- Email: [email protected]
Comfort environment assessment based on bodily sensation in open air: relationship between comfort sensation and meteorological factors
Journal of Wind Engineering and Industrial Aerodynamics 87 (2000) 93±110
Comfort environment assessment based on bodily sensation in open air: relationship between comfort sensation and meteorological factors Ryoji Sasakia,*, Motohiko Yamadab, Yasushi Uematsuc, Hiromu Saekid a
Technical Research Institute, Nishimatsu Construction Co. Ltd., 2570-4, Shimotsuruma, Yamato 242-8520, Japan b New Industry Creation Hatchery Center, Tohoku University, Sendai 980-8579, Japan c Department of Architecture and Building Science, Tohoku University, Sendai 980-8579, Japan d Tohata Architects and Engineers Inc., 4-4-10, Fushimi-cho, Chuou-ku, Osaka 541-0044, Japan Received 6 January 2000; received in revised form 2 April 2000; accepted 10 April 2000
Abstract The purpose of this study is to develop a method for assessing the comfort of an open-air environment, based on a questionnaire answered by inhabitants of urban areas. Eighty-nine inhabitants of three cities in the northern part of Japan and 18 inhabitants from the suburb of Tokyo responded to the survey. The dependence of comfort judgement on various meteorological factors was studied by using multivariate regression analysis. To construct a comfort environment assessment model, the focus was on a discomfort ratio, which represents what percentage of the inhabitants feels uncomfortable under certain ambient conditions. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Comfort assessment; Open air; Questionnaire; Comfort sensation; Meteorological factors; Multivariate regression analysis
1. Introduction A number of studies have been conducted to investigate how comfortable people feel within buildings. Several indices for comfort assessment have been proposed, *Corresponding author. Tel.: +81-46-275-1135; fax: +81-46-275-0094. E-mail address: [email protected] (R. Sasaki). 0167-6105/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 1 6 7 - 6 1 0 5 ( 0 0 ) 0 0 0 1 8 - 0
94
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
and of these, SET [1] and PMV [2] are widely used. Both indices are based on six factors: air temperature, relative humidity, mean radiant temperature, wind velocity, clo value and metabolic rate. The application of these indices to a comfort environment assessment in open air is controversial, because the values of the factors involved in the indices vary over a wider range in open air than they do within buildings. Furthermore, in addition to the above-mentioned factors, solar radiation and weather must be considered. Attempts have also been made to incorporate other outdoor weather parameters with indoor thermal comfort models for the assessment of outdoor human sensation [3±5]. Some indices for human sensation in open air have been proposed in the ®eld of climatology. Thom [6] de®ned the temperature±humidity index, based on the reaction of the human body to a combination of heat and humidity. Wind chill index, which is expressed as a combination of wind speed and air temperature, is often used in cold season [7]. These indices seem to be useful for speci®c climate. Regarding the wind environment assessment, some criteria have been proposed, e.g. by Penwarden [8], Isymov and Davenport [9], Lawson and Penwarden [10], and Melbourne [11]. Most of these criteria are concerned only with wind speed, either mean or gust. It is thought that the human sensation of wind is in¯uenced by such ambient conditions as air temperature and relative humidity. Therefore, it is hoped that a new criterion for wind environment assessment based on bodily sensation can be developed. Murakami and Morikawa [12] proposed a criterion, in which they considered the eects of temperature on human sensation, based on a questionnaire taken in Tokyo. Recently, Soligo et al. [4] and Stathopoulos et al. [5] tried to develop more general criteria for assessing comfort sensation. We have investigated the eects of several meteorological factors on human sensation of comfort in open air, based on a questionnaire given to inhabitants in urban areas. The details of the questionnaire and some results of a preliminary analysis are presented in Sasaki et al [13]. In the present paper, a multivariate regression model for evaluating the human sensation of comfort in open air is constructed. In constructing the model, a regression model for each respondent is ®rst constructed. Then, we focus on a ``discomfort ratio'' which represents what percentage of the inhabitants feel uncomfortable under a certain ambient condition, and present a regression model for the discomfort ratio. Finally, we propose a ``climatic regression model'', which relates the climatic conditions of a reference city to that of another city. Using such a model we can apply the comfort (or discomfort) evaluation model obtained for the reference city to other cities. 2. Questionnaire 2.1. Sites and respondents Two kinds of questionnaire studies were conducted. In the ®rst study, referred to as Study-1 hereafter, the questionnaire was given to urban inhabitants of Aomori, Morioka, and Sendai, three cities in the northern part of Japan's main island. The
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
95
questionnaire covered a total of six months: the six even-numbered months from April 1996 to February 1997. There were 89 respondents (see Table 1). The second study, referred to as Study-2 hereafter, is based on a questionnaire given to the inhabitants in a housing development in Ebina, a suburb of Tokyo. This questionnaire covered August and October 1998 and February 1999. There were 18 respondents (see Table 1). The location of the four cities is shown in Fig. 1. Fig. 2 compares the climates of these four cities. In this ®gure, the closed circles and bars represent the monthly average air temperature and monthly accumulated precipitation, respectively. Cities located near the coast of the Paci®c Ocean, like Sendai and Ebina, have much rain in summer and autumn due to typhoons and stationary fronts. On the other hand, cities located near the coast of the Sea of Japan, like Aomori, have much snow in winter. Aomori and Morioka are very cold in winter, whereas Ebina is very hot in Table 1 Characteristics of subjects City
Subjects Number
Sex
Study-1
Aomori Morioka Sendai
20 35 20 14
Female Female Male Female
Study-2
Ebina
3 15
Male Female
Fig. 1. Location of the four cities used in this study.
96
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Fig. 2. Characteristics of the climate in the four cities (1979±1998).
