Urban Heat Island Intensity Research Articles

Modeling Urban Heat Islands and Thermal Comfort During a Heat Wave Event in East China With CLM5 Incorporating Local Climate Zones

AbstractThe urban expansion‐induced heat can exacerbate heat stress for urban dwellers, especially during heat waves. With a focus on the intra‐urban variability of urban heat islands (UHIs) and thermal comfort, the urban parameterization within the Community Land Model version 5 (CLM5) was modified incorporating the local climate zones (LCZs) framework, named CLM5‐LCZs, to simulate the urban climate during a heat wave (HW) event in the summer of 2013. The evaluation of model performance demonstrated that it did a reasonable job of simulating surface energy balance and thermal regimes in cities against observational fluxes from a flux tower measurement site and temperatures from automatic meteorological stations in Nanjing, China. Then we investigated the characteristics and causes of UHIs associated with local background climate, intra‐urban inhomogeneity and HW intensity in East China. The results exhibited that daytime and nighttime canopy urban heat island intensity (CUHII) were highest in the Compact Low Rise (LCZ3) and the Compact High Rise (LCZ1) areas, respectively, while surface urban heat island intensity (SUHII) peaked in the Large Low Rise (LCZ8) and the Compact High Rise (LCZ1) areas during daytime and nighttime, respectively. Urban dwellers were easier exposed to serious heat environment in LCZ3 and LCZ1 areas over the north subtropical climate zone. Contrasts of CUHII and SUHII among different urban classes could exceed 1.7°C and 5.4°C. The intra‐urban heterogeneity may alter the dominant factors controlling SUHII, which were also modulated by local climate and HW intensity. Unlike other controlling factors, the impact of local climate on the contribution from the urban‐rural contrast of convection efficiency was larger than urban features. Overall, CLM5‐LCZs displayed potential of implementing detailed simulations for inter‐ and intra‐city UHIs at a larger scale, and enhancing the capabilities in modeling urban climate and exploring the causes and controls of UHIs.

Geospatial and statistical analysis of urban heat islands and thermally vulnerable zones in Bangalore and Hyderabad cities in India

Urban Heat Island (UHI) effects caused by urbanization and associated anthropogenic activities have adverse environmental and social impacts. The social impacts of UHIs on Indian cities are not well documented. To fill this void, the current paper examines the spatial variation of the UHI effects on the populations of two Indian cities: Bangalore and Hyderabad. Land Surface Temperature (LST) data for the past two decades is obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to analyse the spatial and temporal variation of the thermal environment of the cities. The regions experiencing different levels of UHI effects within the cities, along with the population in these zones, are identified in the study. The average UHI intensity for the past two decades in Bangalore and Hyderabad is found to be 1.9 °C and 2.44 °C, respectively. The UHI intensity in Bangalore and Hyderabad is found to be increasing annually at a rate of 0.040 °C per year and 0.033 °C per year, respectively. Around 1 million people in Bangalore and 1.8 million people in Hyderabad are inhabitants of the high temperate zone, experiencing an average LST of 3.14 °C and 3.55 °C higher than the rest of the population, respectively. The study emphasizes the need for regionally scaled UHI mitigation strategies and identifies the most vulnerable regions within the study area to prioritize mitigation strategies.

Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study

Latino, Black, and economically disadvantaged individuals in the U.S. have been shown to disproportionately live in areas characterized by urban heat islands, yet little qualitative data exist to inform heat adaptation. In a low-income community of color, we explored residents' heat-related health and well-being outcomes, heat vulnerability, and recommended adaptation strategies. From July–September 2021, we conducted qualitative interviews with 18 economically disadvantaged adults (female = 17, Latino = 16, Black = 2) in an area with high urban heat island intensity in Austin, Texas. We identified six themes using NVivo. First, heat impacted residents' physical health (fatigue, headaches, nausea, dizziness, trouble breathing), mental health (uncomfortable, stress), physical activity, and social relationships. Second, heat exposure was only mentioned outdoors (active transportation, outdoor work, recreation). Third, residents perceived children and those with diabetes and hypertension as most sensitive. Fourth, adjusting to heat included staying home, drinking liquids, changing schedules, and using air conditioning. Fifth, barriers to adjusting to heat were lack of trees, shade, greenspace, and bluespace, along with electricity cost and power outages. Sixth, residents recommended adding trees, shade structures, parks, pools, splash pads, and drinking fountains. Findings complement quantitative data to support municipal efforts in designing equitable, heat-resilient cities.

