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Thermal Comfort in Outdoor Environment
Tsuyoshi HONJO
Faculty of Horticulture, Chiba University
648 Matsudo, Matsudo-shi, Chiba 271-8510, Japan
e-mail: [email protected]
Abstract
Relatively few studies on thermal comfort for outdoor environments have been done compared to indoor environments, although the importance of the former is increasingly recognized with changing climate
and increase of heat stress in cities. The difficulty in assessing thermal outdoor conditions is that the
climatic variables are much more diverse than in indoor settings. Indices such as SET*, PMV and PET are
the most broadly used not only as indoor indices but also as outdoor comfort indices. In the present study,
applications of these indices to outdoor conditions are reviewed. There remain problems in the assessment
of outdoor comfort indices, however, SET*, PMV and PET have proven suitable for application at the current state of the art.
Key words: comfort index, outdoor, PET, PMV, SET*, thermal comfort
1. Introduction
During the last decade, interest in the assessment of
thermal comfort has increased because of climate
changes and increased heat stress in cities. But there
have been relatively small numbers of studies on thermal comfort for outdoor environment (Swaid et al.
1993; Nikolopoulou et al. 2001; Givoni et al. 2003;
Spagnolo & de Dear, 2003). On the other hand, many
studies have been done on thermal comfort especially
for indoor environment.
Climatic chambers became available more frequently
after 1960 which permitted to analyze separately the
influences of the 4 main climatic parameters: 1) ambient
air temperature, 2) radiant (i.e., wall) temperature, 3) air
humidity and 4) airstream velocity (Mayer & Höppe,
1987) on thermal comfort. They enabled extensive
studies to establish indices for indoor thermal comfort
and discomfort. Gagge et al. (1971) promoted the
concepts of New Effective Temperature (ET*) or,
respectively, standard Effective Temperatures (SET*) by
taking into account: 5) different degrees of insulation
measured by “clo” and 6) levels of activity measured by
“met” as additional factors. Fanger (1972) provided an
extensive set of comfort diagrams from which – for
given conditions of “clo” and “met” – Predicted Mean
Values (PMV) of comfort or discomfort were derived by
obtaining a rating scale from large samples of individuals for subjectively felt (dis-)comfort as a useful tool to
establish comfortable indoor conditions by air conditioning, depending on clothing and physical activity.
Theoretically, the indices developed for indoor
Global Environmental Research
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printed in Japan
conditions can also be applied to outdoor environments.
However, Spagnolo and de Dear (2003) pointed out for
SET* that it cannot be used outdoors without modification. The main problem for assessing thermal outdoor
conditions is that the climatic variables may be much
more diverse than in indoor settings. To account for this
problem, the Physiological Equivalent Temperature
(PET) was developed (Mayer & Höppe, 1987) with the
aim to obtain a better estimate of outdoor conditions.
Proceeding from thermophysiological considerations
and taking into account the above-mentioned climatic
parameters, PET, as an index, is similar in definition to
ET* (i.e., its dimension is °C) and presents an outdoor
equivalent to a typical indoor setting, e. g., heat balance
of a human subject at light activity (80 W) and indoor
clothing (0.9 clo). Apart from SET*, PMV and PET, as
the most frequently used indices, climatic outdoor
conditions have been further characterized by less complex indices such as the discomfort index (Thom, 1959),
wind-chill index (Steadman, 1971) and apparent temperature (Steadman, 1979).
While indoor environment can be easily changed by
air conditioning, methods for making an outdoor environment comfortable are very limited. For the outdoor
environment, the indoor comfort indices were sometimes applied. Spagnolo and de Dear (2003) reviewed
the studies on thermal comfort of outdoor conditions
and questioned that the theory developed for an indoor
environment can be applied to an outdoor environment
without modification. The outdoor comfort indices
based on energy balance of the human body should also
use meteorological variables (air temperature, humidity,
©2009 AIRIES
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T. HONJO
radiation, and wind speed) and human factors (clothing
and metabolism). But broader ranges of meteorological
variables and of human factors pose greater difficulties
when assessing outdoor comfort indices compared to
indices for an indoor environment. Currently, in recent
studies on outdoor thermal comfort SET*, PMV and
PET are the most broadly used indices. In the present
study applications to outdoor conditions of the indices
SET*, PMV and PET are reviewed. Specifically compared by presenting examples are the prediction values
of PMV and PET in two different outdoor settings.