summer. Therefore, the focus is on the feeling of discomfort due to the cold of winter in Study-1, and on that due to the heat of summer in Study-2. All the respondents in each city lived or worked near a meteorological observatory or an observation site of the Automated Meteorological Data Acquisition System (AMeDAS) of the Japan Meteorological Agency. Therefore, the meteorological data measured at the meteorological observatories or at the AMeDAS sites can be used for the analysis. In this study, six meteorological factors are used; that is, wind speed and direction averaged over a period of 10 min, air temperature, relative humidity, duration of sunshine, and the ¯ux of global solar radiation over one hour. The mean wind speeds at the predetermined points, 1.5 m above ground, were predicted from the values at the meteorological observatories together with the results of wind tunnel experiments using scale models of the areas. 2.2. Questionnaire form Each respondent ®lled out a questionnaire form almost every day during the period of questionnaire. Table 2 shows the daily questionnaire form used in Study-2; the form used in Study-1 is almost the same, regarding the questions about human sensation. It consists of several questions; namely, the date and time the respondent was at a predetermined point, the weather conditions (wind, thermal conditions) and how the respondent felt about the ambient conditions. The main focus of this study
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
97
Table 2 Daily questionnaire form used in Study-2 Questionnaire for assessment of comfort environment Date /Time Please mark your answer. 1. These questions are concerned with thermal conditions. 1-1. What conditions apply? & sunshine & misty
& shade from trees
& rainy
& shade from buildings
& cloudy
& snowy
1-2. How did thermal conditions feel? & very hot
& hot
& slightly cool
& warm
& cool
& slightly warm
& cold
& neutral
& very cold
1-3. How comfortable were conditions overall? & comfortable
& slightly comfortable
& slightly uncomfortable
& uncomfortable
2. These questions are concerned with wind conditions. 2-1. What were the wind conditions? & strong
& slightly strong
& neutral
& slightly weak
& weak
2-2. What were the indications of wind movement? & trees
& branches
& leaves
& nothing
& other
2-3. What did the wind feel? & cold
& fresh
& uncomfortably warm
& hot
& nothing
& other
2-4. What did you hear in the wind? & train
& trac
& children
& housework
& animals
& usual
& other
2-5. What did you smell in the wind? & trees
& moisture
& garbage
& ®elds
& nothing
& other
3. How did the greenery seem? & overgrown & bright
& clear
& usual
& fresh
& beautiful
& moist
& unpleasant
& other
was on the comfort sensation, which is divided into four categories: comfortable, slightly comfortable, slightly uncomfortable, and uncomfortable. 3. Relation between comfort sensation and meteorological factors First, the general dependence of the comfort sensation on each meteorological factor was investigated separately: air temperature t, relative humidity f, wind speed
98
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
v, and weather. Regression analysis was applied to the data for all respondents, irrespective of location or season. 3.1. Air temperature Fig. 3(a) and (b) illustrate the variation of comfort sensation with air temperature obtained from Study-1 and Study-2, respectively. Several features can be detected from these results. First, the percentage of ``comfortable'' and ``slightly comfortable'' responses is highest at air temperature t, ranging from 20 to 228C. This indicates that many people feel most comfortable when t 202228C. The percentage generally decreases with an increase in t when t > 228C, and with a decrease in t when t5208C. The percentage has another peak at t 128C in Fig. 3(a). When t5128C, the percentage of the ``uncomfortable'' response increases signi®cantly with a decrease in t in both studies. This feature may be due to a coupled eect of air
Fig. 3. Variation of comfort sensation with air temperature. (a) Study-1; (b) Study-2.
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
99
temperature and wind; that is, the discomfort may be quite sensitive to wind speed when t5128C. 3.2. Wind speed Fig. 4(a) and (b) show the variation of comfort sensation with wind speed v obtained from Study-1 and Study-2, respectively. The percentage of the ``comfortable'' response slightly decreases as the wind speed increases in Study-2. On the other hand, in Study-1 any clear dependence of the comfort sensation on wind speed cannot be detected. This is related to the fact that all data irrespective of season are included in the ®gure. Therefore, it may be reasonable to consider that the relation between comfort sensation and wind speed depends on the season, especially on the air temperature. 3.3. Relative humidity Fig. 5(a) and (b) show the variation of comfort sensation with relative humidity f obtained from Study-1 and Study-2, respectively. The results of Study-1 indicate that the percentage of the ``comfortable'' response generally decreases with increasing f. In Study-2, a similar trend can be seen only when f > 50%. The data for f > 50% are mainly from the investigations in summer and autumn. During these seasons more people feel comfortable as the relative humidity decreases. On the other hand, the data for f550% are mainly from the investigation in winter. In winter, the relative humidity is fairly low in the Tokyo area, including Ebina; for example, the monthly average value of f is approximately 50% in Tokyo. Therefore, the comfort sensation may be aected by the other factors. This results in no dependence of comfort sensation on f when f550%.
Fig. 4. Variation of comfort sensation with wind speed. (a) Study-1; (b) Study-2.
100
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Fig. 5. Variation of comfort sensation with relative humidity. (a) Study-1; (b) Study-2.
Fig. 6. Variation of comfort sensation with weather. (a) Study-1; (b) Study-2.
3.4. Weather Fig. 6(a) and (b) show the dependence of the comfort sensation on weather, obtained from Study-1 and Study-2, respectively. The results clearly indicate that most of the respondents feel comfortable during ®ne weather, even when they are in the shade. On the other hand, most respondents feel uncomfortable in rainy or snowy weather, as can be expected.
4. Evaluation of human sensation of discomfort In this chapter, a multivariate regression model for human sensation of comfort is constructed. Based on the results of the preceding analysis, we use four
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
101
meteorological factors, i.e., air temperature (t), wind speed (v), relative humidity (f), and percentage of the duration of sunshine per 1 h (termed sun hereafter), which is regarded as a parameter corresponding to weather conditions. As mentioned above, the Japan Meteorological Agency's AMeDAS network covers the entire country. The number of observation sites is approximately 840, one site per area of approximately 21 square kilometers. This system measures average wind speed and direction for ten minutes, air temperature, duration of sunshine, and precipitation, at every hour. Relative humidity is measured at approximately 80 meteorological observatories throughout the country. Therefore, the assessment method based on the above-mentioned four meteorological factors may be useful in practical applications. Each person has a unique sensation of comfort to these meteorological factors. Therefore, if the data for many respondents is used in multivariate regression analysis, the results will show considerable scatter. On the other hand, the data scatter should be smaller and the correlation coecient higher if multivariate regression analysis is applied to the data for each respondent. Furthermore, it may be reasonable to evaluate the comfort environment based on a discomfort ratio that represents what percentage of people feels uncomfortable under a certain condition. The idea is the same as that used in evaluating the ``Discomfort Index''. The procedure of the analysis is shown in Fig. 7. In this study, multivariate regression analysis is applied to the data for each respondent at the ®rst step. Approximately one half of the data was used for this analysis; the rest of the data was used for testing the application of the model in Section 4.3. Since the replies of comfort sensation are qualitative data, i.e., ``comfortable'', ``slightly comfortable'', ``slightly uncomfortable'' and ``uncomfortable'', they are converted to numerical values, i.e., from 1 to 4 (denoted comf hereafter). At the second step, the values of comf are computed for various ambient conditions by using the multivariate regression models for all respondents, which were constructed at the ®rst step. Based on the results, a multivariate regression model for the discomfort ratio (denoted discomf hereafter) is constructed as a function of the four meteorological factors. The multivariate regression models obtained at the ®rst and the second step are denoted Comfort equation and Discomfort equation, respectively (see Fig. 7). According to the results for the dependence of comfort sensation on air temperature (Fig. 3), it may be reasonable to divide the air temperature level into three ranges, i.e., t4128C; 125t4228C, and t > 228C; these ranges are referred to as Low-, Middle-, and High-temperature levels, respectively, hereafter. 4.1. Comfort sensation for the individual Table 3 shows sample results of multivariate regression analysis applied to the data of several respondents in Ebina (Study-2); the temperature level is Low in this case. In the analysis, the comfort sensation, comf, is the object variable and the four meteorological factors (t, v, sun, and f) are the explanatory variables. For example, the multivariate regression model called Comfort equation for respondent No.1 is
102
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Fig. 7. Procedure of the analysis.
given by the following equation: comf ÿ0:210t 0:275v ÿ 0:067sun ÿ 0:004f 4:658:
1
The multiple correlation coecient R of this equation is 0.82. The value of R for most of the respondents ranged from 0.6 to 0.8. Considering that the model deals with human sensation, and judging from the relatively high values of R, we may conclude that the models give a reasonable evaluation of the comfort sensation.
