as in some agricultural fields without vegetation during the particular time of the year when the satellite imagery was retrieved. The highest NDVI values reached values of up to 0.7 or more and these corresponded to forests and grassland areas (Parlow, 2003). In the hotspots of The Hague, Delft, Leiden, Gouda, Utrecht and Den Bosch, the average NDVI ranges from 0.31 to 0.39. Even though the average NDVI values is pretty similar for all the hotspots, the NDVI visualisation (Figure 5.9) suggests that there might be some consistent NDVI differences within the hotspots. These maps provide an indication of the areas with the lowest values, thus the areas where to increase the vegetation. Overall, the storage heat flux hotspots of these six cities are located in the historical citycentres and it seems delicate to suggest increasing NDVI at street level without analysing in detail the design implications of such a mitigation proposal. The implementation of green roofs would therefore be the most plausible option. Several studies (Kurn et al., 1994; Sailor, 1995) estimate that the near-surface air temperatures over vegetated areas were 1°C lower than background air temperatures.
The vegetation cover examined in the present work had a seasonal effect on air temperature and reduced the UHI intensity, although LAI peaks were relatively low (2.05 to 2.52), and vegetation types and their phenological activity were reduced because many species are deciduous during the cold season, and also as a result of the predominant summer rainfall distribution. In other studies a decrease in the UHI effect is reported, but the LAI is typically higher due to the temperate nature of vegetation and higher and more uniform rainfall regimes (Potchter et al., 2006; Leuzinger et al., 2010). Although the watering costs may reduce the environmental benefits of urban veg- etation (Doick and Hutchings, 2013), here we have shown that vegetation types adapted to low rainfall are useful for reducing climate change effects. The cooling effect of urban vegetation, with likely higher water availability, was similar in magnitude to that of native species present in suburban areas and located at hill slopes. A further work should examine the
Luke Howard, a British meteorologist, is attributed to be the first to record an “excess of heat” back in 1818 when he compared the temperatures inside the city of London with those of the surrounding countryside (Oke T. R., The energetic basis of the urbanheatisland, 1982; Gartland, 2008). However, he did not use the term “heatisland” in his work, as the phrase was coined later by meteorologists who noted the resemblance between urban isotherm maps and the topographic map of an island. Isotherms at city limits usually feature high thermal gradients that resemble ‘cliffs’ surrounded by an ‘ocean’ of lower values. Moving ‘inland’ towards the city centre, these transform into ‘plateaus’ with weak thermal gradients. In some spots within the city, the temperature tends to rise again forming ‘peaks’ (Montavez et al., 2000; Oke, 2004; American Meteorological Society, 2015). This can be summarised as follows:
In order to investigate the urbanheatislandeffect in Greater Manchester, it is essential to monitor and record the temperature around the city. Near ground (up to a few meters above ground) surface temperature was logged in this project instead of ground surface temperature. Air temperatures were recorded over Greater Manchester using different weather monitoring stations. Other weather parameters such as cloud level, wind speed, wind direction, rainfall and air temperatures could be obtained from Met Office ground observation station around Manchester (Woodford, Ringway and Hulme Library). These data could be incorporated into a heat canyon model to calculate the air/surface temperature in an urban canyon. These results could be validated by comparing with the actual air temperature measured from seven canyons in the Manchester city centre. After validation, this canyon model could be used by engineers during design to estimate the heatislandeffect around Manchester. In order to monitor and record air temperature, a suitable monitoring method should be selected. In this chapter, the selection of monitoring method and the location of monitoring stations will be discussed. The selection of sensor-loggers and the associate calibrations as well as data processing technique will be discussed in the next chapter.
However, the combination of factors such as size, type, the composition of the species, geographic factors, and meteorological variables influences the potential green space effect on temperature [ 19 , 20 ]. This combination can lead to an unwanted climatic effect that is warmer than the surrounding areas. In Tel Aviv, Israel, Potchter et al. [ 21 ] observed that parks with grass and few trees were warmer than built-up areas. Analyzing 61 parks in Taipei, Taiwan, Chang et al. [ 22 ] showed that approximately one-fifth of them were warmer than their urban surroundings, and in summer, at noon, parks with ≥ 50% paved coverage and few trees and shrubs were on average warmer than their surroundings. These authors concluded that large parks were on average cooler than the smaller ones, but this relationship was non-linear. In Mexico City, Jauregui [ 23 ] reported that on sunny mornings the park heats up more slowly than the built-up area and two hours after midday there was not a significant difference in temperature between the park and surroundings. Thus, real-time climatological monitoring of the PCI is critical to understand the beneficial function of green spaces in the city as well as to assess the potential of any unwanted climatic features.
