Heat Island Intensity Research Articles

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.

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.

Variations in Surface Urban Heat Island and Urban Cool Island Intensity: A Review Across Major Climate Zones

Variations in Surface Urban Heat Island and Urban Cool Island Intensity: A Review Across Major Climate Zones

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.

Urban Expansion and The Microclimate Characteristics Evolution of Lhasa

Urban expansion is the process of growing urban population and expanding land for residential and productive use. Microclimates are small-scale climates with relatively uniform and changeable climatic characteristics. In the context of climate change on the Tibetan Plateau, the microclimate of Lhasa will respond to urban expansion. With the support of landset8 OLI_TIRS data, this study uses the unsupervised classification method ISO-data algorithm to extract information on construction land and calculate the intensity of urban sprawl. In order to analyze and investigate the evolution of urban expansion and microclimate characteristics in Lhasa city from 2014 to 2021, the radiative transfer equation method was chosen to invert the surface temperature and calculate the heat island effect characteristic-related index. The results of the study show that the area of built-up land in Lhasa city increases and the heat island intensity rises during 2014-2021. This study contributes to urban planning in Lhasa and provides a case study of climate change and cities on the Tibetan Plateau.

How Urban Morphology Relates to the Urban Heat Island Effect: A Multi-Indicator Study

Urban morphology quantitatively expresses a city’s spatial structure, internal relationships, and physical form. It has advantages for predicting urban growth and analyzing the current state of cities in the literature. A comprehensive study on the complex relationships between urban morphology and urban heat island intensity (UHII) is of great importance for mitigating the urban heat island (UHI) effect for megacities. This study models urban morphological indicators in fine resolution based on three aspects: building morphology, ecological infrastructure, and human activities. The model accurately captures UHII by employing the definition of UHI effects. The relationship between urban morphology and UHII was further examined using extreme gradient boosting (XGBoost) and Shapley additive explanations (SHAP). By taking central Beijing, China as study area, major findings include the following: (1) Significant daytime UHI effects were observed within the research area, particularly during the summer months, when it appears to be most severe. More than 90% of the region experiences varying degrees of the UHI effects. (2) UHI is significantly correlated with both 2D and 3D urban morphological indicators. Low sky view factor (SVF) and high SVF tend to mitigate UHI, whereas moderate SVF tends to aggravate UHI. (3) In densely populated areas, tall trees may be more effective than other forms of vegetation at mitigating UHI. Based on the aforementioned findings, this article suggests that urban morphology optimization should focus on seasonality, spatial specificity, and indicator specificity for megacities in urban design and spatial planning aimed at mitigating UHI.

Evaluating the Spatial and Temporal Characteristics of Summer Urban Overheating through Weather Types in the Attica Region, Greece

In this study, we investigated the association between weather type (WT) and urban heat island intensity (UHII) in the region of Attica (Greece). The application of the methodology resulted in ten WTs over the Attica region. The UHII was calculated for every hour of the day from 2008 to 2017, using a new air temperature dataset produced by Copernicus Climate Change Service. To obtain more definitive findings about the relationship between WTs and UHII, we also used the upper 5% of UHII (urban overheating, UO). UO was estimated for two time intervals (daytime and nighttime) and for the warm period (June–September). The UHII frequency distribution, as well as the spatiotemporal characteristics of the UO, were also investigated. It was found that UO was amplified under WT2 during the night, while WT10 was mainly related to increased UO magnitudes in the daytime in all months. Furthermore, analysis results revealed that the UO effect is more pronounced in Athens during the night, especially at the Athens center. The daytime hot spots identified were mainly in suburban and rural areas. Therefore, this methodology may help with heat mitigation strategies and climate adaptation measures in urban environments.

Drivers of global surface urban heat islands: Surface property, climate background, and 2D/3D urban morphologies

Local climate of the city is modulated by not only land surface properties and climate background but also urban morphology. While the modulations of the former two have been extensively studied, there is still a lack of understanding of the importance of urban morphology, especially the three-dimensional (3D) morphology at a global scale. Here we examine the surface urban heat island (SUHI) intensities in 688 urban agglomerations worldwide and explore their driving factors in terms of surface properties, climate background, and 2D/3D urban morphologies, by using stepwise multiple linear regression and interpretable machine learning method. The results show that the interpretable machine learning method performs better in modeling SUHIs than traditional linear regression. Notably, urban morphologies, particularly its 3D pattern, have significant impacts on SUHIs. The overall contribution of urban morphology is comparable to that of surface property, and even surpasses the latter during the daytime in autumn and winter. Specifically, increased urban patch size or higher urban density can significantly amplify the SUHI intensities, while urban expansion characterized by scattered or multi-centered development exhibits the potential in mitigating SUHIs. In terms of 3D urban morphology, building height emerges as a crucial factor in shaping SUHIs. A rougher urban surface characterized by high-rise buildings can yield a cooling effect during the daytime while exacerbating the warming effect at night. Overall, our research provides valuable insights into the role of urban morphology in shaping SUHIs, offering guidance for urban planning strategies that promote sustainable and resilient cities.

