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The impact of thermal environment on occupant IEQ perception and productivity
Accepted Manuscript The impact of thermal environment on occupant IEQ perception and productivity Yang Geng, Wenjie Ji, Borong Lin, Yingxin Zhu PII:
S0360-1323(17)30202-0
DOI:
10.1016/j.buildenv.2017.05.022
Reference:
BAE 4912
To appear in:
Building and Environment
Received Date: 13 March 2017 Revised Date:
10 May 2017
Accepted Date: 11 May 2017
Please cite this article as: Geng Y, Ji W, Lin B, Zhu Y, The impact of thermal environment on occupant IEQ perception and productivity, Building and Environment (2017), doi: 10.1016/j.buildenv.2017.05.022. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.
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The impact of thermal environment on occupant
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IEQ perception and productivity Yang Geng a, b, Wenjie Ji a, b, c, Borong Lin a, b, *, Yingxin Zhu a, b, c
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ABSTRACT
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In this paper, the effects of thermal environment on occupant IEQ perception and productivity were studied. Seven groups of experiments were carried out in a controlled office environment and the physical parameters, including air temperature, globe temperature, relative humidity, carbon dioxide concentration, illuminance and background noise level, were measured. In the experiments, indoor air temperature was the independent variable, which varied from 16 oC to 28 oC with a step of 2 oC, and other constant IEQ parameters were the control variables. The dependent variable would be human perception of various control variables and productivity. Subjects (9 females and 12 males) were recruited to participate every experiment for 2 hours. During each experiment, they voted their perceptions of thermal comfort, indoor air quality, lighting and acoustic environment, and performed simulated office tasks to evaluate the productivity. The results showed that the variation of thermal environment not only affected thermal comfort but also had a “comparative” impact on the perception of other IEQ factors. When thermal environment was unsatisfactory, it weakened the “comfort expectation” of other IEQ factors, which accordingly resulted in the less dissatisfaction with other IEQ factors. Conversely, when thermal environment was quite satisfying, it raised “comfort expectation” of other IEQ factors, which lowered the evaluation of the real performance of other IEQ factors retroactively. The quantitative relationship between productivity and thermal environment was established. The optimal productivity was obtained when people felt “neutral” or “slightly cool”, and the increase of thermal satisfaction had a positive effect on productivity.
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Department of Building Science, Tsinghua University, Beijing 100084, China Key Laboratory of Eco Planning & Green Building, Ministry of Education, Tsinghua University, China c Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, China
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Corresponding email: [email protected] (B. Lin).
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KEYWORDS
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Indoor environment quality (IEQ); Air temperature; Thermal comfort; Environmental satisfaction; Occupant productivity
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1. Introduction
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Indoor Environmental Quality (IEQ) covers many factors, including indoor air quality (IAQ), thermal comfort, lighting and acoustic [1]. Over the past few decades, there has been a considerable amount of literature that recognizes the significance of IEQ in response to the increasing human desire to enhance comfort and health [2]. Previous studies have proved that IEQ exerts a significant effect on occupant’s satisfaction, health and productivity [3-6]. The following challenge faced by many researches is to understand how IEQ affects human perception, health and performance. Existing work can be divided into two main categories: experimental study and field study. Experimental study generally refers to the researches carried out in either a climate chamber or a given space with environmental parameters controlled, in which participants are recruited to vote their perceptions of the environment, measure physiological parameters or perform certain tests [7]. In order to study the impact of one specific IEQ factor on people’s satisfaction, health or productivity, there has been numerous experiments performed by different researchers. Fanger [8,9] used climate chamber data to build up PMV-PPD1 model to predict thermal comfort based on physical parameters (air temperature, humidity, mean radiant temperature and air speed) and human factors (metabolic rate and clothing level). Fisk [10,11] demonstrated that IEQ significantly influence health symptoms and worker performances and estimated that the improvement of health and productivity can be realized by providing better indoor environments. Mendell [12] reviewed the findings of 32 studies and pointed out several IEQ factors associated with occupant health, such as temperature, humidity and ventilation rate. A qualitative study by Wargocki [13] found that increasing ventilation rate had benefits for health, comfort and productivity. Allen [14] explored the associations of cognitive performance with carbon dioxide, ventilation, and VOCs in a controlled office environment. Schiavon [15] studied the impact of personally controlled air movement on thermal comfort, perceived air quality and cognitive performance in tropical climates. Koehn [16], Thomas [17], Hancher [18], Mohamed [19], Akimoto [20] and Lan [21-24] have conducted different experiments to investigate the relationship between thermal environment and labor productivity. They also established mathematical models to forecast the change of productivity due to air temperature variations. Despite of the different models, these studies all revealed that deviation from thermal neutral condition led to productivity loss. Field study, that is frequently used in the domain of IEQ, can broadly be defined as researches conducted in real buildings. To investigate the actual IEQ of buildings, post occupancy evaluation
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Predicted Mean Vote (PMV): an index that predicts the mean value of the thermal sensation votes of a large group of persons; Predicted percentage of dissatisfied (PPD): an index that establishes a quantitative prediction of the percentage of thermally dissatisfied people determined from PMV. 2
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(POE) has been applied as the primary approach [25,26]. In recent years, POE has been developed from the limited subjective survey of occupants to a comprehensive assessment tool, which combined environmental measurements with survey data [27]. Based on comprehensive POE, many researchers studied the mechanism between objective IEQ parameters and occupants’ satisfaction, health and productivity in real building environments. Newsham [28] collected physical and questionnaire data from 95 workstations at an office building in the US. A model linking the physical environment (including thermal, lighting, and acoustic) to job satisfaction was built up and tested. Lee [29] explored whether IEQ could affect occupants’ satisfactions and work performances through a field study in 15 LEED-certified buildings in the US. The results showed that IAQ have a significant positive impact on occupants’ performances in overall workspace. Wong [30] and Lai [31] investigated objective IEQ parameters and occupants’ satisfactions in office and residential buildings in Hong Kong. Empirical models have been proposed to approximate an overall IEQ acceptance based on four environmental parameters: operative temperature, carbon dioxide concentration, noise level and illumination level. Kim [32,33] and Geng [34] used Kano’s model to study the impacts of individual IEQ factors on overall satisfaction. Both of them have found the nonlinear relationships between IEQ factors and overall satisfaction. Nawawi and Khalil [35,36] found 74% of the aspects in Malaysia’s public building performance are in high correlation with the occupants’ satisfaction. Vischer [37] reviewed the previous literature and discussed how people were affected by environments for work, which generated directions for future research. In addition, there has been a large body of literature that investigated IEQ parameters, environmental satisfactions and health in green and conventional buildings [38-41]. These studies have found that occupants of green buildings voted higher environmental satisfaction and well-being than occupants of conventional buildings, which was mostly due to the superior IEQ performance in green buildings. The above section has shown the previous experimental and field studies exploring the impact of IEQ factors on occupants’ satisfaction, health and productivity. However, most of the literature overlooked the interaction between different IEQ factors, which meant the variation of one IEQ parameter not only affected occupant’s perception of this IEQ factor but also influenced the perception of other IEQ factors indirectly. Fang [42,43] made a primary exploration that indoor thermal environment can influence people’s perception of IAQ, but there was still a lack of deeper studies on this issue. The second shortage of previous researches was the restriction to simple comparison of human satisfaction, health or productivity under different environments, instead of revealing the whole characteristics of the relationship between objective IEQ and human factors. Therefore, this study tried to overcome the shortages in the previous experimental and field studies. The main research objectives were (1) to explore how thermal environment2 affects the perception of IAQ, lighting and acoustic, as well as overall environment, and (2) to investigate the whole influencing law/model between thermal environment and occupant’s satisfaction and productivity.
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2 Thermal environment is the primary IEQ factor which has a significant influence on occupant’s satisfaction, productivity and health [1], thus this study selects thermal environment as the research object.
