Understanding differences in thermal comfort between urban and rural residents in hot summer and cold winter climate

Building and Environment 165 (2019) 106393

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Understanding differences in thermal comfort between urban and rural residents in hot summer and cold winter climate

T

Yan Xionga,c, Jianlin Liub,∗, Jungsoo Kimc a

Department of Architecture, School of Urban Design, Wuhan University, Wuhan, Hubei, PR China College of Environmental Science and Engineering, Donghua University, Shanghai, PR China c School of Architecture, Design and Planning, The University of Sydney, Sydney, Australia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Thermal comfort Field measurement Urban-rural differences Residential building Hot summer and cold winter

Rapid urbanization in China resulted in different architectural characteristics between the rural (traditional, more open to external environment) and urban (modern, relatively closed to external environment) residential building stock. Such differences can influence the way residents regulate indoor thermal environment to maintain their comfort, particularly in hot summer and cold winter (HSCW) climate. This study aims to better understand the differences in the perceptions of thermal comfort and related adaptive behaviours between urban and rural residents. Thermal comfort field experiments are conducted in the central region of China that has climatic characteristics of HSCW. A total of 513 and 2171 survey responses have been collected and matched with concurrent environmental measurements in typical residential buildings in Wuhan city (urban) and Luotuoao village (rural), respectively. The majority of survey respondents accept their indoor thermal environments, even though instrumentally measured physical conditions fall well beyond the comfort zone prescribed in the international standards. The differences of indoor thermal comfort between rural and urban residents are attributed to clothing habits, daily activity patterns, housing openness and potential thermal expectation. Results also indicate that rural residents tend to be more tolerant of cold conditions in winter but less tolerant of hot conditions in summer, compared to the urban residents. The findings are helpful in optimizing housing design in HSCW climate region in order to improve indoor thermal conditions.

1. Introduction In modern buildings, a large amount of energy is consumed in the provision of comfort by regulating the indoor thermal environment through air conditioning systems. As one of the effective approaches to reduce energy consumption and maintain a comfortable indoor environment, the adaptive thermal comfort model is prescribed in several international and national standards, such as ASHRAE Standard 55 [1], EN15251 [2], and Chinese Standard GB/T 50785–2012 [3]. These standards are mostly based on the recent studies of adaptive thermal comfort conducted in office environment across different climate zones, while some uncertainties still exist in the context of residential environments [4]. Thermal adaptation can be addressed through occupants' adjustments to the surrounding environment, including physiological, psychological and behavioural factors [5]. It is reasonable to assume that occupants' thermal adaptation process in residential settings can be different from that in office settings because of a higher degree of adaptive opportunities available to residents. It seems

∗

somewhat questionable that we directly apply the existing thermal comfort standards to residential buildings given the contextual difference between office and residential buildings, such as comfort expectation [6], dress-code and personal control [7]. Therefore, adaptive thermal comfort is worthy of further investigation in residential contexts. In this paper we focus on China's distinct differences in urbanrural residential settings resulted from rapid urbanization. China is a typically developing country in which approximately 45% of the population live in suburban and rural regions [8]. Local household registration policy and low income cause noticeable urbanrural differences in terms of its housing characteristics and living conditions. For instance, it is rare to have central heating or cooling systems in a building in rural areas in southern China. Rural residents are expected to make their own efforts to adapt to the local environment to minimize thermal discomfort both in summer and winter. On the other hand, air-conditioners in summer and heating devices in winter are readily available to those who live in urban areas. According to the previous study into thermal comfort of rural residents in hot and humid

Corresponding author. E-mail addresses: [email protected] (Y. Xiong), [email protected] (J. Liu).

https://doi.org/10.1016/j.buildenv.2019.106393 Received 28 June 2019; Received in revised form 19 August 2019; Accepted 2 September 2019 Available online 05 September 2019 0360-1323/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. Climatic zones defined by civil building design in China (left), and the investigated locations: Wuhan city and Luotuoao village (right).

conditions between urban and rural in the HSCW climate, which are critical for the building design. The present study aims to understand the differences in residents' thermal comfort and adaptive behaviours between urban and rural dwellings situated in the same climate zone (HSCW). The paper also addresses seasonal and regional differences by examining the objective indoor thermal conditions, the participants’ subjective responses (thermal sensation and preference), clothing behaviour and daily activities.

