Characteristics Of Urban Heat Island Research Articles

High Resolution WRF Modelling of Extreme Heat Events and Mapping of the Urban Heat Island Characteristics in Athens, Greece

In recent decades, large-scale urbanisation has developed rapidly, resulting in significant changes in the local and regional environment and climate. Large metropolitan areas worldwide induce significant changes in local atmospheric circulation and boundary layer meteorology by modifying the underlying surface characteristics and through the emission of anthropogenic heat and pollutants into the atmosphere. We investigate the urban heat island (UHI) characteristics in the city of Athens, Greece, which is one of Europe’s largest metropolitan complexes with a population of approximately 3.7 million inhabitants. The UHI effect is intense due to the city’s size, dense construction, high incident solar radiation, and almost complete lack of natural vegetation, with previous studies suggesting a temperature rise of 4 °C on average in the city centre compared to summer background conditions. We used high-resolution WRF simulations (1-km horizontal grid) driven with ERA5 reanalysis data to produce surface temperature maps in the city of Athens and the surrounding areas (Region of Attiki) during the summer period of 1 July–20 August 2021. Different model parameterizations were tested, both with respect to urban characteristics and physical parameters. The daily minimum and maximum temperatures (Tmin and Tmax) derived from the model were validated against observational data from a dense network of weather stations covering metropolitan Athens and surrounding locations. We further investigate the influence of different meteorological conditions on the UHI gradients as produced by the model and the observational dataset, including the extreme heat wave of 28 July–5 August 2021, during which persistent maximum temperatures of >40 °C were recorded for nine consecutive days. The results indicate a strong correlation between WRF output and recorded minimum and maximum temperatures throughout the test period (R ranges from 0.80 to 0.93), with an average mean absolute bias (MAB) of 1.5 °C, and reveal the intensity and spatiotemporal variability of the UHI phenomenon in the city of Athens, with UHI magnitude reaching 8–9 °C at times. Our work aims to maximise the potential of using high-resolution WRF modelling for simulating extreme heat events and mapping the UHI effect in large metropolitan complexes.

Heat exposure assessment based on high-resolution spatio-temporal data of population dynamics and temperature variations

Urban heat waves pose a significant risk to the health and safety of city dwellers, with urbanization potentially amplifying the health impact of extreme heat. Accurate assessments of population heat exposure hinge on the interplay between temperature, population spatial dynamics, and the epidemiological effects of temperature on health. Yet, many past studies have over-simplified the matter by assuming static populations, leading to substantial inaccuracies in heat exposure assessments. To address these issues, this study integrates dynamic population data, fluctuating temperature, and the exposure-response relationship between temperature and health to construct an advanced heat exposure assessment framework predicated on a population dynamic model. We analyzed urban heat island characteristics, population dynamics, and heat exposure during heat wave conditions in Beijing, a major city in China. Our findings highlight significant intra-day population movement between urban and suburban areas during heat wave conditions, with spatial population flow patterns showing clear scale-dependent characteristics. These population flow dynamics intensify heat exposure levels, and the disparity between dynamic population-weighted temperature and average temperature is most pronounced at night. Our research provides a more comprehensive understanding of real urban population heat exposure levels and can furnish city administrators with more scientifically rigorous evidence.

Assessing the urban heat island effect of different local climate zones in Guangzhou, China

The urban heat island (UHI) effect has significant implications on urban development, human health, and climate change. Local climate zones (LCZs) provide an objective and systematic classification of field sites for studying the UHI effect. Accurately characterizing the UHI features in each LCZ is fundamental to mitigating the UHI effect. This study aimed to analyze the UHI effect in six LCZs in Guangzhou. Temperature data were collected simultaneously using fixed-point measurements over 405 days from October 1, 2019 to March 31, 2022. The UHI characteristics of the LCZs under various weather conditions were analyzed by examining the hourly, daily, and seasonal UHI intensities (UHIIs). Consistent diurnal variation patterns were observed in the daily average UHII under different weather conditions but with varying peak values. Ideal days with weak winds and clear skies significantly contribute to the development of maximum UHII, occurring most frequently between 18:00 and 23:00 with a frequency of 45%. In comparison, UHI events on precipitation days have a lower frequency. Furthermore, UHI effects varied significantly among different LCZs, with higher UHII observed in high-density built-up LCZs compared to open low-rise LCZs. Variations were particularly pronounced during the night in autumn and winter with a maximum UHII mean of 5.30 °C. UHI event frequencies and hourly warming/cooling rates were also examined to provide a more comprehensive assessment of the UHI effects on LCZs. This study is of great significance for understanding the UHI characteristics within Guangzhou, and for guiding urban planning and management to mitigate UHI in LCZs.

