- Email: [email protected]
Thermal comfort in outdoor and semi-outdoor environments
269
Environmental Ergonomics Y. Tochihara and T. Ohnaka Crown Copyright 9 2005 Published by Elsevier Ltd. All rights reserved.
Thermal comfort in outdoor and semi-outdoor environments Richard de Dear*, Jennifer Spagnolo Division of Environmental and Life Sciences, Macquarie University, Sydney, Australia
Abstract: Thermal comfort research to date has been focused on indoor applications, but in recent years attention has turned to the comfort requirements of people using outdoor and semi-outdoor spaces. Two different approaches have been discerned in the literature. The first simply transfers the assumptions and models usually associated with indoor thermal environmental engineering to the outdoor context, while the second approach accepts that various contextual features of semi-outdoor and outdoor spaces may affect subjective thermal perceptual processes as much, if not more so, than the conventional heat-balance variables found in indoor thermal comfort models. This chapter reports examples of recent work using both of these approaches.
Keywords: Thermal comfort, Acceptability, Outdoors, Hot-humid, Sub-tropical
1. Introduction
The human body maintains thermal homeostasis in the face of enormous variability in both internal (metabolic) and external (environmental) heat loads. Equilibrium is achieved through a combination of physiological controls (shivering, sweating and vasomotor responses) and behavioural thermoregulation (clothing, architecture, heating, ventilation and air-conditioning (HVAC), etc.). Both these control systems optimise the heat balance between body and environment through a variety of heat transfer mechanisms (radiant, convective, latent and conductive). Therefore, it is not surprising that simple air temperature has long been regarded as an inadequate characterization of the human thermal environment. As well as air temperature, ambient humidity, short- and longwave radiant flux densities, wind speed, even turbulence intensity within airflows, also affect
*Corresponding author. E-mail: [email protected] (R. de Dear)
the heat balance. In response to this widely acknowledged complexity, countless indices of human thermal environments have been proposed over the years to characterize outdoor microclimates in terms of their effects on the human body. Some of the better known examples are the Wet-Bulb Globe Temperature, Heat Stress Index, Predicted 4-h Sweat Rate, Apparent Temperature, Wind Chill Index, Relative Strain Index, to mention but a few. It is fair to say that most of the outdoor indices developed thus far have been intended for use primarily in stressful thermal environments. WBGT, for example, is widely used in outdoor workplaces to manage exposures to dangerously high heat loads. Most of the scientific effort on thermal indices and thermophysiological models in the last 30 y has been focused on moderate thermal conditions associated with indoor built environments. The purpose of models such as Predicted Mean Vote (PMV) and 2-node (ET* and SET*) has been to quantify thermal comfort, rather than stress or strain. The approach of these indoor comfort models is a full-blown heat balance involving all
270
six of the basic thermal comfort parameters: air temperature, radiant temperature, air velocity, humidity, clothing insulation and metabolic rate. This stands in contrast to the simplicity of the one-, two-, and occasionally three-parameter outdoor thermal stress indices mentioned above. These observations beg the questions of why there has been so much more scientific effort put into indoor thermal comfort models, and why have they evolved so much further than their outdoor counterparts? The most obvious explanation must surely be the injection of research funding into the science of thermal comfort by the HVAC industry. With such a clearly identifiable end-user for their science, indoor thermal comfort researchers have been relatively well-supported over the last three or four decades through organizations such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). So, what about thermal comfort (as opposed to thermal stress) outdoors? Recently we have seen an increasing awareness of the importance of microclimatology in urban planning because of its direct implications for tourism and the related service economy. Successful public open spaces attract people, who in turn attract businesses that, in turn, generate employment and lift real-estate values (1). Another potential end-user for outdoor thermal comfort research is the weather forecasting industry. Bureaux of meteorology are interested in valueadding by forecasting comfort instead of plain air temperature. Given the obvious weather/climate sensitivity of the tourism industry, a tool for describing and forecasting climatic resources would be a very useful thing to have there as well. Likewise, with outdoor events organizers who should realize that scheduling activities for outdoor or "semi-outdoor" events based on thermal comfort represents a more sophisticated approach than just trying to avoid rainy days. Yet more examples of potential end-uses for outdoor thermal comfort research can be found in the design and engineering of semi-outdoor spaces so that they match endusers' needs or preferences. An example is the work of Fiala and Lomas (2) who numerically simulated the microclimate inside S y d n e y ' s Olympic 2000 stadium, along with the spectators' subjective responses to them, under Sydney's
Typical Reference Year of meteorological data. The project aimed to optimize design and material selection for the main 2000 Olympic stadium. While the demand for outdoor thermal comfort information and models can readily be demonstrated, an appropriate strategy for meeting that demand is not so obvious. Clearly, the indices of thermal stress described earlier in this chapter are not suitable. They have neither been calibrated nor validated against empirical thermal comfort assessments by human subjects. The physiological parameters relevant to their validation are sweat rates, core temperatures, heart rates and other physiological indicators, but thermal comfort models need to be validated against conscious thermal comfort ratings, thermal sensations and thermal preferences. In view of the paucity of actual thermal comfort data collected in outdoor contexts, the most common approach to outdoor thermal comfort modeling has, to date, been the transfer of indoor thermal comfort models such as PMV and SET* to outdoor conditions. A recent case in point is Kennedy's book How's the Weather Find Your Outdoors Comfort Paradise in the USA