3.3 Selecting Stationary Monitor Sites
Several points in the LA1 and SFV5 study areas were identified as localized UHI or UCI areas based on model and initial mobile-transect results. The research team shared these points and study areas with project partners—including the City of Los Angeles, County of Los Angeles, and LAUSD—and collectively developed a list of their facilities that could serve as potential sites for the stationary monitors. Upon further evaluation of these sites via examination of three-dimensional imagery (for example, Google Earth Pro) and location visits, certain sites were assigned a lower priority because of such factors as unavailability of flat roofs (sloped roofs can be an issue for installing weather stations), lack of secure sites in open areas (at ground level), and physical characteristics of the site. These factors resulted in a top tier of 10 potential sites, some of which are shown in Figure 7.
mainly in urban. In August 30, 2011 in Guiyang, most areas were in the medium and high temperature zone, and expanded significantly compare to 2007. Lower, low and higher temperature zone were only sporadic presence. The temperature zones change can be seen from Table 2, gone with these eight years, the lower temperature zone disappeared, the low temperature zone was sharp decline, the medium temperature zone increased, high and higher temperature zone soared. This three surface temperature retrieval maps were dealt with matrix change analysis, results can be seen from Table 3 that from 2003 to 2007, the low zone transformed into the medium zone was the largest area, 250.77 Km2, accounting for about 10% of Guiyang City area; the area of medium zone into high zone was followed, 184.56 Km2; medium zone transformation for the low zone was also larger area, 175.49 Km2; the area of other transformation was smaller. As can be seen from Table 4, from 2007 to 2011, area of low zone transformed into the medium zone was the largest, 787.22 Km2, accounting for about 33% of Guiyang City area; followed by medium zone into high zone, an area of 540.97 Km2, accounting for about 23% of Guiyang City area; other converted area was smaller. The overall trend is low grade transformed into high grade temperature zone.
Several recent studies quantify the consequences of climate change on com- mon urban tree species worldwide and how newly introduced species from other climate regions will perform as urban trees (Böll et al., 2014; City of London, 2014; Pretzsch et al., 2015b). Higher tree species diversity in cities will likely in- crease urban biodiversity and the resistance of the whole urban tree stand of a city to pests and diseases (Raupp et al., 2006; Tubby & Webber, 2010), and pro- vide a wider range of aesthetic features and ecosystem services to mitigate the consequences of global warming and worsening climate scenarios in city centers (Bassuk et al., 2009; Cregg & Dix, 2001; Sjöman et al., 2012). Surprisingly, con- trary positive effects despite all the mentioned negative consequences of global warming on tree growth have been found as well (Fang et al., 2014; Kauppi et al., 2014). In their study about forest tree growth, Pretzsch et al. (2014) highlighted a faster growth of forests since the last decades. Global warming and higher im- missions of nutrients and pollutions accelerated tree growth.
Urban areas are major contributors to air pollution and climate change, causing impacts on human health that are amplified by the microclimatological effects of buildings and grey infrastructure through the urbanheatisland (UHI) effect. Urban greenspaces may be important in reducing surface temperature extremes, but their effects have not been investigated at a city-wide scale. Across a mid- sized UK city we buried temperature loggers at the surface of greenspace soils at 100 sites, stratified by proximity to city centre, vegetation cover and land-use. Mean daily soil surface temperature over 11 months increased by 0.6 °C over the 5 km from the city outskirts to the centre. Trees and shrubs in non-domestic greenspace reduced mean maximum daily soil surface temperatures in the summer by 5.7 °C compared to herbaceous vegetation, but tended to maintain slightly higher temperatures in winter. Trees in domestic gardens, which tend to be smaller, were less effective at reducing summer soil surface temperatures. Our findings reveal that the UHI effects soil temperatures at a city-wide scale, and that in their moderating urban soil surface temperature extremes, trees and shrubs may help to reduce the adverse impacts of urbanization on microclimate, soil processes and human health.
