Advertisement

Relevance of Thermal Indices for the Assessment of the Urban Heat Island

  • Andreas MatzarakisEmail author
  • Letizia Martinelli
  • Christine Ketterer
Open Access
Chapter

Abstract

Urban areas, with their specific characteristics, modify the atmosphere and produce their own meso- and micro climate. The major aspect of this chapter is the discussion of methods for the quantification and assessment of the urban micro-climate and the most known and world-wide studied phenomenon, the Urban Heat Island (UHI). Four urban measurement stations and one rural measurement station are used to quantify the temporal and spatial climatic characteristics in Stuttgart, Germany. For the quantification of the urban micro-climate and the UHI human thermal, comfort indices were applied. These indices, namely Physiologically Equivalent Temperature and the Universal Thermal Climate Index, are used to describe the integral effect of urban thermal atmosphere, based on the energy exchange of the human body. These indices, following the concept of equivalent temperature, are applied to quantify the integral effect of air temperature, air humidity, wind and radiation fluxes, expressed as mean radiant temperature.

Keywords

Thermal Comfort Physiologically Equivalent Temperature Human Biometeorology 

4.1 Introduction

Urban areas, with their artificial materials and specific morphology, act as an obstacle to the atmosphere, altering energy-balance, the chemical composition as well as the wind field (Landsberg 1981; Oke 1982; Oke and Cleugh 1987; Helbig et al. 1999). The urban heat island (UHI), describing the urban-rural surface and air temperature differences, is the most prominent and world-wide studied phenomenon of urban climate (eg Böhm and Gabl 1978; Katsoulis and Theoharatos 1985; Kuttler et al. 1996; Runnalls and Oke 2000; Johansson and Emmanuel 2006).

In fact, the intensity of the urban heat island depends on eg land-use, building ratio, population density and vegetation (eg Landsberg 1981; Oke 1981; 1982; 1988). Before establishing mitigation and adaptation measures counteracting the urban heat island, city planners and officials need to comprehend the spatial and temporal dimensions of the meteorological and climatological conditions in a city (Matzarakis et al. 2008; Ketterer and Matzarakis 2014a, b). As city dwellers are the main target of city planners, the integral effect of air temperature, air humidity, wind speed and radiation fluxes on humans in a city has to be quantified and assessed (Eliasson 2000; Ketterer and Matzarakis 2014a, b). Hence, modern human-biometeorological methods for quantification of the spatial and temporal distribution of the UHI as well as to assess mitigation and adaptation measures for improving outdoor meteorological conditions have to be applied (Kuttler 2011, 2012; Matzarakis 2013).

Urban planners require information about the human biometeorological conditions in terms of frequencies (eg number of days or hours per year or season), as well as the quantification of temperature differences between different planning scenarios Fröhlich and Matzarakis (2011, 2013). The quantification of heat stress and its reduction by planning measures is a big challenge, especially in the light of climate change (Matzarakis and Endler 2010). Due to climate change, the mean air temperature is expected to increase and also heat waves are assumed to became more frequent, more intense and longer lasting (Matzarakis and Amelung 2008; Meehl and Tebaldi 2004; Schär et al. 2004; Muthers and Matzarakis 2010; Matzarakis and Nastos 2011). Thus, there is a demand for the assessment and quantification of adaptation measures improving the urban climate, ie street morphology, different types of vegetation (Hwang et al. 2011; Ketterer et al. 2013; Lin et al. 2012; Matzarakis 2001, 2006, 2007, 2010). This approach is twofold: the analysis and description of single places for urban planning measures and the construction of maps for the detection of areas with frequent heat stress (Svensson et al. 2003).

The aim is to show and describe methods based on long term measured data and their analysis for a comprehensive quantification of urban-rural differences and possible strategies for adaptation and mitigation in urban areas, focused on micro scale conditions.

4.2 Methods and Data

The application of thermal indices based on the human energy balance gives detailed information on the effect of complex thermal environments on humans (Höppe 1999). It is related to the close relationship between the human thermoregulatory mechanism and the human circulatory system. The human body does not have any selective sensors for the perception of individual climatic parameters.

Thermoreceptors can register the temperature of the skin and blood flow passing the hypothalamus and response thermoregulatorily (Höppe 1984, 1993, 1999). These temperatures, however, are influenced by the integrated effect of all climatic parameters, which are in some kind of interrelation, ie affect each other (VDI 1998; Höppe 1999).