(3) in which Fanger's PMV was directly applied to outdoor climatological norms across the USA to derive "comfort maps" for various times of the year. The untested assumption underlying this transfer is that perceptual and semantic aspects of thermal environments are completely independent of contextual factors such as indoors versus out that there is no range-effect in operation, no adaptation effects, and so on. In another recent project, de Dear applied an outdoor version of the Standard Effective Temperature index (OUT_SET*) to standard climatological data with the aim of producing thermal comfort guidelines for artificially generated windspeeds in semi-outdoor spaces (13). The end-user of the guidelines wanted to make its clientele, the general public, more comfortable while they were queuing (up to 45 min) to use its services. Given the hot and oftentimes humid nature of the semioutdoor spaces in question (Orlando, FL, Los Angeles, CA, and Hong Kong), increasing air speed in the queuing areas was the only practicable strategy for enhancing thermal comfort. The research question came down to: "What was the
271
design air speed required at various times of day and at various times of year to deliver thermal comfort to the occupants of these semi-outdoor spaces?" On the basis of earlier work it was assumed that 26~ was a realistic comfort target temperature in semi-outdoor contexts. After making assumptions about typical clothing insulation values and metabolic rates and mean radiant temperatures (MRT), de Dear applied various combinations of air temperature and humidity to the two-node model while iterating incrementally higher air speeds, until an OUT_SET* value of 26~ was attained. After numerous results had been solved in this iterative fashion, "isotachs" were plotted on a psychrometric chart. An example is given in Fig. 1. The diagonal isotachs in that chart represent the air speeds required to bring a human subject with a metabolic rate of 1.2 met, clothing insulation of 0.45 clo units, standing in partial shade outdoors in Orlando, Florida in July to a particular temperature. The end-user (engineer) of such a design guideline can select a design temperature/humidity combination for, say 3 pm
on a typical July, and enter those coordinates into the psychrometric chart. They would then be able to interpolate from the nearest isotachs to find the air speed they needed to provide within the occupied zone where their clientele are queuing in semioutdoor spaces at any time during July. Perhaps the most difficult assumption in this approach was the estimation of MRT. This was based on mathematic conversion of solar angles and intensity, ground reflectivity, and partial shade-factors overhead, into an equivalent surface temperature of an hypothetical enclosure around the subject (10). Despite being a fairly complicated application of comfort theory to outdoor and semi-outdoor problems, the research approach just described was premised on the direct transferability of the SET* index and its associated two-node model from an indoor context to the outdoors, but there was no empirical basis underpinning that assumption. One of the few research papers in which actual thermal comfort data from human subjects outdoors were reported was by Nikolopoulou et al. (1). They interviewed users of public spaces in the city-center
Fig. 1. Lines of ambient air speed required to produce thermally acceptable semi-outdoor environmental conditions for standing human subjects wearing light summer casual clothing in Orlando, Florida, USA during July.
272
of Cambridge in the UK and collected simultaneous meteorological observations. One of their key findings was that physical environmental parameters were not enough to characterize subjective thermal states outdoors. Furthermore, actual thermal sensations were widely discrepant from those predicted by indoor comfort indices such as PMV, but that could be due, in part, to the way solar radiant loads were assessed with a black globe thermometer instead of being explicitly converted from absorbed solar radiation into equivalent MRTs. Nikolopoulou et al. (1) concluded that other factors beyond the physics of the body' s heatbalance, such as psychological adaptation (available choice, environmental stimulation, thermal history, memory effect, expectations) were responsible for discrepancies between predictions of comfort models based on indoor research, and the actual subjective evaluations by human subjects in outdoor field settings. Against this theoretical backdrop of thermal comfort outdoors, the aims of the present chapter, are to: 9 describe a full-scale outdoor thermal comfort study in a subtropical city (Sydney), 9 collect all data required for calculation of current-generation indoor and outdoor thermal comfort models, 9 compare predictions of indoor comfort models with actual thermal comfort assessments outdoors, 9 compare actual indoor and outdoor thermal comfort assessments by human subjects, 9 examine adaptation effects with regard to summer and winter seasons.
comfort, were measured on site in various outdoor and semi-outdoor microclimates with a mobile meteorological station named "TROJAN". The sensors monitored global and diffuse solar radiation, short-wave radiation reflected from the ground, long-wave radiation down-welling from the sky, long-wave radiation up-welling from the ground, air temperature, humidity and wind speed. A camera tripod was used as the skeleton of the station and spirit-levels were used to align the radiometers with the horizon before each measurement cycle. Subjects were approached at random in various outdoor and semi-outdoor recreational and public transit spaces in Sydney. Once recruited, thermal comfort data were recorded from the subjects using a questionnaire adapted from those used in recent ASHRAE indoor field studies (e.g. 5). Some new items, such as one dealing with sun/ shade preferences, were introduced. The questionnaire was designed to require less than 1 min to complete so as to minimize the rejection rate. The most important question requested subjects to indicate their Actual Thermal Sensation Vote (ATSV) on the following scale: - 3 = c o l d , - 2 = cool, - 1 = slightly cool, 0 = neutral, + 1 = s l i g h t l y warm, + 2 = w a r m , +3=hot, taking into account the clothing they were wearing at the time of interview. Temperature preference at the time of interview was also assessed (want warmer, want no change, want cooler). While the subject completed the questionnaire, a measurement sequence was initiated with the T R O J A N instrument package as described above. The summer sample size was 432 while the winter sample had 585 subjects.