climate region, the possible causes of distinct thermal comfort conditions in rural areas are due to the local culture, residents' thermal expectation and their attitudes towards the environment [9]. Those nonclimatic factors, that can potentially influence residents’ thermal comfort and related behaviours, deserve further investigation in order to advance our understanding. China has five climatic zones according to the thermal design code for civil building [10], including severe cold zone (SC), cold zone (C), hot summer cold winter zone (HSCW), hot summer warm winter zone (HSWW) and mild zone (M) (Fig. 1). The HSCW zone is defined as regions where the coldest month's average outdoor temperature falls between 0 and 10°C and the hottest month's average outdoor temperature falls between 25 and 30°C [10]. The typical climate in this zone is described as hot and humid summer as well as cold and humid winter. Central heating is not provided in residential buildings both in cities and rural regions in this HSCW zone [11] as per the national heating policy [10]. This means that residents in this zone have to face challenges to maintain comfort indoors both in hot summer and cold winter. At the same time, while the HSCW zone in China is economically and culturally prosperous with high-dense population, the currently the energy demand for heating in this zone is only one-tenth of that of zones with central heating in northern China [12]. With residents' rising expectation of better thermal comfort in winter, the heating energy consumption of China has increased 4 times than that ten years ago [13]. Given the high population density and severe climate conditions, there is no doubt that the HSCW zone will face big challenges to cope with increasing energy demand especially for space heating over the course of its rapid urbanization. In recent years, thermal comfort investigations have been carried out in residential buildings in different climate zones in China, including SC zone [14–17], C zone [15,16,18], HSWW zone [15,16], HSCW zone [15,16,19,20] and M zone [15]. Comparative thermal comfort studies between urban and rural settings also have been carried out in several regions such as Beijing [21], Hunan [22], Liaoning [23], Turfan [24] and Guangdong [8,9]. In general, the previous findings indicated that climate conditions and living conditions can affect residents’ thermal comfort level. More specifically, residents in the HSCW zone are more adaptive and tolerant of poor thermal conditions presumably because of the long history of habitation under the local climate. Rural residents showed a higher acceptance to the given indoor thermal environment, registering a cooler winter comfort temperature in cold zones (SC & C) and a warmer summer comfort temperature in warm zones (HSWW), when compared against urban residents. However, the aforementioned studies have not systematically investigated the regional and seasonal differences in thermal comfort

2. Methods 2.1. Locations and investigated buildings Field measurements were conducted in Wuhan city (latitude 30°59′ north and longitude 114°31′ east) and Luotuoao village (latitude 30°78′ north and longitude 115°40′ east). Wuhan is the capital and biggest city in Hubei province, and Luotuoao is a typical rural village in Luotian county in Hubei. These two locations fall within the HSCW climatic zone as shown in Fig. 1. The investigated urban buildings are multistory apartments in Wuhan, while most of the investigated rural buildings were separated houses or townhouses (Fig. 2). The urban sample buildings were built between 1990 and 2005, as per the local building energy codes. The rural sample buildings were built between 1995 and 2010, without any building energy codes enforced. Although both urban and rural sample buildings were mainly constructed with concrete and bricks, there were some noticeable differences between the two groups. Firstly, the layouts of the rural buildings (houses or townhouses) were more open to external environments with larger surface areas (i.e. external walls and roofs) being directly exposed to the external condition, compared to the urban buildings (predominantly apartments). Front doors of the surveyed rural buildings always remained open during the field monitoring period regardless of seasons. In contrast, the urban sample buildings' front doors were closed in winter and partially closed in summer mainly because of privacy and security issues. Secondly, the urban buildings’ envelope system generally had higher heat resistance resulting from better airtightness and insulation than the rural ones. It would have benefited in reducing heat loss in winter and heat gain in summer. In contrast, the rural sample buildings typically were equipped with single-glazed windows, a large open front door and concrete flat roofs without any thermal insulation. All investigated buildings were in free-running mode during the field monitoring period, although some of them were equipped with split air-conditioning systems.

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Fig. 2. Indoor and outdoor photos of the typical sample buildings in (a) Wuhan (urban) and (b) Luotuoao (rural).

upon the completion of the survey. In this study, it should be noted that the urban investigation was conducted between 2008 and 2010, while the rural field study was done more recently from 2014 to 2015. We received less finical support from the funding body during our urban survey campaigns enabling us to recruit as many subjects and research assistants as the earlier one. Neverthless, the number of samples we collected for each subject groups was large enough (Rural n = 2171, Urban n = 513) to adequately represent householders in the regions we studied. The field data were collected, by utilizing the standard thermal comfort survey and simultaneous in-situ measurements of indoor and outdoor climatic parameters. Each measurements process lasted for 3 h, and the interval for the administration of thermal comfort questionnaires was minimum 30 min. TESTO 400 was installed in the center of the room near the participant (~1 m) and recorded the indoor environmental parameters, including air temperature (Tin , °C), radiant

