Analysis Of Urban Heat Island Research Articles

Quantitative assessment and driving factors analysis of surface urban heat island of urban agglomerations in China based on GEE.

The urban heat island (UHI) effect generated by the development of high-speed urbanization has become one of the major problems affecting the urban ecological environment. As the main body of urbanization in China, China's urban agglomerations are the core areas of urban heat island effect. The purpose of this study is to study the spatial-temporal characteristics and driving factors of surface urban heat island in 19 urban agglomerations in China, with a view to providing theoretical references for the prevention of urban thermal environmental risks. Based on Google Earth Engine (GEE), this paper estimated the surface urban heat island intensity (SUHII) of 19 urban agglomerations in China from 2003 to 2019 using MODIS land surface temperature (LST) data. Correlation analysis and regression analysis were used to explore the correlation between the change of SUHII and driving factors. Finally, the driving factors of SUHII were detected by the geo-detector model. Results showed that (1) the SUHII of 19 urban agglomerations in arid and semi-arid areas of northwestern China is higher than that in humid areas of eastern and southeastern China. (2) The SUHII of 19 urban agglomerations in China generally shows a decreasing trend, and the spatial variation of the change trend is significant. (3) There are positive correlations between SUHII and reference evapotranspiration (ET0), population density (POP), gross domestic product (GDP), nitrogen dioxide (NO2), ozone (O3), and ultraviolet aerosol index (UVAI); negative correlations with normalized difference vegetation index (NDVI), DEM, sulfur dioxide (SO2), carbon monoxide (CO), and formaldehyde (HCHO); the correlations all pass the significance test of P < 0.05 and are statistically significant. (4) The factor detection results showed that NDVI, land cover type (LC), and UVAI were the main driving factors of SUHII. The interaction detection results showed that the interaction between O3 and UVAI had the most significant impact on SUHII.

Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing

Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 km2 and 2293 km2, while the maximum nighttime differences were 484 km2 and 265 km2. Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands.

Comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta region

Compared with surface temperature, the near-surface temperature is more related with human health. However, extensive researches have been conducted on the UHI effect globally using surface temperature considering its accessibility. In this study, a comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta Region is investigated. This study first proposed a spatialization method suitable for air temperature in highly urbanized areas with complex land cover. Based on this method, a dataset of 1-km gridded air temperature is developed, and an in-depth analysis of the changes of near-surface and surface heat island is further carried out. Results show that both the near-surface urban heat island intensity (NSUHII) and surface urban heat island intensity (SUHII) are rather strong over the past 20 years, presenting similar spatial distributions as well. However, in the rapidly expanding urban areas especially during summer and winter seasons, the difference in magnitude and time variations (R) between NSUHII and SUHII are pronounced. Hence, adaptions and mitigation strategies on NSUHI and SUHI should be developed and implemented separately in such occasions, which is especially important for developed areas such as Yangtze River Delta Region.

Remote sensing-based seasonal surface urban heat island analysis in the mining and industrial environment.

Cooling spaces have an optimistic influence on surface urban heat islands (SUHI). Blue spaces benefit from balancing thechanging climate and heat variations. Because of the rapid deforestation and SUHI increase, the climate is gradually changing in Paschim Bardhhaman, West Bengal state, India. Paschim Bardhhaman has two sectors: specifically, Durgapur is the main industrial centre and Asansol has coal mines. This investigation aims to categorize spatiotemporal variations and seasonal differences in cooling spaces and their influence on SUHI, land use and land cover (LULC), and thermal differences using Landsat datasets for the years 1992, 2004, 2012, and 2022 in summer and winter. The coal mining and industrial range decreased from 10,391.92 (1992) to 3591.1ha (2022), respectively. Open pit mining distresses fresh water by heavy water uses in ore processing, and mining water was applied to excerpt minerals. Among the two sub-divisions, the blue space amount was higher in Asansol because mining actions were higher in Asansol than in Durgapur. The open vegetation volume has reduced from 46,441.03 (1992) to 25,827.55ha (2022) and dense vegetation has erased from 7368.02 (1992) to 15,608.56ha (2022). Dense vegetation improved because of heavy precipitation in those regions. Mostly, Raghunathpur, Saraswatiganja, Bhagabanpur, Bistupur, Paschim Gangaram, Garkilla Kherobari, and Gourbazar have dense vegetation. The outcomes similarly demonstrate that the total built-up part has increased by 8412.82ha in between 30years. The built-up zone changes near the southeast and western Paschim Bardhhaman district. Those region needs appropriate attention and planning to survive soon.