Commonly used thermal indices, based on the human energy balance, are Predicted Mean VotePMV (Fanger 1972), Physiologically Equivalent Temperature PET (Mayer and Höppe 1987; Höppe 1999; Matzarakis et al. 1999), Standard Effective Temperature SET* (Gagge et al. 1986) or Outdoor Standard Effective Temperature Out_SET* (Spagnolo and Dear 2003), Perceived Temperature pT (Staiger et al. 2012) and Universal Thermal Climate Index UTCI (Jendritzky et al. 2012). These thermal indices require the same meteorological input parameters: air temperature, air humidity, wind speed, short and long wave radiation fluxes. These input parameters have a temporal and spatial variability, which have a huge influence on thermal indices. Wind speed and mean radiant temperature have the highest variability and are modified by surroundings and obstacles in complex urban areas. Thus, it is particularly important to calculate correctly these parameters and to perform the measurements with high quality and exactness (ie including artificial ventilation and radiation shield for air temperature measurements).

The basis for these thermal indices is the energy balance equation for the human body:
$$ \mathrm{M}+\mathrm{W}+\mathrm{R}+\mathrm{C}+{E}_D+{E}_{Re}+{E}_{Sw}+\mathrm{S}=0 $$
(1)
where, M represents the metabolic rate (internal energy production), W the physical work output, R the net-radiation of the body, C the convective heat flow, ED the latent heat flow to evaporate water diffusing through the skin (imperceptible perspiration), ERe the sum of heat flows for heating and humidifying the inspired air, ESw the heat flow due to evaporation of sweat, and S the storage heat flow for heating or cooling the body mass.

The individual terms in this equation have positive signs if they result in an energy gain for the body and negative signs in the case of an energy loss (M is always positive, W, ED and Esw are always negative). The unit of all heat flows is in Watt (Höppe 1999).

The individual heat flows in Eq. 1, are controlled by the following meteorological parameters (VDI 1998; Höppe 1999):
  • air temperature: C, ERe

  • air humidity: ED, ERe, ESw

  • wind velocity: C, ESw

  • radiant temperature: R

The thermo-physiological parameters heat resistance of clothing (clo) and activity of humans (in W) are required in addition (Fig. 4.1).
Fig. 4.1

Flowchart of the human-biometeorological assessment of the thermal environment

From the energy balance, which is primarily designed for the calculation of a thermal index like PET, are the indices which enable the user to predict “real values” of thermal quantities of the body, ie skin temperature, core temperature, sweat rate or skin wetness.

For this purpose, it is necessary to take into account all basic thermoregulatory processes, like the constriction or dilation of peripheral blood vessels and the physiological sweat rate (Höppe 1993; 1999).

An example of thermophysiological heat-balance model is the Munich Energy Balance Model for Individuals (MEMI) (Höppe 1993), which is the basis for the calculation of the Physiologically Equivalent Temperature, PET.

4.2.1 Physiologically Equivalent Temperature

The Physiologically Equivalent Temperature (PET) is the equivalent temperature at a given place (outdoors or indoors) to the air temperature in a typical indoor setting with core and skin temperatures equal to those under the conditions being assessed. Thereby, the heat balance of the human body with a work metabolism 80 W (light activity, added to basic metabolism) and a heat resistance of clothing 0.9 clo) is maintained (Höppe 1999).

The following assumptions are made for the indoor reference climate:
  • mean radiant temperature equals air temperature (Tmrt = Ta).

  • air velocity (wind speed) is fixed at v = 0.1 m/s.

  • water vapor pressure is set to 12 hPa (approximately, equivalent to a relative humidity of 50 % at Ta = 20 °C).

The procedure for the calculation of PET contains the following steps:
  1. 1.

    calculation of the thermal conditions of the body with MEMI for a given combination of meteorological parameters.

     
  2. 2.

    insertion of the calculated values for mean skin temperature and core temperature into the model MEMI and computation the energy balance equation system for the air temperature Ta (with v =0.1 m/s, VP = 12 hPa and Tmrt = Ta).

     
  3. 3.

    the resulting air temperature is equivalent to PET.