Predicting Urban Heat Island severity on the census-tract level using Bayesian networks

Urban development and population growth have resulted in several critical impacts on the society, environment, and economy. One of the main impacts is the increase in temperature observed in urban areas, also known as the Urban Heat Island (UHI) effect. UHI has become the focus of several research studies due to the associated negative implications it causes. Despite that some research efforts examined different characteristics of the UHI phenomenon, there is a gap in the literature in terms of developing interpretable machine learning models that can accurately estimate or predict the severity of UHI (rather than air temperature or other UHI characteristics) as well as that can provide predictions on the census-tract level in the US. To that extent, this paper fills this gap by developing a knowledge-based white-box Bayesian network model that predicts UHI severity based on demographic, meteorological, and land use/land cover factors. First, a dataset for all census tracts in the state of New Jersey, USA was developed, which is comprised of 13 independent variables or factors affecting UHI severity. Second, expert knowledge was obtained from 10 UHI experts using the systematic three-round Delphi method to develop four different Bayesian network models. Third, the performance of the four Bayesian models was assessed and compared to choose the optimal model with the highest accuracy. Finally, sensitivity analysis was conducted to assess the influence of each key factor on the UHI severity. The results showed that the optimal model is a tree-augmented Bayesian network that can predict or estimate the UHI severity with an accuracy of 87.88%. The outcomes of this paper also reflected that the following 8 variables are the key factors that impact UHI severity: NDVI during winter season, NDVI during summer season, imperviousness, tree canopy, building area, population density, water body areas, and annual rainfall. The findings also identified the land use/land cover category as the major category affecting UHI severity compared to demographic- and meteorological-related factors. The proposed white-box Bayesian network model in this paper adds to the body of knowledge by allowing practitioners and researchers to perform micro-level UHI predictions and inferences about future or unknown UHI severity pertaining to individual communities or small/localized geographical areas. This enables more focused and targeted UHI mitigation actions to be planned, designed, and implemented in the most affected communities, which ultimately results in better decisions, actions, and outcomes to reduce or address the UHI effect.

Assessing the synergies between heat waves and urban heat islands of different local climate zones in Guangzhou, China

Urban heat island (UHI) effects and heat waves (HWs) increase, posing a threat to human health and urban ecological systems. However, the synergistic mechanisms of UHI and HWs are yet to be fully elucidated. This study aims to assess the characteristics of UHI under HWs in different Local Climate Zones (LCZs) using temperature observations from Guangzhou, China. Fixed-point measurements were employed to simultaneously collect air temperature from nine LCZs in 2021. The results showed a localised interplay between HWs and UHI. During HWs, the UHI effect was significantly amplified in built-type LCZ, where the UHI intensity (UHII) was amplified by 0.1–0.5 °C throughout the day, 0.4–1 °C at night, but slightly reduced by 0–0.4 °C during daytime. The UHII was significantly reduced in land-cover type LCZ, and the urban cold effect was amplified during HWs. Furthermore, with strong solar radiation and low wind speed in HWs, the daily maximum UHII of LCZs increased 0.4–1.1 °C. Finally, correlations between UHII and morphological characteristics of LCZs are examined in HWs and non-heat waves (NHWs). During HWs, the increase in impervious surface fraction (ISF) and building surface fraction (BSF) and the decrease in sky view factor (SVF) exacerbate UHII, particularly at night. The augment in surface vegetation fraction (Fveg), does not significantly contribute to reducing the UHI and is less effective than NHWs at night. Therefore, different measures should be taken in different LCZs to mitigate UHII amplification during HWs. This study can serve as a reference for preventing responses to climate extremes.

