The inclusion of the urban heat island effect in design weather for net-zero buildings

On top of climate change and its consequent temperature rises, urban areas have the added burden of the urban heat island (the urban area being warmer than the rural area especially at night under calm, cloud-free conditions). The urban heat island intensity (the difference between the rural air temperature and that in the city centre) can be as large as 10K for the major cities such as London. The urban heat island intensity, consequently, can have a significant effect on the sizing of heating, ventilating and air-conditioning plant and its energy consumption. At present, designers have access to empirical factors for design days only in June, July and August from the Chartered Institution of Building Services Engineers Guide. Or they can use the latest Design Summer Year which implicitly includes the urban heat island intensity. However, the empirical model discussed in this paper allows the designer to add on the hourly urban heat island intensity for central London to any recent year’s hourly weather data set from London Heathrow or Bracknell, a more rural site. The model is similar to one for Manchester, suggesting that the model may well be of application to other UK cities. Practical applications: Most buildings that building services engineers and other building designers are involved with are in urban or city centres. However, the weather data for their designs are based on near-rural weather data, which does not include the urban heat island effect. This paper describes the urban heat island effects that a designer needs to consider and the adjustments that can be made, related to London.

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.

Temperature measurements for UK and world cities are often taken at local airports. These are usually on the outskirts of the urban areas and so not subject to the urban heat island (UHI) effect. Hence they are not representative of urban and city area temperatures. As most buildings are built in urban and city areas, designers need to know these temperatures. It is therefore very important to study the UHI effect and adjust the weather data used for design to include the UHI effect. This paper emanates from a project to measure the UHI effect in Greater Manchester, UK. With the advent of miniature temperature sensors and data loggers a simpler and cheaper shield was designed and produced. This paper describes the construction and testing of a new, low cost radiation shield that can accommodate a new, low cost combined miniature temperature sensor and data logger. The shield and data loggers are both tested and shown to give very reliable results. Practical application: The low cost, easily fabricated radiation shield with its miniature sensor—logger was designed to measure the dry bulb air temperature in Greater Manchester to investigate the UHI effect. The shield is designed to be mounted, with two stainless steel bands, on a lamppost column, typically at 4 m height. A telescopic pole can be used to unhook the sensor—logger for data collection. The shield can also be used to measure the dry bulb air temperature around buildings by fixing it to an external wall. Tests have shown the shield to perform well in comparison to a Stevenson screen and to greatly reduce any influence of solar irradiance.

The urban heat island intensity is the difference in temperature between a site close to the centre of a city and a site close to but outside the city (the rural site). The urban heat island intensity varies continuously throughout the day and is strongly dependent on the weather conditions at the time. The most important weather parameters are the wind speed, the cloud cover and the solar radiation. We have developed an empirical model for the urban heat island intensity and applied it to a site near the centre of Manchester and a rural site at Rostherne, approximately 17 km away. Weather data from the Met Office station at Rostherne are available from the British Atmospheric Data Centre. Our model uses the measured wind speed, the measured cloud cover and the measured solar radiation from Rostherne. The parameters of the model are adjusted to give a best fit to the measured urban heat island intensity for the year 2014. The model is then used to predict the hourly urban heat island intensity for the first six months of 2015, obtaining good results especially as the values of the parameters are not changed throughout the year and the model does not make use of the temperatures at either site. The accuracy of the model is such that if used for a basic heating and cooling load calculations the accuracy of the annual demand is high. Practical applications: Many buildings that building services engineers and other building designers design are in urban or city centres. However, the weather data for their designs are based on near-rural weather data which do not include the urban heat island effect. This paper describes a method to ascertain the urban heat island effect in the centre of Manchester. A designer could apply this for Manchester and as an initial indication to other similar urban areas. This will allow the rural weather data to be adjusted on an hourly basis for the urban heat island effect throughout the year.

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.

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.

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.