ha (in 2009). Moreover, the high number of population also leads to increase greenhouse gas emissions from the vehicles, followed by an increase surface temperature that has created heatislandeffect. In the long term, high temperature in urban area resulted in inconvenience living environment. One effort to adapt to the urbanheatisland is to build a green open space. Green open space has very important role in decreasing surface temperature through the absorption of CO 2 in the process of photosynthesis. There were several researches on land use and land cover changes in Jakarta capital city [2, 3], however, its relation to surface temperature has not been discussed. The research is to analysis land use and land cover changes of Jakarta and its impact on surface temperature of Jakarta. The study result would benefit for the city planners to prioritize area for green space development based on surface temperature. 2. Method
The urbanheatislandeffect is a phenomenon observed worldwide, i.e. evening and nocturnal temperatures in cities are usually several degrees higher than in the surrounding countryside. In contrast, cities are sometimes found to be cooler than their rural surroundings in the morning and early afternoon. Here, a general physical explanation for this so-called daytime urban cool island (UCI) effect is presented and validated for the cloud-free days in the BUBBLE campaign in Basel, Switzerland. Simulations with a widely evaluated conceptual atmospheric boundary-layer model coupled to a land-surface model, reveal that the UCI can form due to differences between the early morning mixed-layer depth over the city (deeper) and over the countryside (shallower). The magnitude of the UCI is estimated for various types of urban morphology, categorized by their respective local climate zones.
The evolution of heat-wave risk in cities is related to regional climate change in interaction with urbanheatisland. Land planning and urban transport policies, due to their long- lasting impact on city’s size and shape, can also have an influence. However, these com- bined effects are complex and strongly depend on the indicators used to quantify heat- wave risk. With Paris area as a case study and using an interdisciplinary modelling chain, including a socio-economic model of land-use transport interaction and a physically-based model of urban climate, air temperature in the city during heat waves is simulated for five urban expansion scenarios. The urbanheatisland is always higher at night and affects pref- erentially the city centre. Its intensity and spatial extension are moderately impacted by densification process and choice in urban form. But the variation of heat-wave risk with the densification dynamics is not limited to the effect on urbanheatisland, and also depends on exposure to heat of population. Spatial distribution of population in the city differs according to urban expansion scenarios. The results show that the compact city, by concentrating the inhabitants in areas the most impacted by heatislandeffect, amplifies the overall vulnerability of population.
Figure 7. Predicted indoor (solid line) and outdoor temperature (dot line) at city centre (red) &boundary (blue) Results – Task 8: Indoor overheating benchmarking
The indoor temperatures for the 2 identical case study buildings located at city centre and fringe over the 7 months period were compared against CIBSE overheating benchmarks CIBSE Guide A 2006 and CIBSE TM52/BS EN 15251 adaptive thermal comfort standard. The results in table 2 shows that rooms in city centre is significant warmer than rooms at city fringe. Therefore, the mitigation of the urbanheatisland in city centre is the key. Different strategies to reduce UHI effects can be adopted, based on properly designing the urban texture in order to obtain the health benefits, examples including, the increase of urban surface reflectivity and urban vegetation (green roofs, street trees, and green spaces).
UGSs implementation within London city centre would be different in terms of the type of UGS, its percentage, where it is applied and that would be based on street orientation where S1 streets does not need much vegetation as S2 streets that is mainly due to solar radiation and the buildings height within the canyon limits sunlight rays to reach street level due to building shading. For S1 street, applying high albedo surfaces for pavements would increase comfort through reducing PET by 0.21°C. The latter was due to its high effect on lowering surface temperature while its reflectivity had increased Tmrt. while other vegetation percentages, PET improved by -0.35 to -1.05°C. With regards to S2 canyon, a very high thermal stress was found compared to S1, HPA increased thermal stress by 1.3°C due to its high Tmrt, while trees lowered PET by 3.08 and 5.84°C for 25% and 50%, espectively. While for LF 25% and 50%, -1.17°C and -0.09°C was found as a Tr which is almost negligible for LF 50%.
Changes in land cover due to increased urbanization can affect the urban environment and climate substantially (Seto and Shepherd, 2009). One well-documented effect of urbanization is the UrbanHeatIsland (UHI); that is, higher temperatures in urban areas compared with surrounding rural areas, par- ticularly during the night (Arnfield, 2003). Urban expansion has the potential to enhance UHI effects further (for example, Pauleit et al., 2005; Argüeso et al., 2014; Yang et al., 2016; Morris et al., 2011; Liu et al., 2018) and hence understand- ing the impacts of future urban expansion on the UHI is very important. A number of features, such as building fabric, building form, thermal properties of construction materials, synoptic condition and wind flow, and anthropogenic heat, combine to generate the UHI in urban areas (Oke et al., 1991; Harman and Belcher, 2006). The UHI is driven by the higher thermal heat capacity and heat storage of urban infrastructure and reduced evapotranspiration due to the loss of vegetation and increase in impermeable surfaces. The UHI results in higher air temperatures at screen level in urban areas that can contribute to heat-related illnesses including heart disease, which can lead to mortality, particularly during summer, and these effects can be exacerbated during heatwaves in Australia (Department of Infrastructure and Regional Development, 2013). For example, the deaths of a total of 4,555 people were attributed to extreme heat over the period 1900–2011 in Australia, which is 55.2% of the total natural hazard deaths (Coates et al., 2014). In addition, higher temperatures in urban areas also affect urban ecosystems, as well as human thermal comfort, and increase the rate of energy consumption (Block et al., 2012).