On-site measurement and numerical simulation study on characteristic of urban heat island in a multi-block region in Beijing, China

The urban heat island (UHI) effect can lead to an increase in winter temperatures in cold regions, which in turn increases energy consumption and air pollution within urban areas. Field measurements of UHI effects can provide high-precision and high-resolution data, making them suitable for investigating the spatial-temporal distribution of UHI. However, there have been relatively few studies on the field testing of UHI effects in multiple blocks, especially those that involve continuous monitoring. Additionally, while many studies have demonstrated the impact factors of UHI intensity, there is a scarcity of research on quantitative and weight analyses of these factors. In this study, on-site measurements of UHI characteristics were carried out in a typical multi-block region in Beijing. The on-site sampling was conducted in both summer and winter seasons for 29 consecutive sampling days with 20 sampling points. The average urban heat island intensity (UHII) during the sampling period was 1.7 ± 1.2 °C in summer and 3.7 ± 1.8 °C in winter. Temperatures measured at different sampling locations varied significantly, with a maximum spatial difference of 7.5 °C in summer and 4.1 °C in winter. Simulation results showed that, in summer, each ±10% change in wind speed, albedo, or solar radiation intensity would cause the UHII to change by ∓0.16 °C, ∓0.21 °C, or ± 0.17 °C, respectively. In winter, the effects of these factors were very limited. The effect weights of wind speed, albedo, and solar radiation intensity on UHII in summer were 31.3%, 36.6%, and 32.1%, respectively. The position of green space in relation to the dominant wind direction of the city would have a greater effect on UHI mitigation in summer than in winter. This study provides a theoretical basis for mitigating UHI effects and is of great significance for sustainable urban design.

STUDY OF URBAN HEAT ISLAND IN YOGYAKARTA CITY USING LOCAL CLIMATE ZONE APPROACH

Yogyakarta urban area has increased significantly for the past ten years and altered ecological features, such as inducing urban heat island (UHI). Our objectives are examining UHI characteristics in Yogyakarta urban with atmospheric variables which include air temperature and relative humidity and analyzing the UHI distribution using the LCZ method. This study uses the LCZ classification to spatially compare thermal characteristic and explain how land use and building geometry affect UHI. The system comprises 17 standard classes at the local scale, using quantitative approach which includes numerical data, such as aspect ratio, building heights, and street canyons to support the classification system. Three LCZ classes found in Yogyakarta urban area are LCZ 3, LCZ 5, and LCZ 6. The LCZ variables which affect temperature and relative humidity are building heights, width of street canyons, and land use. The biggest thermal difference is ΔLCZ3-LCZ5 and ΔLCZ3-LCZ6, which happened during 08.00 – 12.00 and 16.00 – 20.00. Small UHI magnitude (< 2K) is affected by small difference in morphology and fabric. Medium UHI magnitude (2 – 5K) is mostly caused by large difference in fabric and small difference in morphology. In the future, the LCZ should be widely applied for urban planning regarding climatic mitigation.

Impact of land use land cover on variation of urban heat island characteristics and surface energy fluxes using WRF and urban canopy model over metropolitan cities of India

The present study focuses on the assessment of urban heat island (UHI) and urban energy fluxes over Indian metropolitan cities. For this purpose, a high-resolution numerical Weather Research and Forecasting (WRF) model and a coupled single-layer urban canopy model (WRF-UCM) has been implemented over Delhi, Kolkata and Hyderabad to evaluate the performance of these models in capturing the impact of urban areas in simulating the UHI over different land use land cover (LULC) classes. The model initial conditions are obtained from NCEP-FNL reanalysis data. The model simulations reveal that the air temperature at 2 m height (T2) and UHI intensity are overestimated by WRF whereas the WRF-UCM simulations are matching well with ERA5-Land reanalysis data considered as observations. For the WRF-UCM simulation, the statistical errors in T2 and UHI intensity are within the desirable limits and the index of agreement (IoA) is more than 0.8 over the built-up areas (BAs). By comparing with the satellite observations, the relative surface UHI is noticed to be better captured over the BA by WRF-UCM simulation. The sensible heat flux simulated by WRF is highly overestimated whereas WRF-UCM is in good agreement with the satellite observation over the BA. Interestingly, the latent heat flux is reasonably well simulated over all the LULC classes. The results derived from the present study have shown the performance of WRF-UCM in simulating the spatial variation of UHI and fluxes of sensible and latent heat-related varied LULC classifications over metropolitan cities of India.