2. Energy Balance of the Human Body
In the thermal indices which consider the thermal
physiology, energy balance of the human body is important to evaluate the thermal comfort. Equation of the
energy balance, which is the basis for the comfort indices, is expressed as follows (Höppe, 1999).
M+W+R+C+ED+ERe+ESw+S = 0,
where M is the metabolic rate, W is the physical work
output, R is the net radiation of the body, C is the
convective heat flux, ED is the latent heat flux diffusing
through the skin, ERe is of heat flux by the respiration,
ESw is the heat flux due to evaporation of sweat, and S
is the storage heat flow for heating or cooling the body
mass. These terms in this equation have positive signs if
they gain energy for the body (M is always positive, W,
ED and ESw are always negative).
The individual terms are mainly influenced by the
meteorological parameters. C and ERe are affected by
air temperature, ED, ERe, ESw are affected by humidity,
Ultrasonic anemometer
Pyrgeometer
Pyranometer
Thermometer/
hygrometer
Globe
thermometer
Data logger
Fig. 1 Photograph Showing the Setup of Meteorological Instruments for the Measurement of Outdoor Comfort Indices.
Relations between factor and senser are shown below.
Factor
Air temperature
Globe temperature
Relative humidity
Wind speed
Incoming short-wave radiation
Incoming long-wave radiation
(according to Thorsson et al., 2007)
Sensor
Thermometer
Globe thermometer
Hygrometer
Ultrasonic anemometer
Pyranometer
Pyrgeometer
C and ESw are affected by wind velocity and R is calculated as the exchange between environment and human
body by short wave and long wave radiation. A typical
setup of meteorological instruments to assess these environmental variables is shown in Fig. 1.
In a sedentary condition we feel comfortable when
sweat production is small or ESw is near zero. At higher
levels of activity, comfort is attained at modest levels of
sweating or skin wettedness. That means that combinations of skin temperature and the body’s core temperature indicating the comfortable range are activitydependent. The other important condition for the comfort is that changes of S do not exceed a certain small
value, i.e., the amount of metabolic heat generated in the
body (M +W) is approximately equal to the amount of
heat lost from the body.
3. Outdoor Comfort Indices and Their
Application
3.1 SET*
The new effective temperature (ET*) is based on
human energy balance and two-node model (Gagge
et al., 1971). With ET* the thermal conditions can be
compared to the conditions in a standardized room with
a mean radiant temperature equal to air temperature and
a constant relative humidity of 50%. Gagge et al. (1986)
improved ET* and proposed the new standard effective
temperature (SET*) which is used frequently both as
indoor and outdoor comfort index. Ishii et al. (1988)
compared several thermal comfort indices and concluded that SET* is better suited in evaluating outdoor
comfort. Kinouchi (2001) also found that SET* can be
used as an index for the outdoor environment. Pickup
and Dear (1999) improved the SET* and developed
thermal indices especially for outdoor conditions OUTSET*.
Many CFD (Computational Fluid Dynamics) models
have been used in the analysis of outdoor thermal
environment (Bruse & Fleer, 1998; Tominaga et al.,
2004; Stathopoulos, 2006). In recent studies, distribution
of SET* is used as an output of CFD model (Lin et al.,
2008). Chen et al. (2004) measured the outdoor environment of apartment blocks and simulated the effects of
changes in the outdoor thermal design on calculated
SET*. With the CFD model and Genetic Algorithm
(GA) Ooka et al. (2008) simulated the optimum arrangement of trees and Chen et al. (2008) simulated the
influence of the arrangement of buildings on the outdoor
thermal environment. Both studies show the distribution
of SET* at the pedestrian level.