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2. Methodologies
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2.1. Overview of experiment
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In order to study how thermal environment affects occupant IEQ perception, productivity and health, a series of experiments was carried out during November 2016 in a controlled office environment at Tsinghua University in Beijing, China. The total floor area of the office was 35 m2 and the ceiling height was 2.7m. The envelope of the room was constructed by heat-insulating walls and windows in order to reduce the interference of the external environment. The heat transfer coefficients of the walls and the windows were 0.4 W/(m2 ▪ K) and 1.4 W/(m2 ▪ K). Furthermore, the external shadings were used to avoid the direct solar radiation into the room. The door and windows were all closed during the experiments. Inside the room, two sets of multi-split air conditioners with the function of dehumidification, which hung on the top of the room, were equipped to control dry-bulb temperature and humidity. The carbon dioxide concentration was controlled with two fresh air units placed in the corners of the room. The positions of air conditioners and fresh air units can be seen in Figure 1A. In addition, the lighting environment of the office was controlled with electric blinds inside the windows and four illumination-stepless adjusted LED lights on the ceiling. Seven groups of experiments were conducted within one week. In the experiments, indoor air temperature was the independent variable, which varied from 16 oC to 28 oC with a step of 2 oC. Other IEQ parameters were served as the control variables, which remained constant. Subjects were recruited to participate every experiment for 2 hours on each day, which ensured the repeated measures of the same individual under different thermal environments. During each experiment, participants were exposed to the unchanged environmental condition, reading books or using their own laptops. The first 60 minutes for participants was to adapt to the indoor environment. Then they needed to complete a series of questionnaires on comfort and IEQ perception and to conduct simulated office tasks to assess productivity (see Figure 1B and 1C). Meanwhile, the environmental parameters, including air temperature, globe temperature, relative humidity, carbon dioxide concentration, illuminance and background noise level, were measured by instruments and informed to all participants before filling in questionnaires. Figure 1A shows the positions of environmental instruments.
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Twenty-one participants from Tsinghua University were recruited and Table 1 shows the human body data. All participants have more than 2-year experiences living in the North, which is long enough to avoid the influence of different thermal adaptabilities [44]. In the experiments, participants were instructed to wear long trousers, long-sleeved sweater or long-sleeved flannel shirt, long underclothes on the top and bottom, socks and shoes (1.0 clo), which is the typical clothing indoors during the winter in northern China. The chairs used in this experiment has a cushion and mesh back (0.15 clo). Thus, the total clothing insulation of each participant is 1.15 clo, including the insulation of the chair. During the experiment, participants were not allowed to move around or leave the room until all experimental tasks have been done. In addition, participants were told to eat normally, have enough sleep and to avoid strenuous exercise during the 24 hours before the experiment. Illness was also excluded from the experiment. Furthermore, in order to avoid the influence of the unfamiliarity with experiment, all participants were required to attend a training regarding the experimental procedure, survey questionnaire and productivity test before the formal experiment.
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2.2. Participants
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Fig. 1. (A) Office layout; (B) photo of the experiment; and (C) experimental procedure.
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Table 1
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Human body data (mean value±standard deviation) of participants. Gender Male
Sample size
Age (y)
Height (m)
Weight (kg)
BMI (kg/m2)
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22.7±3.0
1.74±0.06
73.0±8.1
24.0±2.0
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20.3±1.1
1.65±0.05
51.9±3.1
19.1±1.2
Total
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21.7±2.6
1.70±0.07
64.0±12.4
21.9±3.0
Note: Body mass index (BMI) = Weight (kg) / [Height (m)]2.
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2.3. Questionnaire and productivity test
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The questionnaire consisted of the perception of each IEQ factors, including thermal environment, air quality, lighting and acoustic, as well as the overall environment. Thermal sensation votes (TSV) used the ASHRAE 7-point scale as follows: cold (-3), cool (-2), slightly cool (-1), neutral (0), slightly warm (1), warm (2), and hot (3). Satisfaction votes of thermal environment and other IEQ factors were also casted on the 7-point scale [45] – strongly dissatisfied (-3), dissatisfied (-2), slightly dissatisfied (-1), neutral (0), slightly satisfied (1), satisfied (2), and strongly satisfied (3). The survey repeated three times according to the preset schedule during each experiment and participants were equipped with laptop computers to vote their perceptions on website. Productivity test was made up of three tasks and each task was chosen to measure one aspect of working efficiency. To encourage participants to take the test seriously, they were informed of a cash bonus that would be awarded depending on their test scores. Test was arranged at the end of experiment and was simulated by software, which could be taken on computers. The following is the detailed information of each task: (1) Icon Matching was to examine the processing capacity of discerning graphical information. Five icon libraries, each of which contained 32 different but similar icons, were pre-prepared in the computer. In each trial, the program would select a library and choose 14 icons in the library randomly, of which two icons would be copied by program to generate two matching pairs. Then, a total of 16 icons were arranged in 4 × 4 matrix and participants were asked to click the same icon as quickly as possible. Twenty trials were scheduled and the completion time, together with the number of corrects and errors, was recorded to generate a comprehensive score. (2) Number Summing was to measure the alertness and processing speed of digit arithmetic. The program generated three units digits (zero was excluded) randomly in a row and participants needed to type the summing result as fast as they can. Sixty trials were scheduled and the completion time, together with the number of corrects and errors, was recorded. (3) Text Memory and Typing was concentration and short-term memory task. A random string of 6 letters would appear on the computer screen for 2 seconds. During the display, participants could only try to remember the letter string and cannot input anything. Two seconds later, the letter string disappeared and participants needed to quickly input the letter string with memory. Thirty trials were scheduled and the program would record the completion time, together with the number of corrects and errors.
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2.4. Statistical method The data analysis toolkit SPSS was used to perform statistical analysis. Firstly, the perception and performance data was tested for normal distribution using the Shapiro-Wilk test [46]. Secondly, to examine the robustness of data, the repeated measurements of each individual were analyzed using the t-test (for normally distributed data) [47] or the analysis of variance (for non-normally distributed data) [48]. The significance level of the above tests were all set to be 0.05, which means the results were statistically significant when P-value < 0.05. Thirdly, the Spearman or Pearson correlation coefficient between two variables were calculated, if necessary. In addition, the distribution of data was presented in the form of boxplots, which revealed the average, maximum and minimum, as well as the 1st and 3rd percentiles.
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3. Results
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3.1. The measured environmental parameters
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Table 2 shows the measured environmental parameters on each experimental group, including air temperature, relative humidity, carbon dioxide concentration, illuminance and background noise level. Air temperature of each experimental group was controlled as intended, while all the other environmental parameters were almost at the same level. Moreover, the measured globe temperature was approximate to air temperature, so it was not shown in Table 2. The reason is that there was no direct solar radiation or radiant heating terminal in the room. Thus, the air temperature was assumed to approximate the mean radiant temperature and the operative temperature in this study.
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Table 2
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Indoor environmental conditions (mean value ± standard deviation) of each experimental group.
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Relative humidity (%)
CO2 (ppm)
Illuminance (lx)
Noise (dB(A))
16.2 ± 0.5 17.9 ± 0.3 20.0 ± 0.4 22.3 ± 0.3 24.1 ± 0.3 26.2 ± 0.5 27.7 ± 0.5
28.3 ± 1.7 27.8 ± 2.9 25.1 ± 3.3 25.8 ± 4.2 23.2 ± 2.5 21.5 ± 3.0 21.1 ± 2.3
725 ± 136 783 ± 124 705 ± 138 696 ± 90 680 ± 142 712 ± 85 738 ± 101
442 ± 34 401 ± 38 425 ± 26 420 ± 29 423 ± 33 408 ± 34 431 ± 30
43.5 ± 1.6 43.1 ± 2.3 45.0 ± 2.2 44.3 ± 1.8 42.7 ± 1.5 43.3 ± 2.6 44.6 ± 2.0
3.2. Thermal comfort and thermal satisfaction
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Air temperature. (oC)
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Figure 2 illustrates the results of thermal sensation votes and thermal satisfaction votes under different indoor air temperature conditions. According to ASHRAE Standard 55-2013 [45], the thermal sensation vote was predicted to be “neutral” at 24 oC, meanwhile, the percentage of thermal dissatisfaction was expected to be lowest. This prediction was verified by Figure 2, which showed 75% of the thermal satisfaction voted concentrate on “neutral” and none of the subjects felt dissatisfied under the experimental condition of 24 oC. The thermal sensation vote moved to the cool/cold side with the air temperature decreasing and to the warm/hot side with the air temperature increasing. When the air temperature deviated from 24 oC, more subjects felt thermal dissatisfied. However, the thermal dissatisfaction was more significant under cool or cold conditions, compared to warm or hot conditions.
