Evaluation of thermal comfort in two neighboring climatic zones in Eastern India—an adaptive approach

Journal Pre-proof

Evaluation of thermal comfort in two neighboring climatic zones in Eastern India – an Adaptive ApproachBy Samar Thapa , Madhavi Indraganti PII: DOI: Reference:

S0378-7788(19)31733-5 https://doi.org/10.1016/j.enbuild.2020.109767 ENB 109767

To appear in:

Energy & Buildings

Received date: Revised date: Accepted date:

4 June 2019 18 October 2019 7 January 2020

Please cite this article as: Samar Thapa , Madhavi Indraganti , Evaluation of thermal comfort in two neighboring climatic zones in Eastern India – an Adaptive ApproachBy, Energy & Buildings (2020), doi: https://doi.org/10.1016/j.enbuild.2020.109767

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.

Highlights 

   

Conducted ASHRAE class II Protocol based thermal comfort field survey studies in 10 different buildings in 5 neighboring locations experiencing two different climatic region, i.e. Hot and humid climate and Cold climate Clothing Insulation was significantly higher in cold climatic region than in hot and humid region Comfort temperature in cold climatic region was significantly lower than that in hot and humid region Variation of proportion of use of fans is provided by a logistic regression equation Variation of proportion of wearing of warm clothing is provided by a logistic regression equation

1

Evaluation of thermal comfort in two neighboring climatic zones in Eastern India – an Adaptive Approach By 1

Samar Thapa , Madhavi Indraganti2 1 2

Department of Environmental Studies, Salesian College, Darjeeling (India); Architecture and Urban Planning Department, Qatar University, Doha, Qatar

Thermal comfort standards determine the sustainability and comfort in buildings. In order to determine the comfort conditions in the real environment and to include the adaptability of the subjects, numerous field survey-based comfort studies were conducted across the globe. Apart from the four environmental factors and two personal factors that affect thermal comfort, several interpersonal and nonthermal factors affect adaptation. However, previous studies reported from different climatic conditions also had their subjects as different natives, thus involving subjects with different interpersonal factors altogether. In this paper, we present the results of a cross-sectional thermal comfort study conducted with 436 subjects having close interpersonal factors, i.e. similar group of people in close geography but separated by two different climates, i.e. hot and humid type and cold type. Clothing insulation was significantly higher in cold climatic region than in the hot and humid region, while comfort temperature was significantly lower in the cold climate than in the hot and humid region. Compared to the published reports, the comfort temperatures varied widely: 18.4 °C to 36.1 °C in the hot and humid region and 11.1 °C to 30.1 °C in the cold climate region. We present a logistic regression model to predict the probability of fans running in the hot and humid region given the temperature variation. Also, presented is the probability of wearing of warm clothing with plummeting temperature. ABSTRACT

Keywords – Hot and Humid; Cold; Interpersonal factors; Clothing Insulation; Comfort; Preference; Logit Model 1. INTRODUCTION

Thermal balance in human beings involves different and complex mechanisms, like metabolism, thermodynamics, heat and mass transfer and individual behaviour. However, the deep body temperature of a normal human being varies around 36.5 °C – 37.5 °C [1]. It is essential that the indoor environmental and thermal conditions inside the buildings are good enough to maintain the mental and physical well-being and maximize productivity level. In an air-conditioned building, the indoor environment is maintained at a particular set point condition irrespective of the outdoor ambient condition, using some external energy [2]. In fact, 48 % of total energy consumption in building sector is used for providing indoor comfortable condition [3]. In contrast, naturally ventilated (NV) buildings use natural measures like windows, curtains, overhangs, orientation, fans etc., to provide comfortable conditions indoors, and thus are lesser energy intensive. However, if these NV buildings are improperly designed the indoor conditions may become uncomfortable which may lead to retrofits with heating and air conditioning devices. This may further lead to higher energy consumption in these buildings [4]. Thus, defining the thermal comfort conditions is required for the success of a building not only to make the occupants comfortable, but also to conserve energy. As per ASHRAE‟s standard 55 [5], thermal comfort is that state of mind which expresses satisfaction with the thermal environment. Two models are usually used to determine the thermal comfort inside a building. First is the climate chamber based Fanger‟s predicted mean vote (PMV) – percentage predicted dissatisfied (PPD) model [6]. The international standards like the ASHRAE Standard 55 [5] and ISO 7730 [7] are based on this climate-chamber based model for determination of thermal comfort. However, Nicol and Humphreys [8] found that the PMV – PPD model often either overestimates or underestimates the actual condition felt by the subjects in real indoor environments of warm or cold climates, respectively. Nicol [9] found that the adaptation in subjects leads to amelioration of thermal discomfort in stressful condition. The

2

adaptive principle states that if any change occurs that causes discomfort to the subjects, they react in ways, which try to restore back their comfort [8]. 1.1 Literature Review – Indian Scenario In order to accurately determine the thermal comfort conditions of the indoor environment, several field survey-based studies were conducted across the globe [10 - 12]. In order to include the adaptive effects, de Dear and Brager [13] provided revisions to the ASHRAE Standard 55 [5] using the database of 160 buildings located in 4 continents. However, Indian data was not a part of this database. It may be noted that India has a wide diversity in geography, climate, culture, ethnicity, etc., which affect adaptation of occupants. In India however there are no thermal comfort standards, yet. The National Building Code [14] prescribes two narrow limits, 23 °C – 26 °C for summers and 21 °C – 23 °C for winters for all buildings and across all climatic regions. Further these temperature ranges are only recommendatory and not mandatory in nature. However, in India, several isolated reports based on adaptive thermal comfort field studies were published [15 – 19]. Even though the NBC [14] classifies India into 5 climatic zones, i.e. hot and dry, warm and humid, moderate, composite and cold, most of the thermal comfort studies in India were conducted either on hot and humid region [4] and [15] or in composite climate [16] and [17], respectively. Very few studies report the cold climate of India. Manu et al. [18] reported a study based on all five climates and had its cold climatic data from Shimla in the western Himalayas which is comparatively drier than the study of Singh et al. [19], which reports from three different climates of north east India and had cold and cloudy climate of Cherapunji which is more humid. Though NBC [14] doesn‟t differentiate between the cold and dry or cold and cloudy type of climate, the eastern Himalayas which has a higher cloud cover are relatively humid. In these regions, the seasonal temperature variation is narrower than day-night variation than in comparison to that in the western Himalayas which is drier [20]. Further, the above studies [18] and [19] had fewer responses for the said climatic region. In cold climate region outside India, Wang [21] found the comfort temperatures of 20.9 °C and 21.9 °C and a clothing insulation of 1.33 clo and 1.42 clo for male and female, respectively in Harbin, Northeast China. Similarly, Luo et al. [22] found the comfort temperature to be as low as 17.2 °C in Shanghai. Shengxian and Ming [23] found that the PMV values were lower in rooms facing north than in rooms facing south in Yunnan Province. In the Himalayan region, Rijal et al. [24] found that the comfort temperature decreased with elevation in different districts of Nepal and consequently their overall comfort also decreased with elevation. Subjects used variety of measures like butter tea to make themselves more comfortable in the cold climate. Fuller et al. [25] reported that the indoor thermal comfort level was poor and far below what is internationally acceptable in the Himalayan houses of Nepal. Secondly, apart from the four environmental parameters and two personal parameters, which affect thermal comfort [6], several others inter and intra personal factors affect thermal adaption among subjects [26]. Maiti [27] found that in tropical natives there is no dripping of sweat which results in more effective heat loss. However, most of the studies report on the basis of variation in climate, since past experience is a more prominent factor in adaptation [28]. Further previous studies in different climatic region involved different natives, thus the interpersonal factors were also different. Cao et al. [29] reported a field study with subjects belonging to north and south of River Yangtze and living in Beijing. However, very few studies are reported from climatically diverse regions involving occupants having similar inter personal factors of adaptation like nativity, origin, culture, ethnicity etc. 1.2 Objectives of the Study The district of Darjeeling in Eastern India, though geographically small (3149 km²) [30] has interesting geographic and climatic diversity. The three international borders of Nepal, Bhutan and Bangladesh surround this small region experiences (figure 1). It experiences two climatic conditions, first, the sub Himalayan region having cold climate as per NBC [14] or Subtropical highland oceanic climate (Cwb) as per Koppen 3

climate classification and second, the terai plains having hot and humid climate as per NBC [14] or Subtropical humid (Cwa) as per Koppen. A field study was conducted in 10 different buildings at 5 different locations having different elevations and two different climatic conditions, i.e. hot and humid climate and cold climate in Darjeeling district in the year 2015. An earlier paper [31] presented the results of the study with the difference in elevation in a macro scale. The current paper presents the difference in thermal comfort in two different climatic regions but with the subjects having close interpersonal characteristics like ethnicity, culture, tradition, fashion, etc. which is due to their close locations. All the locations are lesser than 40 km apart. The difference in clothing insulation, thermal sensation votes, thermal preference votes and comfort temperature between the people of hot & humid and cold climate are presented in this paper. 2. THE RESEARCH DESIGN

