Study on outdoor thermal comfort on a campus in a subtropical urban area in summer

Sustainable Cities and Society 22 (2016) 164–170

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Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs

Research article

Study on outdoor thermal comfort on a campus in a subtropical urban area in summer Lingjun Zhao a,b , Xiaoqing Zhou a,b , Li Li b,c,∗ , Shiquan He b , Raochao Chen a,b a b c

College of Civil Engineering, Guangzhou University, Guangzhou 510006, China Building Energy Research Center, Guangzhou University, Guangzhou 510006, China College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China

a r t i c l e

i n f o

Article history: Received 7 November 2015 Received in revised form 25 January 2016 Accepted 14 February 2016 Available online 17 February 2016 Keywords: Outdoor thermal environment Thermal comfort SET* Evaluation index

a b s t r a c t Most domestic outdoor thermal comfort evaluation indices were proposed by developed, temperate climate regions in Europe. Therefore, the study developed an evaluation index according to subtropical Guangzhou, China to guide outdoor environment work more accurately. A thermal comfort study was conducted for Guangzhou University campus. Field measurements and a questionnaire survey were used to assess the thermal comfort of subjects. The results showed that 90% acceptable thermal temperature limit is 28.54 ◦ C, which is significantly higher than the western/middle European limits. However, 46.7% of humidity sensation votives are neutral. Finally, a new thermal comfort index model was developed for Guangzhou area. © 2016 Elsevier Ltd. All rights reserved.

Contents 1. 2.

3.

4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 2.1. 2.2. Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Results and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 3.1. SET* statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Neutral temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 3.2. 3.3. Preferred temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 3.4. Percent of dissatisfied respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 3.5. Thermal comfort indices model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

1. Introduction Achieving a pleasant outdoor thermal environment is important for any outdoor space. The outdoor thermal environment in subtropical urban areas is extremely hot, and many people suffer

∗ Corresponding author. Tel.: +86 135 6010 2369. E-mail addresses: [email protected] (L. Zhao), zhou [email protected] (X. Zhou), lili [email protected] (L. Li). http://dx.doi.org/10.1016/j.scs.2016.02.009 2210-6707/© 2016 Elsevier Ltd. All rights reserved.

summer heat strokes each year (Xi, Li, Mochida & Meng, 2012). Research has shown that a comfortable outdoor thermal environment can promote outdoor activity and improve physical and mental health (Thach, Zheng, Lai, Wong & Chau, 2015). The outdoor environment has received considerable research attention regarding the improvement of living standards and comfort, including several outdoor thermal comfort studies (Singh, Mahapatra, & Teller, 2015; Lai, Guo, Hou, Lin & Chen, 2014; Yang, Wong, & Zhang, 2013a; Katafygiotou & Serghides, 2014; Rossi, Anderini, Castellani, Nicolini & Morini, 2015). Evaluation indices provide the basis for assessing the outdoor environment. Environmental planners and designers use indices to make clear decisions