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
103
Table 3 Regression coecients and multiple correlation coecient R in Comfort equation at Low-temperature level in Ebina Subject no.
Number of data
t (8C)
v (m/s)
Sun (%/h)
f (%)
Const.
R
1 2 3 4 5
13 13 14 11 13
ÿ 0.210 ÿ 0.008 ÿ 0.075 ÿ 0.117 ÿ 0.145
0.275 ÿ 0.196 0.643 ÿ 0.422 0.449
ÿ 0.067 ÿ 0.083 ÿ 0.081 ÿ 0.013 ÿ 0.101
ÿ 0.004 ÿ 0.013 0.002 ÿ 0.027 ÿ 0.016
4.658 3.490 3.078 4.272 5.416
0.82 0.50 0.72 0.60 0.88
Table 4 Number of Comfort equations for constructing Discomfort equation; the terms in parentheses are the number of data Low t4128C
Middle 125t4228C
High 228C5t
Study-1
Aomori Morioka Sendai
13 (750) 13 (304) 20 (406)
15 (948) 22 (514) 17 (374)
16 (366) 4 (30) 8 (119)
Study-2
Ebina
15 (180)
16 (162)
14 (246)
4.2. Discomfort ratio Using the Comfort equation obtained for the individuals in the preceding section, we can evaluate the comfort sensation for each respondent for any set of ambient conditions. The discomfort ratio (%), discomf, is de®ned as the ratio of the number of the respondents who feel uncomfortable (comf 5 2.5) under a certain ambient condition to that of all respondents. For example, if 10 among 20 respondents in Aomori feel uncomfortable for some ambient conditions, the value of discomf will be 50%. The values of discomf were computed for various combinations of meteorological factor, which had been obtained in the questionnaire studies. A multivariate regression analysis was again applied to the results, in which discomf is the object variable and the four meteorological factors are the explanatory variables. In the analysis, Comfort equations with R50:5 were not used; the numbers of the respondents used for constructing the model of discomf are listed in Table 4. The result of the multivariate regression analysis is summarized in Table 5. As shown in Table 4, the number of replies to High-temperature level in Morioka and Sendai is rather limited. Therefore, the results for these two cases should be questionable from a statistical point of view; this data will be excluded in the following discussion. Except for these two cases, the value of R is fairly high. From the results in Table 5, the following features can be detected: (1) The regression coecient for t is positive when t > 228C, while negative when t4228C. This feature corresponds well to the result in Fig. 3. It is found that
104
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Table 5 Regression coecients and multiple correlation coecient R in Discomfort equation t(8C)
v (m/s)
Sun (%/h)
f (%)
Const.
R
High 228C5t
Aomori Morioka Sendai Ebina
4.36 4.46 4.73 3.32
8.10 2.29 2.97 1.75
ÿ 0.42 ÿ 0.08 0.13 ÿ 0.17
0.40 0.16 0.76 0.44
ÿ 92.23 ÿ 90.93 ÿ 144.32 ÿ 84.95
0.94 0.34 0.57 0.89
Middle 125t4228C
Aomori Morioka Sendai Ebina
ÿ 3.00 ÿ 5.24 ÿ 2.07 ÿ 5.49
8.22 3.62 ÿ 1.05 4.83
ÿ 0.59 ÿ 0.14 ÿ 1.48 ÿ 0.26
0.55 0.97 0.40 0.44
64.02 63.40 31.49 93.76
0.94 0.94 0.81 0.94
Low t4128C
Aomori Morioka Sendai Ebina
ÿ 2.38 ÿ 0.73 ÿ 5.50 ÿ 4.25
11.35 7.70 4.95 5.65
ÿ 0.53 ÿ 0.62 ÿ 1.93 ÿ 0.34
0.58 0.48 0.10 ÿ 0.07
32.01 45.27 75.83 97.93
0.95 0.96 0.89 0.94
more people feel comfortable as the air temperature increases up to about 228C; beyond this temperature, the situation is reversed. (2) The regression coecient for v is generally positive and its value is larger at lower temperature levels. This feature indicates that more people feel uncomfortable as wind speed increases, in particular, when air temperature is low. It is expected that the wind makes us feel comfortable when the air temperature is very high, for example, t > 308C. The present result is not in accordance with such an expectation. This discrepancy may be due to the fact that the amount of data for such a high-temperature range is limited in the present study. (3) The duration of sunshine has a negative correlation with the discomfort ratio. The magnitude of the regression coecient is relatively large in northern cities (Aomori and Morioka). This is due to the fact that the duration of sunshine is rather short in winter in Aomori; the monthly average duration of sunshine for February is only approximately 75 hours. (4) The relative humidity generally has a positive correlation with the discomfort ratio. The only exception is the Low-temperature level in Ebina. This is due to the fact that the relative humidity is fairly low in winter in Ebina, as mentioned above. Fig. 8 shows the discomfort ratio as a function of t and v for Aomori when f 70%, in which sun is assumed to be 100%/h or 0%/h. The ®gure clearly shows the dependence of the discomfort ratio on wind speed and air temperature. Furthermore, sun signi®cantly in¯uences the discomfort ratio. 4.3. Tests for discomfort ratio The application of the above-mentioned model for the discomfort ratio was investigated by comparing the results predicted by the model (Comfort equation) with
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
105
Fig. 8. Distribution of discomfort ratio (%) in Aomori as a function of air temperature and wind speed (f 70%). (a) sun=100%/h; (b) sun=0%/h.
Fig. 9. Relation between Ratio-A and Ratio-B at Low-temperature level in Aomori.
106
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
those of the questionnaire. Fig. 9 shows the result of such a comparison for the Lowtemperature level in Aomori. In this ®gure, Ratio-A represents the discomfort ratio computed by using the Discomfort equation directly when the values of four meteorological factors are given. Ratio-B is the ratio of the number of the respondents giving comf 5 2.5 in the Comfort equation (i.e., who feel uncomfortable) for the same ambient condition. Both ratios are expressed as a percentage. From this ®gure, it is found that the agreement between Ratio-A and Ratio-B is relatively good. Table 6 summarizes the results of the comparison for all cases, in which the mean, standard deviation, kurtosis and skewness of the dierence between the two results are listed. In particular, the agreement is fairly good for the High-temperature level in Ebina, the Middle-temperature level in Aomori and the Low-temperature level in Sendai. 4.4. Application of the Discomfort equation to other cities The results in Table 5 indicate that the Discomfort equation is dependent on the city, as mentioned in the preceding section. This means that people of each city have their own criteria for uncomfortable conditions, based on their experience in the regional climate of the location. Therefore, the Discomfort equation obtained for a city cannot be applied to other cities directly. Strictly speaking, the Discomfort equation should be obtained for each city or region. However, this is impossible in practice. So, in order to apply the equations obtained in this study to other cities, we propose a simple method based on a regression model, representing the relation between two cities located in the same climatic zone. This regression model is referred to as the Climatic equation hereafter. The regression model for two cities Table 6 Statistics of the dierence between Ratio-A (%) and Ratio-B (%) (Ratio-A) ± (Ratio-B) Average
S.D.