     
PET offers the advantage of a widely known unit (degrees Celsius), which makes results more comprehensible to regional or urban planners, who are not necessarily very familiar with the modern human-biometeorological methods (Matzarakis et al. 1999). The assessment classes of PET (Table 4.1) are valid only for the assumed values of internal heat production (80 W) and thermal resistance of the clothing (0.9) (Matzarakis and Mayer 1997).
Table 4.1

Ranges of the physiological equivalent temperature (PET) for different grades of thermal perception by human beings and physiological stress on human beings, internal heat production: 80 W, heat transfer resistance of the clothing: 0.9 clo (according to Matzarakis and Mayer 1996, 1997)

PET

Thermal perception

Grade of physiological stress

 

very cold

extreme cold stress

4 °C

----------------------

----------------------------

 

cold

strong cold stress

8 °C

----------------------

----------------------------

 

cool

moderate cold stress

13 °C

----------------------

----------------------------

 

slightly cool

slight cold stress

18 °C

----------------------

----------------------------

 

Comfortable

no thermal stress

23 °C

---------------------

----------------------------

 

slightly warm

slight heat stress

29 °C

------------------------------

------------------------------------------

 

warm

moderate heat stress

35 °C

----------------------

----------------------------

 

hot

strong heat stress

41 °C

----------------------

----------------------------

 

very hot

extreme heat stress

The meteorological input parameters have to be measured or transferred to the average height of a standing person’s gravity center, 1.1 m above ground (Matzarakis et al. 2009). These meteorological parameters can be measured or calculated by numerical models. PET can be calculated by models like RayMan (Matzarakis et al. 2007, 2010).

4.2.2 Universal Thermal Climate Index

The Universal Thermal Climate Index UTCI (Jendritzky et al. 2012) is defined as the air temperature (Ta) of the reference condition causing the same model response as the actual condition. Thus, UTCI represents the air temperature, which would produce, under reference conditions, the same thermal strain as in the actual thermal environment. Both meteorological and non-meteorological (metabolic rate and thermal resistance of clothing) reference conditions were defined:
  • wind speed (v) of 0.5 m/s at 10 m height (approximately 0.3 m/s in 1.1 m),

  • mean radiant temperature (Tmrt) equal to air temperature,

  • vapor pressure (VP) that represent relative humidity of 50 %, at high air temperatures (>29 °C) the reference air humidity is defined as 20 hPa.

  • representative activity to be that of a person walking with a speed of 4 km/h (1.1 m/s). This provides a metabolic rate of 2.3 MET (135 W/m2).

The adjustment of clothing insulation is a powerful behavioral response to changing atmospheric conditions. Thereby, the conception behind UTCI was to consider seasonal clothing adaptation habits of Europeans based on available data from field surveys, in order to obtain a realistic representation of this behavioral action.

The categorization of UTCI is based on physiological response of an organism at actual environmental conditions depending on the responses for the reference conditions and thermal load (ie heat or cold stress) (Table 4.2). UTCI values between 18 and 26 °C may comply closely with the definition of the “thermal comfort zone” supplied in the Glossary of Terms for Thermal Physiology (International Union of Physiological Sciences – Thermal Commission 2003) as: “The range of ambient temperatures, associated with specified mean radiant temperature, humidity, and air movement, within which a human in specified clothing expresses indifference to the thermal environment for an indefinite period”.
Table 4.2

UTCI equivalent temperature categorized in terms of thermal stress

UTCI (°C) range

Stress category

Physiological responses

above +46

extreme heat stress

Increase in Tretime gradient

Steep decrease in total net heat loss

Averaged sweat rate >650 g/h, steep increase

+38 to +46

very strong heat stress

Core to skin temperature gradient < 1 K (at 30 min)

Increase in Tre at 30 min

+32 to +38

strong heat stress

Dynamic Thermal Sensation (DTS) at 120 min > +2

Averaged sweat rate > 200 g/h

Increase in Tre at 120 min

Latent heat loss >40 W at 30 min

Instantaneous change in skin temperature > 0 K/min

+26 to +32

moderate heat stress

Change of slopes in sweat rate, Tre and skin

temperature: mean (Tskm), face (Tskfc), hand (Tskhn)

Occurrence of sweating at 30 min

Steep increase in skin wetness

+9 to +26

no thermal stress

Averaged sweat rate > 100 g/h

DTS at 120 min < 1

DTS between −0.5 and +0.5 (averaged value)

Latent heat loss >40 W, averaged over time

Plateau in Tre time gradient

+9 to 0

slight cold stress

DTS at 120 min < −1

Local minimum of Tskhn (use gloves)

0 to −13

moderate cold stress

DTS at 120 min < −2

Skin blood flow at 120 min lower than at 30 min (vasoconstriction)

Averaged Tskfc < 15 °C (pain)

Decrease in Tskhn

Tre time gradient < 0 K/h

30 min face skin temperature < 15 °C (pain)

Tmsk time gradient < −1 K/h (for reference)

−13 to −27

strong cold stress

Averaged Tskfc < 7 °C (numbness)

Tre time gradient < −0.1 K/h

Tre decreases from 30 to 120 min

Increase in core to skin temperature gradient

−27 to −40

very strong cold stress

120 min Tskfc < 0 °C (frostbite)