1.1. Methods
2. Results
The underlying concept of this field research project was to collect simultaneous micrometeorological measurements and subjective thermal comfort assessments from a large sample of subjects in various outdoor settings. Micrometeorological sensors were selected in accordance with the specifications outlined in ASHRAE's Handbook o f Fundamentals (4). All four basic environmental parameters, influencing thermal
Sydney has a subtropical climate with a mean daily maximum temperature of 26-28~ during summer, and a mean daily winter maximum of 17~ Mean minimum daily temperatures range from 5 - 8 ~ in winter to 17-18~ over the summer months (6). Due to its coastal location, Sydney does not experience large diurnal or seasonal variability. Average wind speeds range from 11.5 to 14 km h-~ (at 10 m), corresponding to a range of
273
1.8-2.2 m S-1 at the height of a human subject (1 m) using the Power law with an urban terrain friction exponent. Table 1 describes some basic micrometeorological observations made during the two seasons' field campaigns. The average air temperature measured was 27.8~ in summer and in winter 18.2~ The average air velocities were lower than the climatological averages, probably due to sheltering from urban structures. Low minimum and average radiation values probably result from our use of some semi-outdoor locations with shading. Fig. 2 shows the distribution of questionnaire votes by season. The distribution of Actual Thermal Sensation Votes (ATSV votes in Fig. 2a) indicates a seasonal skewing of thermal sensations towards the "cool" end of the scale in winter and the "warm" end in summer, as might be expected. The air temperature preference votes (Fig. 2b) show a large proportion of the sample in both seasons wanting "no change" in air temperature, and the seasonal skewing noted in ATSV (Fig. 2a) was also evident in thermal preference votes (Fig. 2b). The seasonal bias was again evident in air movement preference votes (Fig. 2c), with summer subjects "wanting more air movement" and winter subjects "wanting less air movement". The winter results for sun/ shade preference (Fig. 2d) showed a majority of subjects voting "want more sun", but in summer, the largest proportion of the sample voted "no change". It is likely that both summer and winter samples had a large percentage of subjects being interviewed in the shade, prompting the winter subjects to request more sunshine while the summer subjects found a shady microclimate preferable. Table 1 Statistics on the micrometeorological observations during the summer and winter field campaigns in Sydney. Air temperature (~
Air velocity (m s - l )
Solar radiation (W m -2)
Summer
Winter
Summer
Winter
Summer
Winter
18.2 28.2 12.6 3.5
0.7 2.1 0.07 0.4
0.7 2.0 0.06 0.4
207.0 1031.0 10.2 352.7
147.8 844.8 3.6 193.2
Mean 27.8 Max 43.3 Min 20.4 SD 5.0
Perceived temperature (PT) is an index developed by Jendritzky et al. (7) and defined as the air temperature of a standardized environment in which the same PMV (8) is registered as in the real environment. For warm conditions, PMV is corrected for humidity effects by substitution of the new Effective Temperature, ET*, in place of operative temperature (9). In the standardized environment, the MRT is set equal to the air temperature, rh -- 50% and the wind speed (vel) is locked in at 0.12 m s- 1. The PT (7) refers to a person standardized as follows: male, age 35 y, height 1.75 m, weight 75 kg, walking at 4 km per h on a horizontal plain related to an internal heat production of 172.5 W. The person varies the thermal resistance of clothing (clo) in the range of c l o - - 1 . 7 5 (winter) and c l o - 0 . 5 0 (summer) to achieve thermal comfort ( P M V - - 0 ) if possible. In summary, PT is based exclusively on an indoor thermal comfort index PMV with the main difference from indoor PMV being Jendrizky's conversion of solar and terrestrial radiation fluxes into their equivalent MRT. Based on Gagge's (9) 2-node model, the new Effective Temperature (ET*) is the temperature of a standard environment ( r h - 5 0 % , T a - - M R T and v e l - 0 . 1 2 m s -1) in which a subject would experience the same net heat balance, skin wettedness and mean skin temperature as in the actual environment. Where ET* assumes 70 W/ m 2 metabolic rates and 0.6clo, the Standard Effective Temperature (SET*) extended ET* to include variable activities and clothing levels. In the reference environment Ta-- MRT and vel - 0.12 m s-1. SET* was modified for outdoors (OUT_SET*) by Pickup and de Dear (11) by incorporating short- and terrestrial radiation fluxes into the MRT calculations. Again the difference between the indoor index and its outdoor implementation (OUT_SET*) was the inclusion of solar and terrestrial radiation, and for the present chapter we have adopted the same MRT sourcecode as used in the PT model (10). The two indices here have different assumptions and gave different results, particularly in summer (see Table 2). The mean OUT_SET* was 28.9~ in summer and 23.9~ in winter, while the mean
274
Fig. 2. Frequency of questionnaire responses in Sydney by season.
PT was observed to be 34.9~ in summer and 24.0~ in winter. The discrepancy is probably the result of the inclusion of observed clo values in OUT_SET* calculations (mean values were 0.6 clo in summer and 0.9 clo in winter), while the PT index assumes fixed insulation. Table 3 indicates widely discrepant neutralities between the two
heat-balance indices and plain air temperature, probably again for the same reasons. By pooling the summer and winter samples in Table 3 we were able to estimate an all-year-round outdoor thermal neutrality on the OUT_SET* index. The result of 26.2~ came in significantly higher than the indoor SET* neutrality of 24~ (9). To test whether or not the subjective response to a given thermal environment was the same under
Table 2 Calculated thermal index summary. OUT_SET* (~
Mean Max Min SD
Table 3 Observed thermal neutralities on various thermal indices.