2.2. Field measurements Due to the limited availability of instrument and human resources, field measurements carried out in urban and rural areas span across multiple years. Measurements in the urban sample buildings were conducted in Wuhan both in summer (July ~ August 2008) and winter (December 2008, January and February 2010). A total of 513 valid sets of samples comprised of objective and subjective data were obtained (301 sets for summer and 212 sets for winter). A total of 2171 rural samples were collected in summer (July 2014) and winter (Jan 2015) in Luotuoao, including 1540 and 631 valid data sets for summer and winter, respectively. The standard thermal comfort questionnaires (see Table 2) were administered to the participating householders. As per the ethical and research office of Wuhan University guidelines, all the participants were clearly explained about the full process prior to the survey, and each participant received a honorarium of 15 RMB per hour Table 1 Measurement ranges and accuracies of the used instruments. Description

instruments

Environmental parameters

Range

Accuracy

TESTO 400

Tin Tr RHin Va

-40∼150°C -40∼150°C 0∼100% 0∼20m/s

± 0.2°C ± 0.2°C ± 1% ± 0.03m/s

HOBO 12

Tout RHout

-20∼70°C 5∼95%

± 0.35°C ± 2.5%

3

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Table 2 Summary of questionnaire items and rating scale used in this study.

Table 3 Sample sizes categorized by region and season.

No.

Questionnaire item

Voting scale

1

Thermal Sensation Vote (TSV): Please tick your feeling on temperature right now?

Hot (3) Warm (2) Slightly warm (1) Neutral (0) Slightly cool (−1) Cool (−2) Cold (−3) Warmer (1) No change (0) Cooler (−1) Acceptable (0) Unacceptable (1) Too damp (1) A little damp (2) Just right (3) A little dry (4) Too dry (5) Too still (1) A little still (2) Just right (3) A little windy (4) Too windy (5)

2

Thermal Preference Vote (TPV): Would you like to be?

3

Thermal Acceptability Vote (TAV): Can you accept the current temperature? Humidity Sensation (HSV): Please tick your feeling on humidity right now?

4

5

Air Movement Sensation (AMS): Please tick your feeling on air movement right now?

Region

Numbers of Sample

Survey season

Numbers of Sample

Female (%)

Male (%)

Rural

2171

Urban

513

summer winter summer winter

1540 631 301 212

50.3 64.5 41.9 42.9

49.7 35.5 58.1 57.1

2.4. Data processing and analysis It should be noted that data uncertainties from subjective thermal votes are inevitable in thermal comfort field studies [29]. In this research, standardized thermal comfort questionnaires and measurement instruments were used. All the levels of significance for statistical analysis were set at p < 0.05 to guarantee the accuracy of results. Independent-Samples T Test was used to perform the mean comparison of each measured variable between the urban and rural sample groups. Pearson correlation analysis was used to evaluate the relevance between indoor and outdoor air temperatures. Linear regression was applied to establish the relation between mean TSV and PMV/SET. 3. Results

temperature (Tr , °C), relative humidity (RHin , %) and air velocity (Va , m/ s). HOBO 12 was set near the investigated building (~3 m) to record outdoor environmental parameters including air temperature (Tout , °C) and relative humidity (RHout , %). Table 1 shows the measurement range and accuracy of the using instruments, which all complied with ISO 7726 [25]. The measurement interval was set as 1 min. Meanwhile, the air temperature of Wuhan and Luotuoao during the investigation days were also recorded for reference from nearby weather stations. Indoor operative temperature (To ) was calculated considering the effects of air temperature, radiant temperature and air velocity, which was calculated as [1]:

To = ATin + (1 − A) Tr

3.1. Measured thermal environments and building thermal performance 3.1.1. Measured thermal environments Table 4 summarizes the measured indoor and outdoor thermal parameters categorized by the location and season. For indoor parameters, the urban group shows 1.6 °C higher mean Tin and To compared to the rural group in summer. In winter, there are no noticeable differences between the urban and rural samples in terms of Tin and To . For outdoor parameters, the urban group presents 3.7 °C higher mean Tout and 15.5% lower mean RHout compared with the rural group in summer. In contrast, in winter the urban group shows 2.0 °C lower mean Tout and 10.5% higher mean RHout . Because of low indoor air velocity and no obvious effect of radiation observed in the sample houses, the Paired-Sample T test shows that there is no significant difference between Tin and To . Therefore, in the subsequent analysis presented in this paper, the simple index of indoor air temperature Tin is widely used. However, To is still used whenever deemed necessary. In order to broadly characterize the investigated thermal environments, To measured at the time when each questionnaire has completed and plotted against the psychometric chart (Fig. 3). Red data points represent the urban group, while blue data points represent the rural group. Purple zone and pink zone respectively represent the comfort zone corresponding to 0.5 clo and 1.0 clo [1]. It is clear that almost all observations fall outside the comfort zone typically used for conventional HVAC office buildings. Since the present samples are collected in the free-running residential buildings, the data is also compared against the adaptive comfort model that prescribed for naturally-ventilated spaces in Fig. 4. All the data points are plotted and compared against the 80% acceptability ranges defined by ASHRAE standard 55 adaptive model (blue zone) [1], the adaptive model proposed for Sydney residential buildings (pink zone) [4], and acceptable thermal range defined by Chinese GB/T 50785 as Class II in free-running building in HSCW climatic zone (PPD≤25%) (grey zone). It is shown again that most of the investigated thermal environments in this study still fall beyond the adaptive model's 80% acceptability range, and the trend is more pronounced in winter.