Synergistic Effects of Roadside Trees and Spatial Geometry on Thermal Environment in Urban Streets: A Case Study in Tropical, Medium-Sized City, Taiwan

With the global warming effect and the rapid growth of global urbanization, the concept of urban heat islands (UHIs) has become one of the most important environmental issues in the world. Early studies on UHIs mostly focused on highly developed, large cities and found that urban heat island intensity (UHII) can be as high as 4~7 °C. In recent years, it has also been found that the UHI of medium-sized cities can also reach 4–6 °C. Previous studies have also found that planting, street orientation, and aspect ratio individually have a great impact on the thermal environment of streets, but there are not many studies that comprehensively discuss the synergistic effects of these factors. Therefore, this study takes a tropical, medium-sized city, Chiayi City, as a case study to use the ENVI-met numerical simulation tool to comprehensively compare and analyze the influence of the trees and geometric characteristics of streets on the microclimate and comfort in the streets. As a result, in a tropical, with sea winds (west winds), medium-sized city, by comparison of 12 street schemes with different roadside tree situations (planting or not), orientations (E–W, N–S), and aspect ratios (0.3, 0.7, 1.0), the improvement benefits and possible mechanisms of air temperature, wind speed, MRT, PET, SET, absolute humidity, etc. at the pedestrian street level (H = 1.4 m) were obtained and show that the cooling effect of trees was deeply affected by the street orientation and geometry. An analysis of changes at different heights was also obtained. Finally, design strategy suggestions, such as the street orientation, should be prioritized to be parallel to the prevailing wind; modifying tree shapes or building forms on streets perpendicular to the prevailing wind for creating cool and comfortable streets in future tropical, medium-sized cities were proposed.

Automatic Detection and Dynamic Analysis of Urban Heat Islands Based on Landsat Images

Given rapid global urban development, increases to impervious surfaces, urban population growth, building construction, and energy consumption result in the urban heat island (UHI) phenomenon. However, the spatial extent of UHIs is not clearly mapped in many UHI studies based on a remote sensing approach. Therefore, we developed a method to extract the spatial extent of the UHI during the period from 2000 to 2021 in Nanjing, China, and explored the impact of urban two- and three-dimensional expansion on UHI spatial extent and UHI intensity. After cropland effects (i.e., bare soil) were eliminated, our proposed method combines the Getis-Ord-Gi* and the standard deviation of the normalized difference vegetation index (NDVI STD) to extract the UHI area from Landsat 5 and Landsat 8 images using land surface temperature (LST) spatial autocorrelation characteristics and the seasonal variation of vegetation. Our results show the following: (1) Bare farmland has a large influence on the extraction results of UHI—combined with the seasonal variation characteristics of NDVI STD, the impact of bare soil on UHI extraction was highly reduced, strongly improving the accuracy of UHI extraction. (2) The dynamics of the UHI area are consistent with the changes in the built-up area in Nanjing at both spatial and temporal scales, but with the increase of the urban green ratio, the UHI area of mature urban areas trends to decrease due to the cooling effect of green space. (3) The accumulation of population and GDP promote the vertical expansion of urban buildings. When the two-dimensional expansion of the city reaches saturation, the UHI intensity is primarily affected by three-dimensional urban expansion.