Comparison of Urban Heat Island Diurnal Cycles under Various Atmospheric Conditions Using WRF-UCM

The rapid growth of urbanization significantly influences local atmospheric conditions and life quality of residents living in urban areas by creating a localized phenomenon known as an urban heat island. Urban heat island characteristics are strongly formed by prevailing atmospheric conditions influencing their magnitude and intensity. In this study, we used the Weather Research and Forecasting model to investigate the effects of different airmass and windspeed characteristics on the diurnal cycle of the urban heat island phenomenon during four real weather situations presenting typical scenarios with clear sky conditions. The scenarios consisted of warm dynamic and non-dynamic situations and cold dynamic and non-dynamic situations identified with respect to temperature profiles, humidity and wind speed. The comparison of urban heat island intensity between all four scenarios showed a significant effect of wind speed on urban heat island characteristics and intensity as well as the role of humidity and airmass temperature in diurnal changes. The results showed that urban heat island is best defined by conditions with strong radiative heating and weak wind speed regardless of temperature. Air humidity appears to have significant influence on UHII with cold non-dynamic situations in dry air showing a very high UHII amplitude during the daylight period and high intensity during the night. The comparison of warm and cold dynamics situations showed the influence of vertical heat exchange with strong mixing of air between near-surface levels and lower levels of the troposphere with a mild effect on UHII during the warm situation and strong influence on decreasing UHII during the cold scenario.

Diverse cooling effects of green space on urban heat island in tropical megacities

Cities in tropical regions are experiencing high heat risks by overlaying the urban heat island (UHI) effect. Urban green space (UGS) can provide local cooling effect and reduce UHI. However, there still lack a comprehensive exploration of the characteristics of UHI and cooling effect of UGS due to high cloud coverage and limited number of available remote sensing observations. In this study, the enhanced spatial and temporal adaptive reflectance data fusion method was employed to develop an enhanced land surface temperature data in winter seasons in three tropical megacities, Dhaka, Kolkata, and Bangkok. The spatiotemporal variations of surface urban heat island (SUHI) were explored from 2000 to 2020 with a 5-years interval. The optimal size of UGS associated with its cooling effects was assessed by using the threshold value of efficiency (TVoE). The relationship between the intensity and range of urban cooling island (UCI) and four landscape metrics of green space patches, total area (P_Area), shape index (P_SI), normalized difference vegetation index (P_NDVI), and land surface temperature (P_LST), were analyzed. The results show that the average SUHI intensity increased by 0.98°C, 1.42°C, and 0.73°C in Dhaka, Kolkata, and Bangkok, respectively, from 2000 to 2020. The maximum intensity of UCI ranges from 4.83°C in Bangkok to 8.07°C in Kolkata, and the maximum range of UCI varies from 300 m in Bangkok to 420 m in Kolkata. The optimal size of green space is 0.37 ha, 0.77 ha, and 0.42 ha in Dhaka, Kolkata, and Bangkok, respectively. The P_NDVI and P_Area had significant positive effects on UCI intensity and range, while the background temperature had significant negative effects. With higher background temperature, the optimal patch size of UGS is larger. This study provides useful information for developing effective heat mitigation and adaptation strategies to enhance climate resilience in tropical cities.