(1)

where A was the coefficient of the air temperature and radiant temperature, depending on air velocity. A was 0.5 when Va below 0.2 m/s, while it was 0.6 when Va was between 0.2 m/s and 0.6 m/s. A equalled 0.7 if the value of Va was above 0.6 m/s.

2.3. Questionnaire The thermal comfort questionnaire used the standard “right-hereright now” type questions and it was written in Chinese. The questionnaire was designed to collect subjects' thermal responses to ambient thermal stimulus, including thermal sensation vote (TSV), thermal preference vote (TPV), thermal acceptability vote (TAV), humidity sensation vote (HSV) and air movement sensation vote (AMS). The questionnaire items, rating scales and coding schemes are summarized in Table 2. Each questionnaire items were answered by the participants by ticking on the printed paper sheets. In the meantime, each participant's clothing insulation (Icl , clo) and metabolic rate (MET, met) were estimated by the researchers referring to ASHRAE standard 55 [1] and ISO 7730 [26]. Two frequently used thermal indices, predicted mean vote (PMV) [27] and standard effective temperature (SET) [28] were calculated for each of the samples. All the household members were invited to complete the questionnaires at his or her convenience, provided that the participant had lived in the HSCW climate zone for more than 4 years and their age was in the range from 14 to 74. The survey responses were carefully checked to remove any irrational or uncompleted responses. Table 3 summarizes the sample size and the brief demographic information (gender) of the participants.

3.1.2. Building thermal performance Differences of outdoor environmental parameters between the urban and rural groups are mainly resulted from the difference in local microclimate, whereas the difference in indoor thermal parameters can 4

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Table 4 Descriptive statistics of the measured indoor and outdoor environmental parameters. Environmental parameters

Tin (°C)

To (°C) RHin (%) Va (m/s)

Tout (°C) RHout (%)

Region

Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural

Summer

Winter

Min

Max

Mean

SD

Min

Max

Mean

SD

26.4 25.0 26.1 25.1 32.9 55.6 0.0 0.0 25.8 24.3 37.8 57.9

36.1 34.8 36.5 34.6 81.8 92.7 1.8 4.2 40.6 34.9 79.4 92.5

31.3 29.7 31.3 29.7 63.9 73.8 0.2 0.3 33.4 29.7 58.7 74.2

1.66 2.15 1.73 2.13 7.78 7.37 0.23 0.27 2.51 3.27 8.06 11.05

6.6 5.4 6.6 5.2 23.6 30.3 0.0 0.0 2.9 3.8 25.4 14.9

16.0 16.7 18.3 17.1 87.9 85.9 0.1 0.8 16.2 23.0 89.0 90.6

11.1 11.1 10.9 10.9 60.6 52.3 0.0 0.1 9.9 11.9 59.2 48.7

2.00 2.45 2.02 2.44 13.04 9.93 0.02 0.17 3.17 3.32 15.14 12.02

3.2. Residents’ thermal behaviours

be the result from the building's thermal property. In summer, as shown in Table 4, the mean Tin is 2.1 °C lower than the mean Tout in the urban group. In contrast, there are no noticeable differences between indoor and outdoor temperatures in the rural group. In winter, the mean Tin is 1.2 °C higher than the mean Tout in the urban group, but 0.8 °C lower in the rural group. As seen in Table 4, the higher Va observed in the rural group indicates that more air movement is achieved inside the buildings, no matter in winter or summer. Table 5 shows the comparisons of Pearson correlation coefficients between indoor and outdoor temperature records in the subsamples, categorized by season. Tin is significantly correlated with the Tout at 0.01 level (2-tailed), regardless of the location and season. The higher coefficients in the rural group in both seasons imply that the rural buildings have poorer thermal performance (in terms of separating indoors from outdoors) compared to urban buildings. Meanwhile, the higher coefficients in winter than summer, regardless of urban or rural location, also implicate that these buildings in winter do less work to improve indoor thermal environments by letting outside cold weather through indoor spaces.