Surface urban heat island mitigation network construction utilizing source-sink theory and local climate zones

Negative impacts of urban heat island (UHI) are exacerbated by rapid urbanization. To address insufficient on construction of key areas for UHI mitigation, source-sink theory and local climate zone (LCZ) were developed to build networks. This study discriminated source and sink landscapes, and applied multiple spatial analyzes to identify key surfaces, corridors and barrier points of heat source and sink landscapes at urban and main urban district scales. 30 important heat source and sink landscape surfaces were identified, primarily comprised open high-rise and open mid-rise in heat source landscapes, accounting for 34.07%, 22.19% at urban scale, and 48.63%, 29.68% at main urban scale, composed of low plants and water in heat sink landscapes, representing 68.63%, 12.28%, and 45.99%, 31.80%, respectively. 62 and 58 heat source corridors identified were dominated by open high-rise, and low plants, accounting for 16.02%, 31.67%, and 21.70%, 22.41%. 60 and 46 heat sink corridors identified composed of open high-rise, low plants, and water, with percentages of 19.94%, 41.13%, 13.25%, and 32.97%, 30.09%, 7.42%, respectively. Barrier points of 38 and 28 heat source landscapes identified were characterized mainly by low plants, and water, representing proportions of 52.10%, 20.24%, and 42.73%, 18.31%, respectively. Barrier points of 41 and 20 heat sink landscapes identified were primarily associated with open high-rise, open mid-rise accounting for proportions of 41.17%, 20.03%, and 54.17%, 18.89%, respectively. Given these key areas, it is recommended to split heat source landscape, increase area of smaller sink landscape, renew of LCZs to those with lower UHI intensity.

Analysis of urban heat island and human thermal comfort in a Mediterranean city: A case study of Lecce (Italy)

A Mediterranean city was studied to examine the Urban Heat Island (UHI), with near-surface temperature measurements at five locations in both urban and suburban areas over a nine-year period from 2013 to 2021. The study aims to analyse the intensity of UHI and evaluate urbanisation effects on human thermal comfort also considering the effect of heat waves. The UHI study indicated that the intensity of the phenomenon peaked at night (ranging from 1.1 to 1.6 °C) in the city's core, while its impact was comparatively weaker during the central hours of the day. The diurnal peak of the UHI intensity was between 1–3 °C, but in unfavourable conditions, it exceeded 5–6 °C. The intensity and daily maximum of UHI decreased when the wind speed from north exceeded 3 m s−1. Thermal comfort analysis indicated the worst conditions in August, with high discomfort and heat stress risk during the early afternoon. The analysis shows a significant deterioration of thermal comfort over the investigated period. Results achieved with this work point out the importance of implementing measures to mitigate thermal discomfort, particularly when temperatures are already high and exacerbated by the UHI effect.

Measuring nocturnal near-surface urban heat island intensity in the small, mid-latitude city of Inverness, Scotland

ABSTRACT Few publications have considered the urban heat island (UHI) effect in small settlements. This paper, therefore, presents the findings of a UHI study conducted in the small, mid-latitude city of Inverness, Scotland (population: 63,220). The study aimed to provide an initial appraisal of the scale of UHI phenomena in Inverness and to understand the factors associated with its presence. Mobile vehicular traverses of a study transect were conducted on 30 dates during June, July & August 2019. Measurements of near-surface air temperature were recorded at 19 local climate zone observation points between 20:00 and 23:00 BST. Daily mean UHI intensity (UHII) during the study ranged from 0.7 to 3.5°C, with an overall mean UHII of 1.6°C. Land use characteristics impacted the UHII, with areas with higher fractions of impervious surface cover returning significantly higher air temperatures. UHII increased with the onset of sunset, and the highest UHII occurred on dates with up to 1–2 oktas of cloud cover, low relative humidity, and high cloud base height. The results align with previous studies and comparisons are drawn between Inverness and other settlements globally. Further research in Inverness is recommended to better understand UHI effects and influence national planning policy.

