Estimation of urban heat island intensity and trends in Spanish cities

The definition and quantification of urban heat island (UHI), the air temperature differences between urban and rural areas, remains problematic. This is due, in large part, to the difficulty of operationalizing the terms “urban” and “rural”, especially with regard to classifying the weather stations that provide data. This thesis studies the urban heat island (UHI) intensity in Hong Kong and there are three foci in the research. The first focus of this study is the determination of the urban and rural weather stations in Hong Kong. The Local Climate Zones (LCZ) system has been employed to classify 17 weather stations and field observation was the main technique to collect the necessary metadata. Six field trips were arranged in the summer of 2009 and 2010. Hong Kong Observatory Headquarters (HKO) is considered as the only representative urban station, whilst Tsak Yue Wu station (TYW) is deemed as the representative rural station because of its Forest Zone (NCZ1) classification. Ta Kwu Ling station (TKL) is another reference rural site. The second focus is the quantification of the UHI intensities at six pairs of stations in Hong Kong and their diurnal and seasonal variations. The 19-year annual UHI intensities in Hong Kong suggest that the representative rural sites (TYW and TKL) also record representative UHI intensities for the region. The differences of the cooling rate at urban and rural stations drive the diurnal cycle of urban heat island. The seasonal variations of UHI intensities are also driven by the cooling rate differences of urban and rural stations in different seasons. Since the mean maximum urban cooling rate does not vary considerably throughout the seasons (0.4 – 0.5 °C/hr), it is the alteration of the rural cooling rate (1.0 – 1.6 °C/hr at TYW; 0.9 – 1.2 °C/hr at TKL) which determines the seasonal variations of UHI intensities. The mean daily maximum UHI intensities in Hong Kong are greatest in winter. The final focus is the meteorological impacts on the UHI intensity in Hong Kong. Five meteorological elements, including air temperature, wind speed, vapour pressure, cloud cover, and cooling rate, have been separately investigated to establish their impacts on the UHI intensity. Under fine weather conditions, the first four elements are negatively related to the UHI intensity. Sixteen regression models were built after the use of stepwise procedures which optimize the combination of independent variables. Rural air temperature is considered the most important meteorological factor on the UHI intensity. The models also suggest that there are other factors affecting the UHI intensities in spring and summer.

Urban Heat Island (UHI) intensity is one of vital parameters in studying urban boundary layer meteorology as well as urban planning. Because the UHI intensity is defined as air temperature difference between urban and rural sites, an objective sites selection criterion is necessary for proper quantification of the spatial variations of the UHI intensity. This study quantified the UHI intensity and its spatial pattern, and then analyzed their connections with urban structure and metabolism in Seoul metropolitan area where many kinds of land use and land cover types coexist. In this study, screen-level temperature data in non-precipitation day conditions observed from 29 automatic weather stations (AWS) in Seoul were analyzed to delineate the characteristics of UHI. For quality control of the data, gap test, limit test, and step test based on guideline of World Meteorological Organization were conducted. After classifying all stations by their own local climatological properties, UHI intensity and diurnal temperature range (DTR) are calculated, and then their seasonal patterns are discussed. Maximum UHI intensity was <TEX>$4.3^{\circ}C$</TEX> in autumn and minimum was <TEX>$3.6^{\circ}C$</TEX> in spring. Maximum DTR appeared in autumn as <TEX>$3.8^{\circ}C$</TEX>, but minimum was <TEX>$2.3^{\circ}C$</TEX> in summer. UHI intensity and DTR showed large variations with different local climate zones. Despite limited information on accuracy and exposure errors of the automatic weather stations, the observed data from AWS network represented theoretical UHI intensities with difference local climate zone in Seoul.