3.2.1. Clothing insulation Table 6 summarizes residents' clothing insulation grouped by season, region and gender. In the same season for the same gender, all the mean values of clothing level are significantly different at p < 0.05 level between the urban and rural groups. The region difference and season difference are much more significant than gender difference. Then Fig. 5 further illustrates that how the residents (both genders) adjusted their clothing insulation level as the indoor air temperature increased. Both for urban and rural groups, noticeable change is observed in the participants’ clothing insulation level between the two seasons. However, urban residents are more active in adjusting their clothing than the rural ones. On average they wear 0.58 clo more in winter but 0.09 clo less in summer than the rural residents. 3.2.2. Metabolic rate Table 7 summarizes the participants' metabolic rate grouped by season, region and gender. The participants' activity levels within an hour prior to answering the questionnaire are included in the

Fig. 3. Measured indoor thermal conditions plotted against the psychometric chart. 5

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Fig. 4. Measured indoor thermal conditions plotted against the adaptive model's 80% acceptability range.

Table 5 Pearson correlation test of Tin and Tout grouped by season and region. Season

Region

Pearson Correlation Coefficient

Sig. (2-tailed)

Summer

Urban Rural Urban Rural

.494** .736** .637** .825**

.000 .000 .000 .000

Winter

calculation of metabolic rate in the present study. The rural group's mean metabolic rates fall in the range of 1.39 met~1.50 met, while the urban group's mean metabolic rates are slightly less, falling in the range of 1.24 met ~1.34 met. The differences in metabolic rate between the urban and rural samples in the present study are mainly due to high levels of activities such as household work or farmland work in which the rural residents are often engaged.

Fig. 5. Relationship between clothing insulations and indoor air temperature for the urban and rural residents.

3.2.3. Behavioral adjustments in response to thermal discomfort Fig. 6 shows the percentage breakdown of various behavioural adjustments frequently used by the residents in an attempt to reduce thermal discomfort. The total sample sizes are 119 (winter urban), 87 (winter rural), 306 (summer urban) and 83 (summer rural) residents in the studied regions. Note that not all respondents in this survey participated in the “right-here-right now” thermal comfort survey. The information illustrated by Fig. 6 only gives a supplementary explanation to the main results from the “right-here-right now” thermal comfort survey. Some behavioural differences between urban and rural residents

were observed (Fig. 6). In summer, the clothing adjustment and space cooling using air-conditioners occur more frequently in urban buildings than in the rural buildings. In winter use of portable heat source, air conditioner heating, being lazy in bed and closing doors and windows occur more frequently in the urban context than in the rural context. The result indicates that, firstly, residents in the more developed urban area are more dependent on air-conditioners for both cooling and heating than rural residents. Lower usage of air-conditioners in rural settings is probably because of the poor building airtightness and relatively expensive electricity in rural areas. Secondly, the very low percentage of “closed windows/doors” for the rural group in winter

Table 6 Comparisons of clothing insulation (Icl ) grouped by season, region and gender. Season

Summer Winter

Region

Urban Rural Urban Rural

Male

Female

All

Min

Max

Mean

SD

Min

Max

Mean

SD

Min

Max

Mean

SD

0.13 0.06 1.30 0.82

0.44 0.78 2.20 2.49

0.25 0.32 2.02 1.42

0.06 0.10 0.22 0.27

0.03 0.18 1.80 0.79

0.35 0.78 2.40 2.40

0.21 0.36 2.05 1.47

0.09 0.14 0.15 0.31

0.03 0.06 1.30 0.79

0.44 0.78 2.40 2.49

0.23 0.34 2.03 1.45

0.08 0.12 0.19 0.29

6

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Table 7 Comparison of metabolic rates (met) grouped by season, region and gender. Season

Summer Winter

Region

Urban Rural Urban Rural

Male

Female

All

Min

Max

Mean

SD

Min

Max

Mean

SD

Min

Max

Mean

SD

0.80 0.45 1.00 0.48

3.00 0.48 1.60 4.50

1.24 1.44 1.25 1.39

0.43 0.42 0.18 0.39

0.70 0.70 1.00 0.95

3.00 3.50 1.90 4.00

1.34 1.43 1.24 1.50

0.48 0.45 0.20 0.36

0.70 0.70 1.00 0.85

3.00 3.50 1.88 4.50

1.29 1.44 1.24 1.46

0.45 0.43 0.19 0.37

generally reports around “0.5” higher TSV value. While in summer, although the rural group is much more sensitive to Tin variation, once other thermal comfort factors are considered (includingTr , Va RHin , Icl and MET in SET calculations) the urban group and rural showing similar thermal sensitivity.

indicates that residents would like to move between indoor and outdoor spaces freely, possibly for the convenience for their daily activities involving diverse outdoor works. This can partially explain a high correlation between the indoor and outdoor temperature in the rural group (Table 5), and a high level of air movements observed in the rural group (Table 4). Thirdly, the lower percentage of “fewer clothes” voted by the rural residents is consistent with the results showed in section 3.2.1. Rural residents tend to wear slightly more clothes in summer than urban residents.