PT (~
Summer
Winter
Summer
Winter
28.9 45.1 16.3 5.7
23.9 38.7 6.4 6.6
34.9 52.4 23.6 6.9
24.0 45.0 12.8 6.4
Ta (~ Summer Winter All year
PT (~
OUT_SET* (~
23 (22.0-23.8) 28.1 (26.8-29.3) 23.3 (22.1-24.3) 26.6 # (22.8-44.7) 31.6(29.4-34.9) 33.3 (30.7-37.5) 21.9 # (21.0-22.9) 28.4 (27.6-29.2) 26.6 # (25.1-27.2)
# indicates weaker significance (p > 0.05).
275
both indoor and outdoor exposures, we accessed some thermal comfort data collected inside an airconditioned office building in Sydney by Rowe (12) and calculated neutralities using the same statistical methods as used on our own outdoor thermal comfort study results. Fig. 3 indicates significant differences between statistical estimates of outdoor and indoor neutral index values in winter. Outdoor summer comfort temperatures expressed in simple air temperature (Ta) were not significantly different between indoor and outdoor contexts, and this similarity between indoor and out persisted even when radiant temperature, air speed, humidity, insulation and metabolism were taken into consideration (with OUT_SET*). The sign of the winter difference (outdoor neutrality warmer than indoors) along with the very wide confidence interval require cautious interpretation. They may possibly be an artifact resulting from the skewed distribution of microclimatic observations towards colder-thanneutral conditions. They may also reflect a misinterpretation of the questionnaire item by some of our subjects - they were asked to evaluate their personal thermal state (including the effects of clothing), but some may have erred by focusing exclusively on environmental warmth (ignoring the clothing effect).
3. Discussion
The obvious need for a thermal comfort model suitable for outdoor applications has prompted the International Society of B iometeorology (ISB) and the World Meteorological Organization (WMO) to form a specialist Working Commission (Number 6) to develop a Universal Thermal Climate Index (UTCI). The UTCI will be: (a) thermo-physiologically significant, (b) valid in all climates, seasons and scales, (c) applicable to daily forecasts, warnings, bioclimatic mapping, epidemiological studies, and climate impact research, and (d) independent of age, gender, activities and clothing. While the model underlying UTCI was undecided at the time of writing, it is more likely to resemble the current generation of indoor thermal comfort models than the traditional outdoor thermal stress indices discussed in the introduction to this chapter. The results from the field study reported in this chapter carry implications for the work of the UTCI Commission 6. It seems clear that, while the physics and, to some extent, physiology underpinning UTCI may be suitable for global standardization, the model's outputs will probably need to be calibrated against local subjective comfort data. An index rating of "x" may not elicit the same subjective response from Berliners as, say from Sydney-siders. This reinforces Nikolopoulou et al.'s (1) point that factors beyond the physics of the body's heatbalance, such as psychological adaptation (available choice, environmental stimulation, thermal history, memory effect, expectations) will undermine standardized outdoor thermal comfort models.
4. Conclusions
Fig. 3. Seasonal thermal neutralities in Sydney: indoor versus outdoor (Ta or SET*).
This chapter identifies a paucity of empirical thermal comfort research conducted in outdoor settings. More such work is clearly needed because factors such as the range-effect, adaptation, expectation, and a plethora of other contextual factors are likely to undermine any uncritical transfer of indoor thermal comfort models to outdoor applications.
276
The chapter described an outdoor comfort study from subtropical Sydney. All microclimatic parameters pertaining to the human heat balance were recorded and a variety of indices (derived from indoor thermo-physiological models) were calculated and statistically analyzed alongside standard thermal questionnaire responses collected from subjects. Thermal index values considered by the subjects to be neutral, varied between Sydney's relatively mild subtropical summer and winter seasons. When the summer and winter samples were pooled, year-round thermal neutrality was greater than 2~ warmer than that found inside Sydney's air-conditioned office buildings. Replications of such outdoor thermal comfort field studies with larger samples and across a greater variety of climate zones are required in order to calibrate the new generation of universal thermal climate indices expected to emerge in the near future. The present chapter represents a possible "methodological template" study upon which those regional validation exercises can be based.
References 1. Nikolopoulou, M., Baker, N. and Steemers, K. 2001. Thermal comfort in outdoor urban spaces: understanding the human parameter. Solar Energy, 70: 227-235.
2. Fiala, D. and Lomas, K. 1999. Application of a computer model predicting human thermal responses to the design of sports stadia, CIBSE National Conference UK, pp. 492-499. 3. Kennedy, J. 2002. How's the Weather? Find Your Outdoors Comfort Paradise in the USA, Ask Analytic Services Inc. 4. ASHRAE 2001. Handbook of Fundamentals. ASHRAE Inc. 5. de Dear, R.J. and Fountain, M.E. 1994. Field experiments on occupant comfort and office thermal environments in a hothumid climate. ASHRAE Trans., 100: 457-475. 6. Bureau of Meteorology, 1991. Sydney NSW Climatic Survey, AGPS. 7. Jendritzky, G., Gr~itz, A. and Friedrich, M. 2000. The assessment of human thermal climates in cities. In: de Dear, R.J., Kalma, J.D., Oke, T.R. and Auliciems, A. (eds), Biometeorology and Urban Climatology at the Turn of the Millennium, WCASP 50: WMO/TD No.1026. WMO, Geneva, pp. 65-69. 8. Fanger, P.O. 1970. Thermal Comfort. Danish Technical Press, Copenhagen. 9. Gagge, A.P., Fobelets, A. and Berglund, L.G. 1986. A standard predictive index of human response to the thermal environment. ASHRAE Trans., 92: 709-731. 10. Jendritzky, G. and Staiger, H. 2001. Personal Communication. 11. Pickup, J. and de Dear, R. 2000. An outdoor thermal comfort index (OUT_SET*) - Part I - The model and its assumptions. In: de Dear, R.J., Kalma, J.D., Oke, T.R. and Auliciems, A. (eds), Biometeorology and Urban Climatology at the Turn of the Millennium, WCASP 50: WMO/TD No.1026. WMO, Geneva, pp. 279-283. 12. Rowe, D.M. 2001. Activity rates and thermal comfort of office occupants in Sydney. J. Therm. Biol., 26: 415-418. 13. de Dear, R.J. 2003. Design Guidelines for Thermally Acceptable Outdoor and Semi-Outdoor Micro-Climates. Macquarie Research Ltd, Sydney.