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Fig. 2. (A) Thermal sensation votes and (B) thermal satisfaction votes for the seven indoor air
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The boxplots of thermal sensation and satisfaction votes are presented in Figure 3. The voting results of adjacent experimental condition were analyzed using the t-test and there was a significant difference between each group of data at the p<0.05 level, except for the data under 18 o C. As can be seen from the Figure 3(A), a positive correlation was found between the thermal sensation and air temperature and the median value of thermal sensation vote increased from “-3” at 16 oC to “1” at 28 oC. In Figure 3(B), the thermal satisfaction votes firstly increased and then decreased with the air temperature moving from 16 oC to 28 oC, and subjects attained highest thermal satisfaction (the median value is “2”) at experimental condition of 24 oC. In addition, regression analysis was used to explore the quantitative impact of air temperature on thermal comfort and the results were compared with the PMV-PPD model in Figure 4. As Figure 4 (A) shows, the average value of thermal sensation vote had a strong linear relationship with air temperature (R2=0.98), which was similar with the PMV model. However, there was a “scissors difference” phenomenon between the actual thermal sensation votes and PMV model and the intersection point was just at the “neutral” temperature 24 oC. The possible reason was that participants were informed of the real air temperature before voting, while the PMV model was established on the votes without knowing actual environmental parameter. This information symmetry of environmental parameter weakened the psychological effect on thermal sensation voting and it might lead to greater differences on voting when the thermal condition diverged from “neutral”. It can be seen from Figure 4 (B) that there was an obvious quadratic regression relationship between thermal dissatisfaction ratio and air temperature (R2=0.92). The regression curve had the similar shape with the PPD model and the minor difference was mainly due to the information symmetry of environmental parameter, which was discussed above.
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Fig. 3. Boxplots of (A) Thermal sensation votes and (B) thermal satisfaction votes.
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3.3. Perception of other IEQ factors
In order to explore how thermal environment affects the perception of other IEQ factors, participants were asked about the satisfaction of IAQ, lighting, acoustic and overall environment at the seven thermal conditions (16oC ~ 28oC), while the carbon dioxide concentration, illuminance and background noise were almost at the same level (Table 2). Figure 5 shows the voting results of IAQ, lighting, acoustic and overall environment at different air temperatures. Except for the overall environment, similar satisfaction votes were obtained at different air temperature conditions and more than 80% of the participants felt “neutral or satisfied” with IAQ, lighting and acoustic environment. However, the percentage of IAQ, lighting and acoustic dissatisfaction was relatively high at 22oC and 24oC, which was beyond the authors’ expectation. Such dissatisfaction had little to do with the poor performance of IAQ, lighting, acoustic, because the measured carbon dioxide concentration, illuminance and background noise all conformed to the related indoor climate standards [49-51]. The probable reason was the impact of thermal environment, which was the only changed factor, and it will be further discussed below. The satisfaction of overall environment was significantly related to air temperature. For example, 70% of participants felt “dissatisfied” with overall environment at 16oC, while the percentage of overall environment dissatisfaction fell down to zero at 24oC.
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compared with the PMV-PPD model.
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Fig. 4. The quantitative impact of air temperature on thermal sensation and thermal dissatisfaction,
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Fig. 5. Satisfaction votes of (A) indoor air quality, (B) lighting environment, (C) acoustic environment and (D) overall environment under different air temperature conditions.
In Figure 6, the dissatisfaction ratio of IAQ, lighting, acoustic and overall environment is shown and compared with the thermal dissatisfaction percentage. It can be seen that the dissatisfaction ratios of IAQ and lighting were both significantly high at 22 oC and 24 oC (P<0.05), while the conditions of 22 oC and 24 oC were generally regarded as comfortable or satisfactory thermal environment. Furthermore, the dissatisfaction ratio of acoustic at 22 oC and 24 oC was also greater than at other thermal conditions, despite of the non-significant distinction (P>0.05). This phenomenon revealed that the satisfaction of thermal environment might have a negative effect on perception of other IEQ factors, which was defined as “comparative” impact in this paper. The 11
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triggered by human different “comfort expectations” on IEQ factors. When thermal environment was too cold or too hot, such as the air temperature conditions of 16 oC, 18 oC and 28 oC, the thermal dissatisfaction became relatively high, which weakened the “comfort expectation” of other IEQ factors. Thus, fewer participants felt dissatisfied with IAQ, lighting and acoustic environment. Conversely, when thermal environment was close to neutral, just like 22 oC ~ 24 oC, the percentage of thermal dissatisfaction was at a low level and most of participants felt comfortable about thermal environment, which raised “comfort expectation” of other IEQ factors. Hence, the high thermal satisfaction did not reduce instead increased the percentage of IAQ, lighting and acoustic dissatisfaction in this study. In order to further prove this “comparative” impact, the correlation coefficients between dissatisfaction ratios of thermal environment and other IEQ factors have been calculated by SPSS. It is found that IAQ and lighting dissatisfaction ratios had a strong negative correlation with thermal dissatisfaction ratio (correlation coefficients = -0.76 and -0.78). In addition, the dissatisfaction ratio of acoustic environment was also negatively correlated with the dissatisfaction ratio of thermal environment, although the correlation coefficient (-0.54) was not significant enough. The results verified that the “comparative” impact of thermal environment on the perception of IAQ, lighting and acoustic really existed. When it comes to the overall environment, its perception was positively related to thermal comfort, as expected. Because thermal environment was involved in overall environment and the experiment only changed the objective parameter of thermal environment, the variation of thermal comfort would directly reflect in the satisfaction of overall environment. The overall environment dissatisfaction ratio had a significant positive correlation with thermal dissatisfaction ratio (correlation coefficients = 0.93).
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Fig. 6. The dissatisfaction ratio of (A) indoor air quality, (B) lighting environment, (C) acoustic environment and (D) overall environment and the correlation with thermal dissatisfaction ratio.
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3.4. Performance of productivity test The performance of productivity tests, including icon matching, number summing and text memory and typing, is presented in Table 3. In order to evaluate the performance of each task quantitatively, accuracy and reaction time were used as two main indicators, which were also adopted in the researches of Lan [21-24]. The higher accuracy and lower reaction time indicated the performance was better. All the performance data was tested for robustness using the t-test analysis and most of the data was statistically robust under the significant level of 0.05. Furthermore, the performance data of each individual was standardized by Equation (1), so that the productivity can be easily compared between different thermal conditions. In Equation (1), RP is the relative productivity compared to the average productivity, pi,j is the productivity of subject i 13
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at condition j and n is the amount of experimental conditions. RP = s p , =
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Table 3
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The performance data (mean value ± standard deviation) of each task.
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92.6 ± 7.7 91.3 ± 7.1 91.8 ± 6.1 93.0 ± 9.7 95.9 ± 4.6 93.1 ± 8.5 94.7 ± 6.2 94.9 ± 6.8 93.8 ± 6.0 93.3 ± 6.4 94.5 ± 6.6 91.7 ± 11.6 93.3 ± 5.6 85.1 ± 6.8
12.79 ± 3.33 2.09 ± 0.46 2.04 ± 0.44 11.72 ± 1.87 2.00 ± 0.34 1.93 ± 0.36 11.45 ± 2.28 2.07 ± 0.54 1.93 ± 0.53 12.23 ± 2.12 1.95 ± 0.40 1.97 ± 0.48 12.35 ± 3.27 2.13 ± 0.58 1.92 ± 0.51 14.23 ± 3.58 2.07 ± 0.48 2.01 ± 0.61 12.99 ± 2.55 2.26 ± 0.69 2.16 ± 0.55
The performance data is statistically robust through t-test (P<0.05).