L. Zhao et al. / Sustainable Cities and Society 22 (2016) 164–170

on thermal environment levels. Numerous evaluation indices have been proposed over the past 100 years (Epstein & Moran, 2006). Several indices have integrated thermal environmental factors and human energy balance properties to optimize outdoor thermal comfort. Various comfort parameters include the predicted mean vote (PMV) (Fanger, 1970), the new effective temperature (ET*), the SET* (Gagge, Fobelets, & Berglund, 1986), standard effective temperature for the outdoors (OUT-SET*) (Pickup & Dear, 1999; Spagnolo & De Dear, 2003), the physiologically equivalent temperature (PET) and universal thermal climate index (UTCI). Bröde, Krüger, Rossi and Fiala (2012) used general additive models to study the effects of temperature, humidity, and wind, long-wave radiant heat fluxes and short-wave radiant heat fluxes as summarized by the recently developed Universal Thermal Climate Index (UTCI). The study suggested that the UTCI provided a suitable planning tool for urban thermal comfort in sub-tropical regions. Cheng, Ng, Chan and Givoni (2012) conducted an outdoor thermal comfort survey based on using longitudinal experiments in Hong Kong to addresses the effects of changing wind and solar radiation conditions on thermal sensation. The study also estimated predictive formulas based on the physiological equivalent temperature (PET) thermal index. Yang, Wong, and Jusuf (2013b) conducted a thermal comfort study of outdoor urban spaces in Singapore and suggested that individuals who typically subsist in outdoor environments are more tolerant to heat stress than those who subsist in indoor environments in tropical climates. Xi et al. (2012) investigated the influences of various design elements on the outdoor thermal environment around campus clusters, noting the subjective responses of Guangzhou students in subtropical urban areas. The study established an outdoor thermal comfort calculation model based on the new standard effect temperature (SET*) evaluation index. The SET*, PMV and PET indices have been commonly used in recent outdoor thermal comfort studies. The PMV index is based on the predicted mean vote of a large group of people, who assess an actual thermal sensation. The PMV index uses the ASHRAE 7-point scale of thermal sensation scale. However, several studies have reported poor correlations between the PMV and subjective thermal perception (Cheng et al., 2012; Höppe, 2002; Nikolopoulou, 2010; Nikolopoulou, Baker, & Steemers, 2001; Thorsson, Lindqvist, & Lindqvist, 2004). SET* and PET are based on climate-chamber analyses of the human energy balance and integrate the effects of air temperature (Ta ), vapor pressure (VP ) (or relative humidity (RH)), mean radiant temperature (Tmrt ) and air speed (v). SET* is considered one of the most widely used indices for outdoor thermal environment studies (Xi et al., 2012). Compared to other evaluation indices, SET* aims to improve warm or humid condition evaluations (Blazejczyk, Epstein, Jendritzky, Staiger & Tinz, 2012) and comprehensively consideres the effects of outdoor thermal parameters on the human body heat balance. The index has been verified via numerous experiments and theoretical studies (Xi et al., 2012; Gagge et al., 1986). In addition, the American Society of Heating Refrigerating and Air-conditioning Engineer (ASHRAE) has adopted the SET* analysis process. Because this study is also concentrate on university in Guangzhou of subtropical area, this article take SET* as evaluation index. The analysis focuses on revising the thermal comfort evaluation index for students on the subtropical region campus based on field measurements and a questionnaire survey. The questionnaire survey and field measurement results were then used to develop the new evaluation index model. The newly developed thermal comfort calculation model provides an appropriate evaluation index for designing a green campus environment and is suitable for Guangzhou inhabitants.

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2. Methodology 2.1. Study area The study area encompasses Guangzhou University in south China. The university is located at a longitude of 112.8◦ E and latitude between 22.3◦ N and 24.1◦ N. The summer season lasts for six months from May to mid-October. The daily mean air temperature is approximately 30 ◦ C. The humidity is typically high, often exceeding 90%. Guangzhou is a typical subtropical city with uniformly high temperatures, high humidity and abundant summer rainfall. The field surveys and measurements were conducted daily in August through mid-October 2014 from 9:00 to 18:00. Most university students are from the Guangzhou area and represent the characteristics of the local residents. The sampling points were established at popular student locations. Each study area was carefully selected to represent different microclimatic conditions, including shaded roads in living areas, sidewalks, recreational areas and teaching areas. The sampling points shown in Fig. 1(c) and (e) represent shaded locations, which can reduce the summer temperature and improve comfort. The former point is located under a pavilion, while the latter is located under trees. The points shown in Fig. 1(b) and (d) are not shaded. The main difference between these two points is related to the ground composition. The point in Fig. 1(b) is characterized by grass, while ceramic tiles characterize the point in Fig. 1(d). Therefore, these sampling points encompass various characteristics of the study area and represent different microclimates. This study chose sampling locations with significant microenvironmental similarities. The sampling points are separated by long distances, ensuring that each respondent has walked for a significant period before arriving at a sampling point. Thus, the respondents can sufficiently adapt to the microenvironment, which is shown in Fig. 1. 2.2. Data collection The investigation includes physical measurement and subjective assessments. All field surveys were conducted on days with suitable weather to avoid extreme weather interference. The physical measurement aimed to collect microclimatic parameters such as air temperature, relative humidity, globe temperature and wind velocity. The two former parameters were measured using a ZDR-20 at a 5-min interval. A JTR10 recorded the global temperature, which is a combination of air temperature, wind velocity, air humidity and the radiation emitted from the surroundings. KANOMAX MODEL KA22 and CASELLA units measured the wind velocity. The KA22 unit measured smaller speeds, while CASELLA unit measured higher speeds. The measurement height was 1.1 m, corresponding to the average height of the centre of gravity for adults. The objective physical measurements lasted 15–20 min during each visit. The subsequent analysis used the average values of each measured variable. Table 1 summarizes detailed measurement instrument information for each physical parameter. 1582 samples were collected via the survey. The questionnaire consisted of Parts A and B. Part A asked the respondents to access the thermal sensation, thermal acceptability and thermal preference. The analysis used the traditional ASHRAE 7-point scale thermal sensation vote (TSV), as shown in Fig. 2(a). The thermal acceptable thermal vote (ATV) was based on a direct assessment (acceptable and unacceptable), as shown in Fig. 2(b). The preference thermal vote (PTV) utilized the 3-point Mclntyre preference scale, as shown in Fig. 2(c). The respondents finished part A, after the investigators explained about each question. Part B collected demographic information such as gender, height, activity level