Thermal characterization of Ajmer City: Insights into urban heat dynamics

Human development, urbanization, and ongoing environmental issues have led to environmental disturbances and climate changes in Ajmer, a crucial rest station for migrating birds. Congested and poorly planned urban areas, reduced vegetation cover, and increasing emissions from industries and automobiles all contribute to climate challenges. The rise in land surface temperature (LST) has led to the formation of urban heat islands (UHI). The goal of this research is to investigate UHI in Ajmer City, India. Using remotely sensed LANDSAT data and Geographic Information System (GIS), we analyzed LST and Normalized Difference Vegetation Index (NDVI) for the summer months (March to June) in Ajmer from 2015 to 2022, including the period when nationwide curfews were implemented due to Covid-19. The extraordinary measures taken to restrict human activities during the pandemic resulted in remarkable temporary declines in aerosols and atmospheric pollutants (NO2, CO, SO2, and PM) mainly due to restrictions on fossil fuel burning and transportation emissions. The analysis reveals that Urban LST was 312.07 K in 2019 and decreased to 308.228 K in 2020 due to the strict Covid-19 lockdowns. With the gradual easing of restrictions and resumption of human activities, LST slightly increased to 308.27 K in 2021 and further to 311.71 K in 2022. Researchers can now explore the effects of reduced surface vehicle heat and air pollution on LST and UHI intensity in Western Indian cities, providing valuable insights for urban planning and climate mitigation strategies.

MODERN CHANGES OF THE URBAN HEAT ISLAND IN MOSCOW

According to the data of weather stations for the period 1991–2020, as well as radiometric measurements of the surface temperature TS of Moscow region by Terra and Aqua satellites for the period 2000–2020, the daily course and long-term changes of the urban ‘heat island’ (UHI) of Moscow in the field of both the air temperature T at a height of 2 m and the TS were studied. For the analysis of TS 1379 satellite images were selected for 21 years in clear and slightly cloudy sky, when the UHI analysis is possible. The average annual values of TS were obtained using their normalization for separate seasons. The daily course of Moscow UHI in the air is weakly expressed, especially when compared with the countryside not only of the city center, but also of its entire area: only the weakening of this phenomenon in autumn and in the pre-winter season (from October to December) is reliable. According to satellite data, the ‘surface UHI’ is marked by a maximum in June-July with the strongest vegetation development, a minimum in autumn when leaves fall, and intermediate values in winter and spring. The rapid growth of T took place in 1991–2020 both in Moscow and in rural areas with approximately the same speed. As a result, there have been no statistically significant directional changes in the UHI intensity both in the air and on the surface as a whole over the past 20–30 years. The probable reasons for the general stabilization of the UHI in Moscow are both natural and socio-economic factors: the approximate constancy of the probability of clear weather (which promotes the strengthening of this phenomenon) and the vegetative activity in the region (which determines heat losses due to transpiration by plants), slowing down and stopping the growth of the population and energy consumption of the city, as well as its deindustrialization. Additional reasons are the gradual relocation of residents to the new outskirts of Moscow, as well as quarantine restrictions due to the COVID-19 pandemic in 2020.

Influence of non-urban reference delineation on trend estimate of surface urban heat island intensity: A comparison of seven methods

Increasing concern over urban heat mitigation creates an emerging necessity to track the evolution of urban heat island intensity (UHII) to assess the effectiveness of existing plans in combating urban heat. However, complex human-environment interactions make it difficult to accurately isolate urban effects in UHII evolutions due to unstable non-urban references. This study provided the first test on the sensitivity of surface UHII (SUHII) trend estimate to seven methods of non-urban reference delineation across 281 Chinese cities. The corresponding seven non-urban references include administrative non-urban areas, administrative non-urban areas excluding extreme elevations, administrative low plant areas, suburb, outer suburb, outer suburbs progressively excluding unstable areas interfered by evident greening/desertification trends and the SUHI footprint of neighboring cities, respectively. Results revealed that the selection of different non-urban references significantly altered SUHII and the nature of observed surface thermal island (heat or cold) in 74% and 8% of cities, respectively. Besides, due to diverse and inconsistent land use and land cover changes across and nearby non-urban areas, the way of non-urban reference delineation also affected the amplitude and even direction of statistically significant SUHII trends in 41% and 21% of cities, respectively. Among the seven methods, the use of suburb which was collectively interfered by urban and non-urban changing factors, produced a large number of misestimated significant downtrends (2.7-4.5 times more cities than other methods) and was therefore in general not recommended. Administrative non-urban areas were not suitable to cities with evident greening/desertification trends, yet the applicability can be largely enhanced by removing areas with extreme elevations. Low plant areas were in general feasible, except for the cities with suburban low plants additionally warmed up by urban areas. Outer suburb was relatively the optimal, particularly the areas with all concerned interferences excluded. Overall, this study provided empirical evidence supporting the need to use stable non-urban references for reliable trend estimate of urban-induced SUHII variations. The results may benefit more accurate assessments of the causes, progress, and consequences of SUHI evolution, thereby minimizing the chances of policy failure in SUHI mitigation.