The ASHRAE Standard 55 [5] class II protocol was followed while conducting the thermal comfort survey in these 10 different buildings at different places in Darjeeling Hills. The indoor air temperature (T i), indoor globe temperature (Tg), relative humidity (RH) and air movement (Va) were measured at a height of 1.1 meters above the floor using pre calibrated WBGT heat stress meter and a hand-held anemometer while the respondents filled up the subjective questionnaire [31]. The buildings that were surveyed included classrooms, offices and residential living spaces. Though these buildings were functionally different and in thermal comfort studies such combination of data from different buildings are least desired, the cold climate in India are prevalent in high altitude region only. Hence, it was felt essential to conduct study at different locations having different elevation to make the study more representative. Further, it was constraining to get access to functionally similar buildings at all locations; such combination of data was inevitable. However, such combination of data from buildings of different types is previously seen in the literature by Dhaka et al. [16] and Kumar et al. [17]. In the present study, these investigated buildings were situated in two different climatic zones as per the National Building Council (NBC) [14]. The details of the buildings investigated are illustrated in table 1. All the investigated buildings were naturally ventilated. The first location, i.e. S0135 is in Siliguri under Darjeeling district where 8 different classrooms and 2 halls were investigated in a college building. The location lies in the terai plains of Darjeeling district, which has hot and humid climate as per the NBC [14] and experiences hot and humid condition during the warm season, i.e. from March to October and moderately cool season between November and February. The investigated building had envelope as 1.5 cm plaster – 12 cm burnt brick – 1.5 cm plaster and the top roof was not exposed, since there was an auditorium above, which was not investigated. The remaining locations, i.e. K1420, M1640, S1950 and T2565 are in Kurseong, Mirik, Sonada and Tiger Hills, all are sub Himalayan town located in Darjeeling district. These locations fall under cold climatic region as per NBC. However, all the five locations are close by, i.e. within the same district and within 40 km by road from each other, the exact distance being even lesser (figure 1) due to the contour in the hilly terrain. It was thus observed that subjects of all the locations and both the climatic condition are similar to one another in terms of factors like ethnicity, culture, habit, etc. The investigated buildings in these locations include classrooms, office buildings and residential houses. The residential buildings in K1420 and the office buildings in M1640 had the building envelope in layers of 1.5 cm plaster – 12 cm burnt brick – 1.5 cm plaster. The top exposed roofs in these buildings were galvanized iron sheet with a false ceiling in the interior. The campus building in S1950 had the building envelope in layers of 1.5 cm plaster – 12 cm hollow brick – 1.5 cm plaster and roofs of all the investigated spaces in this building were not exposed as there was an auditorium above, which was not investigated. It was interesting to note that the buildings in the two climatic regions under investigation did not differ much except that the residential houses in T2565 had tin-sheet outer envelope with wooden inner layer. They had relatively smaller openings and windows. Further, the height of these houses were relatively smaller (lesser than 3 meters), respectively. The mean radiant temperature (T mrt) was calculated using the relation (1) given by ASHRAE Standard 55 [5]. 4

(

(

)

)

(1)

Where, ϵ stands for emissivity of the black globe and D is the diameter of the globe both provided by the manufacturer. The indoor environmental conditions are represented by the indoor operative temperature (T op) in most of the studies [16], [17] which is given by ASHRAE Standard 55 [5] as eq. (2), respectively. , where,

⁄

(2)

Where, hc and hr are the convective and radiation heat transfer coefficient of the clothed body in W/m² °C. However, A is taken as 0.5 for air movement below 0.2 m/s, 0.6 for air movement 0.2 – 0.6 m/s and 0.7 for air movement 0.6 – 1.0 m/s for most practical purpose [5]. It was interesting to note that, only in the building that was investigated in hot and humid climate, i.e. S0135 ceiling fans were present, whereas all the other buildings which were located in cold climate region were devoid of any fans. Further the fans in S0135 were observed to be functioning only during the warm season and were not in use during the cool season. Thus, in majority of the case, still air condition prevailed. Since, the air movement was measured using a pre calibrated hand held anemometer whose resolution was that of 0.1 m/s, an air movement lower than 0.1 m/s could not be detected. However, there exists air movement relative to human body arising due to bodily movement and convection current even during still air condition [32]. In this regard, Humphreys [33] had earlier found that the air movement will have appreciable effect on the comfort temperature only when the velocity is above 0.1 m/s (eq. 3). (3)

Hence, the still air condition was taken as 0.1 m/s as done in previous studies [32]. As the investigated buildings were located in rugged location each having varying elevation and terrain, the outdoor temperature varied differently in different locations. Thus, the daily outdoor maximum and minimum temperature for each location were measured using a digital hygrometer located outside each location at a height of over 2 meters from the ground level in a shaded location. The average of the daily outdoor maximum and minimum temperature gave the daily mean temperature. However, the outdoor environmental data collected for each location was cross-verified with the online available data, (http://www.worldweatheronline.com). The influence of past experience of environmental condition upon the adaptive model of thermal comfort is of importance. The running mean of outdoor temperature (Trm) which was calculated as in eq. (4) below, represents the past experience of environmental condition [8]. (4)

Where, Tr-n is the mean outdoor air temperature before nth day to the day of observation and α is a constant such that 0 < α ≤ 1, and a higher value of α represents a more significant effect of past temperature. The value of α till α < 0.90 does not affect the correlation between outdoor temperature and the comfort temperature [28]. Thus a 30 – day running mean of outdoor air temperature was calculated taking α = 0.80 [28]. The questionnaire had three sections. First the bio physical data of each respondent, i.e. gender, age, height and weight were noted. This information was noted only once at the beginning of the survey. Table 2 illustrates the anthropological data of the respondents who were investigated. Since several subjects denied giving response regarding their bio physical information, the sample size (N) varied in each case of table 2. In the hot and humid climate, the mean age of the respondents was 19.2 years (N 114, sd 1.40 years), whereas in cold climate the mean age was 25.7 years (N 291, sd 10.33 years). Further, this difference in age was statistically significant, t (df 403, p<0.001): -6.653. The respondents in the hot and humid climate consisted of mostly college students whereas those in cold climate included college students, office workers and residents, which explain the lesser variation in age among the subjects in hot and humid climate. The mean height of the subjects in hot and humid region was 166 cm (sd 9.67 cm, N 96) whereas that in cold region was 159.3 cm (sd 9.90 cm, N 251) and this difference was also statistically significant, t (df 345, p<0.001): 5.662. In fact, the respondents of S0135 and S1950 were college students of the same institution and showed similar difference in height, as found in an earlier study [34]. This higher average 5

height in the subjects of hot and humid climate calls for a further research on the growth rate of individuals in different climatic conditions. The mean weight of subjects in hot and humid climate was 56.4 kg (sd 10.89 kg, N 86) while that in cold climate was 56.3 kg (sd 10.68 kg, N 263). This difference however was not significant. Thus, the body mass index (BMI) which was calculated as the ratio between the weight and the square of the height of subject had a mean value of 20.5 kg/m² (sd 3.28 kg/m², N 80) in hot and humid climate and a mean value of 22.3 kg/m² (sd 4.12 kg/m², N 240) in cold climate, respectively. It is pertinent to mention here that 18.5 kg/m² ≤ BMI < 25.5 kg/m² is considered as normal [35]. In the next section of the questionnaire, the thermal comfort responses on thermal sensation and thermal preference regarding the indoor air temperature, relative humidity and the air movement were noted in the monthly survey. The thermal sensation was noted in the seven-point ASHRAE scale, i.e., -3 cold, -2 cool, -1 slightly cool, 0 neutral, 1 slightly warm, 2 warm and 3 as hot, respectively. The thermal preference was noted in five-point Nicol Scale, i.e., -2 much warmer, -1 a bit more warmer, 0 no change, 1 a bit cooler and 2 much cooler respectively. In this section, the responses on overall comfort, self-judged productivity, thermal acceptability and level of perspiration (sweating or shivering) were also queried. The overall comfort was judged in 6 point scale, ranging from 1 as very comfortable to 6 as very uncomfortable. Similarly, the self judged productivity was measured in 5 point scale, from +2 as much higher than normal to -2 as much lower than normal. The thermal acceptability was measured either as acceptable or unacceptable. The sweating and shivering level was judged in 7 point scale, i.e. -3 highly shivering, -2 moderately shivering, -1 slightly shivering, 0 neither shivering nor sweating, 1 slightly sweating, 2 moderately sweating and 3 highly sweating, respectively. In the last section of the questionnaire, a checklist of clothing was provided where the respondents indicated the garment worn by them at the time of the survey. The clothing insulation value was obtained from ASHRAE standard 55 [5] in clo. As some of the Indian clothing like salwar kameez, saree etc is not listed in ASHRAE Standard 55 [5], their respective clo value were calculated as per the eq. (5) below given by Hanada et al. [36]. (5) Where W is the weight of the garment in grams. Also the upholstery insulation was added as per ASHRAE Standard 55 [5] for the seated respondents. In this section of questionnaire, the list of activities was provided where the subjects could indicate the activities they were involved in the last half hour. The metabolic activity level in met was calculated as the mean activity of the respondents in the last half hour as per ASHRAE Standard 55 [5]. The monthly survey was conducted in all the buildings in the year 2015 at a frequency of 4 – 6 weeks wherein 436 subjects (192 female and 244 male) participated voluntarily. Several readings were taken in the same day from the subjects but at a gap of about 3 hours. Though the same subjects were tried to be retained for survey in all the months, some subjects denied taking part later and left while some new subjects joined in. Hence the sample size varied in each month. Though, the survey was conducted in each building every month, in order to have sufficiently data for analysis, the data were combined in two primary season prevalent in both the climatic region, i.e. warm season from March to October, when the fans were functional in hot and humid region. The second season was cool season from November to February when the fans in hot and humid region were not used. Secondly, this seasonal classification is also based on the fact that the office buildings in M1640 which functions under the Govt. of West Bengal, India has a working time till 5:30 PM in the warmer months, i.e. March to October and till 4:30 PM in the cooler months, i.e. November to February respectively. The data of the monthly survey were stored in excel files. The outliers were removed along all the columns which were outside ± 2.5 times the standard deviation. Table 3 illustrates the sample size in each climatic region investigated and in each location during the two seasons. For analysis of the data, SPSS version 20 and R version 3.3.1 were used. The automatic linear modeling and binary logistic function under 6

regression in SPSS were used to obtain the linear and logistic regression as discussed in this paper. Further, multilevel perceptron under neural network in SPSS and Stanford University‟s Machine Learning Code in GNU Octave version 5.0 was used to obtain the neural network model. 3. RESULTS AND DISCUSSION