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Fig. 1. Major sampling points on the campus (a) study area; (b) grass; (c) pavilion; (d) commons; (e) shaded road.

Table 1 Measurement instrument information.

3. Results and analysis

Test factor

Type of product

Measurement range

Accuracy

3.1. SET* statistics

Dry-bulb temperature & relative humidity Wet-bubble globe temperature Wind velocity

ZDR-20

Temperature: −40–100 ◦ C RH: 0–100%

±0.5 ◦ C; ±3%

JTR10

5–120 ◦ C

±0.2 ◦ C

SET* is a comfort index that was developed based on the dynamic two-node model (2 NM) of human temperature regulation (Gagge et al., 1986; ASHRAE, 2013;ASHRAE, 1989; Gagge, 1971). A transient energy balance states that the rate of heat storage is equal to the net heat gain minus the heat loss. The thermal model is described by two coupled heat balance equations:

KANOMAX MODEL KA22 CASELLA

0–4.99 m/s

±2%

0.4–25 m/s

±2% + 0.2

Wind velocity

Scr = (M − W − Cres + Eres ) − (tcr − tsk ) × (5.28 + 1.163 × skbf)

(1)

Ssk = (tcr − tsk ) × (5.28 + 1.163 × skbf) − (C + R + Esk )

(2)

(W/m2 ),

Fig. 2. Field test voting scales (a) thermal sensation vote (TSV), (b) acceptable thermal vote (ATV), (c) preference thermal vote (PTV).

(sitting, standing, or walking) during the 15-min periodand current clothing type. Human activity levels largely affects metabolism. This relationship is generally assessed using the ASHRAE 55-2013 and ISO7730 standards, which are based ontypical male metabolisms and activity levels. The clo values of clothing were calculated according to ASHRAE Standard 55 (ASHRAE, 2013). The ensemble clo value represents the sum of the partial garment clo values. Underwear garments were assigned standard clo values of0.03 and 0.04 for men and women, respectively. These values were then added to the total clo insulation values.

where Scr is the heat storage rate in the core node Ssk is the heat storage rate in the skin node (W/m2 ), Cres is the convective heat loss rate via respiration (W/m2 ), Eres is the evaporative heat loss rate via respiration (W/m2 ), tcr is the the core node temperature (◦ C), tsk is the skin node temperature (◦ C), skbf is the peripheral blood flow (L/h m2 ), C is the sensible heat loss from the skin via convection (W/m2 ), R is the sensible heat loss from the skin via radiation (W/m2 ), and Esk is the total evaporative heat loss from the skin (W/m2 ). The heat storage rate in the body is based on internal energy ratio variations. The storage rate can be separately written for each compartment based on thermal capacity and temporal temperature rate variations of each compartment:



Scr = (1 − ˛) mcp,b

 Ssk = ˛ mcp,b

dtcr /d AD

dtsk /d AD



(3)



(4)

where ˛ is the body mass fraction in the skin compartment, m is the body mass (kg), cp,b is the specific heat capacity of the body (kJ/kg),  is the time (s), and AD is the Dubois surface area (m2 ). The SET* index is defined as the equivalent temperature of an isothermal environment at 50% RH, in which a subject

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Table 2 Environmental factors and SET* statistics. Parameter

Air temperature (◦ C)

Relative humidity (%)

Wind velocity (m/s)

Mean radiant temperature (◦ C)

SET* (◦ C)

Average value Standard deviation Minimum value Maximum value

30.0 2.27 24.70 34.60

62.9 9.06 37.60 89.70

0.71 0.56 0.00 3.47

33.65 4.59 25.60 65.93

29.5 3.07 38.6 20.5

42

36

34

SET* /oC

SET* /oC

38

39

(a)

30 26

33 30 27 24

22 18 23

(b)