An analysis of urban heat island based on the temperature inversion across the atmospheric boundary layer in Mashhad, Iran (2018–2022)

An analysis of urban heat island based on the temperature inversion across the atmospheric boundary layer in Mashhad, Iran (2018–2022)

Surface urban heat island analysis based on local climate zones using ECOSTRESS and Landsat data: A case study of Valencia city (Spain)

Extreme heat events in cities are becoming more intense, frequent and prolonged. The spatiotemporal dynamics of surface temperature are closely related to land cover and atmospheric conditions, especially for urban areas with extensive surface heterogeneity. Two high spatial resolution satellites, ECOSTRESS and Landsat, provide land surface temperature (LST) for a typical Mediterranean climate city, Valencia, Spain. In total of 17 images throughout two heatwaves in the period of July and August from 2022 to 2023 were selected to monitor LST and surface urban heat island (SUHI). The Local Climate Zone (LCZ) scheme and hotspot analysis were applied to analyze the patterns of LST and SUHI during and after the heatwaves. Daytime images show LCZ 2/3/6/8 and LCZ C/E/F exhibit elevated SUHI intensity, meanwhile the LCZ 4, A/B and G have lower SUHI. The SUHI intensity of compact buildings (LCZ 1/2/3), LCZ 8/E/G are significantly higher at night, especially on heatwave days, the SUHI of all types increased by 0.1–0.9 °C. Furthermore, the meteorological parameters were introduced trying to explain the obvious diurnal difference of SUHI during heatwaves. This research serves as a proxy for understanding the spatiotemporal dynamics of SUHI during heatwave events, offering valuable insights for city planners and policymakers to enhance thermal comfortable level and effectively cope with extreme weather.

The Effect of Land Cover Change on Urban Heat Island Phenomenon Based on Remote Sensing in Probolinggo City

Probolinggo City is located in East Java Province, Indonesia. Based on BPS data for the City of Probolinggo in 2023, it shows an increase in population growth rate of 1.7% annually. The development of the number and increase in the activity of the population of Probolinggo City certainly affects the increase in the number of built-up areas to meet human needs. An increase in the number of built-up land has led to changes in the environment and urban land use, characterized by declining air quality and global warming due to the increasing phenomenon of Urban Heat Island (UHI). This research was conducted to determine the spatial distribution of surface temperature and the Urban Heat Island (UHI) phenomenon and the effect of land cover on the UHI phenomenon. The research was conducted using UHI analysis using the Landsat 8 OLI remote sensing approach to obtain surface temperature values and high-resolution aerial photographs through data extraction. Based on the data processing results carried out over five years in 2013, 2017, and 2022, it is known that high and very high temperatures dominate the surface temperature of Probolinggo City. In addition, there is an effect of land cover on surface temperature based on the results of a simple linear regression analysis.

Machine Learning for Urban Heat Island (UHI) Analysis: Predicting Land Surface Temperature (LST) in Urban Environments

This study investigates how urban configuration influences the distribution of heat, known as the Urban Heat Island (UHI) effect, in Shiraz, Iran. Several Machine Learning algorithms are employed to analyze Land Surface Temperature (LST) data across various land cover types, including built-up, soil, and vegetation. The analysis reveals that Deep Neural Networks (DNNs) and Extreme Gradient Boosting (XGB) models excel at predicting LST, outperforming other methods. These results highlight the significant impact of land use on LST patterns within the metropolitan regions. Furthermore, the study assesses the influence of specific configuration metrics within each land cover category. This allows researchers to pinpoint which urban morphology features most significantly affect LST. These insights can inform targeted interventions and management strategies implemented to mitigate heat and improve thermal comfort in specific areas of Shiraz.