Global mapping of urban thermal anisotropy reveals substantial potential biases for remotely sensed urban climates

Urban thermal anisotropy (UTA) drastically impacts satellite-derived urban surface temperatures and fluxes, and consequently makes it difficult to gain a more comprehensive understanding of global urban climates. However, UTA patterns and associated biases in observed urban climate variables have not been investigated across an adequate number of global cities with diverse contexts; nor is it known whether there are globally measurable factors that are closely related to the UTA intensity (UTAI, quantified as the maximum difference between the nadir and off-nadir urban thermal radiation). Here we investigate the UTAI over more than 5500 cities worldwide using multi-angle land surface temperature (LST) observations from 2003 to 2021 provided by Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that the global mean UTAI can reach 5.1, 2.7, 2.4, and 1.7 K during summer daytime, winter daytime, summer nighttime, and winter nighttime, respectively. Using nadir LST observations as a reference, our analysis reveals that UTA can lead to an underestimation of satellite-based urban surface sensible heat fluxes (H) by 45.4% and surface urban heat island intensity (Is) by 43.0% when using LST observations obtained from sensor viewing zenith angles (VZAs) of ±60°. Practitioners can limit the biases of H and Is within ±10% by using LSTs from sensor VZAs within ±30°. We also find that UTAI is closely related to urban impervious surface percentage and surface air temperature across global cities. These findings have implications for angular normalization of satellite-retrieved instantaneous LST observations across cities worldwide.

Water-energy-vegetation nexus explain global geographical variation in surface urban heat island intensity

Surface urban heat island (SUHI) is a key climate risk associated with urbanization. Previous case studies have suggested that precipitation (water), radiation (energy), and vegetation have important effects on urban warming, but there is a lack of research that combines these factors to explain the global geographic variation in SUHI intensity (SUHII). Here, we utilize remotely sensed and gridded datasets to propose a new water-energy-vegetation nexus concept that explains the global geographic variation of SUHII across four climate zones and seven major regions. We found that SUHII and its frequency increase from arid zones (0.36 ± 0.15 °C) to humid zones (2.28 ± 0.10 °C), but become weaker in the extreme humid zones (2.18 ± 0.15 °C). We revealed that from semi-arid/humid to humid zones, high precipitation is often coupled with high incoming solar radiation. The increased solar radiation can directly enhance the energy in the area, leading to higher SUHII and its frequency. Although solar radiation is high in arid zones (mainly in West, Central, and South Asia), water limitation leads to sparse natural vegetation, suppressing the cooling effect in rural areas and resulting in lower SUHII. In extreme humid regions (mainly in tropical areas), incoming solar radiation tends to flatten out, which, coupled with increased vegetation as hydrothermal conditions become more favorable, leads to more latent heat and reduces the intensity of SUHI. Overall, this study offers empirical evidence that the water-energy-vegetation nexus highly explains the global geographic variation of SUHII. The results can be used by urban planners seeking optimal SUHI mitigation strategies and for climate change modeling work.

Experimental Research with Computer Simulation (Case Study Of Urban Cool Island)

This research will describe experimental research methodology using computer simulation with the case study of urban cool island research. The urban cool island is one of the urban heat island mitigation technology strategies. This technology uses a geometric and material intervention process that forms an urban cool island in an area with high urban heat island intensity. The urban cool island will expand and reduce the temperature of the hot islands so that the urban heat island gets smaller. In the research process, a series of experimental methodologies were carried out. Experimental methodology is carried out on a regional scale will be difficult and expensive. So a computer simulation is needed to facilitate urban cool island experiments in the area. The stages of experimental research with computer simulations use two process stages (1) Quasi-measurement experiments and field observations in the UCI area, (2) Validation of field measurements versus simulation models, (3) Experiments with computer simulations by creating test models and comparison models. The scope of research on the scale of urban areas with experimental methodology becomes efficient with the help of computer simulations. In Urban Cool Island, several countries have also used this methodology. Computer simulation used: ENVI-MET, CFD-Ansys, and others. Before starting the research, a clear frame or research design is needed, determining variables, replicating the real conditions, mastering the use of simulations, and always paying attention to the results, which are continuously controlled and validated to produce simulations that are close to the real conditions.