3.1 Physical and Environmental Measurements A total of 2608 responses were obtained from 436 subjects. The mean age of the subjects was 23.8 years (standard deviation, s.d.= 9.27 years, N = 405), mean height 161.1 cm (s.d. = 10.27 cm, N = 347) and the mean weight 56.4 kg (s.d. = 10.72 kg, N = 349), respectively. 3.1.1 Outdoor environmental Condition

Table 4 illustrates a brief summary of the indoor and outdoor air temperature in the two climatic regions studied during different seasons. During the cool season, the mean outdoor air temperature varied between 18.5 °C - 23.6 °C (mean = 21.1 °C, s.d. = 2.36 °C, N = 224) in the lower elevated region having hot and humid type of climate and between 5.7 °C – 18.3 °C (mean = 11.6 °C, s.d. = 2.68 °C, N = 684) in the cold climatic hilly region. This difference in mean outdoor air temperature between the two climatic region during the cooler season was statistically significant, t = 47.483 (d.f. = 906, p<0.001). Similarly, during the warm season, the mean outdoor air temperature varied between 26.5 °C - 31.3 °C (mean = 29.8 °C, s.d. = 1.37 °C, N = 346) in the lower elevated hot and humid climatic region and between 11.1 °C – 24.0 °C (mean = 17.4 °C, s.d. = 3.54 °C, N = 1354) in the hilly cold climatic region, respectively. This difference in mean outdoor air temperature between the two climatic region during the warmer season was also statistically significant, t = 63.98 (d.f. = 1698, p<0.001). It was interesting to note that the variation in outdoor mean air temperature was more evident in the hilly cold climatic region (18.3 °C) than that in the hot and humid climate (12.3 °C). The higher relative humidity could be a reason which forbade the rapid change in temperature in hot and humid climate especially during the warm season which includes the monsoon, as the water molecules in air which have higher heat capacity acts as heat sink or source [37]. 3.1.2 Indoor environmental conditions Table 4 also shows the details of variation of indoor operative temperature (T op) in the two climatic regions. During the cooler season, the indoor operative temperature varied between 22.4 °C – 29.0 °C (mean = 25.0 °C, s.d. = 1.38 °C, N = 224) in the lower hot and humid region and between 9.0 °C – 21.0 °C (mean = 16.1 °C, s.d. = 2.16 °C, N = 684) in the hilly region having cold climate. This difference in mean operative temperature during the cooler season in the two climatically different surveyed locations was statistically significant, t = 58.0 (d.f. = 906, p<0.001). During the warmer season the indoor operative temperature varied between 23.7 °C – 35.1 °C (mean = 30.2 °C, s.d. = 2.61 °C, N = 346) in the hot and humid region and between 12.3 °C – 26.9 °C (mean = 20.3 °C, s.d. = 3.19 °C, N = 1354), respectively. This difference in mean operative temperature during the warmer season was also statistically significant, t = 53.6 (d.f. = 1698, p<0.001). It was interesting to note that unlike outdoor air temperature, the indoor operative temperature varied lesser during the cool season in both the climatic regions. This is due to the fact that during the cooler months most of the openings like windows and doors are kept closed. Consequently, a lesser variation in indoor operative temperature is noted than during the warmer months when most of the windows are open and as a result, the indoor conditions vary more closely with the outdoor conditions. It was also interesting to note that the ceiling fans were present only in the locations of lower elevation, i.e. S0135 having hot and humid climate. Even in this location these ceiling fans were operational only during the warmer season. The mean air velocity was 0.65 m/s (s.d. = 0.516 m/s, N = 346) during the warmer season in the hot and humid climate buildings while in all the other cases the still air condition prevailed, i.e. 0.10 m/s. 7

During the cooler season, which is drier, the indoor relative humidity (RH) varied between 30.5 % 71.3 % (mean = 51.7 %, s.d. = 10.05 %, N = 224) in the lower hot and humid region and between 33.1 % 82.1 % (mean = 60.4 %, s.d. = 11.03 %, N = 684) in the elevated cold climatic region. This difference in mean RH % between the two climatically different region during the cool season was statistically significant, t = 10.45 (d.f. = 906, p<0.001). Whereas during the warmer season which include the monsoons the indoor relative humidity (RH) varied between 31.4 % - 90.9 % (mean = 70.1 %, s.d. = 11.07 %, N = 346) in the lower hot and humid zone and between 49.1 % - 94.0 % (mean = 72.2 %, s.d. = 8.36 %, N = 1354) in the higher cold climatic region. This difference in mean RH % between the two climatically different region during the warm season was also statistically significant, t = -3.97 (d.f. = 1698, p<0.001). It was however interesting to note that during both the seasons, the indoor RH % in the higher cold climatic region was slightly higher than that in the lower hot and humid region. This is explained as follows. The relative humidity is the ratio between the partial vapour pressures of moisture in the air to the equilibrium vapour pressure at air – water interface (also known as saturation vapour pressure) at that temperature. With the decrease in temperature the saturation vapour pressure decreases resulting in the increase of relative humidity with the given amount of water vapour in air [37]. 3.1.3 Clothing Insulation It has been seen in previous studies, that subjects effectively use clothing insulation, i.e. add or remove clothing layers in order to adjust with the environmental condition [15]. Figure 2 illustrates the mean clothing insulation in the two climatic regions in the two seasons. During the cool season, the clothing insulation varied between 0.29 clo – 1.34 clo (mean = 0.81 clo, s.d. = 0.210 clo, N = 224) in the lower elevated hot and humid region and between 0.21 clo – 2.30 clo (mean = 1.13 clo, s.d. = 0.320 clo, N = 684) in the hilly cold climatic region. The difference in mean clothing insulation between the two climatic condition region was statistically significant, t = -13.88 (d.f. = 906, p<0.001). Similarly, during the warm season, the clothing insulation varied between 0.07 clo – 1.46 clo (mean = 0.53 clo, s.d. = 0.137 clo, N = 346) in the hot and humid region and between 0.12 clo – 1.97 clo (mean = 0.81 clo, s.d. = 0.301 clo, N = 1354) in the cold climatic region, respectively. This difference in mean clothing insulation between the two climatic zones was also statistically significant, t = -16.58 (d.f. = 1698, p<0.001). It was noticed that the variation in clothing insulation was lesser in hot and humid type of climate than in cold climate in both the seasons, with that in the warm season being even lesser. This is due to the lesser opportunity subjects have at higher temperature, i.e. the minimum amount of cloth covering as decided by the culture and the dress code, whereas at lower temperature subjects can change the inner layer of clothing. Thus at lower temperature the variation of cloth covering is seen to be higher [34]. In the cold climatic region investigated, there were 4 locations, K1420, M1640, S1950 and T2565 with varying elevation, i.e. 1420 m, 1640 m, 1950 m and 2565 m above mean sea level, respectively. It was found in previous study [31] that the factors of thermal comfort in elevated region varied with the elevation of the location of the study. Thus, anticipating group wise variation in clothing insulation within the cold climatic region, testing of null (level 0, no predictors) model was done using SPSS. We obtained the grand mean of the intercept, γ00 = 0.908, however, the interclass correlation coefficient (ICC) <0.05 and Z test obtained a value of Z = 1.395 (p = 0.163) for intercept with variance as different locations. Therefore the null hypothesis that each of the variances within the group is zero was retained and as a result the multi-level modeling was not performed. Secondly, the authors acknowledge the fact that there can be possible effect of clustering of responses from each subject, since the same subject provided multiple responses, which makes the study of variation between participants essential. However, the objective of the present paper is to illustrate the difference in thermal comfort factors between similar groups of people in two different climatic region and secondly, the variation between participants in terms of their body mass index (BMI), age and gender have been previously reported [38]. Therefore, the present paper does not provide subject-wise analysis. We consider the database as a large transverse survey data. 8

The subjects used clothing insulation as a measure of adaptation, which is illustrated in the in figure 3. It is seen that the change in clothing insulation with operative temperature follows a polynomial regression which asymptotes to the minimum clothing insulation value usually dictated by the dress code or culture of the place [15]. Eq. (6) – (7) illustrates a polynomial regression between the clothing insulation value and the indoor operative temperature in the two climatic conditions. The negative correlation coefficient illustrates that the subjects using clothing as a measure for thermal adaptation decreased the clothing insulation with the increase in outdoor air temperature and vice versa. Hot & Humid:

(6)

Cold:

(7)