The application of different scale models in the UHI studies is explained. The theme of studies is prioritized with representing sample works conducted in the last two years for each category. While building and microclimate models have higher resolution in the urban canopy layer, they cannot spatially be extended to cover the entire area of a city due to the extensive computational cost and complexity of the important parameters. Despite the capability of meso-scale models in investigation of the large-scale effect of the UHI, their accuracy is not enough to provide details about the urban canopy layer. This gap thus requires further research to develop spatially and computationally efﬁcient models.
Conservation and improvement in existing green and blue areas in cities and the creation of new spaces to ameliorate the urbanheatislandeffect is hugely important and can have a number of additional benefits such as creating areas for recreation, biodiversity, filtering air and draining and storing water. Vegetation provides cooling through shading and enhanced evapotranspiration. Parks and open water areas are essential. Green and blue roofs lower temperature during dry seasons through insulation and enhancing evapotranspiration. Vegetation areas have a significant effect on the local climate as they incite the production of fresh and cold air, particularly at night, and have a thermally balancing effect during the day due to a high percentage of tress (EEA, 2013). Above all, this smart and integrated approach to spatial planning ensures that cities limited land is turned into areas capable of providing multiple functions for nature and society (EC, 2012a). 6.3. Awareness and Behavior Change
Stewart and Oke  noted that classifying field sites as conventionally “urban” or “rural” is especially diffi- cult in regions where both cities and countrysides are densely populated and land uses intensely mixed. How- ever, the core of the problem of urbanheatisland is that wherever high population and built up area exists, tem- peratures are higher and thus urbanheatislandeffect is felt. Thus to estimate urbanheatisland intensity, a “ru- ral” site may not be required for a region which has mixed land use. In the present study, mostly the green areas (parks and medium dense forests) in the city were observed to have the lowest temperatures. These green areas have thus been considered as baseline to asses UHI at other stations. Urbanheatisland intensity (UHI) is calculated as difference between highest temperature and lowest temperature observed among all micrometeo- rological stations across the study area at a given time.
Abstract: Although transport has resulted in many beneficial effects on society, but their development in fact have negative impacts on the environment. The car policy caused many problems such as: - the spectacular growth of fuel consumption hence the very vast increase in urban pollution, traffic congestion in certain places and at certain times, the increase in the number of accidents. The exhaust emissions from cars and weather conditions are the main factors that determine the level of pollution in urban atmosphere. These conditions lead to the phenomenon of heat transfer and radiation occurring between the air and the soil surface. These exchanges give rise, in urban areas, to the effects of heat islands that correspond to the appearance of excess air temperature between the city and its surrounding space. We perform a numerical simulation of the plume generated by the exhaust gases of cars and show that these gases form a screening effect above the urban cite which cause the heatisland in the presence of wind flow. The study allows us: i- to understand the different mechanisms of interactions between these phenomenons, ii- to consider appropriate technical solutions to mitigate the effects of the heatisland.
The UCL heat islands are the most commonly observed of the two types and are often referred to in discussions of UHIs.
2.4 Maximum UrbanHeatIsland (UHI max )
In general, the UHI effect is determined by measuring air temperatures within the urban area and comparing the observations to nearby rural temperatures (i.e. air temperatures away from the city). Because of the slow release of heat from urban structures after sunset, the UHI intensity is greatest at night. In addition, the mag- nitude of the UHI is dependent on the location within the city. For example, the city centre has more impact on the temperature than suburban areas due to the increased heat capacity of urban structures. As for ambient climatic conditions, wind speed and cloud cover also impact the magnitude of the UHI. High winds and/or cloud cover disrupt the cooling differences between the urban and rural areas and reduces the UHI effect, while calm conditions with clear skies are optimal for large UHI effects. So, a term had to be found for the UHI being quite independent from the period of time. The maximum urbanheatisland intensity abbreviated as UHI max represents this term. It is defined as the maximum hourly average temperature difference between