Kurtosis
Skewness
High 228C5t
Aomori Morioka Sendai Ebina
1.07 0.45 2.21 ÿ 0.37
8.66 25.11 15.99 7.73
3.21 3.81 2.77 3.18
0.26 ÿ 1.10 ÿ 0.06 0.20
Middle 125t4228C
Aomori Morioka Sendai Ebina
0.16 0.31 0.74 ÿ 0.47
11.43 9.66 11.66 7.66
2.71 4.35 2.81 3.22
0.37 ÿ 0.77 ÿ 0.38 ÿ 0.05
Low t4128C
Aomori Morioka Sendai Ebina
0.41 0.47 ÿ 0.02 ÿ 1.41
10.51 8.57 6.09 8.85
4.44 3.15 3.17 12.14
0.28 0.31 ÿ 0.09 ÿ 2.35
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
107
Fig. 10. Climatic division map.
(Cities 1 and 2) may be given by the following equation: f City 1 a g City 2 b;
2
where a and b are constants; f and g represent one of the four meteorological factors (monthly data) at Cities 1 and 2, respectively; City 1 is a reference city where the Discomfort equation was obtained, while City 2 is the other city. The values of the four meteorological factors for City 2 are ®rst converted to the corresponding values for City 1 by using Eq. (2). Then, substituting the converted values in the Discomfort equation for City 1, we obtain the discomfort ratio for City 2. In order to investigate the climatic zones of Japan, a principal component analysis is made by using six kinds of monthly average meteorological factors obtained at 80 meteorological observatories in Japan; they are the average values over a period of 1961±1990. These factors are daily air temperature, daily relative humidity, daily wind speed, dierence between the daily highest and the daily lowest temperature, precipitation, and duration of sunshine. Fig. 10 shows the result represented as a form of climatic division map, in which the country is divided into six climatic zones. This ®gure indicates that Aomori, Morioka, and Sendai belong to the same climatic zone. Therefore, the Discomfort equation obtained for Aomori can be applied to Morioka and Sendai, when combined with the Climatic equations for Aomori (City 1)
108
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Table 7 Regression coecients of Climatic equation
Morioka ! Aomori Sendai ! Aomori
Air temperature
Wind speed
Duration of sunshine
Relative humidity
a
b
a
b
a
b
a
b
0.96 1.09
0.51 ÿ 3.02
1.08 1.20
} }
0.98 0.86
} }
0.70 0.43
23.23 44.26
Fig. 11. Comparison for the discomfort ratio between the results obtained by using Discomfort equation and those predicted by Climatic equations (from Morioka and Sendai to Aomori) together with the Discomfort equation for Aomori.
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
109
and Morioka or Sendai (City 2). Using the data measured in this decade, the climatic regression models for the four meteorological factors are obtained in the form of f Aomori a g Morioka or Sendai b. The values of the constants a and b are listed in Table 7. Fig. 11 shows a comparison for the discomfort ratio between the results obtained by using the Discomfort equations for Morioka and Sendai directly, and those predicted by the Climatic equations for Morioka and Sendai together with the Discomfort equation for Aomori. The results for various ambient conditions are plotted in each ®gure. It is found that the prediction obtained by using the Climatic equations captures the general trend of the discomfort ratio. The agreement between these two results are relatively good, except for two cases, i.e., (a ÿ 1) and (b ÿ 2). 5. Concluding remarks The dependence of the human sensation of comfort (or discomfort) on four meteorological factors has been investigated, based on the questionnaires given to the inhabitants living in four cities of Japan. Multivariate regression analysis was applied to the results. First, a multivariate regression model for the comfort sensation of the individual (Comfort equation) was constructed as a function of the four meteorological factors for each respondent. Then, a multivariate regression model for the discomfort ratio (Discomfort equation) was constructed, which is again a function of the four meteorological factors. Finally, a Climatic equation, which represents the climatic relation between two cities located in the same climatic zone, is proposed to allow the Discomfort equation for one city to be applied to another city. The evaluation method proposed in this study may be useful for the environmental design of a housing development, because we can easily obtain the statistics of the basic meteorological factors from the Japan Meteorological Agency. The number of the respondents is still too limited for making a precise evaluation. Further questionnaire studies are planned for improving accuracy and obtaining a more general assessment method. Acknowledgements A part of the present study was ®nancially supported by the Japan Securities Scholarship Foundation. We would like to express our appreciation to Profs. Katsura and Kuji of Shokei Women's Junior College for valuable discussions. Acknowledgement also is due to Mr. T. Ito, who was then a student of Tohoku University, for his help in data processing. References [1] ASHRAE HANDBOOK Fundamentals, ASHRAE, 1985. [2] P.O. Fanger, Thermal Comfort, Danish Technical Press, 1970.
110
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
[3] E. Arens et al., Thermal comfort under an extended range of environment conditions, ASHRAE Transactions 92 (1) (1986) 18±26. [4] M.J. Soligo, P.A. Irwin, C.J. Williams, Pedestrian comfort including wind and thermal eects, Proceedings of the Third Asia±Paci®c Symposium on Wind Engineering, Hong Kong, Vol. 2, 1993, pp. 961±966. [5] T. Stathopoulos, H. Wu, J. Zacharias, Field survey on outdoor human comfort in an urban climate, Proceedings of the 10th International Conference on Wind Engineering, Copenhagen, Vol. 2, 1999, pp. 797±804. [6] E.C. Thom, The discomfort index, Weatherwise 12 (2) (1959) 57±60. [7] P.A. Siple, C.F. Passel, Measurements of dry atmospheric cooling in subfreezing temperatures, Proceedings of American Philosophical Society 89 (1) (1945) 177±199. [8] A.D. Penwarden, Acceptable wind speeds in towns, Building Sci. 8 (3) (1973) 259±267. [9] N. Isymov, A.G. Davenport, The ground level wind environment in built up areas, Proceedings of the Fourth International Conference on Wind Eects on Buildings and Structures, Heathrow, 1975, Cambridge University Press, Cambridge, 1975, pp. 403±422. [10] T.V. Lawson, A.D. Penwarden, The eects of wind on people in the vicinity of buildings, Proceedings of the Fourth International Conference on Wind Eects on Buildings and Structures, Heathrow, Cambridge University Press, Cambridge, 1975. [11] M.H. Melbourne, Criteria for environmental wind conditions, J. Industrial Aerodyn. 3 (2/3) (1978) 241±249. [12] S. Murakami, Y. Morikawa, Criteria for assessing wind-induced discomfort considering temperature eect, J. Architecture, Planning Environmental Eng. Trans. Architecture Inst. Japan (358) (1984) 9±17. [13] R. Sasaki, M. Yamada, Y. Uematsu, H. Saeki, Wind environment assessment based on bodily sensation in open air, Proceedings of the Second East European Conference on Wind Engineering, Prague, Vol. 1, 1998, pp. 193±200.