We analyzed the annual, monthly, and seasonal variations of urban heat island (UHI) intensity in Istanbul by using meteorological data measured for the period of 1960–2012 at six stations. The UHI on minimum temperature is found to be positive for all seasons, and the average UHI intensity clearly indicates seasonal changes, strongest in summer and weakest in winter. The results demonstrated increase of night time UHI intensity with 0.41–0.50 °C/decade and decrease of daytime UHI intensity with 0.13–0.18 °C/decade at the urban sites. The UHI strengthened with the expansion of the city due to increased population. The influences of meteorological variables on seasonality of the UHI intensity are examined for the days categorized depending on wind, cloud cover, and precipitation values. It is found that the UHI intensity decreases with increasing wind speed and cloud cover. The integrated response of the city atmosphere to wind speed changes differ such that daytime UHI in urban atmosphere intensifies rapidly from calm conditions to the wind speeds of 2–3 m/s, then slightly increases until 4–5-m/s wind speeds and starts to decline afterwards. On the other hand, the nighttime UHI intensities in urban sites continuously decline with the same rate until the wind speeds reach to 5–6 m/s. The difference of daytime UHI between rainy summer days and dry days is around 1 °C which is almost independent of the precipitation amount. Both nighttime and daytime UHI intensities depend on the season and site range approximately between 0.24 and 1.74 °C and − 0.62 and 2.61 °C, respectively. However, the UHI based on minimum temperature for the selected dry days with low wind and clear sky conditions increases to 1.70–3.08 °C. Land surface data from Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra show areal extension of the UHI through the north along the Bosphorus between 2000 and 2012, especially in the night observations. The continuous increase of built-up areas, paved roads, and decrease of green areas caused the growth of UHI intensity. The estimated UHI based on land surface temperature (LST) at the most urbanized locations of Istanbul reach to 8 °C for daytime and 6 °C for nighttime.

New methods of urban heat island (UHI) center/movement and usage of standard deviation (SD) of temperature for the analysis of UHI intensity (UHII) are presented in this study. UHI deviation is defined as difference between temperature of a measurement point and mean temperature of all measuring points. New definition of UHII is used as the difference between maximum and minimum UHI deviations. The UHI center is set as gravity center of relatively hot area and the movement of the UHI can be observed by the course of the center. These UHI metrics are suitable in analyzing the dense network for UHI measurement. As an application of these method, AMeDAS data is used to analyze the UHI effect in Tokyo metropolitan area (TMA) from the point of view of UHI movement and UHII. Clear difference of summer and winter pattern of the UHI in TMA was observed. In the summer pattern, the monthly average UHI area with the high UHI deviation was located from the coastal area to the north inland. About hourly change in a day, the UHI located along the coast at night and after the sunrise, the UHI gradually extended to inland. With this change, the UHI center moved from south to north and returned from north to south. In the winter pattern, the high UHI deviation area was located along the coast and the UHI center was located in the same area for all the day. The method of movement analysis is very effective to clarify the UHI characteristics of the area, especially coastal areas. The relation between the UHII and the SD of the temperature was analyzed. The UHII has strong linear relationship with the SD and the UHII is nearly four times of the SD (UHII ≅ 4×SD). Especially, in the observation of a dense network which has many measuring points, the SD is considered as more robust index of the UHII.

Interdecadal variations and trends of the Urban Heat Island in Athens (Greece) and its response to heat waves

&amp;lt;p&amp;gt;Historical changes, spanning 1971&amp;amp;#8211;2016, in the Athens Urban Heat Island (UHI) over summer were assessed by contrasting two air temperature records from established meteorological stations in urban and rural settings. When contrasting two 20-year historical periods (1976&amp;amp;#8211;1995 and 1996&amp;amp;#8211;2015), there is a significant difference in summer UHI regimes. The stronger UHI-intensity of the second period (1996&amp;amp;#8211;2015) is likely linked to increased pollution and heat input. Observations suggest that the Athens summer UHI characteristics even fluctuate on multi-annual basis. Specifically, the reduction in air pollution during the Greek Economic Recession (2008-2016) probable subtly changed the UHI regime, through lowering the frequencies of extremely hot days (T&amp;lt;sub&amp;gt;max&amp;lt;/sub&amp;gt; &amp;gt; 37 &amp;amp;#176;C) and nights (T&amp;lt;sub&amp;gt;min&amp;lt;/sub&amp;gt; &amp;gt; 26 &amp;amp;#176;C).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Subsequently, we examined the future temporal trends of two different UHIs in Athens (Greece) under three climate change scenarios. A five-member regional climate model (RCM) sub-ensemble from EURO-CORDEX with a horizontal resolution of 0.11&amp;amp;#176; (~12 &amp;amp;#215; 12 km) simulated air temperature data, spanning the period 1976&amp;amp;#8211;2100, for the two station sites. Three future emissions scenarios (RCP2.6, RCP4.5 and RCP8.5) were implanted in the simulations after 2005. The observed daily maximum and minimum air temperature data (T&amp;lt;sub&amp;gt;max&amp;lt;/sub&amp;gt; and T&amp;lt;sub&amp;gt;min&amp;lt;/sub&amp;gt;) from two historical UHI regimes (1976&amp;amp;#8211;1995 and 1996&amp;amp;#8211;2015, respectively) were used, separately, to bias-adjust the model simulations thus creating two sets of results.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This novel approach allowed us to assess future temperature developments in Athens under two different UHI intensity regimes. We found that the future frequency of days with T&amp;lt;sub&amp;gt;max&amp;lt;/sub&amp;gt; &amp;gt; 37 &amp;amp;#176;C in Athens was only different from rural background values under the intense UHI regime. There is a large increase in the future frequency of nights with T&amp;lt;sub&amp;gt;min&amp;lt;/sub&amp;gt; &amp;gt; 26 &amp;amp;#176;C in Athens under all UHI regimes and climate scenarios; these events remain comparatively rare at the rural site.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This study shows a large urban amplification of the frequency of extremely hot days and nights which is likely forced by increasing air pollution and heat input. Consequently, local mitigation policies aimed at decreasing urban atmospheric pollution are expected to be also effective in reducing urban temperatures during extreme heat events in Athens under all future climate change scenarios. Such policies therefore have multiple benefits, including: reducing electricity (energy) needs, improving living quality and decreasing heat- and pollution related illnesses/deaths.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;