Correlation Analysis Between Urban Heat Island Intensity and Temperature Criticality Value in Denpasar City

The compactness of buildings in Denpasar resulted in the formation of urban heat islands (UHI), which can be evaluated through the Urban Thermal Field Variance Index (UTFVI) and Environment Criticality Index (ECI). ECI is the ratio of land surface temperature to the Normalized Difference Vegetation Index (NDVI). It can be transformed into Temperature Criticality Value (TCV) using air temperature and Index-based Built-up Index (IBI). This study aims to identify the UHI intensity, the impact of land cover changes, and its association with the TCV. The study employs Landsat 8 imagery and field measurements data, and the findings demonstrate that the study area was mainly composed of built-up areas that had grown from 2015 to 2021. TFVI indicates the most intense UHI (>0.02) in the built-up areas, whereas the mean value of NDVI suggested a reduction in vegetation density. The density of built-up areas (IBI) had increased, while vegetation had decreased. TCV in 2015 ranged from -11.15°C.IBI to 6.42°C.IBI; 2018 between -9.96°C.IBI to 6.79°C.IBI; and 2021 between -10.84°C.IBI to 6.87°C.IBI showed that the environment had become increasingly critical from 2015 to 2021. A transect analysis revealed that more vigorous UHI intensity, denser buildings, and a more critical environment were present in urban centers compared to the suburbs. The correlation coefficient (r) between TCV and UTFVI was relatively robust (0.75–0.82), indicating that the growth of UHI intensity was associated with a more critical environment. TCV has the strongest (r=0.99) and strong correlation (r>0,80) with Built-up Index but inverse correlation with NDVI. Therefore, limiting the expansion of built-up areas and increasing vegetation could help to control the environment's criticality.

Quantifying the effects of urban development intensity on the surface urban heat island across building climate zones

Urban heat island (UHI) has gradually been a critical issue in the context of global environmental change and regional sustainability. The urban development intensity (UDI) increases with rapid urbanization and exacerbates the UHI problem, but the effects and contributions of UDI on UHI remain unclear. Based on Landsat 8 satellite images, this study quantified the effects of UDI on surface UHI intensity (SUHII) in 20 important cities across 5 building climate zones of China by using regression analysis, correlation analysis and boosted regression tree (BRT), and analyzed their potential influence mechanisms. Results indicated that there was apparent spatial heterogeneity of SUHII in different building climate zones (ranging from 3.26 °C to 4.43 °C) and cities (ranging from 1.44 °C to 6.61 °C). The effect of UDI on SHUII in cities of north was stronger than that in south, the average R2 values of the UDI-SUHII relationship in severe cold zone and cold zone were higher than the other climate zones. Building density, as the most critical predictor of SUHII among UDI metrics in all cities, was significantly positively correlated with SUHII. The R2 values showed significant positive correlation with annual average sunshine duration and aridity, and significant negative correlation with annual average precipitation, humidity and soil clay content. Our findings can offer new insights and decision-support in climate-adapted urban planning and heat-related risk management across different building climate zones.