Natural-anthropogenic environment interactively causes the surface urban heat island intensity variations in global climate zones

The inconstant climate change and rapid urbanization substantially disturb the global thermal balance and induce severe urban heat island (UHI) effect, adversely impacting human development and health. Existing literature has revealed the UHI characteristics and driving factors at an urban scale, but interactions between the main factors of a global grid scale assessment on the context of climate zones remain unclear. Therefore, based on the multidimensional climatic and socio-economic statistical datasets, the multi-time scale of surface urban heat island intensity (SUHI) characteristics was investigated in this study to analyze how natural-anthropogenic drivers affect the variance of SUHI and vary in their importance for the changes of other interaction factors. The results show that the mean value of SUHI in summer is higher than in winter, and in daytime is higher than in nighttime on a seasonal and daily scale. SUHIs in different global climate zones have significant differences. When analyzing drivers’ contributions and interactions with LightGBM model and SHAP algorithm, we know that monthly precipitation (PREC), the estimated population (POP) and surface pressure (PRES) are the three major drivers of daytime SUHI. The nighttime SUHI is mainly PREC, POP and anthropogenic heat emission (AHE), the influence rules of the natural driversare mostly opposite to that of daytime. This study highlights the fundamental role of background climate for designing strategies. Irrigation or artificial rainfall will be effective to mitigate SUHI in low rainfall areas, while it is more effective to reduce AHE in high rainfall areas. In where greening can be difficult in the most developed cities, reducing AHE, increasing per capita GDP and controlling the population scale may also contribute to alleviating the SUHI. This study provides ideas for developing responsive urban heat island mitigation policies in a more realistic setting.

Characteristics of Urban Heat Island in China and Its Influences on Building Energy Consumption

Urban heat island (UHI) draws more attention as it affects not only the health of residents but also the energy consumption of buildings at the city scale. To achieve carbon neutrality goals, it is crucial to better understand the mechanism of the UHI influences on building energy consumption. The characteristics of urban heat island intensity (UHII) and the relationship between the UHII effect and building electricity and related coal consumption were analyzed, based on the long period of monitoring data with hourly weather data from 1 January to 31 December 2019. Results show that a strong correlation between the annual mean UHII and the median daily mean UHII exists. The synthetic diurnal UHII of most cities presents a U-shaped variation trend. In different building climate zones in China, namely, severe cold region (SCR), cold region (CR), hot summer cold winter region (HSCWR), hot summer and warm winter region (HSWWR), and mild region (MR), the influences of UHII on building energy consumption were analyzed. The existence of UHI reduces building energy consumption in 96.7% of SCR cities and 60.8% of CR cities, while in HSCWR, HSWWR, and MR cities, the percentage of cities where the building energy consumption is increased by UHI is 69.4%, 80%, and 63.6%, respectively. Urban climate strongly influences building energy consumption, indicating that it should be considered and analyzed in detail for making future urban development or carbon emission reduction strategies.

Surface urban heat island and its relationship with land cover change in five urban agglomerations in China based on GEE.

The development of urbanization has changed the original land cover and exacerbated the urban heat island effect, seriously affecting the sustainable development of the ecological environment. Research on urban heat island characteristics and land cover changes in five major urban agglomerations in China to provide a reference for preventing thermal environmental risks and urban agglomeration construction planning. This paper estimates the surface urban heat island intensity (SUHII) of the five major urban agglomerations in China from 2003 to 2019 based on Google Earth Engine (GEE) through the urban-rural dichotomy, analyzes their trends through the Sen + M-K trend analysis method, and combines the detrending rate matrix to analyze the impact of land cover type shift on urban heat island change. Research shows that (1) the land cover types of the five major urban agglomerations in China have changed considerably from 2003 to 2019, and all five major urban agglomerations in China experienced varying degrees of urban expansion. (2) The annual average value of SUHII decreases in Beijing-Tianjin-Hebei, Yangtze River Delta, and middle reaches of the urban agglomerations, while the annual average value of SUHII increases in Chengdu-Chongqing and Pearl River Delta urban agglomerations. (3) The spatial composition of land cover types in the five major urban agglomerations in China is highly spatially correlated with urban heat islands, with urban land and bare land urban heat islands being the most pronounced. (4) The land cover type shift has the most significant heat island impact on Beijing-Tianjin-Hebei, Yangtze River Delta, and Chengdu-Chongqing urban agglomerations. (5) The land cover change (LCC) with an increasing trend in SUHII is mainly bare land converted to arable land, and water bodies, grassland, forest land, and arable land converted to urban land.