Comfort environment assessment based on bodily sensation in open air: relationship between comfort sensation and meteorological factors Ryoji Sasakia,*, Motohiko Yamadab, Yasushi Uematsuc, Hiromu Saekid a
Technical Research Institute, Nishimatsu Construction Co. Ltd., 2570-4, Shimotsuruma, Yamato 242-8520, Japan b New Industry Creation Hatchery Center, Tohoku University, Sendai 980-8579, Japan c Department of Architecture and Building Science, Tohoku University, Sendai 980-8579, Japan d Tohata Architects and Engineers Inc., 4-4-10, Fushimi-cho, Chuou-ku, Osaka 541-0044, Japan Received 6 January 2000; received in revised form 2 April 2000; accepted 10 April 2000
Abstract The purpose of this study is to develop a method for assessing the comfort of an open-air environment, based on a questionnaire answered by inhabitants of urban areas. Eighty-nine inhabitants of three cities in the northern part of Japan and 18 inhabitants from the suburb of Tokyo responded to the survey. The dependence of comfort judgement on various meteorological factors was studied by using multivariate regression analysis. To construct a comfort environment assessment model, the focus was on a discomfort ratio, which represents what percentage of the inhabitants feels uncomfortable under certain ambient conditions. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Comfort assessment; Open air; Questionnaire; Comfort sensation; Meteorological factors; Multivariate regression analysis
1. Introduction A number of studies have been conducted to investigate how comfortable people feel within buildings. Several indices for comfort assessment have been proposed, *Corresponding author. Tel.: +81-46-275-1135; fax: +81-46-275-0094. E-mail address: [email protected] (R. Sasaki). 0167-6105/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 1 6 7 - 6 1 0 5 ( 0 0 ) 0 0 0 1 8 - 0
94
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
and of these, SET [1] and PMV [2] are widely used. Both indices are based on six factors: air temperature, relative humidity, mean radiant temperature, wind velocity, clo value and metabolic rate. The application of these indices to a comfort environment assessment in open air is controversial, because the values of the factors involved in the indices vary over a wider range in open air than they do within buildings. Furthermore, in addition to the above-mentioned factors, solar radiation and weather must be considered. Attempts have also been made to incorporate other outdoor weather parameters with indoor thermal comfort models for the assessment of outdoor human sensation [3±5]. Some indices for human sensation in open air have been proposed in the ®eld of climatology. Thom [6] de®ned the temperature±humidity index, based on the reaction of the human body to a combination of heat and humidity. Wind chill index, which is expressed as a combination of wind speed and air temperature, is often used in cold season [7]. These indices seem to be useful for speci®c climate. Regarding the wind environment assessment, some criteria have been proposed, e.g. by Penwarden [8], Isymov and Davenport [9], Lawson and Penwarden [10], and Melbourne [11]. Most of these criteria are concerned only with wind speed, either mean or gust. It is thought that the human sensation of wind is in¯uenced by such ambient conditions as air temperature and relative humidity. Therefore, it is hoped that a new criterion for wind environment assessment based on bodily sensation can be developed. Murakami and Morikawa [12] proposed a criterion, in which they considered the eects of temperature on human sensation, based on a questionnaire taken in Tokyo. Recently, Soligo et al. [4] and Stathopoulos et al. [5] tried to develop more general criteria for assessing comfort sensation. We have investigated the eects of several meteorological factors on human sensation of comfort in open air, based on a questionnaire given to inhabitants in urban areas. The details of the questionnaire and some results of a preliminary analysis are presented in Sasaki et al [13]. In the present paper, a multivariate regression model for evaluating the human sensation of comfort in open air is constructed. In constructing the model, a regression model for each respondent is ®rst constructed. Then, we focus on a ``discomfort ratio'' which represents what percentage of the inhabitants feel uncomfortable under a certain ambient condition, and present a regression model for the discomfort ratio. Finally, we propose a ``climatic regression model'', which relates the climatic conditions of a reference city to that of another city. Using such a model we can apply the comfort (or discomfort) evaluation model obtained for the reference city to other cities. 2. Questionnaire 2.1. Sites and respondents Two kinds of questionnaire studies were conducted. In the ®rst study, referred to as Study-1 hereafter, the questionnaire was given to urban inhabitants of Aomori, Morioka, and Sendai, three cities in the northern part of Japan's main island. The
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
95
questionnaire covered a total of six months: the six even-numbered months from April 1996 to February 1997. There were 89 respondents (see Table 1). The second study, referred to as Study-2 hereafter, is based on a questionnaire given to the inhabitants in a housing development in Ebina, a suburb of Tokyo. This questionnaire covered August and October 1998 and February 1999. There were 18 respondents (see Table 1). The location of the four cities is shown in Fig. 1. Fig. 2 compares the climates of these four cities. In this ®gure, the closed circles and bars represent the monthly average air temperature and monthly accumulated precipitation, respectively. Cities located near the coast of the Paci®c Ocean, like Sendai and Ebina, have much rain in summer and autumn due to typhoons and stationary fronts. On the other hand, cities located near the coast of the Sea of Japan, like Aomori, have much snow in winter. Aomori and Morioka are very cold in winter, whereas Ebina is very hot in Table 1 Characteristics of subjects City
Subjects Number
Sex
Study-1
Aomori Morioka Sendai
20 35 20 14
Female Female Male Female
Study-2
Ebina
3 15
Male Female
Fig. 1. Location of the four cities used in this study.
96
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Fig. 2. Characteristics of the climate in the four cities (1979±1998).