3.3.2. Actual (TSV) vs. predicted (PMV) thermal sensation The linear regression models between TSV and PMV are plotted separately for winter, summer and the whole year in Fig. 9. The slope of each regression model plays the same role as Fanger's expectation value [30]. Fanger and Toftum [30] argues that the PMV model also can well predict thermal sensation in non-air-conditioned buildings in a warm climate by adopting expectation factor, which varies between “0.5” to “1.0” depending on the local weather and the usage of air-conditioners. As shown in Fig. 9, the rural group has a lower slope in winter while the urban group has a lower slope in summer. It means the rural group has a lower thermal expectation in winter but higher thermal expectation in summer compared with the urban group. However, compared with Fanger's expectation value ranging from “0.5” to “1”, the slopes shown in Fig. 9 are much lower than what the literature [30] suggested, regardless of seasons. This can probably be attributed to the residents' long-term habituation to these hot summer and cold winter conditions without any mechanical heating or cooling systems available in their homes. Plus, if winter and summer data are fitted together into a whole year data, the very similar slope values of “0.53” for the rural group and “0.58” for the urban group can be drawn. These values are very close to Fanger's lowest expectation value “0.5” for Bangkok [30]. The slopes indicate PMV model tend to overestimate the actual thermal sensation votes approximately twofold in the free-running residential buildings, no matter in urban or rural settings. Fig. 9(c) also indicates that the rural group consistently felt about “0.5” unit warmer than the urban group at the same PMV value. According to our results, the expectation factor for PMV model can

3.3. Subjective perceptions of thermal environment 3.3.1. Thermal sensation and thermal sensitivity Fig. 7 and Fig. 8 display mean thermal sensation vote distributions against Tin and SET for the rural and urban groups. Tin and SET are binned by 1 K. A linear regression was fitted between the two variables to estimate thermal sensitivity (i.e. the gradient of the regression line). Firstly, a simple thermal index Tin is considered (Fig. 7). In winter, the urban and rural groups show very similar thermal sensitivity, registering very similar regression coefficients of “0.09” and “0.10”. However, the rural group shows consistently “0.5” higher TSV value than the urban group. In summer, the rural group is found to be more sensitive to Tin variations than the urban group, with the regression coefficient of “0.27”. In contrast, the urban group's TSV values almost remain unchanged, with the regression coefficient of “0.035”. Secondly, more comprehensive thermal index SET is considered (Fig. 8). In winter, both the urban and rural groups report similar thermal sensitivities, with the rural group still generally registering “0.6” higher TSV value than the urban group. In summer, although there is a slight difference between urban and rural groups in terms of coefficients of “0.09” and “0.13”, the differences between the regression gradients are not as pronounced as Fig. 7. It suggests that, in winter, the urban group and rural group show similar thermal sensitivity, but the rural group

Fig. 6. Percentages of engaging various adaptive actions in response to thermal discomfort in summer (a) and winter (b). 7

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Fig. 7. Mean TSV regressed on indoor air temperature (Tin ), (a) winter and (b) summer.

be suggested as “0.5” for naturally ventilated residential buildings for both urban and rural settings under the investigated climate. Also it is worth noting that the rural residents consistently reported around “0.5” unit warmer thermal sensation than the urban residents at the same PMV value estimated. It is probably because of the fact that the rural group's mean MET value is around 1.45 met, which is 0.2 met higher the urban group's value of 1.25 met as already discussed in Section 3.2.2. Humphreys and Nicol [31] have shown that when the mean MET value was higher than 1.4 met, the bias between PMV and TSV became not negligible. In the present analysis, the 0.2 met higher metabolic rate in the rural group can explain about “0.2” unit higher TSV value, compared to that of the urban group. In another words, higher metabolic rate of our rural residents can partially explain the discrepancy between PMV and TSV, and it also can explain 40% of the urban-rural differences in TSV voting.

cold environment [1], and therefore the rural group's likeness of movement possibly makes the residents accept more cold conditions in winter and reduce their tolerance to warm discomfort conditions in summer.