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Icon matching Number summing Text memory and typing* Icon matching* Number summing* Text memory and typing* Icon matching Number summing* Text memory and typing* Icon matching Number summing* Text memory and typing Icon matching* Number summing Text memory and typing* Icon matching* Number summing* Text memory and typing* Icon matching* Number summing* Text memory and typing
Reaction time (s)
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Figure 7(A) shows the relative productivity of each subject at different thermal conditions. Relative productivity above or below 100% indicated participants performed better or worse than their average level. It can be found that the relative productivity increased first and then decreased when the air temperature varied from 16 oC to 28 oC and participants generally performed best at 22 oC, with the mean relative productivity of 106.5%. In addition, the relationship between relative productivity and air temperature was found to be significantly quadratic (R2=0.90). For further analysis, the relationship between relative productivity and thermal sensation votes (thermal satisfaction votes) was also studied. From Figure 7(B), it can be seen that the optimal productivity was obtained when people felt “neutral” or “slightly cool”, i.e. the thermal sensation vote equaled to 0 or -1, and the sensation of either cold or hot had a negative influence on productivity. For example, the relative productivity was 104.3% and 101.3% when the thermal 14
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(2)
0.0005" 1.0242 0.012" RP = 0.0219" 0.9917
(3) (4)
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where T is the indoor air temperature (oC), X1 stands for thermal sensation votes and X2 stands for thermal satisfaction votes.
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sensation was 0 and -1, however, it fell down to 91.8% when thermal sensation was -3 and to 98.3% when thermal sensation was 2. The regression analysis also showed a strong quadratic relationship between relative productivity and thermal sensation (R2=0.90). As can be seen from Figure 7(C), the relative productivity increased from 90.8% when the thermal satisfaction was -3 to 105.0% when the thermal satisfaction was 3, which indicated that the productivity had a positive correlation with thermal satisfaction. Furthermore, this positive correlation between productivity and thermal satisfaction was found to be clearly linear (R2=0.76). In order to describe the quantitative relationship between productivity and thermal environment, the variation of relative productivity as a function of air temperature, thermal sensation or thermal satisfaction can be generated by Equation (2) ~ (4), as presented in Figure 7:
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Fig. 7. The relationship between relative productivity and (A) air temperature, (B) thermal sensation votes and (C) thermal satisfaction votes.
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4. Discussions
The impact of thermal environment on occupant IEQ perception is one of the key contents in this paper and a “comparative” impact of thermal environment on the perception of indoor air quality, lighting and acoustic environment was found. In the previous work, Kim [32,33] and Geng [34] found the nonlinear relationships between IEQ factors and overall satisfaction; Fang [42,43] studied the impact of temperature and humidity on the perception of indoor air quality. However, there still lacks much literature on this issue. This study just made a primary exploration and raised the attention on the interaction between different IEQ factors. There are also several limitations which can affect the result. The first limitation is the amount of subjects. There were twenty-one participants recruited in 16
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Therefore, there were totally 441 (21×7×3) subjective questionnaire samples, which was the
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considerable data to analyze. Furthermore, how to determine a reasonable number of subjects is always a controversial issue in experimental studies. For example, the number of subjects was 12 in the study [24], while there were 20 subjects in another study [52]. An extreme instance is the study by Gagge [53], who studied comfort and thermal sensations and associate physiological responses at various ambient temperatures with only 3 subjects in the experiment. From the above examples, it can be seen that currently there is not a consensus on the number of subjects for experiments. Generally, it is also difficult for experimental studies to recruit so many subjects, due to the expense of both time and money. But of course, a larger number of subjects is helpful to the reliability of this study and it has been considered as the next step of our work. The second limitation is the sampling bias. The subjects of this research were mainly young and healthy students in university, who might have different perceptions on thermal environment and other IEQ factors, compared to other groups, such as the elder people. Thus, whether the “comparative” impact between thermal environment and IEQ factors widely exists in all groups needs to be further studied. The third limitation is the parameter settings of indoor air quality, lighting and acoustic environment. In this study, all these parameters conformed to the related indoor climate standards, which meant the dissatisfaction caused by objective environment was mostly excluded. Thus, the “comparative” impact caused by thermal environment could greatly affect the perception of indoor air quality, lighting and acoustic environment. However, when the objective performances of indoor air quality, lighting and acoustic environment get worse, the perception of these IEQ factors may be mostly affected by objective parameters, like carbon dioxide concentration, illuminance and background noise, and the “comparative” impact caused by thermal environment becomes negligible. Therefore, more environmental conditions should be included in the further study to validate the strength of the “comparative” impact between thermal environment and other IEQ factors. The impact of thermal environment on occupant productivity is another highlight of this study and the relationship between productivity and thermal sensation votes in this paper was compared with the relationships presented in the previous studies [24,54,55], as shown in Figure 8. It was seen that all relationships between productivity and thermal sensation votes had the similar varying pattern or trend. However, there were two small differences between the relationship in this paper and the relationships developed previously. (1) The curve of the present work was higher than other curves, which was due to the different definitions of relative productivity. In this study, relative productivity was compared to the average productivity and its value could be above or below 100%, which indicated participants performed better or worse than their average level. While, relative productivity defined in the previous work was compared to the maximum performance and its value must be below 100%. (2) Although all the researches indicated that the optimal productivity occurred when the thermal sensation vote lied between -1 and 0, there was a small discrepancy whether the best state of thermal sensation vote should be closer to 0 (as illustrated in present work) or to -1 (as illustrated in previous work). The reason might be the
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different experimental samples, whose productivity could be affected by thermal sensation in different ways. Further study is needed to verify the relationship developed in this paper, as well as the previous relationships.
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Fig. 8. Comparison of the relationship between productivity and thermal sensation votes in this paper with the relationships presented in the previous studies by Lan et al. [24], Jensen et al. [54] and Seppänen et al. [55].
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Through experiments with objective environmental measurement, subjective survey and productivity test under different air temperatures ranging from 16 oC to 28 oC, the impact of thermal environment on occupant IEQ perception and productivity was studied. The key findings are presented as follows. Most of participants felt “neutral” and were satisfied with the thermal environment at 24 oC in the office. When the air temperature deviated from 24 oC, the number of subjects who were dissatisfied with thermal environment increased. The thermal dissatisfaction was found more significant under cool or cold conditions, compared to warm or hot conditions. Thermal sensation vote had a strong linear relationship with air temperature, while the thermal satisfaction firstly increased and then decreased along with the air temperature moving from 16 oC to 28 oC and the highest satisfaction was obtained at 24 oC. These relationships were similar with PMV-PPD model, however a “scissors difference” phenomenon existed between the results and the model, which was possibly due to whether the subjective votes were collected on the premise of information symmetry of environmental parameters or not. The variation of thermal environment not only affected occupant thermal comfort but also had a
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ACCEPTED MANUSCRIPT “comparative” impact on the perception of indoor air quality, lighting and acoustic environment. When thermal environment was unsatisfactory, it weakened the “comfort expectation” of other IEQ factors, which accordingly resulted in the less dissatisfaction with other IEQ factors. Conversely, when thermal environment was quite satisfying, it raised “comfort expectation” of other IEQ factors, which could lower the evaluation of the real performance of other IEQ factors retroactively. The “comparative” impact of thermal environment on indoor air quality or lighting satisfaction was much stronger than that on acoustic satisfaction. The quantitative relationship between productivity and thermal environment was established and the relative productivity could be predicted on the basis of air temperature, thermal sensation or thermal satisfaction. The optimal productivity was obtained when people felt “neutral” or “slightly cool”, and the increase of thermal satisfaction had a positive effect on productivity. Productivity loss emerged along with thermal discomfort caused by too high or too low air temperature.
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Acknowledgments
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This study is supported by the China National Key R&D Program (Grant No.2016YFC0700100), the National Natural Science Foundation of China (Grant number 51561135001), and the Innovative Research Groups of the National Natural Science Foundation of China (Grant number 51521005). Special thanks to all the volunteers for their participation and cooperation in experiments.