summer. Therefore, the focus is on the feeling of discomfort due to the cold of winter in Study-1, and on that due to the heat of summer in Study-2. All the respondents in each city lived or worked near a meteorological observatory or an observation site of the Automated Meteorological Data Acquisition System (AMeDAS) of the Japan Meteorological Agency. Therefore, the meteorological data measured at the meteorological observatories or at the AMeDAS sites can be used for the analysis. In this study, six meteorological factors are used; that is, wind speed and direction averaged over a period of 10 min, air temperature, relative humidity, duration of sunshine, and the ¯ux of global solar radiation over one hour. The mean wind speeds at the predetermined points, 1.5 m above ground, were predicted from the values at the meteorological observatories together with the results of wind tunnel experiments using scale models of the areas. 2.2. Questionnaire form Each respondent ®lled out a questionnaire form almost every day during the period of questionnaire. Table 2 shows the daily questionnaire form used in Study-2; the form used in Study-1 is almost the same, regarding the questions about human sensation. It consists of several questions; namely, the date and time the respondent was at a predetermined point, the weather conditions (wind, thermal conditions) and how the respondent felt about the ambient conditions. The main focus of this study
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
97
Table 2 Daily questionnaire form used in Study-2 Questionnaire for assessment of comfort environment Date /Time Please mark your answer. 1. These questions are concerned with thermal conditions. 1-1. What conditions apply? & sunshine & misty
& shade from trees
& rainy
& shade from buildings
& cloudy
& snowy
1-2. How did thermal conditions feel? & very hot
& hot
& slightly cool
& warm
& cool
& slightly warm
& cold
& neutral
& very cold
1-3. How comfortable were conditions overall? & comfortable
& slightly comfortable
& slightly uncomfortable
& uncomfortable
2. These questions are concerned with wind conditions. 2-1. What were the wind conditions? & strong
& slightly strong
& neutral
& slightly weak
& weak
2-2. What were the indications of wind movement? & trees
& branches
& leaves
& nothing
& other
2-3. What did the wind feel? & cold
& fresh
& uncomfortably warm
& hot
& nothing
& other
2-4. What did you hear in the wind? & train
& trac
& children
& housework
& animals
& usual
& other
2-5. What did you smell in the wind? & trees
& moisture
& garbage
& ®elds
& nothing
& other
3. How did the greenery seem? & overgrown & bright
& clear
& usual
& fresh
& beautiful
& moist
& unpleasant
& other
was on the comfort sensation, which is divided into four categories: comfortable, slightly comfortable, slightly uncomfortable, and uncomfortable. 3. Relation between comfort sensation and meteorological factors First, the general dependence of the comfort sensation on each meteorological factor was investigated separately: air temperature t, relative humidity f, wind speed
98
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
v, and weather. Regression analysis was applied to the data for all respondents, irrespective of location or season. 3.1. Air temperature Fig. 3(a) and (b) illustrate the variation of comfort sensation with air temperature obtained from Study-1 and Study-2, respectively. Several features can be detected from these results. First, the percentage of ``comfortable'' and ``slightly comfortable'' responses is highest at air temperature t, ranging from 20 to 228C. This indicates that many people feel most comfortable when t 202228C. The percentage generally decreases with an increase in t when t > 228C, and with a decrease in t when t5208C. The percentage has another peak at t 128C in Fig. 3(a). When t5128C, the percentage of the ``uncomfortable'' response increases signi®cantly with a decrease in t in both studies. This feature may be due to a coupled eect of air
Fig. 3. Variation of comfort sensation with air temperature. (a) Study-1; (b) Study-2.
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
99
temperature and wind; that is, the discomfort may be quite sensitive to wind speed when t5128C. 3.2. Wind speed Fig. 4(a) and (b) show the variation of comfort sensation with wind speed v obtained from Study-1 and Study-2, respectively. The percentage of the ``comfortable'' response slightly decreases as the wind speed increases in Study-2. On the other hand, in Study-1 any clear dependence of the comfort sensation on wind speed cannot be detected. This is related to the fact that all data irrespective of season are included in the ®gure. Therefore, it may be reasonable to consider that the relation between comfort sensation and wind speed depends on the season, especially on the air temperature. 3.3. Relative humidity Fig. 5(a) and (b) show the variation of comfort sensation with relative humidity f obtained from Study-1 and Study-2, respectively. The results of Study-1 indicate that the percentage of the ``comfortable'' response generally decreases with increasing f. In Study-2, a similar trend can be seen only when f > 50%. The data for f > 50% are mainly from the investigations in summer and autumn. During these seasons more people feel comfortable as the relative humidity decreases. On the other hand, the data for f550% are mainly from the investigation in winter. In winter, the relative humidity is fairly low in the Tokyo area, including Ebina; for example, the monthly average value of f is approximately 50% in Tokyo. Therefore, the comfort sensation may be aected by the other factors. This results in no dependence of comfort sensation on f when f550%.
Fig. 4. Variation of comfort sensation with wind speed. (a) Study-1; (b) Study-2.
100
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Fig. 5. Variation of comfort sensation with relative humidity. (a) Study-1; (b) Study-2.
Fig. 6. Variation of comfort sensation with weather. (a) Study-1; (b) Study-2.
3.4. Weather Fig. 6(a) and (b) show the dependence of the comfort sensation on weather, obtained from Study-1 and Study-2, respectively. The results clearly indicate that most of the respondents feel comfortable during ®ne weather, even when they are in the shade. On the other hand, most respondents feel uncomfortable in rainy or snowy weather, as can be expected.
4. Evaluation of human sensation of discomfort In this chapter, a multivariate regression model for human sensation of comfort is constructed. Based on the results of the preceding analysis, we use four
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
101
meteorological factors, i.e., air temperature (t), wind speed (v), relative humidity (f), and percentage of the duration of sunshine per 1 h (termed sun hereafter), which is regarded as a parameter corresponding to weather conditions. As mentioned above, the Japan Meteorological Agency's AMeDAS network covers the entire country. The number of observation sites is approximately 840, one site per area of approximately 21 square kilometers. This system measures average wind speed and direction for ten minutes, air temperature, duration of sunshine, and precipitation, at every hour. Relative humidity is measured at approximately 80 meteorological observatories throughout the country. Therefore, the assessment method based on the above-mentioned four meteorological factors may be useful in practical applications. Each person has a unique sensation of comfort to these meteorological factors. Therefore, if the data for many respondents is used in multivariate regression analysis, the results will show considerable scatter. On the other hand, the data scatter should be smaller and the correlation coecient higher if multivariate regression analysis is applied to the data for each respondent. Furthermore, it may be reasonable to evaluate the comfort environment based on a discomfort ratio that represents what percentage of people feels uncomfortable under a certain condition. The idea is the same as that used in evaluating the ``Discomfort Index''. The procedure of the analysis is shown in Fig. 7. In this study, multivariate regression analysis is applied to the data for each respondent at the ®rst step. Approximately one half of the data was used for this analysis; the rest of the data was used for testing the application of the model in Section 4.3. Since the replies of comfort sensation are qualitative data, i.e., ``comfortable'', ``slightly comfortable'', ``slightly uncomfortable'' and ``uncomfortable'', they are converted to numerical values, i.e., from 1 to 4 (denoted comf hereafter). At the second step, the values of comf are computed for various ambient conditions by using the multivariate regression models for all respondents, which were constructed at the ®rst step. Based on the results, a multivariate regression model for the discomfort ratio (denoted discomf hereafter) is constructed as a function of the four meteorological factors. The multivariate regression models obtained at the ®rst and the second step are denoted Comfort equation and Discomfort equation, respectively (see Fig. 7). According to the results for the dependence of comfort sensation on air temperature (Fig. 3), it may be reasonable to divide the air temperature level into three ranges, i.e., t4128C; 125t4228C, and t > 228C; these ranges are referred to as Low-, Middle-, and High-temperature levels, respectively, hereafter. 4.1. Comfort sensation for the individual Table 3 shows sample results of multivariate regression analysis applied to the data of several respondents in Ebina (Study-2); the temperature level is Low in this case. In the analysis, the comfort sensation, comf, is the object variable and the four meteorological factors (t, v, sun, and f) are the explanatory variables. For example, the multivariate regression model called Comfort equation for respondent No.1 is
102
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Fig. 7. Procedure of the analysis.
given by the following equation: comf ÿ0:210t 0:275v ÿ 0:067sun ÿ 0:004f 4:658:
1
The multiple correlation coecient R of this equation is 0.82. The value of R for most of the respondents ranged from 0.6 to 0.8. Considering that the model deals with human sensation, and judging from the relatively high values of R, we may conclude that the models give a reasonable evaluation of the comfort sensation.