Impacts of UHI on Heating and Cooling Loads in Residential Buildings in Cities of Different Sizes in Beijing–Tianjin–Hebei Region in China

The heating and cooling energy consumption levels of urban buildings account for a large and rapidly growing proportion of the total end-use energy consumption of society. The urban heat island (UHI) effect is an important factor influencing the spatiotemporal variations in the heating and cooling energy consumption levels of buildings. However, there is a lack of research on the impact of the UHI on the heating and cooling energy consumption of buildings in cities of different sizes in the Beijing–Tianjin–Hebei urban agglomeration, which is the most urbanized region in northern China. We selected rural reference stations using the remote sensing method, and applied an hourly data set from automatic weather stations, to examine the impact of the UHI on the typical residential building heating and cooling loads in three cities of varied sizes in the Beijing–Tianjin–Hebei urban agglomeration through building energy simulation. The main conclusions were as follows. As the UHI intensity (UHII) increased, the heating load difference between urban and rural areas decreased, while the cooling load difference between urban and rural areas increased in the cities. The average daily heating loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 8.14, 10.71, and 2.79% lower than those in their rural areas, respectively, while the average daily cooling loads in the urban areas were 6.88, 6.70, and 0.27% higher than those in their rural areas, respectively. Moreover, the absolute hourly load differences between urban and rural areas were significantly larger during the heating periods than during the cooling periods, with the former characterized by being strong at night and weak during the day. During the peak energy load period, the contribution of the UHI to the peak load of residential buildings varied between the cities. During the stable high-load period, from 18:00 to 07:00 the next day in the heating periods (from 18:00 to 05:00 the next day in the cooling periods), the hourly loads in the urban areas of Beijing, Tianjin, and Shijiazhuang were 3.15 (2.48), 3.88 (1.51), and 1.07% (1.09%) lower (higher) than those in their rural areas, respectively. Our analysis highlights the necessity to differentiate the energy supplies for the heating and cooling of urban buildings in different sized cities in the region.

The Subsurface Urban Heat Island Intensity in Enugu Urban, Nigeria

The Subsurface Urban Heat Island Intensity in Enugu Urban, Nigeria

Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data

Urbanization strongly correlates with land use land cover (LULC) dynamics, which further links to changes in land surface temperature (LST) & urban heat island (UHI) intensity. Each LULC type influences UHI differently with changing climate, therefore knowing this impact & connection is critical. To understand such relations, long temporal studies using remote sensing data play promising role by analysing the trend with continuity over vast area. Therefore, this study is aimed at machine learning centred spatio-temporal analysis of LST and land use indices to identify their intra-urban interaction during 1991–2021 (summer) in Imola city (specifically representing small urban environment) using Landsat-5/8 imageries. It was found that LST in 2021 increased by 38.36% from 1991, whereas average Normalised Difference Built-up Index (NDBI) increased by 43.75%, associating with increased thermal stress area evaluated using ecological evaluation index. Major LULC transformations included green area into agricultural arable-land and built-up. Finally, the modelled output shows that built-up & vegetation index have strongly impacted LST. This study, help to understand the relative impact of land-use dynamics on LST at intra-urban level specifically with respect to the small urban settings. Further assisting in designing and regenerating urban contexts with stable configuration, considering sustainability and liveable climate, for benefit of health of public and fragile population in particular.

Modelling urban dwellers’ indoor heat stress to enhance heat-health warning and planning

Due to climate change, the intensity, duration and frequency of heatwaves are likely to increase in the coming years. Excessive heat events can increase local urban heat island intensity affecting the health and wellbeing of urban dwellers vulnerable to heat stress. Heat-Health Warning Systems (HHWSs) have been developed to warn the public of impending heat events and to advise on preventable negative health outcomes. However, metrics upon which action triggers are made in HHWSs rely on reported critical outcomes, such as heat-related excess death. Thus, human exposure to heat is underestimated in current metrics and consequently, their capacity to prevent heat-related health risks remains uncertain, particularly indoors. This study investigates how indoor heat stress in urban dwellings at a city-scale can be modelled to enhance Heat-Health Warning and Planning. First, the effects of housing typologies on indoor thermal conditions are quantified in a local urban microclimate context. We then model the dynamic relationships between outdoor climate and indoor heat exposure to identify specific outdoor climatic thresholds as action triggers for alerting urban dwellers’ indoor heat stress. Based on urban microclimate data available for a city of Birmingham UK, a proof-of-principle study is presented. The result shows the presence of large variances in the heat-health action triggers across different housing typologies. This is further extended to consider the Birmingham climate projection scenarios provided by the UKCP18. Compared to the current UK Heat-Health Alert Service, we show how indoor heat stress warnings may look like and the implications for long-term heat-health planning.