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Experiments were conducted in a controlled office under conditions from 16 oC to 28 oC Objective measurement, subjective survey and productivity test were carried out The “comparative” impact between thermal environment and other IEQ factors was found The optimal productivity was obtained when people felt “neutral” or “slightly cool” The increase of thermal satisfaction had a positive effect on productivity
S0360-1323(17)30202-0
DOI:
10.1016/j.buildenv.2017.05.022
Reference:
BAE 4912
To appear in:
Building and Environment
Received Date: 13 March 2017 Revised Date:
10 May 2017
Accepted Date: 11 May 2017
Please cite this article as: Geng Y, Ji W, Lin B, Zhu Y, The impact of thermal environment on occupant IEQ perception and productivity, Building and Environment (2017), doi: 10.1016/j.buildenv.2017.05.022. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.
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The impact of thermal environment on occupant
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IEQ perception and productivity Yang Geng a, b, Wenjie Ji a, b, c, Borong Lin a, b, *, Yingxin Zhu a, b, c
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ABSTRACT
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In this paper, the effects of thermal environment on occupant IEQ perception and productivity were studied. Seven groups of experiments were carried out in a controlled office environment and the physical parameters, including air temperature, globe temperature, relative humidity, carbon dioxide concentration, illuminance and background noise level, were measured. In the experiments, indoor air temperature was the independent variable, which varied from 16 oC to 28 oC with a step of 2 oC, and other constant IEQ parameters were the control variables. The dependent variable would be human perception of various control variables and productivity. Subjects (9 females and 12 males) were recruited to participate every experiment for 2 hours. During each experiment, they voted their perceptions of thermal comfort, indoor air quality, lighting and acoustic environment, and performed simulated office tasks to evaluate the productivity. The results showed that the variation of thermal environment not only affected thermal comfort but also had a “comparative” impact on the perception of other IEQ factors. When thermal environment was unsatisfactory, it weakened the “comfort expectation” of other IEQ factors, which accordingly resulted in the less dissatisfaction with other IEQ factors. Conversely, when thermal environment was quite satisfying, it raised “comfort expectation” of other IEQ factors, which lowered the evaluation of the real performance of other IEQ factors retroactively. The quantitative relationship between productivity and thermal environment was established. The optimal productivity was obtained when people felt “neutral” or “slightly cool”, and the increase of thermal satisfaction had a positive effect on productivity.
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Department of Building Science, Tsinghua University, Beijing 100084, China Key Laboratory of Eco Planning & Green Building, Ministry of Education, Tsinghua University, China c Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing, China
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Corresponding email: [email protected] (B. Lin).
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KEYWORDS
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Indoor environment quality (IEQ); Air temperature; Thermal comfort; Environmental satisfaction; Occupant productivity
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1. Introduction
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Indoor Environmental Quality (IEQ) covers many factors, including indoor air quality (IAQ), thermal comfort, lighting and acoustic [1]. Over the past few decades, there has been a considerable amount of literature that recognizes the significance of IEQ in response to the increasing human desire to enhance comfort and health [2]. Previous studies have proved that IEQ exerts a significant effect on occupant’s satisfaction, health and productivity [3-6]. The following challenge faced by many researches is to understand how IEQ affects human perception, health and performance. Existing work can be divided into two main categories: experimental study and field study. Experimental study generally refers to the researches carried out in either a climate chamber or a given space with environmental parameters controlled, in which participants are recruited to vote their perceptions of the environment, measure physiological parameters or perform certain tests [7]. In order to study the impact of one specific IEQ factor on people’s satisfaction, health or productivity, there has been numerous experiments performed by different researchers. Fanger [8,9] used climate chamber data to build up PMV-PPD1 model to predict thermal comfort based on physical parameters (air temperature, humidity, mean radiant temperature and air speed) and human factors (metabolic rate and clothing level). Fisk [10,11] demonstrated that IEQ significantly influence health symptoms and worker performances and estimated that the improvement of health and productivity can be realized by providing better indoor environments. Mendell [12] reviewed the findings of 32 studies and pointed out several IEQ factors associated with occupant health, such as temperature, humidity and ventilation rate. A qualitative study by Wargocki [13] found that increasing ventilation rate had benefits for health, comfort and productivity. Allen [14] explored the associations of cognitive performance with carbon dioxide, ventilation, and VOCs in a controlled office environment. Schiavon [15] studied the impact of personally controlled air movement on thermal comfort, perceived air quality and cognitive performance in tropical climates. Koehn [16], Thomas [17], Hancher [18], Mohamed [19], Akimoto [20] and Lan [21-24] have conducted different experiments to investigate the relationship between thermal environment and labor productivity. They also established mathematical models to forecast the change of productivity due to air temperature variations. Despite of the different models, these studies all revealed that deviation from thermal neutral condition led to productivity loss. Field study, that is frequently used in the domain of IEQ, can broadly be defined as researches conducted in real buildings. To investigate the actual IEQ of buildings, post occupancy evaluation
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Predicted Mean Vote (PMV): an index that predicts the mean value of the thermal sensation votes of a large group of persons; Predicted percentage of dissatisfied (PPD): an index that establishes a quantitative prediction of the percentage of thermally dissatisfied people determined from PMV. 2
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(POE) has been applied as the primary approach [25,26]. In recent years, POE has been developed from the limited subjective survey of occupants to a comprehensive assessment tool, which combined environmental measurements with survey data [27]. Based on comprehensive POE, many researchers studied the mechanism between objective IEQ parameters and occupants’ satisfaction, health and productivity in real building environments. Newsham [28] collected physical and questionnaire data from 95 workstations at an office building in the US. A model linking the physical environment (including thermal, lighting, and acoustic) to job satisfaction was built up and tested. Lee [29] explored whether IEQ could affect occupants’ satisfactions and work performances through a field study in 15 LEED-certified buildings in the US. The results showed that IAQ have a significant positive impact on occupants’ performances in overall workspace. Wong [30] and Lai [31] investigated objective IEQ parameters and occupants’ satisfactions in office and residential buildings in Hong Kong. Empirical models have been proposed to approximate an overall IEQ acceptance based on four environmental parameters: operative temperature, carbon dioxide concentration, noise level and illumination level. Kim [32,33] and Geng [34] used Kano’s model to study the impacts of individual IEQ factors on overall satisfaction. Both of them have found the nonlinear relationships between IEQ factors and overall satisfaction. Nawawi and Khalil [35,36] found 74% of the aspects in Malaysia’s public building performance are in high correlation with the occupants’ satisfaction. Vischer [37] reviewed the previous literature and discussed how people were affected by environments for work, which generated directions for future research. In addition, there has been a large body of literature that investigated IEQ parameters, environmental satisfactions and health in green and conventional buildings [38-41]. These studies have found that occupants of green buildings voted higher environmental satisfaction and well-being than occupants of conventional buildings, which was mostly due to the superior IEQ performance in green buildings. The above section has shown the previous experimental and field studies exploring the impact of IEQ factors on occupants’ satisfaction, health and productivity. However, most of the literature overlooked the interaction between different IEQ factors, which meant the variation of one IEQ parameter not only affected occupant’s perception of this IEQ factor but also influenced the perception of other IEQ factors indirectly. Fang [42,43] made a primary exploration that indoor thermal environment can influence people’s perception of IAQ, but there was still a lack of deeper studies on this issue. The second shortage of previous researches was the restriction to simple comparison of human satisfaction, health or productivity under different environments, instead of revealing the whole characteristics of the relationship between objective IEQ and human factors. Therefore, this study tried to overcome the shortages in the previous experimental and field studies. The main research objectives were (1) to explore how thermal environment2 affects the perception of IAQ, lighting and acoustic, as well as overall environment, and (2) to investigate the whole influencing law/model between thermal environment and occupant’s satisfaction and productivity.
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2 Thermal environment is the primary IEQ factor which has a significant influence on occupant’s satisfaction, productivity and health [1], thus this study selects thermal environment as the research object.