Environmental Ergonomics Y. Tochihara and T. Ohnaka Crown Copyright 9 2005 Published by Elsevier Ltd. All rights reserved.
Thermal comfort in outdoor and semi-outdoor environments Richard de Dear*, Jennifer Spagnolo Division of Environmental and Life Sciences, Macquarie University, Sydney, Australia
Abstract: Thermal comfort research to date has been focused on indoor applications, but in recent years attention has turned to the comfort requirements of people using outdoor and semi-outdoor spaces. Two different approaches have been discerned in the literature. The first simply transfers the assumptions and models usually associated with indoor thermal environmental engineering to the outdoor context, while the second approach accepts that various contextual features of semi-outdoor and outdoor spaces may affect subjective thermal perceptual processes as much, if not more so, than the conventional heat-balance variables found in indoor thermal comfort models. This chapter reports examples of recent work using both of these approaches.
Keywords: Thermal comfort, Acceptability, Outdoors, Hot-humid, Sub-tropical
1. Introduction
The human body maintains thermal homeostasis in the face of enormous variability in both internal (metabolic) and external (environmental) heat loads. Equilibrium is achieved through a combination of physiological controls (shivering, sweating and vasomotor responses) and behavioural thermoregulation (clothing, architecture, heating, ventilation and air-conditioning (HVAC), etc.). Both these control systems optimise the heat balance between body and environment through a variety of heat transfer mechanisms (radiant, convective, latent and conductive). Therefore, it is not surprising that simple air temperature has long been regarded as an inadequate characterization of the human thermal environment. As well as air temperature, ambient humidity, short- and longwave radiant flux densities, wind speed, even turbulence intensity within airflows, also affect
*Corresponding author. E-mail: [email protected] (R. de Dear)
the heat balance. In response to this widely acknowledged complexity, countless indices of human thermal environments have been proposed over the years to characterize outdoor microclimates in terms of their effects on the human body. Some of the better known examples are the Wet-Bulb Globe Temperature, Heat Stress Index, Predicted 4-h Sweat Rate, Apparent Temperature, Wind Chill Index, Relative Strain Index, to mention but a few. It is fair to say that most of the outdoor indices developed thus far have been intended for use primarily in stressful thermal environments. WBGT, for example, is widely used in outdoor workplaces to manage exposures to dangerously high heat loads. Most of the scientific effort on thermal indices and thermophysiological models in the last 30 y has been focused on moderate thermal conditions associated with indoor built environments. The purpose of models such as Predicted Mean Vote (PMV) and 2-node (ET* and SET*) has been to quantify thermal comfort, rather than stress or strain. The approach of these indoor comfort models is a full-blown heat balance involving all
270
six of the basic thermal comfort parameters: air temperature, radiant temperature, air velocity, humidity, clothing insulation and metabolic rate. This stands in contrast to the simplicity of the one-, two-, and occasionally three-parameter outdoor thermal stress indices mentioned above. These observations beg the questions of why there has been so much more scientific effort put into indoor thermal comfort models, and why have they evolved so much further than their outdoor counterparts? The most obvious explanation must surely be the injection of research funding into the science of thermal comfort by the HVAC industry. With such a clearly identifiable end-user for their science, indoor thermal comfort researchers have been relatively well-supported over the last three or four decades through organizations such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). So, what about thermal comfort (as opposed to thermal stress) outdoors? Recently we have seen an increasing awareness of the importance of microclimatology in urban planning because of its direct implications for tourism and the related service economy. Successful public open spaces attract people, who in turn attract businesses that, in turn, generate employment and lift real-estate values (1). Another potential end-user for outdoor thermal comfort research is the weather forecasting industry. Bureaux of meteorology are interested in valueadding by forecasting comfort instead of plain air temperature. Given the obvious weather/climate sensitivity of the tourism industry, a tool for describing and forecasting climatic resources would be a very useful thing to have there as well. Likewise, with outdoor events organizers who should realize that scheduling activities for outdoor or "semi-outdoor" events based on thermal comfort represents a more sophisticated approach than just trying to avoid rainy days. Yet more examples of potential end-uses for outdoor thermal comfort research can be found in the design and engineering of semi-outdoor spaces so that they match endusers' needs or preferences. An example is the work of Fiala and Lomas (2) who numerically simulated the microclimate inside S y d n e y ' s Olympic 2000 stadium, along with the spectators' subjective responses to them, under Sydney's
Typical Reference Year of meteorological data. The project aimed to optimize design and material selection for the main 2000 Olympic stadium. While the demand for outdoor thermal comfort information and models can readily be demonstrated, an appropriate strategy for meeting that demand is not so obvious. Clearly, the indices of thermal stress described earlier in this chapter are not suitable. They have neither been calibrated nor validated against empirical thermal comfort assessments by human subjects. The physiological parameters relevant to their validation are sweat rates, core temperatures, heart rates and other physiological indicators, but thermal comfort models need to be validated against conscious thermal comfort ratings, thermal sensations and thermal preferences. In view of the paucity of actual thermal comfort data collected in outdoor contexts, the most common approach to outdoor thermal comfort modeling has, to date, been the transfer of indoor thermal comfort models such as PMV and SET* to outdoor conditions. A recent case in point is Kennedy's book How's the Weather Find Your Outdoors Comfort Paradise in the USA
