) and Ordinary Least Squares (OLS) model, making it more suitable for exploring these relationships. The findings aim to provide valuable references for town planning, resource allocation, and sustainable development.

Evaluating the Acoustic Absorption of Modular Vegetation Systems: Laboratory and Field Assessments Using an Impedance Gun

Introducing vegetation is an effective strategy for improving air quality and mitigating the heat island effect. Green modules, which consist of modules that support substrates and various plant species, integrate these elements. This study analyzes the acoustic absorption properties of a specific green wall module using an impedance gun and the Scan and Paint method for laboratory and on-site measurements. The impedance gun method is effective for in situ analysis, offering advantages over standardized techniques for inhomogeneous samples. The sound absorption coefficient of the substrate and the effects of different plant species were measured. Key findings reveal that the substrate primarily influences sound absorption, with its coefficient increasing with frequency, similar to porous materials. Vegetation enhances the acoustic absorption of the substrate, depending on coverage and thickness, with 80–90% of absorption attributed to the substrate and 4–20% to vegetation. However, not all dense plant species improve absorption; some configurations may decrease it. Improvement correlates with substrate coverage and vegetation layer thickness, while the impact of plant morphology remains unclear. These findings confirm vegetation’s potential as an acoustic absorption tool in urban settings. Additionally, green walls can enhance acoustic comfort in indoor environments such as offices and schools by reducing reverberation. They also improve air quality and provide aesthetic appeal, making them a multifunctional solution for modern architecture.

Quantitative Simulation and Planning for the Heat Island Mitigation Effect in Sponge City Planning: A Case Study of Chengdu, China

The implementation of sponge cities in China modifies the hydrological conditions of the underlying surface, effectively alleviating the urban heat island effect. However, in planning and construction, heat island mitigation targets are difficult to quantify and lack quantitative design and evaluation methods. To address this issue, two planning schemes were proposed based on sponge city management and control indicators. The WRF-UCM model was used to conduct numerical simulations of the current conditions (case 1) and the sponge city planning schemes (cases 2 and 3), analyzing the impact of sponge city initiatives on the mitigation of the heat island effect. The results indicated that by changing the structure of the underlying surface and increasing the water content of the underlying surface, the sponge city affects the urban energy distribution process and regional horizontal advection pattern. This not only reduces heat accumulation within the urban area but also suppresses regional convection during high-temperature periods, thereby mitigating the urban heat island effect. Moreover, different schemes following the same sponge city design requirements have varying impacts on urban microclimate elements and heat island distributions. Notably, a higher subsurface water content yields a more pronounced inhibition of the heat island effect. Finally, a sponge city planning method with the consideration of heat island mitigation was proposed, facilitating pre-simulation optimization and decision-making in sponge city planning.

Synergistic deployment of green infrastructure and reflective pavements for mitigation of UHI within urban blocks

Urbanisation is increasing globally, leading to the Urban Heat Island (UHI) effect, where urban areas experience higher temperatures than nearby rural regions. Mitigating UHI is a significant challenge with far-reaching implications. Green Infrastructure (GI) and reflective pavements have been shown to be effective for UHI mitigation, but extensive use of each can be impractical. By synergistically combining the benefits of both strategies, we can potentially achieve more effective UHI mitigation. Additionally, optimizing their configuration in relation to the surrounding urban morphology can also play a crucial role. This study explored the optimal distribution and configuration of GI and reflective pavements to maximise the cooling effect and cost efficiency in a generic urban block consisting of a regular grid of 25 buildings. A computational fluid dynamics (CFD) based numerical model is developed by solving the steady Reynolds-Averaged Navier-Stokes (RANS) equations to study the impact of GI and reflective pavements on temperature distributions. The climate of Ahmedabad, India, a hot and dry city, was considered. Various configurations of GI and reflective pavements were evaluated, involving a large central park, smaller distributed parks and converting only part of the roads into reflective ones. The findings indicated that the presence of a large central park and partial use of reflective pavements on only half the roads could reduce peak canopy air temperature by 0.16–0.33 °C, with a significantly higher benefit-to-cost ratio as compared to other configurations. These insights contribute to urban design by offering evidence-based solutions for temperature management, promoting sustainability, and enhancing urban quality of life.

Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation

Abstract. The urban canopy model Town Energy Balance (TEB) is coupled with the radiation model SPARTACUS-Urban to improve the urban geometry simplification and the radiative transfer calculation. SPARTACUS-Urban assumes that the probability density function of wall-to-wall and ground-to-wall distances follows a decreasing exponential. This better matches the distributions in real cities than in the infinitely long street canyon employed by the classical TEB. SPARTACUS-Urban solves the radiative transfer equation using the discrete ordinate method. This allows us to take into account physical processes such as the interaction of radiation with the air in the urban canopy layer and the spectral dependence of urban material reflectivities or specular reflections. Such processes would be more difficult to account for with the radiosity method used by the classical TEB. With SPARTACUS-Urban, the mean radiant temperature, a crucial parameter for outdoor human thermal comfort, can be calculated from the radiative fluxes in the vertical and horizontal directions incident on the human body in an urban environment. TEB–SPARTACUS is validated by comparing the solar and terrestrial urban radiation budget observables with those simulated by the Monte-Carlo-based HTRDR-Urban reference model for procedurally generated urban districts that mimic the local climate zones. Improvement is found for almost all radiative observables and urban morphologies for direct solar, diffuse solar, and terrestrial infrared radiation. The TEB mean radiant temperature diagnostic for a person in the urban environment is also improved with TEB–SPARTACUS compared with the classical TEB. Based on these results, TEB–SPARTACUS could lead to more realistic results for building energy consumption, outdoor human thermal comfort, or the urban heat island effect.

Comparative analysis of land cover changes on outdoor thermal comfort in Doha, Dubai, Kuwait City, Manama, Muscat, and Riyadh

ABSTRACT Rapid urbanization in Gulf cities has driven significant land cover changes, influencing outdoor thermal comfort and land surface temperatures (LST). This study investigates land cover dynamics from 1998 to 2023 across six cities – Doha, Dubai, Kuwait City, Manama, Muscat, and Riyadh – using Landsat imagery to assess LST, Normalized Difference Vegetation Index (NDVI) and approximated wet-bulb globe temperature (AWGBT). Results reveal an increase in urban areas, with Manama and Kuwait City experiencing the largest expansions (47.50% and 47.02%). Vegetation patterns varied, with cities like Dubai and Riyadh showing consistent increases, while Doha stagnated from 2013 to 2023. LST ranged from 42°C to 55°C, with desert areas showing the highest temperatures. Built-up areas had LST comparable to desert land, highlighting a reverse urban heat island effect. Dubai’s LST decreased between 2013 and 2023 due to successful green initiatives, contrasting with rising temperatures in other cities. The mean LST difference between the desert and urban areas was 2.5°C, and vegetation displayed a cooling effect, with a 3.5°C difference between vegetated and desert areas. Thermal comfort maps aligned with LST data, showing increasing heat stress, particularly in Doha and Kuwait City, while Dubai maintained stable comfort levels. This study underscores the critical role of vegetation and sustainable urban planning in mitigating heat stress and enhancing outdoor thermal comfort across Gulf cities.

Optimizing urban block layouts and density distribution: a generative design framework for neighborhood performance

PurposeIn many countries, urban block layouts follow rigid and outdated design guidelines; however, these approaches fail to effectively address settlement construction, neighborhood planning, the design of old urban contexts and the rapid development of emerging cities, as seen in the new towns of Iran and various other regions worldwide. Revising these guidelines requires early-stage decision-making to evaluate building layouts based on their impact on the built environment. This study introduces a workflow for optimizing urban block layouts with constant density while considering design constraints, climate and environmental performance. This framework assists planners and policymakers in refining site density distribution and building massing.Design/methodology/approachA computational urban design method was developed utilizing a grasshopper-based algorithm and Python for data generation. It introduces an innovative approach by incorporating buildings’ placement coordinates as design variables alongside dimensions and orientation. The Wallacei tool optimizes layouts by considering energy use intensity (EUI), outdoor thermal comfort and facades’ received radiation employing Ladybug Tools. Spatial daylight autonomy (sDA) and urban heat island (UHI) effects are also integrated into the workflow. The framework is applied to a hypothetical residential district in Tehran, consisting of nine blocks, each containing nine plots.FindingsThe results for a set of optimal solutions demonstrate that beyond existing urban design policies, environmentally prioritized designs are achievable. Radiation differences between the optimal models and the conventional parallel layout range from 41.3 to 61.1%. However, changes in EUI and universal thermal climate index (UTCI) are minimal; significant improvements are observed in sDA, with 7, 8 or all 9 buildings meeting the acceptable threshold compared to none in the parallel layout. These findings highlight the effectiveness of the design method framework proposed in this research, which has the potential to significantly influence urban planning practices across various regions globally.Originality/valueThe methodology of this study provides a flexible decision-making framework for early design phases, enabling policymakers, architects and urban designers to address the gap between current urban design methods and environmentally driven approaches. Sensitivity analysis of optimal solutions further offers valuable insights into the correlation between design variables and evaluation metrics.

Long-Term Spatiotemporal Heterogeneity and Influencing Factors of Remotely Sensed Regional Heat Island Effect in the Central Yunnan Urban Agglomeration

Long-Term Spatiotemporal Heterogeneity and Influencing Factors of Remotely Sensed Regional Heat Island Effect in the Central Yunnan Urban Agglomeration

Correction: Evaluation of land use land cover dynamics and urban heat island effects over Mumbai metropolitan Region, India

Correction: Evaluation of land use land cover dynamics and urban heat island effects over Mumbai metropolitan Region, India