21 25

27 29 31 33 35 Air temperature /oC

37

18 23 26 29 32 35 38 41 44 47 50 Mean radiant temperature /oC

Fig. 3. Relationship between SET* and both air temperature and mean radiant temperature. (a) Relationship between SET* and air temperature, (b) relationship between SET* and mean radiant temperature.

wearing standardized clothing for a specific activity generates have the same heat stress (skin temperature, tsk ) and thermoregulatory strain (skin wettedness, ω) in a test environment. The isothermal environment refers to the environment at sea level, in which the air temperature is equal to the mean radiant temperature and the air velocity is zero. If Qsk is defined as the heat loss from the skin (the thermal load of the skin) then it can be expressed by the following equation: Qsk = hcSET∗ (tsk − SET∗) + ωheSET∗ (Psk − 0.5PSET∗ )

(5)

where hcSET* is the standard heat transfer coefficient (W/m2 ◦ C), heSET* is the standard evaporative heat transfer coefficient (W/m2 kPa), ω is the fraction of the wetted skin surface, Psk is the water vapor pressure on the skin, which is normally assumed to equal the saturated water vapor at tsk (kPa), and PSET* is the saturated water vapor pressure at SET* (kPa). Eqs. (1)–(5) were used to calculate the SET* statistics at http:// web.arch.usyd.edu.au/∼rdedear/. Table 2 lists the outdoor physical parameters and SET* statistics. The table indicates that the average outdoor air temperature was 30 ◦ C. The mean radiant temperature was 3.7 ◦ C higher than the average air temperature because the majority of the surveys were conducted in shaded areas. The average wind velocity was 0.71 m/s, mean relative humidity was 62.9% and the maximum relative humidity value was 90%. SET* varied over a large range, with a minimum value at 20.5 ◦ C and maximum value at 38.6 ◦ C. Fig. 3(a) shows the relationship between SET* and the air temperature, while Fig. 3(b) shows the relationship between SET* and the mean radiant temperature. Fig. 4 indicates that SET* increases with air temperature and mean radiant temperature. No significant relationships were identified between SET* and the relative humidity or wind velocity. The TSV frequency distribution in Fig. 4 shows that approximately 95.7% of the TSV is greater than or equal zero. TSV = 0 and TSV > 0 accounted for 30% and 65.7% of the responses, respectively. This result suggests that the thermal environment is typically appropriate or overly hot. Fig. 5 illustrates the distribution of humidity sensation vote (HSV) distribution. Approximately half of the responses (46.7%) were zero. The majority of the remaining respondents (34%) voted drier than average, while 14.75% voted

Fig. 4. TSV frequency distribution.

Fig. 5. HSV frequency distribution.

more humid than average. The results indicate that the summer outdoor thermal environment in Guangzhou is typically overheated, but people are not overly sensitive to the sensitive to humidity. The boxplot in Fig. 6 illustrates presents the relationship

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Fig. 6. Relationship between SET* and TSV.

3.0

(Liu, 2011), but higher than the neutral temperature in Dalian (China) (Yu, 2004).

MTSV = 0.205SET* - 4.899 R² = 0.971

2.5 2.0

3.3. Preferred temperature

1.5

Logistic regression analysis are frequently used for predictive land use modeling based on inductive modeling variations. A logistic regression was used to analyze the “prefer cooler” and “prefer warmer” preference votes. A Binary Logistic regression model in SPSS 18.0 was used to assess the goodness of fit of the preference model. The preferred warmer and cooler regression models are provided in Formulas (1) and (2), respectively. The significant probabilities (p) of 0.053 and 0.468, are both above the 0.05 threshold, suggesting that the logistic regression SET* models provide adequate matches.

1.0 0.5 0.0 -0.5

18

21

24

27

30

33

36

39

-1.0 Fig. 7. Relationship between SET* and MTSV.

between SET* and TSV. The temperature ranges generally widely distributed for each bin, and the medians of SET* values significantly increase as the TSV grade increases. 3.2. Neutral temperature The BIN-method divided the temperature range into several data bins with an increment, and was used to research mean thermal sense vote (MTSV) and SET* variations (Yang et al., 2013a; Liu, 2011). SET* was divided into several effective intervals with a 2 ◦ C increment. Fig. 7 illustrates the SET* fitting results. The slope of the fitting line signifies the respondents’ degree of thermal sensitivity towards SET* variation. A rate of 0.205 suggests that the thermal sensation level will increase a grade for every 4.9 ◦ C increase. Neutral temperature indicate that respondents were neither hot nor cold. The nSET*index value is 23.9 ◦ C when MTSV equals zero. Table 3 provides the results from several previous studies. The neutral temperatureresults produced in this survey are similar to the results of Xi et al. (2012). However, the neutral temperature in Guangzhou (China) is 5.3 ◦ C lower than that in Changsha (China)