Regulation of urban morphology on thermal environment across global cities

Urban morphology (UM) significantly alters the urban thermal environment (UTE), but a comprehensive understanding of the spatiotemporal variations of UM and its contribution to the UTE on a global scale is lacking. To address this gap, this study took 7644 global cities as the study area and applied satellite imagery and global human settlement layer data from 2018 to 2020 to evaluate the UM variations across global cities. Then, this study quantified the UTE by calculating the surface urban heat island intensity, and finally examined the relationship and contributions of UM metrics to the UTE using multiple regression and random forest model. The results reveal significant disparities in UM across global cities, with higher levels of landscape diversity and fragmentation, simpler urban shape, less continuity and aggregation in Asian and North American cities. Two-dimensional landscape configuration is found to have the greatest influence on the UTE (39.6%), followed by landscape composition (COMP) (32.1%), 3D configuration (19.3%), and urbanization (9.1%). However, in tropical and arid zones, COMP has the greatest influence. These findings provide valuable insights into global landscape patterns, and may help to design strategies to mitigate the urban thermal environment.

Spatial heterogeneity and impact scales of driving factors of precipitation changes in the Beijing-Tianjin-Hebei region, China

Changes in land surface properties during urbanization have a significant impact on variations in precipitation. Little research has been carried out on spatial heterogeneity and influence strength of the driving factors of precipitation changes at different urbanization scales. Using a trend analysis and multi-scale geographically weighted regression, this study analysed the spatial heterogeneity and impact scale of driving factors of precipitation changes in 156 urban units in the Beijing-Tianjin-Hebei urban agglomeration region (Jing-Jin-Ji). In summer, RAD (radiation), RHU (relative humidity), WIN (wind speed), and POP (urban population density) were found to act on a small regional scale, AOD (aerosol optical depth) on a medium regional scale, and NDVI (normalized difference vegetation index), NLI (night time light intensity), UHI (urban heat island intensity), and AREA (urban area size) on a global scale. In winter, AREA and WIN acted on a medium regional scale, UHI on a large regional scale, and AOD, NDVI and NLI on a global scale. Across the whole year, NDVI and AREA had a medium regional impact and NLI a large regional one. Variations in natural factors, such as RAD and RHU, had a great influence on the spatial heterogeneity of precipitation changes, whereas human factors, such as NLI and UHI, had a small influence. In summer, AOD mainly affected Tangshan and Qinhuangdao in the northeast and Cangzhou in the southeast of the Jing-Jin-Ji. RHU and AREA primarily affected the cities of Handan and Xingtai. In winter, NLI, AREA, WIN, and UHI had significant effects in the cities of Handan and Xingtai, with AREA being the most important factor. In the Shijiazhuang-Hengshui area, RAD and NLI played a significant role; in the Beijing-Zhangjiakou-Chengde area, the most important factor affecting precipitation changes was the variation in POP. These results provide a scientific basis for flood disaster risk management in the Jing-Jin-Ji and the establishment of differentiated climate policies in different cities.

An Assessment of Long-Term Urban Heat Island Impact on Istanbul’s Climate

Istanbul with its high urban population increase and rapid rate of its unplanned urbanization, it is critical to investigate the relations between urban heat island, population growth, and changes in the urban land use. The aim of this work is to study the local climate change and the effects of urbanization on urban climate of megacity Istanbul. Temporal variations of urban heat island (UHI) and UHI intensity in Istanbul were analyzed by using air temperature data measured by five meteorology stations for the period of 1951–2020. Minimum, maximum, and mean temperature data of Istanbul were used to understand the possible impacts of urbanization on the climate of the megacity. In addition, Corine Land Cover (CLC) datasets for the years 1990, 2000, 2012, and 2018, were used to visualize the spread of urban cover throughout the city. Seasonal observations data were statistically tested for monotonic trends. Significant upward trends in temperature were found in all selected stations, both rural and urban. The air temperature in urban areas, densely populated residential areas of the Istanbul, has increased more in years compared to its rural parts. The results showed an increase of night time (minimum temperature) temperature in both urban and rural stations between 0.010 – 0.047 °C yr-1 and an increase of daytime (maximum temperature) in all stations between 0.026 – 0.034 °C yr-1. The urban heat island impact increased over Istanbul with the expansion of the urban areas toward rural areas further reducing green areas.