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2. Methodologies
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2.1. Overview of experiment
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In order to study how thermal environment affects occupant IEQ perception, productivity and health, a series of experiments was carried out during November 2016 in a controlled office environment at Tsinghua University in Beijing, China. The total floor area of the office was 35 m2 and the ceiling height was 2.7m. The envelope of the room was constructed by heat-insulating walls and windows in order to reduce the interference of the external environment. The heat transfer coefficients of the walls and the windows were 0.4 W/(m2 ▪ K) and 1.4 W/(m2 ▪ K). Furthermore, the external shadings were used to avoid the direct solar radiation into the room. The door and windows were all closed during the experiments. Inside the room, two sets of multi-split air conditioners with the function of dehumidification, which hung on the top of the room, were equipped to control dry-bulb temperature and humidity. The carbon dioxide concentration was controlled with two fresh air units placed in the corners of the room. The positions of air conditioners and fresh air units can be seen in Figure 1A. In addition, the lighting environment of the office was controlled with electric blinds inside the windows and four illumination-stepless adjusted LED lights on the ceiling. Seven groups of experiments were conducted within one week. In the experiments, indoor air temperature was the independent variable, which varied from 16 oC to 28 oC with a step of 2 oC. Other IEQ parameters were served as the control variables, which remained constant. Subjects were recruited to participate every experiment for 2 hours on each day, which ensured the repeated measures of the same individual under different thermal environments. During each experiment, participants were exposed to the unchanged environmental condition, reading books or using their own laptops. The first 60 minutes for participants was to adapt to the indoor environment. Then they needed to complete a series of questionnaires on comfort and IEQ perception and to conduct simulated office tasks to assess productivity (see Figure 1B and 1C). Meanwhile, the environmental parameters, including air temperature, globe temperature, relative humidity, carbon dioxide concentration, illuminance and background noise level, were measured by instruments and informed to all participants before filling in questionnaires. Figure 1A shows the positions of environmental instruments.
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Twenty-one participants from Tsinghua University were recruited and Table 1 shows the human body data. All participants have more than 2-year experiences living in the North, which is long enough to avoid the influence of different thermal adaptabilities [44]. In the experiments, participants were instructed to wear long trousers, long-sleeved sweater or long-sleeved flannel shirt, long underclothes on the top and bottom, socks and shoes (1.0 clo), which is the typical clothing indoors during the winter in northern China. The chairs used in this experiment has a cushion and mesh back (0.15 clo). Thus, the total clothing insulation of each participant is 1.15 clo, including the insulation of the chair. During the experiment, participants were not allowed to move around or leave the room until all experimental tasks have been done. In addition, participants were told to eat normally, have enough sleep and to avoid strenuous exercise during the 24 hours before the experiment. Illness was also excluded from the experiment. Furthermore, in order to avoid the influence of the unfamiliarity with experiment, all participants were required to attend a training regarding the experimental procedure, survey questionnaire and productivity test before the formal experiment.
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2.2. Participants
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Fig. 1. (A) Office layout; (B) photo of the experiment; and (C) experimental procedure.
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Table 1
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Human body data (mean value±standard deviation) of participants. Gender Male
Sample size
Age (y)
Height (m)
Weight (kg)
BMI (kg/m2)
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1.74±0.06
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24.0±2.0
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20.3±1.1
1.65±0.05
51.9±3.1
19.1±1.2
Total
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21.7±2.6
1.70±0.07
64.0±12.4
21.9±3.0
Note: Body mass index (BMI) = Weight (kg) / [Height (m)]2.
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2.3. Questionnaire and productivity test
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The questionnaire consisted of the perception of each IEQ factors, including thermal environment, air quality, lighting and acoustic, as well as the overall environment. Thermal sensation votes (TSV) used the ASHRAE 7-point scale as follows: cold (-3), cool (-2), slightly cool (-1), neutral (0), slightly warm (1), warm (2), and hot (3). Satisfaction votes of thermal environment and other IEQ factors were also casted on the 7-point scale [45] – strongly dissatisfied (-3), dissatisfied (-2), slightly dissatisfied (-1), neutral (0), slightly satisfied (1), satisfied (2), and strongly satisfied (3). The survey repeated three times according to the preset schedule during each experiment and participants were equipped with laptop computers to vote their perceptions on website. Productivity test was made up of three tasks and each task was chosen to measure one aspect of working efficiency. To encourage participants to take the test seriously, they were informed of a cash bonus that would be awarded depending on their test scores. Test was arranged at the end of experiment and was simulated by software, which could be taken on computers. The following is the detailed information of each task: (1) Icon Matching was to examine the processing capacity of discerning graphical information. Five icon libraries, each of which contained 32 different but similar icons, were pre-prepared in the computer. In each trial, the program would select a library and choose 14 icons in the library randomly, of which two icons would be copied by program to generate two matching pairs. Then, a total of 16 icons were arranged in 4 × 4 matrix and participants were asked to click the same icon as quickly as possible. Twenty trials were scheduled and the completion time, together with the number of corrects and errors, was recorded to generate a comprehensive score. (2) Number Summing was to measure the alertness and processing speed of digit arithmetic. The program generated three units digits (zero was excluded) randomly in a row and participants needed to type the summing result as fast as they can. Sixty trials were scheduled and the completion time, together with the number of corrects and errors, was recorded. (3) Text Memory and Typing was concentration and short-term memory task. A random string of 6 letters would appear on the computer screen for 2 seconds. During the display, participants could only try to remember the letter string and cannot input anything. Two seconds later, the letter string disappeared and participants needed to quickly input the letter string with memory. Thirty trials were scheduled and the program would record the completion time, together with the number of corrects and errors.
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2.4. Statistical method The data analysis toolkit SPSS was used to perform statistical analysis. Firstly, the perception and performance data was tested for normal distribution using the Shapiro-Wilk test [46]. Secondly, to examine the robustness of data, the repeated measurements of each individual were analyzed using the t-test (for normally distributed data) [47] or the analysis of variance (for non-normally distributed data) [48]. The significance level of the above tests were all set to be 0.05, which means the results were statistically significant when P-value < 0.05. Thirdly, the Spearman or Pearson correlation coefficient between two variables were calculated, if necessary. In addition, the distribution of data was presented in the form of boxplots, which revealed the average, maximum and minimum, as well as the 1st and 3rd percentiles.
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3. Results
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3.1. The measured environmental parameters
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Table 2 shows the measured environmental parameters on each experimental group, including air temperature, relative humidity, carbon dioxide concentration, illuminance and background noise level. Air temperature of each experimental group was controlled as intended, while all the other environmental parameters were almost at the same level. Moreover, the measured globe temperature was approximate to air temperature, so it was not shown in Table 2. The reason is that there was no direct solar radiation or radiant heating terminal in the room. Thus, the air temperature was assumed to approximate the mean radiant temperature and the operative temperature in this study.
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Table 2
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Indoor environmental conditions (mean value ± standard deviation) of each experimental group.
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16.2 ± 0.5 17.9 ± 0.3 20.0 ± 0.4 22.3 ± 0.3 24.1 ± 0.3 26.2 ± 0.5 27.7 ± 0.5
28.3 ± 1.7 27.8 ± 2.9 25.1 ± 3.3 25.8 ± 4.2 23.2 ± 2.5 21.5 ± 3.0 21.1 ± 2.3
725 ± 136 783 ± 124 705 ± 138 696 ± 90 680 ± 142 712 ± 85 738 ± 101
442 ± 34 401 ± 38 425 ± 26 420 ± 29 423 ± 33 408 ± 34 431 ± 30
43.5 ± 1.6 43.1 ± 2.3 45.0 ± 2.2 44.3 ± 1.8 42.7 ± 1.5 43.3 ± 2.6 44.6 ± 2.0
3.2. Thermal comfort and thermal satisfaction
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Air temperature. (oC)
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Figure 2 illustrates the results of thermal sensation votes and thermal satisfaction votes under different indoor air temperature conditions. According to ASHRAE Standard 55-2013 [45], the thermal sensation vote was predicted to be “neutral” at 24 oC, meanwhile, the percentage of thermal dissatisfaction was expected to be lowest. This prediction was verified by Figure 2, which showed 75% of the thermal satisfaction voted concentrate on “neutral” and none of the subjects felt dissatisfied under the experimental condition of 24 oC. The thermal sensation vote moved to the cool/cold side with the air temperature decreasing and to the warm/hot side with the air temperature increasing. When the air temperature deviated from 24 oC, more subjects felt thermal dissatisfied. However, the thermal dissatisfaction was more significant under cool or cold conditions, compared to warm or hot conditions.