Urban heat island of Yangtze River Delta urban agglomeration in China: Multi-time scale characteristics and influencing factors

Because the scale of the urban agglomeration is much bigger than that of a single city, the urban agglomeration may present different spatio-temporal urban heat island (UHI) patterns from those of a single city. Here in this paper, the multi-time scale UHI characteristics in the Yangtze River Delta urban agglomeration were investigated on the basis of remote sensing data, and the factors influencing the UHI effect were also analyzed with the meteorological data and the economic statistics. The results showed that the characteristics of temperature distribution in the daytime were more consistent with the basic definition of UHI than those in the nighttime. The monthly characteristics of UHI in the study area showed a regular movement pattern, and August was the most suitable month for the yearly UHI characteristics analyses. The yearly UHI variations coincided with the urbanization processes in the past 20 years. It was also found that in the meteorological factors, only the average temperature and the precipitation in the central region had linear relationships with the UHI areas. While the correlations between the UHI areas and various economic statistics factors were very significant.

LST-based threshold method for detecting UHI in a complex urban landscape

Urban Heat Island (UHI) explains the occurrence of excessive heat that spread unevenly in urban areas. Intense and complex human activities in urban areas are mainly causing the formation of UHI. This study aims to identify the spatial characteristics of UHI in a complex urban feature, like the Universitas Indonesia campus area and its surroundings in Depok City. The campus and its surroundings might represent a complex urban area and can be used as a reference to understand the widespread impact of UHI. UHI was determined based on the threshold value, calculated from the average and standard deviation of the surface temperature. This study utilized Landsat 8 OLI-TIRS imagery to extract Land Surface Temperature (LST) value. The LST observations were collected for three different periods, including 2013, 2017, and 2021. The LST value tends to be increased during these times, with the LSTmax recorded at 34.68 °C, 35.47 °C, and 36.4 °C for 2013, 2017, and 2021, respectively. Based on the number of the accommodated grid, the UHI was getting larger every year. Started from 313 grids in 2013, the UHI has been expanded to more than 550 grids in 2021.

Temporal characteristics of urban heat island and its response to heat waves and energy consumption in the mountainous Chongqing, China

In this study, the time series of temperature in Chongqing, China, from 1979 to 2018 was statistically analyzed, and both the interdecadal and seasonal variation characteristics of urban heat island intensity (UHII) were studied. The results show that the temperature in Chongqing has risen significantly in the past 40 years and that the temperature of the Chongqing urban area is approximately 1.6 times higher than the global warming trend. During the study period, the annual average UHII at urban and rural stations increased significantly at a rate of +1.3 °C/century. The seasonal variation in the nighttime UHII was higher in summer, while the seasonal variation in the daytime UHII was higher in winter. In addition, the annual average UHII was found to be related to the heat wave (HW) days, cooling degree days (CDD), and heating degree days (HDD). With an increase of 1 °C in the UHII, the HW days in urban area increased by approximately 10 d, the average difference of the CDD increased by approximately 72 °C•d, and the average difference of the HDD decreased by approximately 86 °C•d.

Temperature patterns along an arid coastline experiencing extreme and rapid urbanization, case study: Dubai

Today more than one billion people are living in coastal regions, and coastal urbanization is rapidly growing worldwide. Here, we explore the impact of an extreme and rapid coastal urbanization on temperature patterns, based on MODIS data. We study Dubai, one of the fastest growing cities in the world over the last two decades. Dubai's urbanization centers along its coastline – in land, massive skyscrapers and infrastructure have been built, while in sea, just nearby, unique artificial islands have been constructed. Studying the coastline during the years of intense urbanization, we show that the coastline exhibits surface urban heat island characteristics, where the urban center experiences higher temperatures, by as much as 2.5 °C and more, compared to the adjacent less urbanized zones. During development, the coastal surface urban heat island has nearly doubled its size, expanding towards the newly developed areas. This newly developed zone also exhibits the largest temperature trend along the coast of over 0.1 °C/year on average. In addition, an opposite linear relation was found between the surface temperature and albedo trends. In sea, temperature decreases were observed, particularly over the artificial islands, exceeding −0.1 °C/year, in one of them. The positive trends in land along with the negative trends in sea decreased the coastal sea-land temperature gradient by up to about −30% in only 12 years, which also decreased the land breeze intensity. The findings shown here directly affect the local coastal population and ecosystem, exacerbate the thermal comfort, and add additional burden to this area, which is already considered as one of the warmest in the world.