3.3.4. Thermal acceptability and preference Despite the measured physical conditions predominantly falling outside the conventional comfort zone (Fig. 4), our sample of residents show very high thermal acceptability measured by different rating scales, i.e. TSV, TAV and TPV (see Fig. 11). In Fig. 11(a), TSVs falling within the middle three categories of the 7-point thermal sensation scale (i.e. “-1, 0, 1”) are regarded as the expressions of thermal acceptability. Compared with urban group, rural group reports 10.46% less acceptability in summer but 10.93% more acceptability in winter. In Fig. 11(b), the direct acceptability scale (TAV) is used to estimate the percentage of ‘acceptable’ votes. Rural group reports 31.23% less acceptability in summer but 34.34% more acceptability than urban group in winter. In Fig. 11(c), thermal preference votes (TPV) distribution shows that residents reported a very high percentage of “cooler” in summer and “warmer” in winter, and there is no noticeable difference between urban and rural groups. The results based on the “TSV” and “TAV” scales demonstrate that the rural group accepts more cold conditions in winter, whereas the urban group is more accepted warm summer conditions. Even though the majority of the surveyed occupants indicate that they accept the given thermal conditions, their responses on the TPV scale indicate that they still prefer to feel ‘cooler’ in summer and ‘warmer’ in winter, regardless of urban or rural settings. Probit regression fitted between TPVs and temperature variations is a typical analytical method to derive the preferred temperature in thermal comfort research [32–34]. In the present analysis the preferred temperatures for our rural and urban residents are estimated by probit models, using TPVs (i.e. ‘warmer’ or ‘cooler’ votes) as the dependent variable and Tin (Fig. 12(a)) or SET (Fig. 12(b)) as the independent variable. The intersection of each group's ‘cooler’ curve and ‘warmer’

3.3.3. Air movement sensation The mean air velocity measured in the rural sample houses is only “0.1 m/s” higher than that of the urban group's both in winter and summer (Table 1). To examine the difference of air movement perception between the urban and rural group, Fig. 10 illustrates the mean indoor air velocity corresponding to each category of 5-point air movement sensation scale. There is a significant difference between urban and rural groups. It is clear that the mean indoor air velocities corresponding to each category of air movement sensation scale are consistently higher in the rural group, compared to the urban group. However, most of the indoor air velocity measurements are “0.0 m/s” in winter (48.8% of the total sample size). Thus it is difficult to differentiate air movement sensation's change according to air movement's change, and no further numerical models can be established to estimate the residents' air movement sensitivity. Nonetheless, the results in Fig. 11 hint that the rural residents prefer to have more air movement inside their homes regardless of the season. The draft is known as a significant factor influencing one's thermal comfort assessment in the

Fig. 8. Mean TSV regressed on standard effective temperature (SET), (a) winter and (b) summer. 8

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Fig. 9. Mean TSV distribution against PMV in (a) winter, (b) summer and (c) whole year.

seasonal clothing variations. Rural residents are often engaged in outdoor farming works and they require clothing protection from direct solar radiation even in hot summer days. Our survey of adaptive behaviours show that urban residents are more active in adjusting their clothes in response to climatic variations. Meanwhile, the rural residents seldom close windows or doors even in winter. Such a habit might have contributed to the rural residents’ low expectations for indoor thermal comfort in winter. The differences in air movement sensation observed between the rural and urban groups can be explained by the rural residents' long habituation to varied air movement levels. The rural group is consistently exposed to more air movement than the urban group both in summer and winter, resulting in their higher acceptance of air movement. In addition, rural residents generally have a high degree of freedom of shifting between indoor and outdoor spaces. More spatial freedom might have affected rural residents' thermal perception, and the long-time habitation to the free space probably has made rural residents accept ‘cool feeling’ more, which has also been reported in literature [35,36]. Thermal sensations are also different between the aforementioned

curve is defined as the preferred temperature for the group [32]. Fig. 12 clearly shows that the rural group has a lower preferred temperature (both Tin and SET) than the urban group. This finding is consistent with the results based on TSVs presented in the earlier sections. In Fig. 12(a), the rural group's preferred temperature is about 2°C (Tin) lower than that of the urban group. However, as can be seen in Fig. 12(b), the differences between two groups drop down to only 0.9°C when they are estimated as SET by considering the effects of clothing, metabolic rate, air movement, radiation and humidity. No matter which thermal indices of TSV, TAV or TPV are used, they illustrate the same result: rural residents are more tolerant of cold conditions in winter but less tolerant of hot conditions in summer. 4. Discussion 4.1. Rural vs urban Residents' behavioural characteristics seem to affect their perception of indoor thermal comfort. Compared to the urban residents, rural residents tend to have higher MET both in summer and winter, but less

Fig. 10. Mean air velocity corresponding to each category of 5-point air movement sensation scale in summer (a) and winter (b). (1: too still; 2: a little still; 3: just right; 4: a little windy; 5: too windy). 9

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Fig. 11. Thermal acceptability estimated by (a) thermal sensation, (b) direct thermal acceptability and (c) thermal preference scales.

variations. However, when the rural residents’ clothing behaviour and activity levels are considered in estimating SET, the differences in the estimated thermal sensitivity became negligible.