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Fig. 2. (A) Thermal sensation votes and (B) thermal satisfaction votes for the seven indoor air
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The boxplots of thermal sensation and satisfaction votes are presented in Figure 3. The voting results of adjacent experimental condition were analyzed using the t-test and there was a significant difference between each group of data at the p<0.05 level, except for the data under 18 o C. As can be seen from the Figure 3(A), a positive correlation was found between the thermal sensation and air temperature and the median value of thermal sensation vote increased from “-3” at 16 oC to “1” at 28 oC. In Figure 3(B), the thermal satisfaction votes firstly increased and then decreased with the air temperature moving from 16 oC to 28 oC, and subjects attained highest thermal satisfaction (the median value is “2”) at experimental condition of 24 oC. In addition, regression analysis was used to explore the quantitative impact of air temperature on thermal comfort and the results were compared with the PMV-PPD model in Figure 4. As Figure 4 (A) shows, the average value of thermal sensation vote had a strong linear relationship with air temperature (R2=0.98), which was similar with the PMV model. However, there was a “scissors difference” phenomenon between the actual thermal sensation votes and PMV model and the intersection point was just at the “neutral” temperature 24 oC. The possible reason was that participants were informed of the real air temperature before voting, while the PMV model was established on the votes without knowing actual environmental parameter. This information symmetry of environmental parameter weakened the psychological effect on thermal sensation voting and it might lead to greater differences on voting when the thermal condition diverged from “neutral”. It can be seen from Figure 4 (B) that there was an obvious quadratic regression relationship between thermal dissatisfaction ratio and air temperature (R2=0.92). The regression curve had the similar shape with the PPD model and the minor difference was mainly due to the information symmetry of environmental parameter, which was discussed above.
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Fig. 3. Boxplots of (A) Thermal sensation votes and (B) thermal satisfaction votes.
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In order to explore how thermal environment affects the perception of other IEQ factors, participants were asked about the satisfaction of IAQ, lighting, acoustic and overall environment at the seven thermal conditions (16oC ~ 28oC), while the carbon dioxide concentration, illuminance and background noise were almost at the same level (Table 2). Figure 5 shows the voting results of IAQ, lighting, acoustic and overall environment at different air temperatures. Except for the overall environment, similar satisfaction votes were obtained at different air temperature conditions and more than 80% of the participants felt “neutral or satisfied” with IAQ, lighting and acoustic environment. However, the percentage of IAQ, lighting and acoustic dissatisfaction was relatively high at 22oC and 24oC, which was beyond the authors’ expectation. Such dissatisfaction had little to do with the poor performance of IAQ, lighting, acoustic, because the measured carbon dioxide concentration, illuminance and background noise all conformed to the related indoor climate standards [49-51]. The probable reason was the impact of thermal environment, which was the only changed factor, and it will be further discussed below. The satisfaction of overall environment was significantly related to air temperature. For example, 70% of participants felt “dissatisfied” with overall environment at 16oC, while the percentage of overall environment dissatisfaction fell down to zero at 24oC.
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compared with the PMV-PPD model.
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Fig. 4. The quantitative impact of air temperature on thermal sensation and thermal dissatisfaction,
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Fig. 5. Satisfaction votes of (A) indoor air quality, (B) lighting environment, (C) acoustic environment and (D) overall environment under different air temperature conditions.
In Figure 6, the dissatisfaction ratio of IAQ, lighting, acoustic and overall environment is shown and compared with the thermal dissatisfaction percentage. It can be seen that the dissatisfaction ratios of IAQ and lighting were both significantly high at 22 oC and 24 oC (P<0.05), while the conditions of 22 oC and 24 oC were generally regarded as comfortable or satisfactory thermal environment. Furthermore, the dissatisfaction ratio of acoustic at 22 oC and 24 oC was also greater than at other thermal conditions, despite of the non-significant distinction (P>0.05). This phenomenon revealed that the satisfaction of thermal environment might have a negative effect on perception of other IEQ factors, which was defined as “comparative” impact in this paper. The 11
ACCEPTED MANUSCRIPT “comparative” impact was not directly caused by the environmental parameters. Instead, it was
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triggered by human different “comfort expectations” on IEQ factors. When thermal environment was too cold or too hot, such as the air temperature conditions of 16 oC, 18 oC and 28 oC, the thermal dissatisfaction became relatively high, which weakened the “comfort expectation” of other IEQ factors. Thus, fewer participants felt dissatisfied with IAQ, lighting and acoustic environment. Conversely, when thermal environment was close to neutral, just like 22 oC ~ 24 oC, the percentage of thermal dissatisfaction was at a low level and most of participants felt comfortable about thermal environment, which raised “comfort expectation” of other IEQ factors. Hence, the high thermal satisfaction did not reduce instead increased the percentage of IAQ, lighting and acoustic dissatisfaction in this study. In order to further prove this “comparative” impact, the correlation coefficients between dissatisfaction ratios of thermal environment and other IEQ factors have been calculated by SPSS. It is found that IAQ and lighting dissatisfaction ratios had a strong negative correlation with thermal dissatisfaction ratio (correlation coefficients = -0.76 and -0.78). In addition, the dissatisfaction ratio of acoustic environment was also negatively correlated with the dissatisfaction ratio of thermal environment, although the correlation coefficient (-0.54) was not significant enough. The results verified that the “comparative” impact of thermal environment on the perception of IAQ, lighting and acoustic really existed. When it comes to the overall environment, its perception was positively related to thermal comfort, as expected. Because thermal environment was involved in overall environment and the experiment only changed the objective parameter of thermal environment, the variation of thermal comfort would directly reflect in the satisfaction of overall environment. The overall environment dissatisfaction ratio had a significant positive correlation with thermal dissatisfaction ratio (correlation coefficients = 0.93).
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Fig. 6. The dissatisfaction ratio of (A) indoor air quality, (B) lighting environment, (C) acoustic environment and (D) overall environment and the correlation with thermal dissatisfaction ratio.
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3.4. Performance of productivity test The performance of productivity tests, including icon matching, number summing and text memory and typing, is presented in Table 3. In order to evaluate the performance of each task quantitatively, accuracy and reaction time were used as two main indicators, which were also adopted in the researches of Lan [21-24]. The higher accuracy and lower reaction time indicated the performance was better. All the performance data was tested for robustness using the t-test analysis and most of the data was statistically robust under the significant level of 0.05. Furthermore, the performance data of each individual was standardized by Equation (1), so that the productivity can be easily compared between different thermal conditions. In Equation (1), RP is the relative productivity compared to the average productivity, pi,j is the productivity of subject i 13
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at condition j and n is the amount of experimental conditions. RP = s p , =
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The performance data (mean value ± standard deviation) of each task.
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92.6 ± 7.7 91.3 ± 7.1 91.8 ± 6.1 93.0 ± 9.7 95.9 ± 4.6 93.1 ± 8.5 94.7 ± 6.2 94.9 ± 6.8 93.8 ± 6.0 93.3 ± 6.4 94.5 ± 6.6 91.7 ± 11.6 93.3 ± 5.6 85.1 ± 6.8
12.79 ± 3.33 2.09 ± 0.46 2.04 ± 0.44 11.72 ± 1.87 2.00 ± 0.34 1.93 ± 0.36 11.45 ± 2.28 2.07 ± 0.54 1.93 ± 0.53 12.23 ± 2.12 1.95 ± 0.40 1.97 ± 0.48 12.35 ± 3.27 2.13 ± 0.58 1.92 ± 0.51 14.23 ± 3.58 2.07 ± 0.48 2.01 ± 0.61 12.99 ± 2.55 2.26 ± 0.69 2.16 ± 0.55
The performance data is statistically robust through t-test (P<0.05).