Satellite Time-Series Analysis for Thermal Anomaly Detection in the Naples Urban Area, Italy

Naples is the most densely populated Italian city (7744 inhabitants per km2). It is located in a particular geological context: the presence of Mt Vesuvius characterizes the eastern part, and the western part is characterized by the presence of the Phlegrean Fields, making Naples a high-geothermal-gradient region. This endogenous heat, combined with the anthropogenic heat due to intense urbanization, has defined Naples as an ideal location for Surface Urban Heat Island (SUHI) analysis. SUHI analysis was effectuated by acquiring the Land Surface Temperature (LST) over Naples municipality by processing Landsat 8 (L8) Thermal Infrared Sensor (TIRS) images in the 2013–2023 time series by employing Google Earth Engine (GEE). In GEE, two different approaches have been followed to analyze thermal images, starting from the Statistical Mono Window (SMW) algorithm, which computes the LST based on the brightness temperature (Tb), the emissivity value, and the atmospheric correction coefficients. The first one is used for the LST retrieval from daytime images; here, the emissivity component is derived using, firstly, the Normalized Difference Vegetation Index (NDVI) and then the Vegetation Cover Method (VCM), defining the Land Surface Emissivity (LSɛ), which considers solar radiation as the main source of energy. The second approach is used for the LST retrieval from nighttime images, where the emissivity is directly estimated from the Advance Spaceborne Thermal Emission Radiometer database (ASTER-GED), as, during nighttime without solar radiation, the main source of energy is the energy emitted by the Earth’s surface. From these two different algorithms, 123 usable daytime and nighttime LST images were downloaded from GEE and analyzed in Quantum GIS (QGIS). The results show that the SUHI is more concentrated in the eastern part, characterized by intense urbanization, as shown by the Corine Land Cover (CLC). At the same time, lower SUHI intensity is detected in the western part, defined by the Land Cover (LC) vegetated class. Also, in the analysis, we highlighted 40 spots (10 hotspots and 10 coldspots, both for daytime and nighttime collection) that present positive or negative temperature peaks for all the time series. Due to the huge amount of data, this work considered only the five representative spots that were most representative for SUHI analysis and determination of thermal anomalies in the urban environment.

Feasibility of Urban–Rural Temperature Difference Method in Surface Urban Heat Island Analysis under Non-Uniform Rural Landcover: A Case Study in 34 Major Urban Agglomerations in China

The urban–rural temperature difference is widely used in measuring surface urban heat island intensity (SUHII), where the accurate determination of rural background is crucial. However, traditionally, the entire permeable rural surface has been selected to represent the background temperature, leaving uncertainty about the impact of non-uniform rural surfaces with multiple land covers on the accuracy of SUHII quantification. In this study, we proposed two quantifications of SUHII derived from the primary (SUHII1) and secondary (SUHII2) land types, respectively, which successively occupy over 40–50% of whole rural regions. The spatial integration and temporal variation of SUHII1 and SUHII2 were compared with the result from whole rural regions (SUHII) within 34 urban agglomerations (UAs) in China. The results showed that the SUHII1 and SUHII2 differed slightly with SUHII, and the correlation coefficients of SUHII and SUHII1/SUHII2 are generally above 0.9 in most (32) UAs. Regarding the long-term SUHII between 2003 and 2019, the three methods demonstrated similar seasonal patterns, although SUHII1 (or SUHII2) tended to overestimate or underestimate compared to SUHII. As for the multi-year integration at the regional scale, the day–night cycle and monthly variations of SUHII1 and SUHII were found to be identical for each geographical division separately, indicating that the spatiotemporal pattern revealed by SUHII is minimally affected by the diversity of rural landcover types. The findings confirmed the viability of the urban–rural LST difference method for measuring long-term regional SUHII patterns under non-uniform rural land cover types.

Urban heat island analysis based on high resolution measurement data: A case study in Beijing

Urban heat island (UHI) is a widely concerned urban climate which impacts billions of residents worldwide. Nevertheless, inadequate rigorous air temperature (Tair) measurement has become an obstacle to UHI research and mitigation, especially in megacities like Beijing. In this study, we applied a 1-km hourly Tair dataset interpolated from records of 521 stations in Beijing. The dataset enables us to analyze UHI patterns on a block scale and the impacts of landscapes combined with the local climate zones (LCZ) scheme. Our results indicate that: (1) Tair and its temporal variation in each LCZ vary significantly, which are highly relevant to their morphology and thermal-radiative properties. (2) LCZ 1 and LCZ 2 have the highest nocturnal UHI intensities (UHII). Discrepancies among LCZs also cause various temporal variations in their UHII. (3) The spatial pattern of LCZs affects regional temperature. Area ratio and aggregation extent of LCZ 1&2 show significant positive correlations with regional temperature, while LCZ A&D&G have the opposite regularity. (4) Distinct mitigation strategies are put forward for each subarea in Beijing with various endowments of population and the LCZ structure. The study provides insight into Beijing UHI and plausible mitigation advice in megacities like Beijing.