(3) in which Fanger's PMV was directly applied to outdoor climatological norms across the USA to derive "comfort maps" for various times of the year. The untested assumption underlying this transfer is that perceptual and semantic aspects of thermal environments are completely independent of contextual factors such as indoors versus out that there is no range-effect in operation, no adaptation effects, and so on. In another recent project, de Dear applied an outdoor version of the Standard Effective Temperature index (OUT_SET*) to standard climatological data with the aim of producing thermal comfort guidelines for artificially generated windspeeds in semi-outdoor spaces (13). The end-user of the guidelines wanted to make its clientele, the general public, more comfortable while they were queuing (up to 45 min) to use its services. Given the hot and oftentimes humid nature of the semioutdoor spaces in question (Orlando, FL, Los Angeles, CA, and Hong Kong), increasing air speed in the queuing areas was the only practicable strategy for enhancing thermal comfort. The research question came down to: "What was the
271
design air speed required at various times of day and at various times of year to deliver thermal comfort to the occupants of these semi-outdoor spaces?" On the basis of earlier work it was assumed that 26~ was a realistic comfort target temperature in semi-outdoor contexts. After making assumptions about typical clothing insulation values and metabolic rates and mean radiant temperatures (MRT), de Dear applied various combinations of air temperature and humidity to the two-node model while iterating incrementally higher air speeds, until an OUT_SET* value of 26~ was attained. After numerous results had been solved in this iterative fashion, "isotachs" were plotted on a psychrometric chart. An example is given in Fig. 1. The diagonal isotachs in that chart represent the air speeds required to bring a human subject with a metabolic rate of 1.2 met, clothing insulation of 0.45 clo units, standing in partial shade outdoors in Orlando, Florida in July to a particular temperature. The end-user (engineer) of such a design guideline can select a design temperature/humidity combination for, say 3 pm
on a typical July, and enter those coordinates into the psychrometric chart. They would then be able to interpolate from the nearest isotachs to find the air speed they needed to provide within the occupied zone where their clientele are queuing in semioutdoor spaces at any time during July. Perhaps the most difficult assumption in this approach was the estimation of MRT. This was based on mathematic conversion of solar angles and intensity, ground reflectivity, and partial shade-factors overhead, into an equivalent surface temperature of an hypothetical enclosure around the subject (10). Despite being a fairly complicated application of comfort theory to outdoor and semi-outdoor problems, the research approach just described was premised on the direct transferability of the SET* index and its associated two-node model from an indoor context to the outdoors, but there was no empirical basis underpinning that assumption. One of the few research papers in which actual thermal comfort data from human subjects outdoors were reported was by Nikolopoulou et al. (1). They interviewed users of public spaces in the city-center
Fig. 1. Lines of ambient air speed required to produce thermally acceptable semi-outdoor environmental conditions for standing human subjects wearing light summer casual clothing in Orlando, Florida, USA during July.
272
of Cambridge in the UK and collected simultaneous meteorological observations. One of their key findings was that physical environmental parameters were not enough to characterize subjective thermal states outdoors. Furthermore, actual thermal sensations were widely discrepant from those predicted by indoor comfort indices such as PMV, but that could be due, in part, to the way solar radiant loads were assessed with a black globe thermometer instead of being explicitly converted from absorbed solar radiation into equivalent MRTs. Nikolopoulou et al. (1) concluded that other factors beyond the physics of the body' s heatbalance, such as psychological adaptation (available choice, environmental stimulation, thermal history, memory effect, expectations) were responsible for discrepancies between predictions of comfort models based on indoor research, and the actual subjective evaluations by human subjects in outdoor field settings. Against this theoretical backdrop of thermal comfort outdoors, the aims of the present chapter, are to: 9 describe a full-scale outdoor thermal comfort study in a subtropical city (Sydney), 9 collect all data required for calculation of current-generation indoor and outdoor thermal comfort models, 9 compare predictions of indoor comfort models with actual thermal comfort assessments outdoors, 9 compare actual indoor and outdoor thermal comfort assessments by human subjects, 9 examine adaptation effects with regard to summer and winter seasons.
comfort, were measured on site in various outdoor and semi-outdoor microclimates with a mobile meteorological station named "TROJAN". The sensors monitored global and diffuse solar radiation, short-wave radiation reflected from the ground, long-wave radiation down-welling from the sky, long-wave radiation up-welling from the ground, air temperature, humidity and wind speed. A camera tripod was used as the skeleton of the station and spirit-levels were used to align the radiometers with the horizon before each measurement cycle. Subjects were approached at random in various outdoor and semi-outdoor recreational and public transit spaces in Sydney. Once recruited, thermal comfort data were recorded from the subjects using a questionnaire adapted from those used in recent ASHRAE indoor field studies (e.g. 5). Some new items, such as one dealing with sun/ shade preferences, were introduced. The questionnaire was designed to require less than 1 min to complete so as to minimize the rejection rate. The most important question requested subjects to indicate their Actual Thermal Sensation Vote (ATSV) on the following scale: - 3 = c o l d , - 2 = cool, - 1 = slightly cool, 0 = neutral, + 1 = s l i g h t l y warm, + 2 = w a r m , +3=hot, taking into account the clothing they were wearing at the time of interview. Temperature preference at the time of interview was also assessed (want warmer, want no change, want cooler). While the subject completed the questionnaire, a measurement sequence was initiated with the T R O J A N instrument package as described above. The summer sample size was 432 while the winter sample had 585 subjects.