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
103
Table 3 Regression coecients and multiple correlation coecient R in Comfort equation at Low-temperature level in Ebina Subject no.
Number of data
t (8C)
v (m/s)
Sun (%/h)
f (%)
Const.
R
1 2 3 4 5
13 13 14 11 13
ÿ 0.210 ÿ 0.008 ÿ 0.075 ÿ 0.117 ÿ 0.145
0.275 ÿ 0.196 0.643 ÿ 0.422 0.449
ÿ 0.067 ÿ 0.083 ÿ 0.081 ÿ 0.013 ÿ 0.101
ÿ 0.004 ÿ 0.013 0.002 ÿ 0.027 ÿ 0.016
4.658 3.490 3.078 4.272 5.416
0.82 0.50 0.72 0.60 0.88
Table 4 Number of Comfort equations for constructing Discomfort equation; the terms in parentheses are the number of data Low t4128C
Middle 125t4228C
High 228C5t
Study-1
Aomori Morioka Sendai
13 (750) 13 (304) 20 (406)
15 (948) 22 (514) 17 (374)
16 (366) 4 (30) 8 (119)
Study-2
Ebina
15 (180)
16 (162)
14 (246)
4.2. Discomfort ratio Using the Comfort equation obtained for the individuals in the preceding section, we can evaluate the comfort sensation for each respondent for any set of ambient conditions. The discomfort ratio (%), discomf, is de®ned as the ratio of the number of the respondents who feel uncomfortable (comf 5 2.5) under a certain ambient condition to that of all respondents. For example, if 10 among 20 respondents in Aomori feel uncomfortable for some ambient conditions, the value of discomf will be 50%. The values of discomf were computed for various combinations of meteorological factor, which had been obtained in the questionnaire studies. A multivariate regression analysis was again applied to the results, in which discomf is the object variable and the four meteorological factors are the explanatory variables. In the analysis, Comfort equations with R50:5 were not used; the numbers of the respondents used for constructing the model of discomf are listed in Table 4. The result of the multivariate regression analysis is summarized in Table 5. As shown in Table 4, the number of replies to High-temperature level in Morioka and Sendai is rather limited. Therefore, the results for these two cases should be questionable from a statistical point of view; this data will be excluded in the following discussion. Except for these two cases, the value of R is fairly high. From the results in Table 5, the following features can be detected: (1) The regression coecient for t is positive when t > 228C, while negative when t4228C. This feature corresponds well to the result in Fig. 3. It is found that
104
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Table 5 Regression coecients and multiple correlation coecient R in Discomfort equation t(8C)
v (m/s)
Sun (%/h)
f (%)
Const.
R
High 228C5t
Aomori Morioka Sendai Ebina
4.36 4.46 4.73 3.32
8.10 2.29 2.97 1.75
ÿ 0.42 ÿ 0.08 0.13 ÿ 0.17
0.40 0.16 0.76 0.44
ÿ 92.23 ÿ 90.93 ÿ 144.32 ÿ 84.95
0.94 0.34 0.57 0.89
Middle 125t4228C
Aomori Morioka Sendai Ebina
ÿ 3.00 ÿ 5.24 ÿ 2.07 ÿ 5.49
8.22 3.62 ÿ 1.05 4.83
ÿ 0.59 ÿ 0.14 ÿ 1.48 ÿ 0.26
0.55 0.97 0.40 0.44
64.02 63.40 31.49 93.76
0.94 0.94 0.81 0.94
Low t4128C
Aomori Morioka Sendai Ebina
ÿ 2.38 ÿ 0.73 ÿ 5.50 ÿ 4.25
11.35 7.70 4.95 5.65
ÿ 0.53 ÿ 0.62 ÿ 1.93 ÿ 0.34
0.58 0.48 0.10 ÿ 0.07
32.01 45.27 75.83 97.93
0.95 0.96 0.89 0.94
more people feel comfortable as the air temperature increases up to about 228C; beyond this temperature, the situation is reversed. (2) The regression coecient for v is generally positive and its value is larger at lower temperature levels. This feature indicates that more people feel uncomfortable as wind speed increases, in particular, when air temperature is low. It is expected that the wind makes us feel comfortable when the air temperature is very high, for example, t > 308C. The present result is not in accordance with such an expectation. This discrepancy may be due to the fact that the amount of data for such a high-temperature range is limited in the present study. (3) The duration of sunshine has a negative correlation with the discomfort ratio. The magnitude of the regression coecient is relatively large in northern cities (Aomori and Morioka). This is due to the fact that the duration of sunshine is rather short in winter in Aomori; the monthly average duration of sunshine for February is only approximately 75 hours. (4) The relative humidity generally has a positive correlation with the discomfort ratio. The only exception is the Low-temperature level in Ebina. This is due to the fact that the relative humidity is fairly low in winter in Ebina, as mentioned above. Fig. 8 shows the discomfort ratio as a function of t and v for Aomori when f 70%, in which sun is assumed to be 100%/h or 0%/h. The ®gure clearly shows the dependence of the discomfort ratio on wind speed and air temperature. Furthermore, sun signi®cantly in¯uences the discomfort ratio. 4.3. Tests for discomfort ratio The application of the above-mentioned model for the discomfort ratio was investigated by comparing the results predicted by the model (Comfort equation) with
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
105
Fig. 8. Distribution of discomfort ratio (%) in Aomori as a function of air temperature and wind speed (f 70%). (a) sun=100%/h; (b) sun=0%/h.
Fig. 9. Relation between Ratio-A and Ratio-B at Low-temperature level in Aomori.
106
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
those of the questionnaire. Fig. 9 shows the result of such a comparison for the Lowtemperature level in Aomori. In this ®gure, Ratio-A represents the discomfort ratio computed by using the Discomfort equation directly when the values of four meteorological factors are given. Ratio-B is the ratio of the number of the respondents giving comf 5 2.5 in the Comfort equation (i.e., who feel uncomfortable) for the same ambient condition. Both ratios are expressed as a percentage. From this ®gure, it is found that the agreement between Ratio-A and Ratio-B is relatively good. Table 6 summarizes the results of the comparison for all cases, in which the mean, standard deviation, kurtosis and skewness of the dierence between the two results are listed. In particular, the agreement is fairly good for the High-temperature level in Ebina, the Middle-temperature level in Aomori and the Low-temperature level in Sendai. 4.4. Application of the Discomfort equation to other cities The results in Table 5 indicate that the Discomfort equation is dependent on the city, as mentioned in the preceding section. This means that people of each city have their own criteria for uncomfortable conditions, based on their experience in the regional climate of the location. Therefore, the Discomfort equation obtained for a city cannot be applied to other cities directly. Strictly speaking, the Discomfort equation should be obtained for each city or region. However, this is impossible in practice. So, in order to apply the equations obtained in this study to other cities, we propose a simple method based on a regression model, representing the relation between two cities located in the same climatic zone. This regression model is referred to as the Climatic equation hereafter. The regression model for two cities Table 6 Statistics of the dierence between Ratio-A (%) and Ratio-B (%) (Ratio-A) ± (Ratio-B) Average
S.D.