To calculate the convective heat flux C in the equation of human energy balance, a single value of convective heat transfer coefficient has been mostly used. Ono
et al. (2008) divided the human body into nine parts and
measured the coefficient of each part by wind tunnel
experiments using a thermal manikin. The values of the
coefficients were compared with the CFD model and
used for the calculation of SET* distribution.
Thermal Comfort in Outdoor Environment
3.2 PMV
Fanger (1972) developed PMV (predicted mean
vote) for indoor climates with the aim to provide an index for ratings of thermal (dis-)comfort at various states
of activity and clothing insulation. PMV is also used as
ISO 7730 (ISO, 1994). In Table 1, the relation of PMV
value with thermal perception and physiological stress
level is shown for low activity and normal indoor clothing. To apply PMV to outdoor conditions, Jendritzky
and Nübler (1981) added complex outdoor radiation
(Fig. 2) and the model is known as Klima-Michel Model
(KMM). As an application of KMM model, Jendritzky
and Nübler (1981) showed the distribution map of PMV
in Freiburg from which effects of buildings and heat
islands are obvious.
Matzarakis and Mayer (1997) calculated PMV to
develop a high-resolution map that presents the average
annual number of days with strong heat stress
(PMV>3.0), using Greece as an example. They selected
PMV as the index because PMV with KMM is a wellsuited measure for assessing the thermal environment of
different outdoor-climates. In this study, PMV was
calculated with the meteorological data obtained at 12
Greek stations for the years 1980 to 1989. The map
shows quite broad areas which have to be considered as
areas of high heat stress.
From meteorological measurement in a nearby park,
Vu et al. (1998) calculated PMV and found that inside
the park, the PMV for a walking person is 2.6. The PMV
for the same person in the upwind direction of the park
is 3.2 and the corresponding value of PMV in the downwind direction of the park is 2.8. In this study, the PMV
value itself is not in the comfortable range but the decrease in PMV, due to the presence of the park, indicates
a improvement of the thermal environment.
Thorsson et al. (2004) studied the relationship
between the thermal environment and behavioral patterns in an urban park located in Goteborg, Sweden by
structured interviews, observations of the behavior and
measurements of PMV. In Fig. 3, comparison between
the thermal perception of the interviewees and the PMV
indicated some discrepancy between PMV and actual
perception vote (ASV). 41% of calculated PMV of the
interviewees were outside the range of Fanger’s model
(<–3 and >3). Nikolopoulou et al. (2001) also presented
similar results.
Hodder and Parsons (2007) investigated the effect of
quantity and quality of solar radiation on thermal comfort. They set a semi-outdoor arrangement like a seat of
a car, and changed the intensity of solar radiation, spectral contents with same intensity of solar radiation and
material of the glass through which solar radiation came
in. PMV was kept 0±0.5 where there was no direct
radiation. Increase in the total intensity of solar radiation
was the critical factor affecting thermal comfort.
45
Table 1 Ranges of PMV and physiological equivalent
temperature PET for different grades of thermal
perception and physiological stress; internal heat
production: 80 W, heat transfer resistance of the
clothing: 0.9 clo (according to Matzarakis &
Mayer, 1997).
PMV
PET
(°C)
–3.5
–2.5
–1.5
–0.5
0.5
1.5
2.5
3.5
4
8
13
18
23
29
35
41
Thermal
perception
Very cold
Cold
Cool
Slightly cool
Comfortable
Slightly warm
Warm
Hot
Very hot
Grade of
physiological stress
Extreme cold stress
Strong cold stress
Moderate cold stress
Slight cold stress
No thermal stress
Slight heat stress
Moderate heat stress
Strong heat stress
Extreme heat stress
Fig. 2 The components of the human radiant energy budget.
short-wave radiation (0.28-4um): I = direct solar radiation,
H = diffuse solar radiation, (I+H)refl = reflected short-wave
radiation.
long-wave radiation (4-100um): Eu = atmospheric counter
radiation, EA = long-wave emissions of the surroundings,
W = radiation from the man’s surface.