Previously, Takasu et al. [39] had found similar negative coefficient between clothing insulation and outdoor air temperature among Japanese subjects (eq. (8)). (8) 3.2 Subjective thermal responses 3.2.1 Thermal Sensation Votes (TSV) The thermal sensation votes (TSV) were obtained from the subjects in a 7-point ASHRAE scale, during the survey, simultaneous to the measurement of the indoor environmental parameters by the researcher. A total of 570 sensation votes were obtained in the hot and humid climatic region and 2038 sensation votes were obtained in the cold climatic region. Figure 4 illustrates the percentage variation of the thermal sensation votes in the two climatic regions in the two seasons. A significant difference in the thermal comfort response between the two climatic regions was noticed, χ² (df = 6, N = 2608): 97.852, p<0.001 with higher responses for slightly warmer sensation in hot and humid region and for slightly cooler sensation in the cold climatic region, respectively. In the hot and humid region the maximum response was for slightly warm (28.6 %), followed by neutral (24.4 %), slightly cool (24.2 %), cool (11.6 %), cold (2.5 %) and hot (1.8 %), respectively. In the cold climatic region, the maximum response was for slightly cool sensation (29.2 %), followed by neutral (28.4 %), slightly warm (16.7 %), cool (4.8 %), cold (1.8 %) and hot (0.3 %), respectively. During the cool season, the mean TSV was -0.41 (s.d. = 1.101, N = 224) in the hot and humid climate and -0.55 (s.d. = 1.086, N = 684) in the cold climate. However, this difference was not statistically significant. During the warm season, the mean TSV was 0.15 (s.d. = 1.346, N = 346) in the hot and humid climate and 0.11 (s.d. = 0.961, N = 1354) in the cold climate, respectively. This difference in mean TSV during the warm season was statistically significant, t = 4.089 (d.f. = 1698, p<0.001). This illustrates the fact that during the warm season which includes humid monsoon the hilly cold climatic region are significantly cooler than the hot and humid plains. This also explains the phenomenon of people moving towards hilly region during these warm seasons, in search for relief from the hot and humid condition in the region. 3.2.1.1 TSV Modeling using Neural Networks Thermal sensation is a feedback process which is non linear in nature. Though, adaptation is treated as a black box with the field study measured thermal sensation vote (TSV) being the determining factor for the comfort temperature [9], models based on field study data would help in future prediction for similar bio-climatic setup on adaptive basis. Secondly, the machine learning models returns a robust model when there are sufficient data to train the model. Thus, the TSV which is a 7 (seven) categorical variable were regrouped into 3 (three) groups as per the classification of ASHRAE Std. 55 [5] as follows: -3 (cold) and -2 (cool) as „cold uncomfortable‟, -1 (slightly cool), 0 (neutral) and +1 (slightly warm) as „comfortable‟ and +2 (warm) and +3 (hot) as „hot uncomfortable.‟ This increases the number of training data for each category. A neural network is a way to imitate the functioning of a human brain, by a learning process using information [40]. A multi-class (one versus all) classification was performed using feed-forward neural networks having gradient descent as the back propagation algorithm. The input features contained categorical 9

variables of season (1: cool and 2: warm), climate (1: hot & humid 2: cold), gender (1: female and 2: male) and continuous variable of clothing insulation (in clo), activity level (in met), air movement (in m/s), relative humidity (in %) and indoor operative temperature (in °C). The output had a three category, i.e. cold uncomfortable (1), comfortable (2) and hot uncomfortable (3), respectively. A four layered neural network consisting of two hidden layers and one output layers as represented in figure 5(a) was constructed using SPSS version 22 and cross-verified using GNU Octave version 5.0. The first hidden layer had 7 neurons while the second hidden layer had 5 neurons, respectively. Both the hidden and the output activation function were the sigmoid activation function (figure 5 (c)). 71.7% of the sample was used for training the network, while 14.1 % and 14.2 % for testing and cross-validation, respectively. An accuracy of 76.8 %, 76.6 % and 76.8 % was obtained for training, testing and the cross-validation data-set, respectively. The details of weights and bias of the different layers (2 hidden layers and 1 output layer) are illustrated in table 5. However, in a classification problem having unbalanced class size (77.2 % of the sample size were for class 2, i.e. comfortable in the present study), the accuracy alone would not truly define the performance as the classifier would predominantly predict the majority class. In such cases, ROC curves which is the plots of true positive rate (the ratio of correctly predicted positive class to actual number of positives) versus the false positive rate (the ratio of incorrectly predicted positive class to the actual number of negatives) provides a better performance measure if it passes through the upper left corner, in figure 5 (b). The area under the curve (AUC) for such classifier is high (~ 1.0). Conversely, if the ROC curve is near the diagonal line, the classifier does a poor job of separating the classes and thus the AUC is only 0.5 which means that the classifier only does a random guessing. 3.2.2 Thermal Preference (TP) The thermal preference of the subjects was recorded using Nicol‟s five-point scale, i.e., -2 much warmer, -1 a bit more warmer, 0 no change, 1 a bit cooler and 2 much cooler respectively. A total of 570 thermal preference votes regarding indoor temperature were obtained from the hot and humid climatic region and 2037 sensation votes were obtained from the cold climatic region. Figure 6 illustrates the percentage variation of the thermal preference votes about indoor temperature in the two climatic regions in the two seasons. A significant difference in the thermal preference between the two climatic region was noticed, χ² (df = 5, N = 2608): 473.518, p<0.001 with a more response preferring a cooler sensation in the hot and humid region and more response preferring warmer sensation in cold climate, respectively. In the hot and humid region maximum response was for no change (41.6 %) followed by a bit cooler (33.9 %), a bit warmer (16.8 %), much cooler (6.7 %) and much warmer (1.1 %), respectively. Similarly in the cold climatic region, the maximum response was for no change (46 %), followed by bit warmer (43.2 %), bit cooler (6.3 %), much warmer (4.1 %) and much cooler (0.3 %), respectively. During the cool season, the mean TP was 0.03 (s.d. = 0.792, N = 224) in the hot and humid climate and -0.74 (s.d. = 0.610, N = 684) in the cold climate. This difference in the mean thermal preference in the cool season between the two climatic region was statistically significant, t = 15.07 (d.f. = 906, p<0.001). Similarly during the warm season, the mean TP was 0.45 (s.d. = 0.861, N = 346) in the hot and humid climate and -0.29 (s.d. = 0.680, N = 1353) in the cold climate, respectively. This difference in the mean thermal preference during the warmer season in the two climatic region was statistically significant, t = 17.115 (d.f. = 1697, p<0.001), respectively. 3.3 The Thermal Neutrality 3.3.1 Comfort Temperature The comfort temperature was calculated using Griffiths‟ method (eq. (9)) below as followed by previous researchers [16], [18]. (9) Where, TnG is called the Griffith‟s neutral temperature, Top indoor operative temperature, TSV the thermal sensation vote and R the Griffiths‟ Constant. Nicol and Humphreys [32] prescribe the use of regression 10

coefficient of over 0.40. Hence previous studies [16], [18] have used the value of Griffiths‟ constant as 0.50, which we have followed in this study too. The regression based neutral temperature corresponds to the operative temperature at neutral sensation along the regression line between operative temperature and thermal sensation [15]. Unlike the regression model, the Griffiths‟ method does not return an extraneous value of neutral temperature. However, the Griffith‟s neutral temperature is based on the fact that a 4 °C, 3 °C and 2 °C of adjustment in operative temperature occurs per unit change in thermal sensation with the value of R as 0.25, 0.33 and 0.50, respectively. Table 6 illustrates the mean comfort temperature in the two climatic regions. It was interesting to note that during the cool season, the comfort temperature ranged between 18.4 °C – 33.8 °C (mean = 25.8 °C, N = 224, s.d. = 2.49 °C) in the lower plains of hot and humid climate and between 11.0 °C – 22.3 °C (mean = 17.2 °C, N = 684, s.d. = 2.230 °C) in the hilly region having cold climate. This difference in mean comfort temperature between the two climatic regions in the cooler months was statistically significant, t = 49.41 (d.f = 906, p<0.001). Likewise during the warm season, the comfort temperature varied between 22.1 °C – 36.5 °C (mean = 29.9 °C, N = 346, s.d. = 2.50 °C) in the lower plains of hot and humid climate and between 12.3 °C – 30.1 °C (mean = 20.5 °C, N = 1354, s.d. = 2.94 °C) in the hilly region with cold climate. This difference in mean comfort temperature between the two climatic regions in the warmer months was also statistically significant, t = 54.81 (d.f = 1698, p<0.001). The low comfort temperature (~ 11 °C) exhibited by the subjects of cold climate during the cold season represents the fact that the hilly people were well adapted to the cold climate [31]. They used a variety of measures to make themselves warmer like the use of multiple layers of warm clothing as discussed below in section 3.7.2 or the increased intake of hot drinks and moving to warm places during winters as discussed in previously presented paper [37]. Similarly, the subjects in hot and humid region exhibited a higher comfort temperature during the warmer months. The warmer months are also represented by higher humidity. As discussed by previous researchers [15], there was an increased use of fans to compensate the hot and humid condition. Further, a higher comfort temperature (~ 36.5 °C) in comparison to studies in composite climate [16, 17] illustrates the fact that the subjects in hot and humid region are more concerned about humidity than the temperature. 3.3.1.1 Variation of Comfort Temperature with indoor condition The adaptation of subjects resulting due to the constant exposure in a certain condition is illustrated by their variation of comfort temperature with exposed temperature. Figure 7 illustrates the variation of comfort temperature (TnG) with the indoor operative temperature (Top) in the two climatic regions. The comfort temperature (TnG) obtained using the Griffiths‟ method closely followed the indoor operative temperature (Top) during the survey in both the climatic region (eq. (10) – (11)). This close relationship between the comfort temperatures (T nG) of the subjects with the indoor operative temperature (Top) illustrated by a high slope in these equations, reiterates the fact that the subjects adapted according to the variation in indoor environment at which they were constantly exposed to. (10) (11) Figure 7 also shows the variation of comfort temperature with indoor temperature for few other studies conducted in India. A relatively higher coefficient in the present study than that in Indraganti [15] illustrates that the comfort temperature closely follows the indoor operative temperature. Though, the subjects used various measures to adapt with the indoor condition, the adjustment in the clothing insulation is the primary one. An inverse relationship is seen between the monthly mean values of clothing insulation and comfort temperature in figure 8. It is seen that the clothing insulation decreases in the warmer months while the comfort temperature increases during the same period, which reaffirms the fact that the subjects used adjustments in the clothing insulation as a primary measure to adapt to the changing environmental condition. 3.3.1.2 Variation of Comfort Temperature with outdoor condition 11