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

Impacts of land use/ land cover types on interactions between urban heat island effects and heat waves

Cities exhibit both beneficial and detrimental characteristics, many of which stem from agglomeration effects and are, to a first approximation, influenced by population size. However, urban density also plays a critical role. For example, cities with similar population sizes but higher densities tend to emit less carbon, while simultaneously exhibiting a more pronounced urban heat island (UHI) effect. This trade-off highlights the need for a balanced approach that simultaneously minimizes both carbon emissions and the urban heat island (UHI) effect. To address this challenge, we examine how both carbon emissions and UHI intensity are influenced by the population size and spatial extent of the cities. As objective function we define the some of both quantities where city population and area are variables. Considering the scaling relation between area and population as constraint, we derive a theoretical expression leading to an optimal city size. To validate our approach, we analyze carbon emissions data from cities in Germany and consider UHI parameters from the literature. We find that, in the specific case of German cities, achieving an optimal city size that simultaneously minimizes both carbon emissions and UHI intensity is not physically feasible. From a methodological perspective, only the UHI intensity parameters, together with the exponent of the relationship between population and area, determine whether an optimum exists or not. We argue that instead, the scaling relation between population and area itself should be understood as an optimum.

The mean maximum urban heat island (UHI) intensity in three major cities in Thailand was investigated by using data measured at two meteorological observatories (an urban site and rural site) in each study area for the period 2004 to 2008. Thermal contrasts between the urban and rural sites were positive which indicated that most of the time the air temperatures at the urban sites were higher than the air temperatures at the rural sites. The strongest mean maximum UHI intensity occurred in the nighttime and in the early morning, while the weakest mean maximum UHI intensity was reached during the daytime. The results indicated that UHI events occurred more frequently in the nighttime than in the daytime. Seasonal analyses showed the mean maximum UHI intensity was weakest in the rainy season (May-October) and was strongest in the dry season (November-April).

Urbanization has led to a significant urban heat island (UHI) effect in Beijing in recent years. At the same time, air pollution caused by a large number of fine particles significantly influences the atmospheric environment, urban climate, and human health. The distribution of fine particulate matter (PM2.5) concentration and its relationship with the UHI effect in the Beijing area are analyzed based on station-observed hourly data from 2012 to 2016. We conclude that, (1) in the last five years, the surface concentrations of PM2.5 averaged for urban and rural sites in and around Beijing are 63.2 and 40.7 µg m−3, respectively, with significant differences between urban and rural sites (ΔPM2.5) at the seasonal, monthly and daily scales observed; (2) there is a large correlation between ΔPM2.5 and the UHI intensity defined as the differences in the mean (ΔTave), minimum (ΔTmin), and maximum (ΔTmax) temperatures between urban and rural sites. The correlation between ΔPM2.5 and ΔTmin (ΔTmax) is the highest (lowest); (3) a Granger causality analysis further shows that ΔPM2.5 and ΔTmin are most correlated for a lag of 1–2 days, while the correlation between ΔPM2.5 and ΔTave is lower; there is no causal relationship between ΔPM2.5 and ΔTmax; (4) a case analysis shows that downwards shortwave radiation at the surface decreases with an increase in PM2.5 concentration, leading to a weaker UHI intensity during the daytime. During the night, the outgoing longwave radiation from the surface decreases due to the presence of daytime pollutants, the net effect of which is a slower cooling rate during the night in cities than in the suburbs, leading to a larger ΔTmin.