Steeper decrease in Tre

30 min Tskfc < 7 °C (numbness)

Occurrence of shivering

Tre time gradient < −0.2 K/h

Averaged Tskfc < 0 °C (frostbite)

120 min Tskfc < −5 °C (high risk of frostbite)

below −40

extreme cold stress

Tre time gradient < −0.3 K/h

30 min Tskfc < 0 °C (frostbite)

(Blazejczyk et al. 2010)

Following abbreviations are used: rectal temperature Tre (°C), mean skin temperature Tskm (°C), face skin temperature Tskfc (°C), sweat production Mskdot (g/min), heat generated by shivering Shiv (W), skin wittedness wettA (%) of body area, skin blood flow VblSk (%) of basal value, Dynamic Thermal Sensation DTS

4.3 Exemplary Results

Thermal biometeorological conditions are described using the thermal indices PET and UTCI for Stuttgart. Thereby, the results are focused on the comparison of thermal indices and air temperature.

Figure 4.2 shows the air temperature conditions for the period 2000 to 2011 based on hourly data, where all the hourly data of the examined period is shown. A comparison is made between mean conditions and the specific year 2003, which experienced an extraordinary hot summer. The two upper graphs show the pattern of air temperature for the long period and the year 2003. It can be noted that, during summer 2003, air temperature exceed very often the 30 °C level. This heat wave in 2003 had many negative effects on human health, which results in high mortality and morbidity rate in Western Europe (Koppe et al. 2003; Le Tertre et al. 2006). The daytime PET values are higher than Ta during the day. During the night, PET is lower than air temperature. During the winter, the values of air temperature are mostly higher than the values of PET, due to the effect of wind, low irradiation and air humidity.
Fig. 4.2

Ta (upper graphs) and PET (lower graphs) for the period 2001–2011 and 2003 for station Schwabenzentrum in Stuttgart

Another possibility to describe thermal comfort conditions and urban rural differences can be the long terms analysis for different stations. In Stuttgart are located five stations and they have all been selected for the analysis (Table 4.3).
Table 4.3

Location and altitude of the measurement stations

Measurement station

Lat (N)

Long (E)

Altitude (asl)

Feature

Neckartal (N)

48:47

09:13

224 m

River valley, urban

Schwabenzentrum (S)

48:46

09:10

250 m

City center, on top of a 25 m high Building

Schnarrenberg (Sb)

48:50

09:12

314 m

top of a SW-exposed hill

Hohenheim (H)

48:42

09:02

405 m

Suburb

Echterdingen (E)

48:41

09:14

371 m

Airport

Beanplots, developed by (Kampstra 2008), display the density curve of the data together with median or mean, percentiles or standard deviation. To obtain the typical bean shape, the density curve is mirrored along the central y-axis. Fig. 4.3 depicts beanplots for the described stations in Stuttgart (Neckartal, Schnarrenberg, Schwabenzentrum, Hohenheim and Echterdingen) for the period 2000–2011.
Fig. 4.3

Bean plots for air temperature (upper graphs), PET (middle graphs) and UTCI (lower graphs) for the Stations Echterdingen (E), Schnarrenberg (Sb), Neckartal (N), Schwabenzentrum (S) and Hohenheim (H) for the period 2000–2011

The plots show the seasonal pattern for winter, spring, summer and autumn for Ta (upper graphs), PET (middle) and UTCI (lower graph). The differences between the stations for Ta are small.

In particular, the daily minimum values are higher for the urban stations, compared to suburban site Hohenheim or the rural reference station Echterdingen. This fact can be explained by the heat storage in the city, but also by the differences in altitude, that reach more than 150 m.

The density distribution of PET is governed by radiation fluxes from spring to autumn, which is also the most impacting meteorological variable. In winter, the density distribution of PET is similar to the one of air temperature. Air temperature is the factor that influences PET most during winter.

The minimum values of UTCI (Fig. 4.3 lower panel) are lower than PET. This can be explained by adapting clothing model of UTCI during summer, but also by different assessment scale.