1.1. Methods
2. Results
The underlying concept of this field research project was to collect simultaneous micrometeorological measurements and subjective thermal comfort assessments from a large sample of subjects in various outdoor settings. Micrometeorological sensors were selected in accordance with the specifications outlined in ASHRAE's Handbook o f Fundamentals (4). All four basic environmental parameters, influencing thermal
Sydney has a subtropical climate with a mean daily maximum temperature of 26-28~ during summer, and a mean daily winter maximum of 17~ Mean minimum daily temperatures range from 5 - 8 ~ in winter to 17-18~ over the summer months (6). Due to its coastal location, Sydney does not experience large diurnal or seasonal variability. Average wind speeds range from 11.5 to 14 km h-~ (at 10 m), corresponding to a range of
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1.8-2.2 m S-1 at the height of a human subject (1 m) using the Power law with an urban terrain friction exponent. Table 1 describes some basic micrometeorological observations made during the two seasons' field campaigns. The average air temperature measured was 27.8~ in summer and in winter 18.2~ The average air velocities were lower than the climatological averages, probably due to sheltering from urban structures. Low minimum and average radiation values probably result from our use of some semi-outdoor locations with shading. Fig. 2 shows the distribution of questionnaire votes by season. The distribution of Actual Thermal Sensation Votes (ATSV votes in Fig. 2a) indicates a seasonal skewing of thermal sensations towards the "cool" end of the scale in winter and the "warm" end in summer, as might be expected. The air temperature preference votes (Fig. 2b) show a large proportion of the sample in both seasons wanting "no change" in air temperature, and the seasonal skewing noted in ATSV (Fig. 2a) was also evident in thermal preference votes (Fig. 2b). The seasonal bias was again evident in air movement preference votes (Fig. 2c), with summer subjects "wanting more air movement" and winter subjects "wanting less air movement". The winter results for sun/ shade preference (Fig. 2d) showed a majority of subjects voting "want more sun", but in summer, the largest proportion of the sample voted "no change". It is likely that both summer and winter samples had a large percentage of subjects being interviewed in the shade, prompting the winter subjects to request more sunshine while the summer subjects found a shady microclimate preferable. Table 1 Statistics on the micrometeorological observations during the summer and winter field campaigns in Sydney. Air temperature (~
Air velocity (m s - l )
Solar radiation (W m -2)
Summer
Winter
Summer
Winter
Summer
Winter
18.2 28.2 12.6 3.5
0.7 2.1 0.07 0.4
0.7 2.0 0.06 0.4
207.0 1031.0 10.2 352.7
147.8 844.8 3.6 193.2
Mean 27.8 Max 43.3 Min 20.4 SD 5.0
Perceived temperature (PT) is an index developed by Jendritzky et al. (7) and defined as the air temperature of a standardized environment in which the same PMV (8) is registered as in the real environment. For warm conditions, PMV is corrected for humidity effects by substitution of the new Effective Temperature, ET*, in place of operative temperature (9). In the standardized environment, the MRT is set equal to the air temperature, rh -- 50% and the wind speed (vel) is locked in at 0.12 m s- 1. The PT (7) refers to a person standardized as follows: male, age 35 y, height 1.75 m, weight 75 kg, walking at 4 km per h on a horizontal plain related to an internal heat production of 172.5 W. The person varies the thermal resistance of clothing (clo) in the range of c l o - - 1 . 7 5 (winter) and c l o - 0 . 5 0 (summer) to achieve thermal comfort ( P M V - - 0 ) if possible. In summary, PT is based exclusively on an indoor thermal comfort index PMV with the main difference from indoor PMV being Jendrizky's conversion of solar and terrestrial radiation fluxes into their equivalent MRT. Based on Gagge's (9) 2-node model, the new Effective Temperature (ET*) is the temperature of a standard environment ( r h - 5 0 % , T a - - M R T and v e l - 0 . 1 2 m s -1) in which a subject would experience the same net heat balance, skin wettedness and mean skin temperature as in the actual environment. Where ET* assumes 70 W/ m 2 metabolic rates and 0.6clo, the Standard Effective Temperature (SET*) extended ET* to include variable activities and clothing levels. In the reference environment Ta-- MRT and vel - 0.12 m s-1. SET* was modified for outdoors (OUT_SET*) by Pickup and de Dear (11) by incorporating short- and terrestrial radiation fluxes into the MRT calculations. Again the difference between the indoor index and its outdoor implementation (OUT_SET*) was the inclusion of solar and terrestrial radiation, and for the present chapter we have adopted the same MRT sourcecode as used in the PT model (10). The two indices here have different assumptions and gave different results, particularly in summer (see Table 2). The mean OUT_SET* was 28.9~ in summer and 23.9~ in winter, while the mean
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Fig. 2. Frequency of questionnaire responses in Sydney by season.
PT was observed to be 34.9~ in summer and 24.0~ in winter. The discrepancy is probably the result of the inclusion of observed clo values in OUT_SET* calculations (mean values were 0.6 clo in summer and 0.9 clo in winter), while the PT index assumes fixed insulation. Table 3 indicates widely discrepant neutralities between the two
heat-balance indices and plain air temperature, probably again for the same reasons. By pooling the summer and winter samples in Table 3 we were able to estimate an all-year-round outdoor thermal neutrality on the OUT_SET* index. The result of 26.2~ came in significantly higher than the indoor SET* neutrality of 24~ (9). To test whether or not the subjective response to a given thermal environment was the same under
Table 2 Calculated thermal index summary. OUT_SET* (~
Mean Max Min SD
Table 3 Observed thermal neutralities on various thermal indices.