Seasonal surface urban heat island analysis based on local climate zones

A fundamental aspect of ensuring urban sustainability is a comprehensive understanding of the driving mechanisms behind the urban heat island (UHI) phenomenon. The primary objective of this study is to investigate the spatiotemporal variations and underlying mechanisms of the surface urban heat island (SUHI) in Hefei. The study employed the local climate zone (LCZ) method to analyze land surface morphology and spatial structure for 2014 and 2021. Subsequently, calculations were conducted to derive surface urban heat island intensity (SUHII), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), gravity water index (GWI), building surface fraction (BSF), road density (RD), poi density (PD), and population density (PPD). The exploration of the mechanisms by which factors influence SUHI was conducted by utilizing both Pearson correlation analysis and geographic detector models. The results revealed that sparsely built (LCZ 9) and low plants (LCZ D) predominantly characterized built-up and natural coverage areas, respectively. The summer season was distinguished by the most extensive SUHI distribution and the highest SUHII levels. Significantly, SUHII consistently exceeded those of built-up LCZs when contrasted to natural LCZs. Large lowrise (LCZ 8) consistently displayed the highest SUHII levels, whereas water (LCZ G) consistently exhibited the lowest SUHII values. NDBI took precedence and showed a positive correlation with SUHI. Among the socio-economic factors, building height (BH) demonstrated a superior explanatory capability for SUHI compared to other variables. The interaction between NDBI and NDVI maximized the explanation of SUHI under different seasons. The findings of this study will serve as critical insights for urban planners and policymakers, enabling the development of scientifically-based and efficacious strategies to mitigate SUHI.

Evaluating the Reconstructed All-Weather Land Surface Temperature for Urban Heat Island Analysis

With the continuous improvement of urbanization levels in the Lhasa area, the urban heat island effect (UHI) has seriously affected the ecological environment of the region. However, the satellite-based thermal infrared land surface temperature (LST), commonly used for UHI research, is affected by cloudy weather, resulting in a lack of continuous spatial and temporal information. In this study, focusing on the Lhasa region, we combine simulated LST data obtained by the Weather Research and Forecasting (WRF) model with remote sensing-based LST data to reconstruct the all-weather LST for March, June, September, and December of 2020 at a resolution of 0.01° while using the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST as a reference (in terms of accuracy). Subsequently, based on the reconstructed LST, an analysis of the UHI was conducted to obtain the spatiotemporal distribution of UHI in the Lhasa region under all-weather LST conditions. The results demonstrate that the reconstructed LST effectively captures the expected spatial distribution characteristics with high accuracy, with an average root mean square error of 2.20 K, an average mean absolute error of 1.51 K, and a correlation coefficient consistently higher than 0.9. Additionally, the heat island effect in the Lhasa region is primarily observed during the spring and winter seasons, with the heat island intensity remaining relatively stable in winter. The results of this study provide a new reference method for the reconstruction of all-weather LST, thereby improving the research accuracy of urban thermal environment from the perspective of foundational data. Additionally, it offers a theoretical basis for the governance of UHI in the Lhasa region.