Logit(p) = 0.293SET ∗ −7.724

(6)

Logit(p) = −0.224SET ∗ +3.731

(7)

Fig. 8 illustrates the relationship between the expected probability and SET *. The preferred cooler probability increases as the SET* increases, whereas the preferred warmer probability gradually decreases and approaches zero as SET* increases. The intersection of the two fitted probability lines represents the temperature at which individuals did not prefer either a cooler or a warmer temperature. The resulting preferred temperature was 23.7 ◦ C, 0.2 ◦ C cooler than the neutral temperature (23.9 ◦ C). This result agrees with previous thermal comfort studies, which suggested that individuals in hot and humid climate prefer “cool” temperatures, because the word “warm” implies an undesirable state (Singh et al., 2015; Yang et al., 2013a,b). 3.4. Percent of dissatisfied respondents The ISO7730 standard provides temperatures that 90% of respondents, accept as comfortable in a thermal environment. Thus, 10% of the respondents feel unsatisfied. However, the ASHRAE

Table 3 Thermal sensation linear regression analysis model. Time

Researcher

Location

Thermal sensation model

R2

2012 2011 2004

Tianyu Xi (Fanger, 1970) Jing Liu (Nikolopoulou, 2010) Guanglian Yu (Nikolopoulou et al., 2001)

Guangzhou Changsha Dalian

TSV = 0.1382 × SET* − 3.3469 MTSV = 0.274 × SET* − 8 MTSV = 0.2777 × SET* − 5.92787

0.305 0.91 0.963

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Table 4 Thermal sensation linear regression model of different researchers. Time

Location

Regression model

2008

Brazil

−3.557 + 0.0632Ta + 0.0677Tmrt + 0.0105RH − 0.304v r = 0.925

2004

Japan

1.7 + 0.1118Ta + 0.0019R↓ − 0.322v − 0.0073RH + 0.0054GT

2004

Greece

−0.412 + 0.034Ta + 0.0001R↓ − 0.086v − 0.001RH r = 0.27 −2.079 + 0.049Ta + 0.0001R↓ − 0.051v + 0.014RH r = 0.44 −1.74 + 0.113Ta + 0.0001R↓ − 0.05v − 0.003RH r = 0.57

Italy England 2012

−8.23 + 0.190Ta + 0.0031Rs − 0.775v + 0.195RH

Hong Kong

Fig. 8. Dramatic relationship between SET* and expected probability of thermal comfort.

3.5. Thermal comfort indices model 100%

APD

80%

APD = 0.003SET*2 - 0.171SET* + 2.043 R² = 0.893

60% 40% 20% 0% 20

23

26

29

SET*

32

35

38

/oC

Fig. 9. Relationship between SET* and APD.

55 standard percentage is 20%. Regarding 10% and 20% acceptable rate as boundaries respectively, Fig. 9 illustrates upper outdoor temperature limits of 28.5 ◦ C and 31.1 ◦ C. The 28.5 ◦ C limit is much higher than the 26.8 ◦ C given as the current standard. These results suggest that Guangzhou residents have a lower outdoor thermal comfort environment requirement than an indoor thermal comfort environment requirement based on the high upper limit and wide range of acceptable values. Fig. 9 shows the relationship between the SET* and actual percent dissatisfied (APD).

The results of this study illustrate that Guangzhou residents possess different thermal expectations and heat adaptation abilities due to their cultural habits and customs. Therefore, a thermal comfort evaluation model must be developed for the local area. Human thermal sensation is the result of the coupled relationship between the external environment and internal factors. However, it is often difficult to quantitatively adopt generalized objective parameters to measure the influence of internal factors on human thermal comfort due to internal factor instabilities. The complex human body mechanism model complicates thermal comfort studies. Therefore external factors impacts (environmental objective factors), and statistical methods are often used to compensate for the influence of internal factors. Table 4 presents various thermal environmental factors mathematical models and thermal sensation obtained by researchers using regression analyses (Cheng et al., 2012; Monteiro & Alucci, 2008; Givoni & Noguchi, 2004; Nikolopoulou, 2004). The thermal sensations exhibit significant variations based on location. The outdoor thermal environment investigation results were analyzed using SPSS software and multiple linear regression statistical methods to establish a summer outdoor thermal comfort index model for Guangzhou. The multiple linear regression method seeks to combine multiple independent variables to effectively predict dependent variables and establish a regression model (Lin, 2006). The major objective of this paper is to integrate the air temperature, relative humidity, wind velocity, mean radiant temperature and thermal sensation to produce logistic regression model outputs.