In a naturally ventilated building, the indoor temperature (Top) closely follows the outdoor temperature (Tout). The indoor condition in which a subject is constantly exposed to is thus affected by the outdoor temperature. Thus, the comfort temperature follows the outdoor temperature too. Figure 9 illustrates the variation of comfort temperature (T nG) with outdoor mean air temperature (Tout). The comfort temperature showed a significant positive correlation with the outdoor mean air temperature for the survey day in both type of climatic condition (eq. (12) – (13)). (12) (13) Figure 9 also illustrates the variation of comfort temperature with outdoor temperature in few previous studies, for e.g., de Dear and Brager [13] had found the relation between the comfort temperature and the outdoor temperature with the data of 160 buildings located in 4 continents, Manu et al [18] had found the Indian Model of Adaptive Comfort from the data of 5 different climatic region, while Dhaka et al [16] had given the relation between comfort temperature and outdoor temperature in the composite climate of Jaipur, India. It was however interesting to note that a wider range of comfort temperature was seen in the present study (11.1 °C – 36.1 °C, table 6) in comparison to previous studies mentioned above, i.e., de Dear & Brager [13]: 21.5 °C – 30.5 °C, Manu et al. [18]: 19.6 °C – 28.5 °C and Dhaka et al. [16]: 16.7 °C – 34.8 °C, respectively. The wide variation in comfort temperature in the present study illustrates the fact that the subjects were highly adaptive. There was an effective use of fans in hot and humid climate, while clothing insulation played a major role in cold climate. Secondly, a possible reason could be that, in these eastern Himalayan regions encountering a relatively higher humidity than its western counterpart, the day night variation in temperature is more pronounced than the seasonal variation [37]. This frequent variation in temperature which the subjects encounter, make them more tolerant towards temperature fluctuations and hence showed a wider range of comfort temperature than as compared to the previous studies [13], [16] and [18]. 3.3.2 Plotting of Comfort temperature in Psychrometric chart The ASHRAE Standard 55 [5] provides the graphical comfort zone for estimation of indoor environment, with certain restrictions, i.e. activity level 1.0 – 1.3 met, and clothing insulation between 0.5 – 1.0 clo. The restriction is also laid in the environmental parameters, i.e. humidity ratio less than 0.012 (kg of H 2O/ kg of dry air) and air movement less than 0.2 m/s. Figure 10 (a) and (b) illustrate the comfort votes, i.e. the operative temperature corresponding to TSV -1, 0 and +1 [5] on the ASHRAE graphical comfort zone for the warm and humid climates and cold climates respectively. For the still air condition, the ASHRAE Standard 55 [5] prescribes a lower temperature limit of 20 °C from 20 % RH to 80 % RH represented by a straight vertical line in figure 10 (a) and the upper limit varying from 27 °C for a RH limit of upto 50 %, again represented by a vertical black line, above which it decreases to 25 °C at 80 % RH, which is represented by a slanting line in figure 10 (a), respectively [17]. Though the upper RH limit is due to the thermal factor of effective evaporative cooling, the lower limit on RH is based on nonthermal factors like drying of mucous membrane, etc. However, as seen in the preceding section that fan were used in hot and humid climate resulting in a higher air movement, while increased clothing insulation was used in cold condition. Thus, the upper temperature limits in operative temperature increase with the increase in air movement, which are shown as additional vertical lines beyond 27 °C in figure 10(a), respectively [17]. The corresponding comfort votes as determined by the field study are also shown accordingly. It was seen from figure 10 (a), that the comfort temperature of the subjects for an increased air movement were outside the comfort zone as prescribed by the standard. This reveals the efficiency of increased air movement in combating warm and humid condition is underestimated by the present standard. Similarly, in cold climate, the ASHRAE standard 55 [5] graphical comfort zone is determined by the clothing insulation, which extends the permissible lower and upper limits in operative temperature as in eq. (14) – (15). 12

⁄ ⁄

(14) (15)

Where, Tmin and Tmax are the lower an upper limit in operative temperature for clothing insulation, Icl (clo). In figure 10 (b) comfort temperature for activity level 1.0 – 1.3 MET, air movement upto 0.1 m/s are shown for different clothing level, i.e. 0 – 0.5 clo, 0.5 – 1.0 clo and 1.0 – 1.79 clo, respectively. Also, the respective ASHRAE comfort band for 0.5 clo level, 1.0 clo and 1.79 clo are shown. Both figure 10 (a) and (b) illustrate a huge majority of the comfort votes above the ASHRAE comfort zone. This shows that the current ASHRAE model under predicted the acceptable humidity level for indoor occupants, if we neglect the non thermal factors like difficulty in drying of cloths, etc. 3.4 Thermal Acceptability The thermal acceptability (TA) was measured as a binary variable on ASHRAE scale, with 0 for acceptable and 1 for not acceptable. A total of 2573 thermal acceptability responses were obtained out of which 88.1 % responses accepted the indoor condition. In the hot and humid climate 570 responses for TA were obtained out of which 81.2 % were under acceptable, whereas in cold climate a total of 2003 responses for TA were obtained out of which 90.0 % were acceptable. 3.5 Sweating and Shivering Sweating is a mechanism by which human body loses heat by the evaporation of sweat and takes place usually in warm climate. In contrast, shivering is a mechanism where the metabolic level is increased thereby more heat is generated to compensate with the heat loss in the cooler climate. The sweating and shivering were judged using a seven-point scale, i.e. -3 highly shivering, -2 moderately shivering, -1 slightly shivering, 0 neither shivering nor sweating, 1 slightly sweating, 2 moderately sweating and 3 highly sweating, respectively. A total of 2516 responses for shivering / sweating were obtained with 570 responses for hot and humid climate and 1946 responses for cold climate, respectively. Figure 11 illustrates the percentage variation of the shivering / sweating votes in the two climatic regions in the two seasons. A significant difference in the shivering / sweating response between the two climatic region was noticed, χ² (df = 6, N = 2516): 204.893, p<0.001 with higher responses for sweating sensation in hot and humid region and for shivering sensation in the cold climatic region, respectively. 3.6 Comfort and Self-Judged Productivity Overall comfort (figure 12) was queried in a six-point scale: 1 very comfortable, 2 moderately comfortable, 3 slightly comfortable, 4 slightly uncomfortable, 5 moderately uncomfortable and 6 very uncomfortable, respectively. A total of 570 and 2034 responses regarding overall comfort were received in hot and humid climate and cold climate, respectively. A significant difference in overall comfort response between the two climatic regions was noticed, χ² (d.f. = 5, N = 2604): 107.942, p<0.001 with a comparatively higher response for moderately comfortable in hot and humid climate and for slightly comfortable in cold climate, respectively. The self-judged productivity (figure 13) was asked using a five-point scale, i.e. -2 much lower than normal, -1 slightly lower than normal, 0 normal, 1 slightly higher than normal and 2 much higher than normal, respectively. A total of 570 and 2033 responses for self-judged productivity were received in the hot and humid climate and cold climate, respectively. A significant difference in self-judged productivity among the subjects in the two climatic regions was noticed, χ² (d.f. = 4, N = 2603): 21.674, p<0.001, with a slightly higher than normal productivity in cold climate and slightly lower than normal productivity in hot and humid climate, respectively. 13

The higher comfortable votes in hot and humid region and higher productivity votes in cold climate reveal the fact that the subjects are lesser comfortable in cold climate, but the productivity is higher. This finding is accordance to earlier studies by Manu et al. [18] and Maiti [27] that the Indian subjects are lesser tolerant to cooler condition than towards warm condition. 3.7 Adaptive measures taken by subjects 3.7.1 Use of fans in warm and humid climate It was seen that in the hot and humid region, fans were used during the warm season. The forced convection caused due to moving air provides comfort in hot and humid region and has been well advocated in thermal comfort literature [15]. In order to estimate the proportion of fans running at a particular temperature, a logistic regression analysis was conducted with the condition that “on” state of fans represented by 1 and “off” state was represented by 0. The relationship between the probability of fans running (p) and the temperature is expressed as logit model as expressed below (eq. (16) – (17)). {

}

(16)