In some cases, esp. for application in planning or for the detection of extreme events for human health, issues conditions can be analyzed in terms of thresholds of air temperature or any thermal index. Table 4.4 shows the amount of days per year for different levels of maximum air temperature (Tamax > 30 and > 35), minimum air temperature (Tamin > 18, > 20 and > 23), the conditions for the maximum value of PET (PETmax > 30, > 35 and > 41) according to the assessment classes (Table 3.7.2.1) and also the PETmin for the levels of > 18. It can be seen that there is a difference in the conditions for maxima over Ta > 30 and only 1 or 2 days for Ta > 35 °C. In addition, it can be seen that Tamin conditions over > 18 and > 20 occur mostly in the urban area. Maximum values of PET are represented by a similar picture; however, the PET is generally higher than air temperature, while extreme heat stress (PET > 41 °C) can occur in more than 20 days per year in the city stations. The daily minimum value of PET never exceeds the threshold of 18 °C in Stuttgart (the frequency is below 0.1 %).
Table 4.4

Number of days on which defined thresholds are exceeded

 

Echterdingen

Schnarrenberg

Neckartal

Schwabenzentrum

Hohenheim

Tamax > 30

8

13

10

14

7

Tamax > 35

1

2

1

2

1

Tamin > 18

3

15

11

20

6

Tamin > 20

0

4

2

6

1

PETmax > 30

93

85

96

99

92

PETmax > 35

55

57

58

61

54

PETmax > 41

16

26

19

23

15

PETmin > 18

0

0

0

0

0

Beyond the information given by the amount of days that manifest specific events, the amount of hours can describe more precisely the occurrence of specific conditions, thus providing more valuable information. Table 4.5 depicts the total amount of hours for the thresholds for the examined stations in Stuttgart urban and rural area. It can be clearly observed that the amount of hours for Taand PET is quite higher in the urban areas than in the rural one, esp. for Ta > 35 and PET > 41 during day time (10 to 16 LST). At nighttime (22 to 6 LST) the picture is a clearer indication that urban areas are quite hotter than rural areas.
Table 4.5

Number of hours per year on which defined thresholds were exceeded in the period 2000–2010

 

Echterdingen

Schnarrenberg

Neckartal

Schwabenzentrum

Hohenheim

Daytime (10–16)

Ta > 30

40.2

53.6

70.5

76.5

37.5

Ta > 35

2.3

4.6

7.5

7.2

2.3

Ta > 41

0.0

0.0

0.0

0.0

0.0

PET > 30

534.5

560.3

539.3

587.4

522.5

PET > 35

252.9

271.2

298.0

289.9

244.4

PET > 41

54.9

64.4

97.7

75.1

51.6

Nighttime (22–6)

Ta > 18

11.9

44.6

52.5

121.0

27.1

Ta > 20

4.3

17.5

21.5

61.5

10.1

Ta > 23

0.4

3.9

4.4

21.5

2.1

PET > 18

188.5

305.2

292.8

516.6

277.4

PET > 20

75.0

149.4

154.0

307.5

130.5

PET > 23

12.4

36.3

40.5

115.0

29.6

4.4 Discussion and Conclusion

There is an increased demand for the quantification and the assessment of the Urban Heat Island (UHI). Most of the studies deal with the comparison of different station in urban and rural areas based on different temporal resolutions and many studies report about differences in air temperature about maxima of more than 10 K and for mean conditions about 2–3 K (Ketterer and Matzarakis 2014a, b). In fact, cities are warmer than rural areas and the formation of the UHI depends mostly on energetic aspects of the urban structures. Most influencing factors are the limited horizon, the storage of heat of urban fabrics and anthropogenic heat emissions (Landsberg 1981; Oke 1982). There are several aspects to quantify UHI beside the energetic aspects (heating and cooling) and air pollution. Recent studies focus on the reduction of UHI based on the modification of urban structures, using reflective materials or increasing evaporative cooling, however, these studies are mostly focusing only one some aspects of the energy budget of the urban areas and concentrate only on air temperature reduction. This aim is achieved using models with different spatial resolutions and designing exemplary measurements for case studies. All these results and information are demanded and can have a direct application. However, it has to be mentioned that results of thermal indices (PET or UTCI), compared to studies based only on air temperature, produce different outcomes. For example, increasing albedo of urban surfaces can decrease air temperature, but at the same time it increases the values of PET, due to increased shortwave reflection. This is a relevant topic not only during summer, but throughout the all year, because it affects the use of the urban spaces, where people want to spend time outdoors.

Finally, easy understandable graphs and figure are a possibility for better communication between different users and disciplines.