Table 5 The summary of regression models. Factor number

Model

One

1

Two

Three

Four

2

3

4

Nonstandard coefficient

r

t

Sig.

Tolerance

VIF

−21.825 25.554

0.000 0.000

1.000

1.000

−20.065 24.891 −7.738

0.000 0.000 0.000

0.985 0.985

1.015 1.015

−22.017 21.637 −9.515 8.724

0.000 0.000 0.000 0.000

0.895 0.945 0.885

1.118 1.058 1.129

−21.868 20.504 −9.850 9.644 4.382

0.000 0.000 0.000 0.000 0.000

0.861 0.941 0.818 0.912

1.161 1.063 1.223 1.096

B

Standard deviation

Constant Air temperature

−7.641 0.297

0.350 0.012

0.541

Constant Air temperature Wind velocity

−7.060 0.286 −0.361

0.352 0.012 0.047

0.564

Constant Air temperature Wind velocity Mean radiant temperature

−7.790 0.255 −0.443 0.051

0.354 0.012 0.047 0.006

0.591

Constant Air temperature Wind velocity Mean radiant temperature Relative humidity

−8.527 0.245 −0.457 0.059 0.013

0.390 0.012 0.046 0.006 0.003

0.598

Collinearity statistics

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The human metabolic rate, extra human mechanical work and clo values of clothing are set to 1.2 met (1 met = 58.2 W/m2 ), 0 and 0.3, respectively. The regression analysis results are shown in Table 5. Table 5 indicates that the correlation coefficients of the four models are all greater than 0.5, suggesting moderate correlations. The correlation coefficient gradually increases from model 1 to model 4. Model 1 contains only air temperature, model 2 adds wind velocity and model 3 adds mean radiant temperature to model 2. Model 4 including all the four elements and exhibits the largest correlation coefficient of r = 0.598. This value suggests that the air temperature, relative humidity, wind velocity and mean radiant temperature all have influence on the thermal sensation. Therefore, we believe that model 4 is the best model. Model 4 provides the ASV *(actual sensation vote) equation for the summer outdoor thermal comfort index model of Guangzhou as: ASV∗ = 0.245T˛ − 0.457v + 0.059Tmrt + 0.013RH − 8.527

(8)

where Ta is the air temperature (◦ C), v the wind velocity (m/s), Tmrt the mean radiant temperature (◦ C) and RH the relative humidity (%). 4. Conclusions This paper presents the results of an outdoor thermal comfort study conducted in Guangzhou. The key conclusions of this study are as follows: The slope of the curve representing the relationship between the respondents’ thermal sensitivities and SET* variation is 0.205/◦ C, while the neutral temperature measured is 23.9 ◦ C. Guangzhou residents possess different temperature variation sensitivities compared to individuals from different climate zones. The humidity sensation vote shows that 46.7% of the responses are neutral while only 14.75% of the responses are humid, suggesting that the residents are not sensitive to humidity. The SET* distribution and associated probability were obtained using a logistic regression model. The preferred temperature was determined to be 23.7 ◦ C, 0.2 ◦ C cooler than the neutral temperature. The 80% and 90% acceptable upper temperature limits correspond to 31.10 ◦ C and 28.54 ◦ C, respectively. Both of these temperatures are significantly higher than the thermal comfort standards suggested by ASHRAE55-2013 (26.8 ◦ C) and ISO7730. The acceptable upper limit temperature of 31.7 ◦ C in Singapore is also significantly higher than the standard values, indicating that residents of subtropical and tropical climate zones adapt to thermal environment variations better than residents of other regions. An outdoor thermal comfort evaluation model ASV* was developed. The model is based on the situation of Guangzhou campus during the summer, and provides a reference for local outdoor environmental designers. Acknowledgments This research was supported by the Important Science & Technology Specific Projects in Guangdong Province (No. 2012A010800042, 2012A010800018 and 2012A010800019), the National Natural Science Foundation of China (No. 51108103) and the Foundation of State Key Lab of Subtropical Building Science (No. 2012KB24).

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