⁄ ⁄{ } (17) where, p is the probability of fans open, while b and c are constants determined by performing a probit regression for the Logit function, using the condition, that fan is open [41]. The variation of proportion of fans open with indoor operative temperature (T op) and outdoor mean temperature (T out) are illustrated in figure 14 (a) and (b), respectively. We obtained the logistic regression equations for the proportion of fans running for the indoor operative temperature, T op and mean outdoor air temperature, T out as eq. (18) and (19), shown below. The equations also show the standard error (SE at 95 % CI level) and Negelkerke R² for the regression. (18) (19) A high correlation coefficient (R²) in eq. (18) and (19) reveals the strong adaptation of the subjects with the changing temperature. Figure 14 also illustrates the previous relation regarding the running of fans with indoor and outdoor temperature given by Rijal [42] from the subjects of Japanese buildings and by Indraganti et al. [43] in the office buildings of hot and humid climate in India. It can be noted from figure 14 (a) that at an indoor operative temperature of 30.0 °C, 61.4 % of fans were running in the present study, 51.2 % in Rijal [42] and 92.0 % in Indraganti et al. [43]. It is often advocated that the fans provide forced convectional cooling which is an effective measure to lose heat in hot and humid environment [43]. However, among the two studies in hot and humid climate, a lesser proportion of fans were running in the present study in comparison to that in Indraganti et al. [43]. This indicates either the subjects in the present study were greatly adapted to the hot and humid condition of the region or the region encountered a lesser humidity level than in comparison to Indraganti et al. [43], as the latter study falls in tropical region. 3.7.2 Use of warm clothing during cold climate It is often noticed that people use clothing as a first line of defense to combat with cold weather. As seen in section 3.1.3 above, the subjects effectively used clothing as a measure for adaptation. In order to estimate the percentage of people wearing warm clothing at a particular indoor operative temperature, a logistic regression was conducted with the condition of wearing of warm layer like sweater / jacket / coat / jumpers in addition to a single layer of clothing that is essential to cover the torso as determined by the culture of the region or the dress code of the institution was denoted as 1, otherwise 0. The relationship between the probability of subjects wearing warm clothing (p) and the indoor operative temperature is expressed in similar logit model as expressed in eq. (16) – (17) above, where p is the probability of subjects wearing warm clothing, while b and c are constants determined by performing a probit regression for the Logit function, using the condition, that subjects are wearing warm clothing. Figure 15 (a) shows the proportion of subjects wearing warm clothing 14

over the torso with the variation of indoor operative temperature in the two climatic regions investigated. We obtained the logistic regression equations for the proportion of subjects wearing warm clothing for the indoor operative temperature, T op as eq. (20) – (21), below. The equation also shows the standard error (SE at 95 % CI level) and Negelkerke R² for the regression. Warm and Humid: Cold Climate:

(20) (21)

In naturally ventilated buildings the variation in indoor temperature is governed by that in the outdoor air temperature. Thus, the variation of proportion of people wearing warm clothing with the outdoor mean temperature in the two climatic regions is shown in figure 15 (b). Warm and Humid: Cold Climate:

(22) (23)

It can be noticed from figure 15 (a), that at an indoor operative temperature of 20.0°C almost 94.7 % of the subjects in hot and humid region were wearing warm clothing, while at the same temperature only 78.5 % of subjects were found to wear warm clothing in cold climate region. This lower proportion of subjects wearing warm clothing in cold climate illustrates the fact that the subjects in cold climate were more adapted to the lower indoor operative temperature than that in the hot and humid climate, respectively. With plummeting of temperature a single layer of warm clothing is not often sufficient. Thus, in cold climate, people usually wear more than one layer of warm clothing over their torso, i.e. sweater or pullover or even thermal wear inside outer layer such as coat, jackets or even overcoats. Figure 16 shows the proportion of subjects wearing single (outer) and double (inner) layer of warm clothing in the cold climate region. Thus, in cold climate, we obtained the logistic regression equations for the proportion of subjects wearing more than one layer or inner layer of warm clothing for the indoor operative temperature, T op as eq. (24) below. Additional layer of warm clothing in cold climate: (24) Thus, from figure 16, it is seen that at an average indoor temperature of 15.0 °C in the cold climatic region investigated in this study, around 94.8 % of the subjects would be wearing warm clothing over their torso like sweater, jackets, pullover, etc. However at the same temperature around 53.2 % of the subjects would be wearing more than one layer of warmer clothing like high-necks, thermal, sweater etc underneath the outer layer like jacket, coat, overcoats, etc. This multiple layer of clothing worn by the subjects of cold climate during the cool season allowed them to exhibit a low comfort temperature. This reiterates the fact in cold condition the primary measure of adaptation by the respondents was increased level of clothing insulation. The equations (22) – (24) would thus be helpful to determine the amount of clothing essential in the climatic condition under investigation given the operative temperature. 4. CONCLUSIONS A thermal comfort study following ASHRAE class II protocols was performed in ten different buildings located in two different climatic regions, i.e. hot and humid climate and cold climate in Darjeeling district of India. The differences in clothing insulation, thermal sensation and thermal preference votes and comfort temperature between the subjects at these two closely situated climatic regions are presented in this paper. 1. The clothing insulation was significantly higher in cold climatic region than in hot and humid region in both the seasons. However, the variation in clothing insulation was lesser in the hot and humid region, especially in warmer months due to the constraints posed by culture and dress code to the subjects.

15

2. A significant difference in the thermal comfort responses between the two climatic regions was noticed, with a higher response for slightly warmer sensation in hot and humid region and for slightly cooler sensation in the cold climatic region, respectively. 3. A significant difference in the thermal preference between the two climatic regions was noticed, with a higher response preferring a cooler sensation in the hot and humid region and more response preferring warmer sensation in cold climate, respectively. 4. The comfort temperature varied between 18.4 °C to 36.1 °C in the hot and humid region and between 11.1 °C to 30.1 °C in the cold climate region. 5. There was a significantly higher sweating sensation in the hot and humid climate and higher shivering sensation in the cold climate. Though, the research was a first of its kind in this region, it had some limitations. First, the buildings that were investigated in this study were functionally different and in thermal comfort studies such combination of data from different buildings is least desired. However, it was constraining to get access to functionally similar buildings at all locations of study which are different in terms of elevation. Thus, any further study with same type of buildings would strengthen the cause of study. Secondly, the study was conducted only during the day time due to lack of access. In cold climate region, the extreme temperature is reached during the night time, hence longitudinal studies involving night time surveys would bring about a clearer picture. Nevertheless, it is expected that the findings of this research and various models presented would help building designers, engineers and architects in this region and in regions elsewhere with similar bio-climatic conditions for determination of temperature required to be maintained indoors for comfortable condition. Thus, they will have a window by which they can bring about changes in the design of the buildings before actual construction to meet the above condition. In addition to the thermal comfort factors, several non thermal comfort factors could be estimated, e.g. Darjeeling is one of the tourist destination and using the models given in the paper one can easily estimate the amount of clothing he or she need to carry about. Further, a similar study in cold climatic region of western Himalayas which experiences a lesser humidity level and thus higher variation in seasonal temperature would make the comparisons more robust. ACKNOWLEDGEMENT We wish to thank Dr. Mahesh Bundele, Poornima University for his immense help during the review of research papers. We also thank our supervisors Dr. Ajay Bansal, Central University of Haryana and Dr. Goutam Kr. Panda, Jalpaiguri Government Engineering College for their valuable help and suggestions. We would also like to thank Prof. Michael Humphreys, Prof. H.B. Rijal and Dr. Jyotirmay Mathur for their technical help and support. Further, we extend our gratitude to all the 436 subjects who voluntarily participated and contributed with the thermal comfort responses. FUNDING:

No funding was received for the project

CONFLICT OF INTEREST:

There are no conflict of interest.

7. REFERENCES 1.

H.T. Hammel, J. B. Pierce, Regulation of internal body temperature, Annual Review Physiology, 1968, vol. 30 (1), pp. 641 – 710, doi:10.1146/annurev.ph.30.030168.003233 (ISSN 0066-4278)

2.

ASHRAE Handbook – Fundamentals, 2017

3.

Bureau of Energy Efficiency, Energy Conservation Building Code, 2016

4.

A.K. Mishra, M. Ramgopal, An adaptive thermal comfort model for the tropical climatic regions of India (Koppen climate type A), India, Elsevier, Building and Environment, 2015, vol. 85, pp 134 - 143 16

5.

ASHRAE Standard 55, 2013, Thermal Environmental Conditions for Human Occupancy, Atlanta GA, American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE Standard 55 – 2013)

6.

P.O. Fanger, Thermal Comfort, Analysis and Applications in Environmental Engineering, McGraw Hill, New York, 1972

7.

ISO 7730, Moderate Thermal Environment – Determination of PMV and PPD indices and Specifications of the conditions for thermal comfort, International Organization for Standardization, Geneva, Switzerland, 2005

8.

J.F. Nicol, M.A. Humphreys, Adaptive thermal comfort and sustainable standards for buildings, 2002, Elsevier: Energy and Buildings, vol. 34 (6), p. 563 – 572

9.