Kurtosis
Skewness
High 228C5t
Aomori Morioka Sendai Ebina
1.07 0.45 2.21 ÿ 0.37
8.66 25.11 15.99 7.73
3.21 3.81 2.77 3.18
0.26 ÿ 1.10 ÿ 0.06 0.20
Middle 125t4228C
Aomori Morioka Sendai Ebina
0.16 0.31 0.74 ÿ 0.47
11.43 9.66 11.66 7.66
2.71 4.35 2.81 3.22
0.37 ÿ 0.77 ÿ 0.38 ÿ 0.05
Low t4128C
Aomori Morioka Sendai Ebina
0.41 0.47 ÿ 0.02 ÿ 1.41
10.51 8.57 6.09 8.85
4.44 3.15 3.17 12.14
0.28 0.31 ÿ 0.09 ÿ 2.35
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
107
Fig. 10. Climatic division map.
(Cities 1 and 2) may be given by the following equation: f City 1 a g City 2 b;
2
where a and b are constants; f and g represent one of the four meteorological factors (monthly data) at Cities 1 and 2, respectively; City 1 is a reference city where the Discomfort equation was obtained, while City 2 is the other city. The values of the four meteorological factors for City 2 are ®rst converted to the corresponding values for City 1 by using Eq. (2). Then, substituting the converted values in the Discomfort equation for City 1, we obtain the discomfort ratio for City 2. In order to investigate the climatic zones of Japan, a principal component analysis is made by using six kinds of monthly average meteorological factors obtained at 80 meteorological observatories in Japan; they are the average values over a period of 1961±1990. These factors are daily air temperature, daily relative humidity, daily wind speed, dierence between the daily highest and the daily lowest temperature, precipitation, and duration of sunshine. Fig. 10 shows the result represented as a form of climatic division map, in which the country is divided into six climatic zones. This ®gure indicates that Aomori, Morioka, and Sendai belong to the same climatic zone. Therefore, the Discomfort equation obtained for Aomori can be applied to Morioka and Sendai, when combined with the Climatic equations for Aomori (City 1)
108
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
Table 7 Regression coecients of Climatic equation
Morioka ! Aomori Sendai ! Aomori
Air temperature
Wind speed
Duration of sunshine
Relative humidity
a
b
a
b
a
b
a
b
0.96 1.09
0.51 ÿ 3.02
1.08 1.20
} }
0.98 0.86
} }
0.70 0.43
23.23 44.26
Fig. 11. Comparison for the discomfort ratio between the results obtained by using Discomfort equation and those predicted by Climatic equations (from Morioka and Sendai to Aomori) together with the Discomfort equation for Aomori.
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
109
and Morioka or Sendai (City 2). Using the data measured in this decade, the climatic regression models for the four meteorological factors are obtained in the form of f Aomori a g Morioka or Sendai b. The values of the constants a and b are listed in Table 7. Fig. 11 shows a comparison for the discomfort ratio between the results obtained by using the Discomfort equations for Morioka and Sendai directly, and those predicted by the Climatic equations for Morioka and Sendai together with the Discomfort equation for Aomori. The results for various ambient conditions are plotted in each ®gure. It is found that the prediction obtained by using the Climatic equations captures the general trend of the discomfort ratio. The agreement between these two results are relatively good, except for two cases, i.e., (a ÿ 1) and (b ÿ 2). 5. Concluding remarks The dependence of the human sensation of comfort (or discomfort) on four meteorological factors has been investigated, based on the questionnaires given to the inhabitants living in four cities of Japan. Multivariate regression analysis was applied to the results. First, a multivariate regression model for the comfort sensation of the individual (Comfort equation) was constructed as a function of the four meteorological factors for each respondent. Then, a multivariate regression model for the discomfort ratio (Discomfort equation) was constructed, which is again a function of the four meteorological factors. Finally, a Climatic equation, which represents the climatic relation between two cities located in the same climatic zone, is proposed to allow the Discomfort equation for one city to be applied to another city. The evaluation method proposed in this study may be useful for the environmental design of a housing development, because we can easily obtain the statistics of the basic meteorological factors from the Japan Meteorological Agency. The number of the respondents is still too limited for making a precise evaluation. Further questionnaire studies are planned for improving accuracy and obtaining a more general assessment method. Acknowledgements A part of the present study was ®nancially supported by the Japan Securities Scholarship Foundation. We would like to express our appreciation to Profs. Katsura and Kuji of Shokei Women's Junior College for valuable discussions. Acknowledgement also is due to Mr. T. Ito, who was then a student of Tohoku University, for his help in data processing. References [1] ASHRAE HANDBOOK Fundamentals, ASHRAE, 1985. [2] P.O. Fanger, Thermal Comfort, Danish Technical Press, 1970.
110
R. Sasaki et al. / J. Wind Eng. Ind. Aerodyn. 87 (2000) 93±110
[3] E. Arens et al., Thermal comfort under an extended range of environment conditions, ASHRAE Transactions 92 (1) (1986) 18±26. [4] M.J. Soligo, P.A. Irwin, C.J. Williams, Pedestrian comfort including wind and thermal eects, Proceedings of the Third Asia±Paci®c Symposium on Wind Engineering, Hong Kong, Vol. 2, 1993, pp. 961±966. [5] T. Stathopoulos, H. Wu, J. Zacharias, Field survey on outdoor human comfort in an urban climate, Proceedings of the 10th International Conference on Wind Engineering, Copenhagen, Vol. 2, 1999, pp. 797±804. [6] E.C. Thom, The discomfort index, Weatherwise 12 (2) (1959) 57±60. [7] P.A. Siple, C.F. Passel, Measurements of dry atmospheric cooling in subfreezing temperatures, Proceedings of American Philosophical Society 89 (1) (1945) 177±199. [8] A.D. Penwarden, Acceptable wind speeds in towns, Building Sci. 8 (3) (1973) 259±267. [9] N. Isymov, A.G. Davenport, The ground level wind environment in built up areas, Proceedings of the Fourth International Conference on Wind Eects on Buildings and Structures, Heathrow, 1975, Cambridge University Press, Cambridge, 1975, pp. 403±422. [10] T.V. Lawson, A.D. Penwarden, The eects of wind on people in the vicinity of buildings, Proceedings of the Fourth International Conference on Wind Eects on Buildings and Structures, Heathrow, Cambridge University Press, Cambridge, 1975. [11] M.H. Melbourne, Criteria for environmental wind conditions, J. Industrial Aerodyn. 3 (2/3) (1978) 241±249. [12] S. Murakami, Y. Morikawa, Criteria for assessing wind-induced discomfort considering temperature eect, J. Architecture, Planning Environmental Eng. Trans. Architecture Inst. Japan (358) (1984) 9±17. [13] R. Sasaki, M. Yamada, Y. Uematsu, H. Saeki, Wind environment assessment based on bodily sensation in open air, Proceedings of the Second East European Conference on Wind Engineering, Prague, Vol. 1, 1998, pp. 193±200.