Many studies have investigated urban heat island (UHI) intensity for cities around the world, which is normally quantified as the temperature difference between urban location(s) and rural location(s). A few open questions still remain regarding the UHI, such as the spatial distribution of UHI intensity, temporal (including diurnal and seasonal) variation of UHI intensity, and the UHI formation mechanism. A dense network of atmospheric monitoring sites, known as the Oklahoma City (OKC) Micronet (OKCNET), was deployed in 2008 across the OKC metropolitan area. This study analyzes data from OKCNET in 2009 and 2010 to investigate OKC UHI at a subcity spatial scale for the first time. The UHI intensity exhibited large spatial variations over OKC. During both daytime and nighttime, the strongest UHI intensity is mostly confined around the central business district where land surface roughness is the highest in the OKC metropolitan area. These results do not support the roughness warming theory to explain the air temperature UHI in OKC. The UHI intensity of OKC increased prominently around the early evening transition (EET) and stayed at a fairly constant level throughout the night. The physical processes during the EET play a critical role in determining the nocturnal UHI intensity. The near-surface rural temperature inversion strength was a good indicator for nocturnal UHI intensity. As a consequence of the relatively weak near-surface rural inversion, the strongest nocturnal UHI in OKC was less likely to occur in summer. Other meteorological factors (e.g., wind speed and cloud) can affect the stability/depth of the nighttime boundary layer and can thus modulate nocturnal UHI intensity.

This paper focuses on the monitoring of the urban heat island (UHI) effect with temporal and spatial variation, combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Thematic Mapper (TM) data. Our study area is located in the central urban area of Beijing, which mainly refers to the areas within the fifth ring road. For detecting UHI changes over the years 2002–2006, three ASTER images in the summers of 2003, 2004 and 2006 and two TM datasets in the summers of 2002 and 2005 were collected. For monitoring UHI changes with the seasons, three ASTER images and one TM image in 2004 in winter, spring, summer and autumn, respectively, were employed. To calculate the urban heat island intensity, the land surface temperatures were retrieved iteratively for ASTER data and using a generalized single-channel method for the TM image. Four separated regions located in four directions outside the fifth ring road were selected as representing rural comparative regions. Their averaged land surface temperature was regarded as the rural comparative temperature. The UHI intensity was computed by the difference between the pixel urban land surface temperature in the urban area and the comparative temperature in the rural area. Detection of the UHI effect over 2002 to 2006 indicated that most of the areas with high UHI effect were the industrial land use regions and the areas having a high density of buildings, roads, transportations and residents; and the areas without UHI effect were located around the regions with large areas of grassland, trees and water bodies. Our results also showed that the UHI effect was not proportional to urbanization over time. Statistical UHI data during 20 July to 20 September in 2003–2008 also support this point. The monitoring of the UHI effect over seasons (winter, spring, summer and autumn) showed that the urban area of Beijing city had a high UHI effect except in winter, when the urban area of Beijing was in an urban heat sink; the UHI effect increased in spring, summer and autumn.

City size is an important determinant of the urban heat island (UHI) intensity. While most studies report a logarithmic dependence of UHI intensity on city size, other functions like power-law and logistic functions have also been reported. In addition, the urban form plays an important role and, intuitively, increased UHI intensity is expected for compact cities. However, how to incorporate urban size and form for modeling UHI is less clear. Based on the perception that every urban site interacts with every other one, whereas the intensity of interaction decreases with the distance, we propose an every-pair-interaction model to characterize the UHI intensity. The model combines urban size and fractal dimension non-linearly and regression on the summertime surface UHI intensity of 5,000 European cities shows that it outperforms the simple linear model. Subject to the interplay between the range of the every-pair interaction and the urban fractal shape, it also represents a generalization as it includes power-law, logarithmic, and saturating size dependence of UHI — all three possibilities have been reported empirically in the literature. Our model indicates that the surface UHI intensity saturates with urban size. Whether the UHI saturates with the expansion of the urban area or follows a continuously increasing trend is relevant for sustainable urban development. Our theoretical framework opens up new research perspectives around UHI intensity.

The relationship between urban form and heat island intensity along the urban development gradients