Consideration of Altered Anthropogenic Behavior during the First Lockdown and Its Effects on Air Pollutants and Land Surface Temperature in European Cities

Substantial reductions in human and economic activities such as road traffic for several months in 2020 were one of the consequences of the Coronavirus pandemic. This unprecedented change in urban metabolism also affected temperature and air pollutants. This study investigates the effects of the first COVID-19 lockdown across 43 cities in Europe. It determines the influence of anthropogenic activities on nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5), as well as on land surface temperature (LST) and the surface urban heat island intensity (SUHII) using satellite, modeled, and mobility data. Our findings show that there are great temporal and spatial differences and distinct patterns between the cities regarding the magnitude of change in the variables under study. In general, the results indicate a substantial decrease in NO2 concentrations in most of the studied cities compared with the reference period of 2015–2019. However, reductions could not be attributed to mobility changes such as less traffic at transit stations, contrary to the results of previous studies. O3 levels increased during the first lockdown in accordance with the decreasing NO2 concentrations. The PM pattern was inconsistent over time and space. Similar to the NO2 results, no relation to the altered mobility behavior was found. No clear signal could be detected for LST and the SUHII, likely due to dominating meteorological influences.

Urban microclimate and climate change impact on the thermal performance and ventilation of multi-family residential buildings

Urban settings and climate change both impact on energy use and thermal comfort inside buildings. This paper first presents a study of changes in energy demand in residential buildings considering the overlapping effect of climate change and urban heat island intensity in two European locations; Cadiz (Spain) and London (United Kingdom), representing temperate and hot European climates and moderate and dense urban settings. Future-urban weather files were generated and simulations were run considering energy demand and indoor thermal comfort. In hot climate regions such as the one of Cadiz, future climate will increase the cooling demand and the additional impact of the UHI leads to a further increase of up to +28% of total energy demand compared to the current climate without considering urban effects. Future-urban weather conditions will be detrimental also for buildings in London, where the annual energy demand is predicted to increase by up to the 16% if future climate and urban effects are included. This is due to a higher increase in cooling demand compared to the reduction for the heating need. The paper also presents a method to take into account microclimatic conditions in naturally ventilated buildings, especially the effect of wind variations around the building which impacts natural ventilation rates. Air and surface temperature and wind speeds were studied using ENVImet and the resulting microclimatic conditions were used as inputs to the EnergyPlus Airflow Network model for the calculation of the building ventilation rates. It was found that ventilation rates are reduced (in comparison to meteorological weather files) and this reduction impacts negatively on internal operative temperatures. A thermal comfort analysis was carried out indicating that the selection of a suitable weather file and microclimatic conditions is essential for more accurate predictions of internal thermal comfort and will assist in the sizing of passive and active systems to avoid overheating.

An Analysis of the Spatial and Temporal Evolution of the Urban Heat Island in the City of Zhengzhou Using MODIS Data

A rapid increase in urbanization has caused severe urban heat island (UHI) effects in China over the past few years. Zhengzhou is one of the emerging cities of China where residents are facing strong impact of UHI. By utilizing MODIS data on land surface temperature (LST) and employing 3S technology, this study investigates the UHI phenomenon in Zhengzhou over a 10-year period (2012–2021), aiming to analyze the spatio-temporal evolution characteristics of the UHI effect and the associated land cover changes. To the best of our knowledge, this study represents the first attempt to investigate annual and seasonal changes in different areas of Zhengzhou. It is noted that in the night-time, the intensity of the heat island is stronger than in daytime, which has moderate and weak heat island areas. Seasonal variation showed that in autumn, Zhengzhou has the strong heat island intensity, followed by summer, and the lowest is in winter and spring. The analysis reveals that built-up (construction) areas exhibit the highest LST, whereas forested land and water bodies have the lowest temperature levels. The findings of this study can serve as reference for reducing UHI and increasing thermal comfort in cities.

Divergent urbanization-induced impacts on global surface urban heat island trends since 1980s

Urbanization experiences different speeds and forms under diverse development stages across the globe. However, urbanization-induced impacts on long-term surface urban heat island intensity (Is) trends across global cities and the regulators of such impacts remain understudied Here we estimate interannual trends in daytime Is (i.e., urban-rural differences in surface temperatures) across 511 major cities for 1985–2020 using annual averages calculated using reconstructed land surface temperature data derived from >250,000 Landsat thermal images. Our study reveals that the global mean Is growth rate is 0.156 °C/decade. We further examine Is change associated with per 1% impervious land growth (denoted as β) in each city throughout the research period and during different periods. The global mean β is 0.018 ± 0.025 °C/% (mean ± 1 standard deviation) for the whole period, with greater values in humid than in arid climates; and the β may change during different periods, e.g., it has more than tripled when urban impervious land exceeds 30%, indicating the spatiotemporally divergent impacts of urbanization on Is trends across global cities. The spatial variations in β across global cities are well correlated with rural vegetation abundance and precipitation but not with urban population. Among these three factors, rural vegetation abundance possesses the greatest standardized regression coefficient of partial least-squares model, signifying the critical role of biome background in regulating β. The finding implies that future urbanization over densely vegetated regions should be more carefully and strategically planned due to the greater urbanization-induced surface warming effect.