References

  1. Błażejczyk, K., Broede, P., Fiala, D., Havenith, H., Holmér, I., Jendritzky, G., Kampmann, B., & Kunert, A. (2010). Principles of the new universal thermal climate index (UTCI) and its application to bioclimatic research in European scale. Miscellanea Geographica, 14, 91–102.Google Scholar
  2. Böhm, R., & Gabl, K. (1978). Die Wärmeinsel einer Großstadt in Abhängigkeit von verschiedenen meteorologischen Parametern. Archiv für Meteorologie, Geophysik und Bioklimatologie Serie B, 26, 219–237.CrossRefGoogle Scholar
  3. Eliasson, I. (2000). The use of climate knowledge in urban planning. Landscape and Urban Planning, 48, 31–44.CrossRefGoogle Scholar
  4. Fanger, P. O. (1972). Thermal comfort: Analysis and applications in environmental engineering. New York: McGraw-Hill.Google Scholar
  5. Fröhlich, D., & Matzarakis, A. (2011). Hitzestress und Stadtplanung – Am Beispiel des Platz der alten Synagoge in Freiburg im Breisgau. Gefahrstoffe – Reinhaltung der Luft, 71, 333–338.Google Scholar
  6. Fröhlich, D., & Matzarakis, A. (2013). Thermal bioclimate and urban planning in Freiburg – Examples based on urban spaces. Theoretical and Applied Climatology, 111, 547–558.CrossRefGoogle Scholar
  7. Gagge, A.P., Fobelets, A., & Berglund, L. (1986). A standard predictive index of human response to the thermal environment. ASHRAE Transactions, 92, 709–731.Google Scholar
  8. Helbig, A., Baumüller, J., & Kerschgens, M. J. (Eds.). (1999). Stadtklima und Luftreinhaltung. Berlin/Heidelberg: VDI-Buch. Springer.Google Scholar
  9. Höppe, P. (1984). Die Energiebilanz des Menschen, Wiss. Mitt. Meteorol. Inst. Univ. München No. 49Google Scholar
  10. Höppe, P. (1992). Ein neues Verfahren zur Bestimmung der mittleren Strahlungstemperatur im Freien. Wetter und Leben, 44, 147–151.Google Scholar
  11. Höppe, P. (1993). Heatbalancemodelling. Experientia, 49, 741–746.CrossRefGoogle Scholar
  12. Höppe, P. (1999). The physiological equivalent temperature – a universal index for the biometeorological assessment of the thermal environment. International Journal of Biometeorology, 43, 71–75.CrossRefGoogle Scholar
  13. Hwang, R. L., Matzarakis, A., & Lin, T. P. (2011). Seasonal effect of urban street shading on long-term outdoor thermal comfort. Building and Environment, 46, 863–870.CrossRefGoogle Scholar
  14. International Union of Physiological Sciences – Thermal Commission. (2003). Glossary of terms for thermal physiology. Journal of Thermal Biology, 28, 75–106.CrossRefGoogle Scholar
  15. Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI – Why another thermal index? International Journal of Biometeorology, 56, 421–428.CrossRefGoogle Scholar
  16. Johansson, E., & Emmanuel, R. (2006). The influence of urban design on outdoor thermal comfort in the hot, humid city of Colombo, Sri Lanka. International Journal of Biometeorology, 51, 119–133.CrossRefGoogle Scholar
  17. Kampstra, P. (2008). Beanplot: A boxplot alternative for visual comparison of distributions. Journal of Statistical Software, Code Snippets, 28, 1–9.Google Scholar
  18. Katsoulis, B. D., & Theoharatos, G. A. (1985). Indications of the urban heat island in Athens, Greece. Journal of Climate and Applied Meteorology, 24, 1296–1302.CrossRefGoogle Scholar
  19. Ketterer, C., & Matzarakis, A. (2014a). Human-biometeorological assessment of adaptation and mitigation measures for replanning in Stuttgart, Germany. Landscape and Urban Planning, 112, 78–88.CrossRefGoogle Scholar
  20. Ketterer, C., & Matzarakis, A. (2014b). Human-biometeorological quantification of the urban heat island in a city with complex topography – the case of Stuttgart. Germany: Urban Climate.Google Scholar
  21. Ketterer, C., Ghasemi, I., Bertram, A., Reuter, U., Rinke, R., Kapp, R., & Matzarakis, A. (2013). Veränderung des thermischen Bioklimas durch stadtplanerische Umgestaltungen – Beispiel Stuttgart-West. Gefahrstoffe-Reinhaltung der Luft, 73, 323–329.Google Scholar