PT (~
Summer
Winter
Summer
Winter
28.9 45.1 16.3 5.7
23.9 38.7 6.4 6.6
34.9 52.4 23.6 6.9
24.0 45.0 12.8 6.4
Ta (~ Summer Winter All year
PT (~
OUT_SET* (~
23 (22.0-23.8) 28.1 (26.8-29.3) 23.3 (22.1-24.3) 26.6 # (22.8-44.7) 31.6(29.4-34.9) 33.3 (30.7-37.5) 21.9 # (21.0-22.9) 28.4 (27.6-29.2) 26.6 # (25.1-27.2)
# indicates weaker significance (p > 0.05).
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both indoor and outdoor exposures, we accessed some thermal comfort data collected inside an airconditioned office building in Sydney by Rowe (12) and calculated neutralities using the same statistical methods as used on our own outdoor thermal comfort study results. Fig. 3 indicates significant differences between statistical estimates of outdoor and indoor neutral index values in winter. Outdoor summer comfort temperatures expressed in simple air temperature (Ta) were not significantly different between indoor and outdoor contexts, and this similarity between indoor and out persisted even when radiant temperature, air speed, humidity, insulation and metabolism were taken into consideration (with OUT_SET*). The sign of the winter difference (outdoor neutrality warmer than indoors) along with the very wide confidence interval require cautious interpretation. They may possibly be an artifact resulting from the skewed distribution of microclimatic observations towards colder-thanneutral conditions. They may also reflect a misinterpretation of the questionnaire item by some of our subjects - they were asked to evaluate their personal thermal state (including the effects of clothing), but some may have erred by focusing exclusively on environmental warmth (ignoring the clothing effect).
3. Discussion
The obvious need for a thermal comfort model suitable for outdoor applications has prompted the International Society of B iometeorology (ISB) and the World Meteorological Organization (WMO) to form a specialist Working Commission (Number 6) to develop a Universal Thermal Climate Index (UTCI). The UTCI will be: (a) thermo-physiologically significant, (b) valid in all climates, seasons and scales, (c) applicable to daily forecasts, warnings, bioclimatic mapping, epidemiological studies, and climate impact research, and (d) independent of age, gender, activities and clothing. While the model underlying UTCI was undecided at the time of writing, it is more likely to resemble the current generation of indoor thermal comfort models than the traditional outdoor thermal stress indices discussed in the introduction to this chapter. The results from the field study reported in this chapter carry implications for the work of the UTCI Commission 6. It seems clear that, while the physics and, to some extent, physiology underpinning UTCI may be suitable for global standardization, the model's outputs will probably need to be calibrated against local subjective comfort data. An index rating of "x" may not elicit the same subjective response from Berliners as, say from Sydney-siders. This reinforces Nikolopoulou et al.'s (1) point that factors beyond the physics of the body's heatbalance, such as psychological adaptation (available choice, environmental stimulation, thermal history, memory effect, expectations) will undermine standardized outdoor thermal comfort models.
4. Conclusions
Fig. 3. Seasonal thermal neutralities in Sydney: indoor versus outdoor (Ta or SET*).
This chapter identifies a paucity of empirical thermal comfort research conducted in outdoor settings. More such work is clearly needed because factors such as the range-effect, adaptation, expectation, and a plethora of other contextual factors are likely to undermine any uncritical transfer of indoor thermal comfort models to outdoor applications.
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The chapter described an outdoor comfort study from subtropical Sydney. All microclimatic parameters pertaining to the human heat balance were recorded and a variety of indices (derived from indoor thermo-physiological models) were calculated and statistically analyzed alongside standard thermal questionnaire responses collected from subjects. Thermal index values considered by the subjects to be neutral, varied between Sydney's relatively mild subtropical summer and winter seasons. When the summer and winter samples were pooled, year-round thermal neutrality was greater than 2~ warmer than that found inside Sydney's air-conditioned office buildings. Replications of such outdoor thermal comfort field studies with larger samples and across a greater variety of climate zones are required in order to calibrate the new generation of universal thermal climate indices expected to emerge in the near future. The present chapter represents a possible "methodological template" study upon which those regional validation exercises can be based.
References 1. Nikolopoulou, M., Baker, N. and Steemers, K. 2001. Thermal comfort in outdoor urban spaces: understanding the human parameter. Solar Energy, 70: 227-235.
2. Fiala, D. and Lomas, K. 1999. Application of a computer model predicting human thermal responses to the design of sports stadia, CIBSE National Conference UK, pp. 492-499. 3. Kennedy, J. 2002. How's the Weather? Find Your Outdoors Comfort Paradise in the USA, Ask Analytic Services Inc. 4. ASHRAE 2001. Handbook of Fundamentals. ASHRAE Inc. 5. de Dear, R.J. and Fountain, M.E. 1994. Field experiments on occupant comfort and office thermal environments in a hothumid climate. ASHRAE Trans., 100: 457-475. 6. Bureau of Meteorology, 1991. Sydney NSW Climatic Survey, AGPS. 7. Jendritzky, G., Gr~itz, A. and Friedrich, M. 2000. The assessment of human thermal climates in cities. In: de Dear, R.J., Kalma, J.D., Oke, T.R. and Auliciems, A. (eds), Biometeorology and Urban Climatology at the Turn of the Millennium, WCASP 50: WMO/TD No.1026. WMO, Geneva, pp. 65-69. 8. Fanger, P.O. 1970. Thermal Comfort. Danish Technical Press, Copenhagen. 9. Gagge, A.P., Fobelets, A. and Berglund, L.G. 1986. A standard predictive index of human response to the thermal environment. ASHRAE Trans., 92: 709-731. 10. Jendritzky, G. and Staiger, H. 2001. Personal Communication. 11. Pickup, J. and de Dear, R. 2000. An outdoor thermal comfort index (OUT_SET*) - Part I - The model and its assumptions. In: de Dear, R.J., Kalma, J.D., Oke, T.R. and Auliciems, A. (eds), Biometeorology and Urban Climatology at the Turn of the Millennium, WCASP 50: WMO/TD No.1026. WMO, Geneva, pp. 279-283. 12. Rowe, D.M. 2001. Activity rates and thermal comfort of office occupants in Sydney. J. Therm. Biol., 26: 415-418. 13. de Dear, R.J. 2003. Design Guidelines for Thermally Acceptable Outdoor and Semi-Outdoor Micro-Climates. Macquarie Research Ltd, Sydney.