Fergus Nicol, Adaptive thermal comfort standards in the hot-humid tropics, Elsevier, Energy and Buildings, 2004, vol. 36, pp 628 – 637

10. S. Mors, J.L.M. Hensen, M.G.L.C. Loomans, A.C. Boerstra, Adaptive thermal comfort in primary school classrooms: Creating and validating PMV-based comfort charts, Elsevier, Building and Environment, 2011, vol. 46, pp 2454 – 2461 11. R.V. Andersen, J. Toftum, K.K. Andersen, B.W. Olesen, Survey of occupant behaviour and control of indoor environment in Danish dwellings, Elsevier, Energy and Buildings, 2009, vol. 41, pp 11 – 16 12. S.A. Damaiti, S.A. Zaki, H.B. Rijal, G. Wonorahardjo, Field Study on Adaptive Thermal Comfort in Office Buildings in Malaysia, Indonesia, Singapore and Japan during Hot and Humid season, Elseiver, Building and Environment, 2016, vol. 109, pp 208 -223 13. Richard de Dear, G.S. Brager, Thermal comfort in naturally ventilated buildings: revisions to ASHRAE 55, 2002, Elsevier: Energy and Buildings, vol. 34, p. 549 – 561 14. BIS, Bureau of Indian Standards, National Building Code, 2016 15. Madhavi Indraganti, Ryozo Ooka, Hom B. Rijal, Gail S. Brager, Adaptive model of thermal comfort for offices in hot and humid climates of India, 2014, Elsevier, Building and Environment, vol. 74, p. 284 – 295 16. S. Dhaka, J. Mathur, G. Brager, A. Honnekeri, Assessment of thermal environmental conditions and quantification of thermal adaptation in naturally ventilated buildings in composite climate of India, Elsevier, Building and Environment, 2015, vol. 86, pp. 17 - 29 17. S. Kumar, J. Mathur, S. Mathur, M. K. Singh, V. Loftness, An adaptive approach to define thermal comfort zones on psychrometric chart for naturally ventilated buildings in composite climate of India, 2016, Elsevier, Building and Environment, vol. 109, p. 135 – 153 18. S. Manu, Y. Shukla, R. Rawal, L. E. Thomas, RJ de Dear, Field studies of thermal comfort across multiple climate zones for the subcontinent: India Model for Adaptive Comfort (IMAC), 2016, Elsevier, Building and Environment, vol. 98, p. 55 – 70 19. M.K. Singh, S. Mahapatra, S.K. Atreya, Adaptive thermal comfort model for different climatic zones of North East India, Elsevier, Applied Energy, 2011, vol. 88, pp 2420 – 2428 20. M.K. Singh, S. Mahapatra, S.K. Atreya, Development of bio-climatic zones in north-east India, Elsevier, Energy and Buildings, vol. 39, pp 1250 – 1257 21. Z. Wang, A field study of the thermal comfort in residential buildings in Harbin, Elsevier, Building and Environment, 2006, vol. 41, pp 1034 – 1039 22. M. Luo, B. Cao, X. Zhou, M. Li, J. Zhang, Q. Ouyang, Y. Zhu, Can personal control influence human thermal comfort? A field study in residential buildings in China in Winter, Elsevier, Energy and Buildings, 2014, vol. 72, pp 411 – 418 23. W. Shengxin, L Ming, Field Survey and Analysis of Student Flat Indoor Thermal Environment in winter, IEEE, International Conference on Energy and Environment Technology, 2009, pp 168 - 171 24. H.B. Rijal, H. Yoshida, N. Umemiya, Seasonal and regional differences in neutral temperatures in Nepalese traditional vernacular houses, Elsevier, Building and Environment, 2010, vol. 45, pp 2743 – 2753 17

25. R.J. Fuller, A. Zahnd, S. Thakuri, Improving comfort levels in a traditional high altitude Nepali house, Elsevier, Building and Environment, 2009, vol. 44, pp 479 – 489 26. Z. Wang, Richard de Dear, M. Luo, B. Lin, Y. He, A. Ghahramani, Y. Zhu, Individual difference in thermal comfort: A literature review, Elsevier, Building and Environment, 2018, vol. 138, pp. 181 – 193 27. R. Maiti, Physiological and subjective thermal response from Indians, Elsevier, Building and Environment, 2013, vol. 70, pp 306 – 317 28. K. J. McCartney, J.F. Nicol, Developing an adaptive control algorithm for Europe: results of the SCAT‟s project, 2002, Elsevier: Energy and Buildings, vol. 34 (6), p. 623 – 635 29. B. Cao, Y. Zhu, Q. Ouyang, X. Zhou, L. Huang, Field Study of human thermal comfort and thermal adaptability during summer and winter in Beijing, Elsevier, Energy and Buildings, 2011, vol. 43, pp 1051 - 1056 30. District

Profile,

DM

Office,

Darjeeling

(URL:

http://darjeeling.gov.in/admin_rpt/Annual_Admin_Report201112.pdf) 31. S. Thapa, A.K. Bansal, G.K. Panda, M. Indraganti, Adaptive thermal comfort in the different buildings of Darjeeling Hills in eastern India – Effect of difference in elevation, Elsevier, Energy and Buildings, 2018, vol. 173, pp 649 677 32. Nicol J.F., Humphreys M.A., 2010, Derivation of the adaptive equations for thermal comfort in free running buildings in European standard EN15251, Elsevier: Building and Environment, vol. 45, p. 11 – 17 33. Humphreys M.A., A simple theoretical derivation of thermal comfort conditions, Journal of the institute of Heating and Ventilating Engineers, 1970, vol. 33, pp. 95 – 108 34. S. Thapa, A.K. Bansal, G.K. Panda, Adaptive thermal comfort in the two college campuses of Salesian College, Darjeeling – Effect of difference in altitude, Elsevier, Building and Environment, 2016, vol. 109, pp 25 – 41 35.

S. Yazdanirad, H. Dehghan, Y. Rahimi, M. Zeinodini, M. Shakeriyan, The relationship between overweight and heart rate in hot and very hot weather under controlled condition, Journal of Health Scope, vol. 4 (iss. 4), 2015

36. Hanada, K., Mihira, K. & Sato, Y., 1983, Studies on the thermal resistance of men‟s underwear, Journal of Japan Research Association for Textile End-Users, vol. 24(8), pp. 363 – 369 37. S. Thapa, A.K. Bansal, G.K. Panda, Adaptive thermal comfort in the residential buildings of north east India – An effect of difference in elevation, Springer, Building Simulation, 2017, DOI: https://doi.org/10.1007/s12273-0170404-x 38. S. Thapa, Insights into the thermal comfort of difference naturally ventilated buildings of Darjeeling, India – Effect of gender, age and BMI, Energy and Building, 2019, vol. 193, pp 267 - 288 39. M. Takasu, R Ooka, H.B. Rijal, M. Indraganti, M.K. Singh, Study on adaptive thermal comfort in Japanese offices under various operation modes, Building and Environment, June, 2017, vol. 118, pp 273 - 288 40. T. Songuppakarn, W. Wongsuwan, W. San-um, Artificial Neural Networks Based Prediction for Thermal Comfort in an Academic Classroom, International Conference and Utility Exhibition 2014 on Green Energy for Sustainable Development, Pattaya City, Thailand, 19 – 21 March, 2014 41. J. F. Nicol, M.A. Humphreys, A Stochastic Approach to Thermal Comfort – Occupant Behavior and Energy Use in Buildings, ASHRAE Transaction, 2004 42. H.B. Rijal, Field Investigation of Comfort Temperature and Adaptive Model in Japanese Houses, PLEA2013 – 29th Conference, Sustainable Architecture for a Renewable Future, Munich, Germany, 10 – 12 September, 2013 43. M. Indraganti, R. Ooka, H.B. Rijal, Thermal comfort in offices in India: Behavioral adaptation and the effect of age and gender, Energy and Buildings, 2015, vol. 103, pp 284 - 295

18

Figure 1: Geographical map of Darjeeling district showing the survey locations (Legends indicates the elevation of the location)

Figure 2: Seasonal Variation of mean clothing insulation in the two climatic regions

19

Figure 3: Variation of clothing insulation with indoor operative temperature

Figure 4: Percentage distribution of thermal sensation votes in the two climatic regions

20

Figure 5: (a) Neural network architecture for the one-versus-all classification, (b) ROC curve showing the area under curve (AUC); and (c) Sigmoid transfer function used (θ: weight and X: feature)

Figure 6: Distribution of thermal preference vote in the two climatic regions

21

Figure 7: Variation of comfort temperature with indoor operative temperature

Figure 8: Variation of monthly mean clothing insulation (clo) and monthly mean comfort temperature (°C). Error bars indicate 95% confidence interval (CI)

Figure 9: Variation of comfort temperature with outdoor temperature 22

Figure 10: Comfort votes (TSV-1, 0 and+1) in (a) warm and humid climate and (b) cold climate

23

Figure 11: Percentage response of shivering / sweating in the two climatic regions in the two seasons

Figure 12: Overall comfort votes

Figure 13: Self Judged Productivity

24

Figure 14: Proportion of fans running in hot and humid climate at (A) different indoor operative temperature, Top and (B) different outdoor air temperature, Tout. The data points (black circles) represent the actual proportion of fans in use at the respective temperature bin.

25

Figure 15: Proportion of subjects wearing warm clothing in the two climatic regions with (A) indoor operative temperature, and (B) outdoor mean temperature. The data points (black circles and red triangles) represent the actual proportion of warm clothing worn by the subjects at the respective temperature bin.