Detecting urban landscape factors controlling seasonal land surface temperature: from the perspective of urban function zones.

Understanding the impact on the thermal effect by urbanization is of great significance for urban thermal regulation and is essential for determining the relationship between the urban heat island (UHI) effect and the complexities of urban function and landscape structure. For this purpose, we conducted case research in the metropolitan region of Beijing, China, and nearly 5000 urban blocks assigned different urban function zones (UFZs) were identified as the basic spatial analysis units. The seasonal land surface temperature (LST) retrieved from remote sensing data was used to represent the UHI characteristics of the study area, and the surface biophysical parameters, building forms, and filtered landscape pattern metrics were selected as the urban landscape factors. Then, the effects of urban function and landscape structure on the UHI effect were examined based on the optimal results of the ordinary least squares and geographically weighted regression models. The results indicated that (1) Significant spatiotemporal heterogeneity of the LST was found in the study area, and there was an obvious temperature gradient with "working-living-resting" UFZs. (2) All types of urban landscape factors showed a significant contribution to the seasonal LST, in the order of surface biophysical factors > building forms > landscape factors; however, their contributions varied in different seasons. (3) The major contributing factors showed a certain difference due to the variation of urban function and landscape complexity. This study expands the understanding on the complex relationship among urban landscape, function, and thermal environment, which could benefit urban landscape planning for UHI alleviation.

Analysis of urban heat island characteristics and mitigation strategies for eight arid and semi-arid gulf region cities

The aim of the study is, therefore, to analyze the formation of the UHIs in eight different cities in arid and semi-arid regions. The analysis is based on land cover (LC) classification (urban, green, and bare areas). The study found that bare areas had the highest mean LST values compared to the urban and green areas. The results show that the difference in temperatures between the bare areas and the urban areas ranges between 1 and 2 °C, between the bare areas and green areas ranges between 1 and 7 °C, and between the urban areas and green areas ranges between 1 and 5 °C. Furthermore, the LST values varied for each of the LULC categories, and hence some areas in the three categories had lower or higher LST values than in other categories. Hence, one category may not always have the highest LST value compared to other categories. The outcomes of this study may, therefore, have critical implications for urban planners who seek to mitigate UHI effects in arid and semi-arid urban areas.

Analyzing the Characteristics of UHI (Urban Heat Island) in Summer Daytime Based on Observations on 50 Sites in 11 LCZ (Local Climate Zone) Types in Xi’an, China

Urbanization has induced significant changes on local climate in urban areas. For sustainable urban planning, it is necessary to identify the distribution characteristics of urban heat island (UHI) and the effects of land cover properties. In situ measurements are frequently carried out to obtain critical data in urban climate studies. However, long-time continuous observations on multiple sites are still rare, even though they would be useful in mapping the distribution of UHI intensity. In the current work, three observation campaigns were carried out in Xi’an, China. Pedestrian- level air temperatures (PLAT) were measured with potable micro-environment stations on 50 sites in 11 local climate zone (LCZ) types. The normalized PLAT was used to investigate the canopy layer UHI characteristics and the effects of LCZ types. Results revealed that the land coverage type and the surface structure have significant influence on the local climate. The PLAT in high-surface-fraction building covered areas was higher than that in low-surface-fraction building covered areas. In areas with similar building surface fraction, building height influences the UHI magnitude as well, as tall buildings would provide more shielding on the pedestrian level. The average UHI magnitude and the standard deviation within each LCZ type were calculated by statistical analysis of the observed results, which proved to be useful for UHI mapping based on the LCZ classification results in urban areas.