The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the whole urban area together with rural zone have averaged 1.9 and 0.9 °C, respectively, in recent years. The UHI in Moscow has stabilized over the past decade and is not growing. Under conditions of a strong anticyclone, the maximal UHI intensity in space and time reaches 11–12 °C. Low cloudiness and amplitudes of diurnal air temperature, as well as surface temperature, demonstrate the closest relationship with the UHI intensity among other parameters with the correlation coefficient of up to −0.67 for low cloudiness and the maximal UHI intensity. The effect of wind speed, total cloudiness and relative humidity on the UHI is slightly weaker, but still significant. The relationships of all meteorological parameters with the maximal UHI intensity are closer than those with the average one. The multiple correlation coefficient between the maximal UHI intensity and both parameters (low cloudiness and average daily wind speed) is 0.76–0.82. The UHI intensity function of air temperature has a minimum in the range from −4 to 0 °C; its growth both at lower and higher T is due to the influence of anticyclonic weather. The UHI intensity function of wind speed decreases with wind strength. The threshold value at which this function asymptotically approaches its lower limit is 10 m/s in the 40–200 m air layer. The UHI intensity functions of both total and low cloudiness decrease with increasing cloudiness and the differences between them are significant if the cloud cover is more than 50%.

Improvement of spatial-temporal urban heat island study based on local climate zone framework: A case study of Hangzhou, China

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.

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.

Developing a modified typical meteorological year weather file for Hong Kong taking into account the urban heat island effect

Comparison of the urban heat island intensity quantified by using air temperature and Landsat land surface temperature in Hangzhou, China

— Ankara is the second largest city in Turkey after Istanbul, and the rate of population increase and urbanization are quite high. In this study, the effects of urbanization on temperature variation due to urbanization in Ankara were investigated. The intensities of urban heat island (UHI) for long and short term were analyzed. Analysis of both longand short-term data revealed that there is a significant increase in the intensity of UHI (ΔT(u-r)) in winter during the period analyzed. Analysis of data collected for period of October 2001-September 2002 shows that intensity of maximum UHI is in February. In this month, positive UHI was observed in 26 nights and on all these days wind speed was less than 0.5m s−1. UHI is positive in all seasons and frequency and intensity of UHI in winter are higher than in the other seasons. This characteristic makes Ankara different from other temperate latitude cities.

In this study, the urban heat island of Toronto was characterized and estimated in order to examine the impact of the selection of rural sites on the estimation of urban heat island (UHI) intensity (∆Tu-r). Three rural stations, King Smoke Tree (KST), Albion Hill, and Millgrove, were used for the analysis of UHI intensity for two urban stations, Toronto downtown (Toronto) and Toronto Pearson (Pearson) using data from 1970 to 2000. The UHI intensity was characterized as winter dominating and summer dominating, depending on the choice of the rural station. The analyses of annual and seasonal trends of ∆Tu-r suggested that urban heat island clearly appears in winter at both Toronto and Pearson. However, due to the mitigating effect on temperature from Lake Ontario, the estimated trend of UHI intensity was found to be less at Toronto compared to that at Pearson which has no direct lake effect. In terms of the impacts of the rural stations, for both KST and Millgrove, the trends in UHI intensity were found to be statistically significant and also were in good agreement with the estimates of UHI intensities reported for other large cities in the USA. Depending on the choice of the rural station, the estimated trend for the UHI intensity at Toronto ranges from 0.01°C/decade to 0.02°C/decade, and that at Pearson ranges from 0.03°C/decade to 0.035°C/decade during 1970–2000. From the analysis of the seasonal distribution of ∆Tu-r, the UHI intensity was found to be higher at Toronto in winter than that at Pearson for all three rural stations. This was likely accounted for by the lower amount of anthropogenic heat flux at Pearson. Considering the results from the statistical analysis with respect to the geographic and surface features for each rural station, KST was suggested to be a better choice to estimate UHI intensity at Toronto compared to the other rural stations. The analysis from the current study suggests that the selection of a unique urban–rural pair to estimate UHI intensity for a city like Toronto is a critical task, as it will be for any city, and it is imperative to consider some key features such as the physiography, surface characteristics of the urban and rural stations, the climatology such as the trends in annual and seasonal variation of UHI with respect to the physical characteristics of the stations, and also more importantly the objectives of a particular study in the context of UHI effect.