Relationships between land use types and urban heat island intensity in Hulu Langat district, Selangor, Malaysia

BackgroundUrban heat island (UHI) is an urban climate phenomenon that primarily responds to urban conditions and land use change. The extent of hard surfaces significantly influences the thermal properties of the land. To address this issue, a novel approach quantifying the association between land use and UHI is developed. This study offers a new technique for effectively estimating the effect of land use on the UHI intensity using the combination of urban heat intensity index (UHII) and land contribution index (LCI) derived from Landsat 8 OLI images. The time-series thermal effect of land use on the UHI intensity can be determined according to the ratio in mean temperature between specific land use and the whole study site. The study was conducted in the Hulu Langat district, Malaysia during 2014–2021.ResultsThe UHI intensity rose from 0.19 in 2014 to 0.70 in 2021. The negative value of LCI for vegetation areas and water bodies obtained its negative contribution to the urban heat island, while the positive value of LCI for bare areas and built-up areas showed its positive effect on the urban heat island. The LCI value for urban areas showed a significant increase in the 7 years such as 0.51, 0.66, 0.69, and 0.75 for periods 2014, 2016, 2018, and 2021, respectively. The change in LCI from 2014 to 2021 for the transformation of bare area and forest was recorded to be 0.23 and − 0.02, respectively. Thus, the conversion of forests into urban areas had a negative effect on the increment of UHI intensity. ConclusionsOverall, these findings are useful for policy-making agency in developing an effective policy for reducing high UHI intensity and planning long-term land use management.

Integrating Urban Heat Island Impact into Building Energy Assessment in a Hot-Arid City

Dense cities usually experience the urban heat island (UHI) effect, resulting in higher ambient temperatures and increased cooling loads. However, the typical lack of combining climatic variables with building passive design parameters in significant evaluations hinders the consideration of the UHI effect during the building design stage. In that regard, a global sensitivity analysis was conducted to assess the significance of climatic variables and building design features in building energy simulations for an office building. Additionally, this study examines the UHI effect on building energy performance in Qatar, a hot-arid climate, using both measurement data and computational modeling. This study collects measurement data across Qatar and conducts computational fluid dynamics (CFD) simulations; the results from both methods serve as inputs in building energy simulation (BES). The results demonstrate that space cooling demand is more sensitive to ambient temperature than other climatic parameters, building thermal properties, etc. The UHI intensity is high during hot and transition seasons and reaches a maximum of 13 °C. BES results show a 10% increase in cooling energy demand for an office building due to the UHI effect on a hot day. The results of this study enable more informed decision-making during the building design process.

Seasonal and daily effects of the sea on the surface urban heat island intensity: A case study of cities in the Caspian Sea Plain

This study aimed to explore the effect of the sea on the spatial and temporal changes in surface urban heat island (SUHI) intensity. Therefore, 11 cities located along the Caspian Sea Plain were selected as the case studies. Firstly, the SUHI intensity of both coastal and non-coastal cities was calculated for different seasons and times of day and night. Finally, the effect of distance from the sea on the SUHI intensity was calculated by modeling the relationship between the SUHI intensity and a set of independent parameters such as urban surface biophysical properties, population density, and distance from the coastline. The results showed that by increasing the distance of cities from the sea, the daily SUHI intensity increased, while it reversed during the night. The RMSE values between the actual and simulated SUHI intensities based on population density and surface biophysical properties in spring, summer, autumn, and winter were 0.9, 1.1, 0.5, and 0.3 °C, respectively. By factoring in the distance from the sea in the modeling, the RMSE values for different seasons were obtained at 0.7, 0.8, 0.3, and 0.2 °C, respectively. The results show that distance from the sea has a significant effect on UHI intensity.