  22. Koppe, C., Jendritzky, G., & Pfaff, G. (2003). Die Auswirkungen der Hitzewelle 2003 auf die Gesundheit. Klimastatusbericht, 152–162. Available at: http://www.dwd.de/bvbw/generator/DWDWWW/Content/Oeffentlichkeit/KU/KU2/KU22/klimastatusbericht/einzelne__berichte/ksb2003__pdf/09__2003,templateId=raw,property=publicationFile.pdf/09_2003.pdf.
  23. Kuttler, W. (2011). Climate change in urban areas. Part 2, Measures. Environmental Sciences Europe, 23, 21.Google Scholar
  24. Kuttler, W. (2012). Climate Change on the Urban Scale – Effects and Counter-Measures in Central Europe, Human and Social Dimensions of Climate Change, Prof. Netra Chhetri (Ed.), InTech, DOI:  10.5772/50867. Available from: http://www.intechopen.com/books/human-and-social-dimensions-ofclimate-change/climate-change-on-the-urban-scale-effects-and-counter-measures-in-central-europe.Google Scholar
  25. Kuttler, W., Barlag, A.-B., & Robmann, F. (1996). Study of the thermal structure of a town in a narrow valley. Atmospheric Environment, 30, 365–378.CrossRefGoogle Scholar
  26. Landsberg, H. E. (1981). The urban climate. London: The academic press.Google Scholar
  27. Le Tertre, A., Lefranc, A., Eilstein, D., Declercq, C., Medina, S., Blanchard, M., Chardon, B., Fabre, P., Filleul, L., Jusot, J.-F., Pascal, L., Prouvost, H., Cassadou, S., & Ledrans, M. (2006). Impact of the 2003 heatwave on all-cause mortality in 9 French cities. Epidemiology (Cambridge, Mass.), 17, 75–79.Google Scholar
  28. Lin, T. P., Tsai, K. T., Hwang, R. L., & Matzarakis, A. (2012). Quantification of the effect of thermal indices and sky view factor on park attendance. Landscape and Urban Planning, 107, 137–146.CrossRefGoogle Scholar
  29. Matzarakis, A. (2001). Die thermischeKomponente des Stadtklimas, Wiss. Ber. Meteorol. Inst. Universität Freiburg No. 6Google Scholar
  30. Matzarakis, A. (2006). Weather and climate related information for tourism. Tourism and Hospitality Planning & Development, 3, 99–115.CrossRefGoogle Scholar
  31. Matzarakis, A. (2007). Climate, human comfort and tourism. In B. Amelung, K. Blazejczyk, A. Matzarakis (Eds.), Climate change and tourism: Assessment and coping strategies (pp. 139–154). Freiburg - Warsow - Maastricht: Own publishingGoogle Scholar
  32. Matzarakis, A. (2010). Climate change: Temporal and spatial dimension ofad aptation possibilities at regional and localscale. In C. Schott (Ed.), Tourism and the implications of climate change: issues and actions (Bridging tourism theory and practice, Vol. 3, pp. 237–259). Bingley: Emerald Group Publishing.CrossRefGoogle Scholar
  33. Matzarakis, A. (2013). Stadtklima vor dem Hintergrund des Klimawandels. Gefahrstoffe – Reinhaltung der Luft, 73, 115–118.Google Scholar
  34. Matzarakis, A., & Amelung, B. (2008). Physiologically equivalent temperature as indicator for impacts of climate change on thermal comfort of humans. In M. C. Thomson et al. (Eds.), Seasonal forecasts, climatic change and human health (Advances in global change research, Vol. 30, pp. 161–172). Dordrecht: Springer-Sciences and Business Media.CrossRefGoogle Scholar
  35. Matzarakis, A., & Endler, C. (2010). Climate change and thermal bioclimate in cities: Impacts and options for adaptation in Freiburg, Germany. International Journal of Biometeorology, 54, 479–483.CrossRefGoogle Scholar
  36. Matzarakis, A., & Mayer, H. (1996). Another kind of environmental stress: Thermal stress. WHO collaborating centre for air quality management and air pollution control. Newletters, 18, 7–10.Google Scholar
  37. Matzarakis, A., & Mayer, H. (1997). Heat stress in Greece. International Journal of Biometeorology, 41, 34–39.CrossRefGoogle Scholar
  38. Matzarakis, A., & Nastos, P. (2011). Human-biometeorological assessment of heat waves in Αthens. Theoretical and Applied Climatology, 105, 99–106.CrossRefGoogle Scholar
  39. Matzarakis, A., Mayer, H., & Iziomon, M. G. (1999). Applications of a universal thermal index: physiological equivalent temperature. International Journal of Biometeorology, 43, 76–84.CrossRefGoogle Scholar
  40. Matzarakis, A., Rutz, F., & Mayer, H. (2007). Modelling radiation fluxes in simple and complex environments – application of the RayMan model. International Journal of Biometeorology, 51, 323–334.CrossRefGoogle Scholar