Temporal Analysis of Land Surface Temperature Dynamics and Urban Heat Islands in Chennai using Geo-Spatial Technology

Temporal Analysis of Land Surface Temperature Dynamics and Urban Heat Islands in Chennai using Geo-Spatial Technology

A comparative analysis of surface and canopy layer urban heat island at the micro level using a data-driven approach

The Urban Heat Island (UHI) phenomenon, which is defined as the temperature differential between inner cities and surrounding areas, has been extensively studied over the past few years to unravel its mechanism and develop mitigation strategies. Nonetheless, two distinct types of temperature can be used to measure UHI, namely, (1) air temperatures, which refer to air temperatures at the canopy layer (CUHI), and (2) surface temperature, which refers to the temperatures at the surface layer (SUHI). The two types of UHI can have different deriving mechanisms and any effective mitigation strategies should be able to mitigate both concurrently. While efforts have been made to compare these two types of UHI, the studies so far seldom used consistent data. This is because SUHI is mostly based on remote sensing data, whereas CUHI commonly requires field measurements. These data are often not consistent with respect to spatial resolution, frequency, and accuracy. To address this gap, the present research aims to perform a comprehensive comparative analysis of CUHI and SUHI using data collected by a tailor-made mobile data collection unit at a high frequency and resolution at the micro-level (i.e., street-level). Data was collected from a fixed 8 km-long route in the city of Apeldoorn, the Netherlands, for the period of one year. Two different machine learning techniques, i.e., random forest and neural network, were used to study SUHI and CUHI. The results indicated a considerable variability between air and surface temperatures during the data collection campaign. Air temperatures ranged from -0,3 to 35,3°C, while surface temperatures fluctuated over a wider range, from -12,0 to 48,4°C at a micro-level. This variability of temperatures translated into an average of 0,10°C for CUHI, and -0,48°C SUHI. More importantly, however, the results highlighted the importance of investigating simultaneously the two types of UHI. This is because while urban features do not change dramatically in short periods of time, the impact of these same features on the CUHI and SUHI is different, therefore urban-heat resilience strategies planned for one type of UHI alone could have a different impact on the other type.

Remote sensing‐based geostatistical hot spot analysis of Urban Heat Islands in Dhaka, Bangladesh

Urban Heat Island (UHI) refers to a phenomenon whereby urban areas experience higher temperatures compared to the surrounding areas. Remote sensing‐based Land Surface Temperature (LST) measurements can be utilized to measure UHI. This study emphasized on geostatistical remote sensing‐based hot spot analysis () of UHI in Dhaka, Bangladesh as a way of examining the influences of Land Use Land Cover (LULC) on UHI from 1991 to 2015. Landsat 5 and 7 satellite‐based remote sensing indices were used to explore LULC, UHI and environmental footprints during the study period. The Urban Compactness Ratio (CoR) was used to calculate the urban form and augmented characteristics. The Surface Urban Heat Island (SUHI) intensity (ΔT) was also used to explore the effects of UHI on the surrounding marginal area. Based on our investigations into LULC, we discovered that around 71.34 per cent of water bodies and 71.82 percent of vegetation cover decreased from 1991 to 2015 in Dhaka city. Contrastingly, according to CoR readings, 174.13 km2 of urban areas expanded by 249.77 per cent. Our hot spot analysis also revealed that there was a 93.73 per cent increase in hot concentration zones. Furthermore, the average temperature of the study area had increased by 3.26°C. We hope that the methods and results of this study can contribute to further research on urban climate.

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.

Exploring the relationship between thermal environmental factors and land surface temperature of a “furnace city” based on local climate zones

Numerous studies have explored multiple factors that affect the urban thermal environment. Most current studies focus on establishing the holistic quantitative relationships between the characteristics of urban thermal environmental factors and land surface temperature (LST), but less attention has been given to exploring urban subdivisions in detail. The local climate zone (LCZ) system has been proven effective in establishing the quantitative relationships between urban climate and spatial factors, thereby revealing the characteristics of LST spatial variations. This study utilized the random forest regression model to analyze the relative importance and marginal effects of the urban thermal environmental factors on seasonal LSTs based on the whole study area and the LCZ-derived built type area of Fuzhou city, known as a “furnace city” in southeastern China. The results show that: (1) Building morphology and spatial distribution exert a significant impact on LST, with the LST levels of different building forms ranked as open < compact and high-rise < mid-rise < low-rise. (2) Landscape composition exhibits the greatest influence on seasonal LSTs in the whole study city. (3) Building morphology emerges as the most influential category for LST in the LCZ built type area, with the standard deviation of building height having the highest relative importance at approximately 34%. (4) Concentrated compact low-rise self-built houses experience higher temperatures and should be a focus for future urban renewal efforts. We believe that these findings provide quantitative insights for urban heat island analysis and have substantial implications for climate-friendly urban planning.

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.

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.