(according to Jendritzky & Nübler, 1981)
Fig. 3 Percentage frequency distribution for thermal perception assessed by the predicted mean vote (PMV) index
and the thermal perception of the interviewees (ASV)
(according to Thorsson et al., 2004).
46
T. HONJO
3.3 PET
PET was developed as an index which takes into
account all basic thermoregulatory processes (Höppe,
1993) and is based on a thermo-physiological heat balance model called Munich energy balance model for
individuals (MEMI) (Höppe, 1984; 1999).
According to Mayer and Höppe (1987) and Höppe
(1999), PET is defined as the equivalent air temperature
at which, in a typical indoor condition heat balance of
the human body exists (work metabolism 80 W of light
activity, and clothing of 0.9 clo). The following
assumptions are made for the indoor reference climate:
Mean radiant temperature equals air temperature
(Tmrt=Ta). Air velocity is set to 0.1 m/s. Water vapour
pressure is set to 12 hPa (approximately equivalent to a
relative humidity of 50% at Ta=20°C).
Although PET is the equivalent temperature under a
virtual indoor condition, it is applicable to a wide range
of real outdoor conditions. PET is one of the recommended indices in new German guidelines for urban and
regional planners (VDI, 1998) and is used for the prediction of changes in the thermal component of urban or
regional climates. By using the Software named RayMan
developed by Matzarakis et al. (2007), PET can be
calculated easily. The comparison of PMV with PET is
shown in Table 1. The range of thermal perception and
physiological stress of both indices are also shown.
As applications of PET: Matzarakis et al. (1999)
determined PET for heat stress in Greece proceeding
from the data used by Matzarakis and Mayer (1997) for
evaluation of PMV. Svensson et al. (2003) presented the
distribution map of PET in Goteborg by using the geographic information system. Gulyás et al. (2006) evaluated thermal comfort in Szeged, Hungary. Ali-Toudert
and H. Mayer (2006) studied the effects of aspect ratio
and orientation of an urban street canyon. Bouyer et al.
(2007) evaluated the thermal comfort of a stadium by
using wind tunnel experiments and PET. Alexandri and
Jones (2008) analyzed the effect of greening of walls
and roofs of urban canyons in a simulation model and
based evaluation of thermal comfort on PET.
Thorsson et al. (2007) studied the relation between
the thermal comfort and outdoor activities in a satellite
city near Tokyo, Japan. Comparison of the results of
structured interviews and of PET evaluation in an urban
park and a square are shown in Fig. 4. PET shows good
agreement with the perception of interviewees. It is
superior to that shown Fig. 3 where PMV was used as
an the comfort index. Further studies will be necessary
to decide which index is more suitable for indicating
outdoor comfort, depending on the particular environmental conditions.
4. Conclusions
Although there remain problems in the assessment of
outdoor comfort indices, SET*, PMV and PET have
proven suitable for application at the current state of the
art.
Outdoor and indoor comfort zones may differ from
each other. There may be larger influences of psychological aspects when we stay in outdoor than in indoor
environments. Adaptation or acclimatization to the outdoor environment should be studied. We do not have
enough knowledge about the influence of regional
differences on the comfort zone. An Universal Thermal
Comfort Index (UTCI) is now being developed (Höppe,
2002) but for an improved assessment of outdoor thermal comfort much more measurements and consideration of additional aspects will be necessary.
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Tsuyoshi HONJO
Tsuyoshi HONJO is a professor in Faculty
of Horticulture at Chiba University, Japan.
One of his interests is urban meteorology
especially the effect of urban vegetation.
He is also interested in visualization of
landscape by computer graphics and
various area of computer application in
agriculture.
(Received 15 January 2009, Accepted 23 March 2009)