  41. Matzarakis, A., Röckle, R., Richter, C.-J., Höfl, H.-C., Steinicke, W., Streifeneder, M., & Mayer, H. (2008). Planungsrelevante Bewertung des Stadtklimas – Am Beispiel von Freiburg im Breisgau. Gefahrstoffe - Reinhaltung der Luft, 68, 334–340.Google Scholar
  42. Matzarakis, A., De Rocco, M., & Najjar, G. (2009). Thermal bioclimate in Strasburg – The 2003 heat wave. Theoretical and Applied Climatology, 98, 209–220.CrossRefGoogle Scholar
  43. Matzarakis, A., Rutz, F., & Mayer, H. (2010). Modelling radiation fluxes in simple and complex environments – basics of the RayMan model. International Journal of Biometeorology, 54, 131–139.CrossRefGoogle Scholar
  44. Mayer, H., & Höppe, P. R. (1987). Thermal comfort of man in different urban environments. Theoretical and Applied Climatology, 38, 43–49.CrossRefGoogle Scholar
  45. Meehl, G. A., & Tebaldi, C. (2004). More intense, more frequent, and longer lasting heat waves in the 21st century. Science, 305, 994–997.CrossRefGoogle Scholar
  46. Muthers, S., & Matzarakis, A. (2010). Use of Beanplots in climatology and biometeorology – A comparison with boxplots. Meteorologische Zeitschrift, 19, 639–642.CrossRefGoogle Scholar
  47. Oke, T. R. (1981). Canyon geometry and thenocturnal urban heat island: Compari-son of scale model and field observations. Journal of Climatology, 1(3), 237–254.  http://dx.doi.org/10.1002/joc.3370010304 Google Scholar
  48. Oke, T. R. (1982). The energetic basis of the urban heat island. Quar-terly Journal of the Royal Meteorological Society, 108(455), 1–24.  http://dx.doi.org/10.1002/qj.49710845502 Google Scholar
  49. Oke, T. R. (1988). Street design and urban canopy layer climate. Energy and Buildings, 11(1–3), 103–113.  http://dx.doi.org/10.1016/0378-7788(88)90026-6 Google Scholar
  50. Oke, T. R., & Cleugh, H. A. (1987). Urban heat storage derived as energy balance residuals. Boundary-Layer Meteorology, 39, 233–245.CrossRefGoogle Scholar
  51. Runnalls, K. E., & Oke, T. R. (2000). Dynamics and controls of the near-surface heat island of Vancouver, British Columbia. Physical Geography, 21, 283–304.Google Scholar
  52. Schär, C., Vidale, P. L., Lüthi, D., Frei, C., Häberli, C., Liniger, M. A., & Appenzeller, C. (2004). The role of increasing temperature variability in European summer heat waves. Nature, 427, 332–336.CrossRefGoogle Scholar
  53. Spagnolo, J., & de Dear, R. A. (2003). Field study of thermal comfort in outdoor and semi-outdoor environments in subtropical Sydney Australia. Building and Environment, 38, 721–738.CrossRefGoogle Scholar
  54. Staiger, H., Laschewski, G., & Grätz, A. (2012). The perceived temperature – a versatile index for the assessment of the human thermal environment. Part A: scientific basics. International Journal of Biometeorology, 56, 165–176.Google Scholar
  55. Svensson, M., Thorsson, S., & Lindqvist, S. (2003). A geographical information system model for creating bioclimatic maps – examples from a high, mid-latitude city. International Journal of Biometeorology, 47, 102–112.Google Scholar
  56. VDI. (1998). Methods for the human-biometerological assessment of climate and air hygiene forurban and regional planning. Part I: Climate, VDI guideline 3787. Part 2. Beuth, Berlin.Google Scholar

Copyright information

© The Author(s) 2016

This chapter is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.

The images or other third party material in this chapter are included in the work’s Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work’s Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.

Authors and Affiliations

  • Andreas Matzarakis
    • 1
    • 2
    Email author
  • Letizia Martinelli
    • 3
    • 4
  • Christine Ketterer
    • 1
    • 5
  1. 1.Albert-Ludwigs-University of FreiburgFreiburgGermany
  2. 2.Research Center Human BiometeorologyGerman Meteorological ServiceFreiburgGermany
  3. 3.Chair of Environmental MeteorologyAlbert-Ludwigs-UniversityFreiburgGermany
  4. 4.Department of Planning, Design and Technology of ArchitectureSapienza UniversityRomeItaly
  5. 5.iMA Richter & RoeckleFreiburgGermany

Personalised recommendations