4.2. Potential design strategies for adaptive thermal comfort It is clear that, even though most of urban and rural residents could accept the indoor thermal conditions that fall well beyond the prescribed comfort zone, they still prefer to feel cooler in summer and warmer in wither. There is no clear difference between the urban and rural residents in their thermal preferences as shown in Fig. 11. The fundamental concept of adaptive thermal comfort suggests that occupants can trace the prevailing outdoor climate and adjust their thermal expectations and behaviours, which have major implications in building energy consumption. Dynamic and fluctuating indoor thermal environments in sync with the external weather conditions would have advantages in minimizing energy use than creating perfectly still thermal comfort all year round, especially in residential buildings. Based on the findings drawn from this study, cooling strategies in summer for rural house and heating strategies in winter for urban house seem to be key to achieve adaptive thermal comfort. Passive design strategies should be encouraged in housing design in the HSCW climate zone. Firstly, maximize natural ventilation in housing design, especially for summer in rural region. Since rural housing has relatively less restrictions in building configuration and ceiling height, strategies like cross ventilation, chimney effect and ceiling fans can be considered to maximize convective cooling effects from air movement inside a building. Secondly, the roof insulation must be improved in rural region, where detached houses are typical. The traditional sloping roof and attic would still be a very effective way to minimize heat loss in winter and heat gain in summer. Thirdly, the sunroom or glass house can be also very useful to improve indoor thermal conditions in winter. Lastly, improvements on external walls' thermal insulation and windows’ airtightness are very useful to reduce the heat loss both in urban and rural houses. Fig. 12. Probit regression models estimating the preferred temperatures (a) Tin and (b) SET.

5. Conclusions This study investigates contextual differences between the urban and rural residential environments in the HSCW climate zone and how it can influence residents’ thermal comfort and related behaviours. A total of 2684 sets of field data containing instrumental observations and subjective thermal comfort evaluations are collected and analysed. The main findings of this study are summarized below.

two groups in terms of their dependence on Tin , SET and PMV. In winter the rural group consistently felt about “0.5” TSV warmer than the urban group, suggesting that the rural group are more tolerant of cold conditions in winter but less tolerant of hot conditions in summer. In summer, the two groups show noticeably different sensitivity to Tin 10

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(a) In the monitored free-running homes in China's HSCW climate zone, the rural housing in general show poorer indoor thermal conditions both in summer and winter than the urban one. While physically measured indoor thermal parameters predominantly fall outside of ASHRAE Standard 55's 80% acceptability range, the thermal comfort survey results indicate that the majority of residents still find such conditions ‘acceptable’. (b) Residents in rural and urban regions show noticeably different clothing behaviours and daily activity patterns, and the differences between our subsamples are more pronounced in the winter. On average, rural residents wear 0.58 clo less clothing, and are engaged in 0.2 met higher activities than urban residents in winter. The rural residents tend to feel hotter in summer but the urban residents tend to feel colder in winter. In winter the rural and urban residents show very similar thermal sensitivity to indoor temperature variations (measured as Tin or SET). In summer the rural residents are more sensitive to Tin variations than the urban group, but both groups show equivalent sensitivity to SET variations. (c) PMV tend to overestimate the actual thermal sensation votes approximately by twofold for both urban and rural sample groups. Results' comparisons between PMV and TSV values indicate that the rural residents have lower thermal expectation in winter, but higher thermal expectation in summer than the urban residents. Rural residents tend to accept more natural air movement regardless of seasons. (d) Cooling strategies in summer for rural house and heating strategies in winter for urban house seem to be key to achieve adaptive thermal comfort. Passive design strategies would be encouraged in housing design in the HSCW climate zone.

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Acknowledgments

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This work was supported by the National Natural Science Foundation of China under Grants (No. 51208387, No. 41671442). The authors gratefully acknowledge financial support from China Scholarship Council (CSC) during Dr. Yan Xiong's one-year visit to the Indoor Environmental Quality Lab at the University of Sydney. The authors thank all the households participated in this study. Special thanks are also given to Prof. Richard de Dear and Prof. Jianlei Niu at the University of Sydney for their valuable comments on thermal comfort model.

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Appendix A. Supplementary data [27]

Supplementary data to this article can be found online at https:// doi.org/10.1016/j.buildenv.2019.106393.

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