Figure 16: Proportion of subjects wearing outer (single) and inner (multiple) layer of warm clothing. The data points (black circles and red triangles) represent the actual proportion of warm clothing worn by the subjects at the respective temperature bin. 26

Table 1: Details of the buildings investigated in the different climatic region (MSL: mean sea level, NV: naturally ventilated, GI: galvanized iron) Climatic Elevation Type of No of Envelop Windows Roof of Remark Zone / (above buildings storey / e investigate s Koppen MSL), investigate space d space (Topography latitude d investigate ) and d longitude Hot and S0135: NV 3 storey / 1.5 cm Aluminu Concrete Humid / Sub 135 m, college 8 class plaster – m slide (12 cm) – Tropical 26° 44‟ campus rooms & 2 12 cm with 4 unexposed Humid 47” N, hall brick – mm clear (Cwa) 88° 26‟ 1.5 cm glass (Plains) 40” E plaster Cold / Sub K1420: NV 3 storey / 4 1.5 cm Wooden GI with The Tropical 1420 m, residential living plaster – frame wooden east highland 26° 52‟ spaces 12 cm with 4 false wall of oceanic 58” N, brick – mm clear ceiling the (Cwb) 88° 16‟ 1.5 cm glass house 30” E plaster was below (Himalayan grade elevated up till region) the 1st floor M1640: NV Office 2 storey / 4 1.5 cm Aluminu GI with 1640 m, building office plaster – m slide wooden 26° 53‟ rooms 12 cm with 4 false 34” N, brick – mm clear ceiling 88° 11‟ 5” 1.5 cm glass E plaster S1950: NV 6 storey / 4 1.5 cm Aluminu Concrete 1950 m, college classrooms plaster – m slide (12 cm) – 26° 57‟ campus 12 cm with 4 unexposed 10” N, hollow mm clear 88° 16‟ brick – glass 59” E 1.5 cm plaster T2565: NV 1 storey / 4 Outer Wooden GI with 2565 m, residential detached tinned – (either wooden 26° 59‟ rooms inner unglazed false 40” N, wooden or with ceiling 88° 17‟ 6” layer small E (total opening width: to wall 12 cm) ratio)

27

Table 2: Summary of the anthropometric data of the investigated subjects in the two climatic regions (BMI: Body Mass Index, BMR: Basal Metabolic Rate, s.d.: standard deviation, N: sample size) Climati c Zone

Gend er

Hot and Fema Humid le Male Total Cold

Fema le Male Total

Combin ed

Fema le Male Total

Age (in years) Height (in cm) Weight (in kg) BMI (kg/m2) mea s.d. N mea s.d. N mea s.d. N mea s.d. N n n n n 19.2 1.38 61 159. 7.73 46 50.9 8.88 41 20.1 3.5 36 0 1 19.2 1.44 53 172. 6.25 50 61.5 10.1 45 20.8 3.0 44 4 1 8 19.2 1.40 11 166. 9.67 96 56.4 10.8 86 20.5 3.2 80 4 0 9 8 23.5 8.47 11 153. 7.68 95 50.6 9.19 97 21.7 4.3 88 2 2 3 27.0 11.1 17 162. 9.30 15 59.7 10.0 16 22.6 3.9 15 4 9 9 6 7 6 6 2 25.7 10.3 29 159. 9.90 25 56.3 10.6 26 22.3 4.1 24 3 1 3 1 8 3 2 0 22.0 7.16 17 155. 8.13 14 50.7 9.06 13 21.2 4.1 12 3 1 1 8 6 4 25.2 10.3 23 165. 9.55 20 60.1 10.0 21 22.2 3.8 19 4 2 2 6 8 1 5 6 23.8 9.25 40 161. 10.2 34 56.4 10.7 34 21.8 3.9 32 5 1 7 7 2 9 9 0

BMR (kcal/day) mean s.d. N 1333. 7 1638. 1 1501. 9 1281. 5 1515. 7 1432. 6 1296. 8 1542. 6 1449. 7

100.1 4 146.3 2 198.3 0 76.29

34

143.0 8 168.8 3 86.87

14 9 23 1 11 6 19 1 30 7

42 76 82

152.1 4 177.3 6

Table 3: Details of sample size in each climatic region and in each location in the two seasons

Season

Cool Season Warm Season Total

Climatic region investigated Hot & Cold Climate Humid S0135 K1420 M1640 224 150 159

S1950 195

T2565 180

Total 684

908

346

339

285

382

348

1354

1700

570

489

444

577

528

2038

2608

Total

28

Table 4: Summary of indoor and outdoor environmental variables in the two different climatic zones Climatic Seaso N Outdoor air temp. Indoor operative Indoor air Indoor Relative Zone n (Tout) in °C temp. (Top) in °C movement (m/s) humidity (RH %) mea s.d. Rang mea s.d. Rang mea s.d. Rang mea s.d. Rang n e n e n e n e Hot and Cool 224 21.1 2.3 18.5 25.0 1.3 22.4 0.10 0.00 0.10 51.7 10.0 30.5 Humid 6 – 8 – – 5 – 23.6 29.0 0.10 71.3 Warm 346 29.8 1.3 26.5 30.2 2.6 23.7 0.65 0.51 0.10 70.1 11.0 31.4 7 – 1 – 6 – 7 – 31.3 35.1 2.20 90.9 Yearl 570 26.4 4.6 18.5 28.2 3.3 22.4 0.43 0.48 0.10 62.9 13.9 30.5 y 2 – 7 – 4 – 6 – 31.3 35.1 2.20 90.9 Cold Cool 684 11.6 2.6 5.7 – 16.1 2.1 9.0 – 0.11 0.04 0.10 60.4 11.0 33.1 8 18.3 6 21.0 9 – 3 – 0.60 82.1 Warm 135 17.4 3.5 11.1 20.3 3.1 12.3 0.10 0.07 0.10 72.2 8.36 49.1 4 4 – 9 – 5 – – 24.0 26.9 1.50 94.0 Yearl 203 15.5 4.2 5.7 – 18.9 3.5 9.0 – 0.11 0.06 0.10 68.3 10.8 33.1 y 8 7 24.0 0 26.9 7 – 9 – 1.50 94.0 Combine Cool 908 14.0 4.8 5.7 – 18.3 4.3 9.0 – 0.10 0.04 0.10 58.2 11.4 30.5 d 7 23.6 4 29.0 3 – 2 – 0.60 82.1 Warm 170 19.9 5.9 11.1 22.3 5.0 12.3 0.22 0.32 0.10 71.8 9.02 31.4 0 5 – 6 – 7 – – 31.3 35.1 2.20 94.0 Yearl 260 17.8 6.2 5.7 – 20.9 5.1 9.0 – 0.18 0.27 0.10 67.1 11.8 30.5 y 8 8 31.3 9 35.1 1 – 4 – 2.20 94.0

29

Table 5: Weights and bias of different (2 hidden and 1 output) layers in the neural network architecture Hidden Layer 1 (Bias) Climate (1: hot & humid, 2: cold) Clothing (clo) Activity (met) Air movement (m/s) RH (%) Indoor op. temperature (°C) Gender (1: female, 2: male) Season (1: cool season, 2: warm) Hidden Layer 2 (Bias) H(1:1) H(1:2) H(1:3) H(1:4) H(1:5) H(1:6) H(1:7)

H(1:1) H(1:2) H(1:3) H(1:4) H(1:5) H(1:6) H(1:7) 1.092 1.722 -2.934 4.230 -0.432 0.323 1.582 -3.039

-4.785

-2.978

-3.696

1.610

-0.476

-0.760

-2.266 -2.114 4.542 6.390 -4.727 -1.871 -0.549

3.963 2.137 3.749 -2.473 -0.069 0.656 -3.805

3.086 -2.890 0.200 6.777 9.245 1.044 -0.379

4.598 0.000 -0.361 3.829 -8.327 -1.384 -1.867

-7.726 -4.423 -2.448 1.986 -6.028 3.801 2.098

2.129 -1.472 -2.088 -3.434 2.060 -1.298 -2.947

1.681 -3.819 -1.402 3.802 -1.830 -3.509 -1.096

H(2:1) H(2:2) H(2:3) H(2:4) H(2:5) 1.180 2.280 -0.255 -3.562 2.472 -0.712 -2.197 -1.991 7.602 -3.553 5.278 -0.891 -3.292 2.724 0.487 -4.341 -0.445 3.293 -7.888 -2.201 1.063 -1.344 -2.767 5.940 -4.976 2.040 -0.854 -2.242 7.730 5.656 -5.540 1.020 3.949 -1.898 -1.692 2.511 0.336 -0.938 1.443 6.654

Output Layer 3 Bias) H(2:1) H(2:2) H(2:3) H(2:4) H(2:5)

1: Cold 2: 3: Hot uncomfortable Comfortable uncomfortable 0.6415 -0.5396 1.5403 -1.7692 4.3540 -6.6333 -1.4387 -0.8387 2.1751 -0.6251 -5.2628 2.9826 5.7339 -8.3952 -4.0094 -3.1178 5.7718 -5.6410

30

Table 6: Summary of comfort temperature (Griffiths' method) in the two climatic regions Climatic Season Comfort temperature, Tcomf (°C) Zone mean s.d. N Range Hot and Cool 25.8 °C 2.49 °C 224 18.4 °C – 33.8 °C Humid Warm 29.9 °C 2.50 °C 346 22.1 °C – 36.5 °C Yearly 28.3 °C 3.19 °C 570 18.4 °C – 36.5 °C Cold Cool 17.2 °C 2.20 °C 684 11.0 °C – 22.3 °C Warm 20.5 °C 2.94 °C 1354 12.3 °C – 30.1 °C Yearly 19.4 °C 3.13 °C 2038 11.0 °C – 30.1 °C Combined Cool 19.3 °C 4.37 °C 908 11.0 °C – 33.8 °C Warm 22.4 °C 4.76 °C 1700 12.3 °C – 36.5 °C Yearly 21.3 °C 4.85 °C 2608 11.0 °C – 36.5 °C

31