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Icon matching Number summing Text memory and typing* Icon matching* Number summing* Text memory and typing* Icon matching Number summing* Text memory and typing* Icon matching Number summing* Text memory and typing Icon matching* Number summing Text memory and typing* Icon matching* Number summing* Text memory and typing* Icon matching* Number summing* Text memory and typing
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Figure 7(A) shows the relative productivity of each subject at different thermal conditions. Relative productivity above or below 100% indicated participants performed better or worse than their average level. It can be found that the relative productivity increased first and then decreased when the air temperature varied from 16 oC to 28 oC and participants generally performed best at 22 oC, with the mean relative productivity of 106.5%. In addition, the relationship between relative productivity and air temperature was found to be significantly quadratic (R2=0.90). For further analysis, the relationship between relative productivity and thermal sensation votes (thermal satisfaction votes) was also studied. From Figure 7(B), it can be seen that the optimal productivity was obtained when people felt “neutral” or “slightly cool”, i.e. the thermal sensation vote equaled to 0 or -1, and the sensation of either cold or hot had a negative influence on productivity. For example, the relative productivity was 104.3% and 101.3% when the thermal 14
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sensation was 0 and -1, however, it fell down to 91.8% when thermal sensation was -3 and to 98.3% when thermal sensation was 2. The regression analysis also showed a strong quadratic relationship between relative productivity and thermal sensation (R2=0.90). As can be seen from Figure 7(C), the relative productivity increased from 90.8% when the thermal satisfaction was -3 to 105.0% when the thermal satisfaction was 3, which indicated that the productivity had a positive correlation with thermal satisfaction. Furthermore, this positive correlation between productivity and thermal satisfaction was found to be clearly linear (R2=0.76). In order to describe the quantitative relationship between productivity and thermal environment, the variation of relative productivity as a function of air temperature, thermal sensation or thermal satisfaction can be generated by Equation (2) ~ (4), as presented in Figure 7:
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Fig. 7. The relationship between relative productivity and (A) air temperature, (B) thermal sensation votes and (C) thermal satisfaction votes.
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4. Discussions
The impact of thermal environment on occupant IEQ perception is one of the key contents in this paper and a “comparative” impact of thermal environment on the perception of indoor air quality, lighting and acoustic environment was found. In the previous work, Kim [32,33] and Geng [34] found the nonlinear relationships between IEQ factors and overall satisfaction; Fang [42,43] studied the impact of temperature and humidity on the perception of indoor air quality. However, there still lacks much literature on this issue. This study just made a primary exploration and raised the attention on the interaction between different IEQ factors. There are also several limitations which can affect the result. The first limitation is the amount of subjects. There were twenty-one participants recruited in 16
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Therefore, there were totally 441 (21×7×3) subjective questionnaire samples, which was the
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considerable data to analyze. Furthermore, how to determine a reasonable number of subjects is always a controversial issue in experimental studies. For example, the number of subjects was 12 in the study [24], while there were 20 subjects in another study [52]. An extreme instance is the study by Gagge [53], who studied comfort and thermal sensations and associate physiological responses at various ambient temperatures with only 3 subjects in the experiment. From the above examples, it can be seen that currently there is not a consensus on the number of subjects for experiments. Generally, it is also difficult for experimental studies to recruit so many subjects, due to the expense of both time and money. But of course, a larger number of subjects is helpful to the reliability of this study and it has been considered as the next step of our work. The second limitation is the sampling bias. The subjects of this research were mainly young and healthy students in university, who might have different perceptions on thermal environment and other IEQ factors, compared to other groups, such as the elder people. Thus, whether the “comparative” impact between thermal environment and IEQ factors widely exists in all groups needs to be further studied. The third limitation is the parameter settings of indoor air quality, lighting and acoustic environment. In this study, all these parameters conformed to the related indoor climate standards, which meant the dissatisfaction caused by objective environment was mostly excluded. Thus, the “comparative” impact caused by thermal environment could greatly affect the perception of indoor air quality, lighting and acoustic environment. However, when the objective performances of indoor air quality, lighting and acoustic environment get worse, the perception of these IEQ factors may be mostly affected by objective parameters, like carbon dioxide concentration, illuminance and background noise, and the “comparative” impact caused by thermal environment becomes negligible. Therefore, more environmental conditions should be included in the further study to validate the strength of the “comparative” impact between thermal environment and other IEQ factors. The impact of thermal environment on occupant productivity is another highlight of this study and the relationship between productivity and thermal sensation votes in this paper was compared with the relationships presented in the previous studies [24,54,55], as shown in Figure 8. It was seen that all relationships between productivity and thermal sensation votes had the similar varying pattern or trend. However, there were two small differences between the relationship in this paper and the relationships developed previously. (1) The curve of the present work was higher than other curves, which was due to the different definitions of relative productivity. In this study, relative productivity was compared to the average productivity and its value could be above or below 100%, which indicated participants performed better or worse than their average level. While, relative productivity defined in the previous work was compared to the maximum performance and its value must be below 100%. (2) Although all the researches indicated that the optimal productivity occurred when the thermal sensation vote lied between -1 and 0, there was a small discrepancy whether the best state of thermal sensation vote should be closer to 0 (as illustrated in present work) or to -1 (as illustrated in previous work). The reason might be the
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different experimental samples, whose productivity could be affected by thermal sensation in different ways. Further study is needed to verify the relationship developed in this paper, as well as the previous relationships.
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Fig. 8. Comparison of the relationship between productivity and thermal sensation votes in this paper with the relationships presented in the previous studies by Lan et al. [24], Jensen et al. [54] and Seppänen et al. [55].
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5. Conclusions
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Through experiments with objective environmental measurement, subjective survey and productivity test under different air temperatures ranging from 16 oC to 28 oC, the impact of thermal environment on occupant IEQ perception and productivity was studied. The key findings are presented as follows. Most of participants felt “neutral” and were satisfied with the thermal environment at 24 oC in the office. When the air temperature deviated from 24 oC, the number of subjects who were dissatisfied with thermal environment increased. The thermal dissatisfaction was found more significant under cool or cold conditions, compared to warm or hot conditions. Thermal sensation vote had a strong linear relationship with air temperature, while the thermal satisfaction firstly increased and then decreased along with the air temperature moving from 16 oC to 28 oC and the highest satisfaction was obtained at 24 oC. These relationships were similar with PMV-PPD model, however a “scissors difference” phenomenon existed between the results and the model, which was possibly due to whether the subjective votes were collected on the premise of information symmetry of environmental parameters or not. The variation of thermal environment not only affected occupant thermal comfort but also had a
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ACCEPTED MANUSCRIPT “comparative” impact on the perception of indoor air quality, lighting and acoustic environment. When thermal environment was unsatisfactory, it weakened the “comfort expectation” of other IEQ factors, which accordingly resulted in the less dissatisfaction with other IEQ factors. Conversely, when thermal environment was quite satisfying, it raised “comfort expectation” of other IEQ factors, which could lower the evaluation of the real performance of other IEQ factors retroactively. The “comparative” impact of thermal environment on indoor air quality or lighting satisfaction was much stronger than that on acoustic satisfaction. The quantitative relationship between productivity and thermal environment was established and the relative productivity could be predicted on the basis of air temperature, thermal sensation or thermal satisfaction. The optimal productivity was obtained when people felt “neutral” or “slightly cool”, and the increase of thermal satisfaction had a positive effect on productivity. Productivity loss emerged along with thermal discomfort caused by too high or too low air temperature.
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Acknowledgments
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This study is supported by the China National Key R&D Program (Grant No.2016YFC0700100), the National Natural Science Foundation of China (Grant number 51561135001), and the Innovative Research Groups of the National Natural Science Foundation of China (Grant number 51521005). Special thanks to all the volunteers for their participation and cooperation in experiments.
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Experiments were conducted in a controlled office under conditions from 16 oC to 28 oC Objective measurement, subjective survey and productivity test were carried out The “comparative” impact between thermal environment and other IEQ factors was found The optimal productivity was obtained when people felt “neutral” or “slightly cool” The increase of thermal satisfaction had a positive effect on productivity