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Title

環境による快適性と研究者科学研究への影響の数学モデ
ルに関する研究

Author(s)李, 知恒

CitationIssue Date2022-03

Type

Thesis or Dissertation

Text version

ETD

URL

http://hdl.handle.net/10119/17784

RightsDescription

Supervisor:KIM, Eunyoung, 先端科学技術研究科, 博士Japan Advanced Institute of Science and Technology

Doctoral Dissertation

Research on the mathematical model about the influence mechanismof indoor environment on researchers’ comfort and productivity

LI ZHIHENG

Supervisor: Prof. Eunyoung Kim

Graduate School of Advanced Science and Technology
Japan Advanced Institute of Science and Technology(Knowledge Science)
March 2022

Abstract
With the development of society, the core of modern work has gradually shifted from the manufacturing industry to
the knowledge-based departments in indoor office environments. In line with the foregoing, indoor environment quality
is an important indicator of the status of the indoor environment. It not only reflects the comfort level of the researchers
in the building but also affects their performance, particularly in research institutions. However, due to the inherent
correlation among various environmental comfort indexes, it is difficult to evaluate the influence of specific environment
physical parameters on researchers’ comfort and their research performances. Therefore, the main objective of this paper
is to develop a mathematical model that would determine the relationship between environment physical parameters andresearch performance.
This study was based on an experiment carried out at a controlled research office in a pharmaceutical researchcompany located in the northeast of China. The controlled research office was equipped with a radiant floor heating
system that supplied heat in winter. A total of 32 researchers were recruited and divided into four experiment groups. Eachexperiment group was required to conduct daily research activities under 12 different environment conditions. Data werecollected from physical environment measurements, subjective questionnaire surveys, and performance tests.
The results showed that changes in the thermal, visual, and acoustic environments had significant influences on the
researchers’ environmental perceptions and satisfactions. Moreover, the environment physical parameters exerted
significant impacts on the researchers’ response times in the performance tests and, consequently, had significant effects
on their research performance. For the influence weight of the items under environmental comforts, thermal comfort had
the highest weight, followed by visual comfort. Meanwhile, acoustic comfort had the least impact. In addition, there wasa positive correlation among the thermal, visual, and acoustic environments.
This paper also developed a mathematical model for evaluating the researchers’ performances based on the indoor
environment physical parameters. In order to establish the mathematical model, the improved environmental comfort
index was obtained by enhancing the three existing mathematical models. Based on a factor analysis of environment
comfort, the weight of each comfort index was obtained. Finally, through a nonlinear regression analysis between theperformance index and the indoor environment quality index, the relationship between research performance andenvironment physical parameters was obtained.

Keywords: Indoor environment quality, environmental comfort, environmental perception, environmental satisfaction
research performanceIContent
Abstract ...............................................................................................................................................I
Content...............................................................................................................................................II
Table List...........................................................................................................................................VI
Figure List.........................................................................................................................................IX
Chapter I: Introduction......................................................................................................................1
1.1 Research Background...............................................................................................................1
1.2 Current Research Tendency.......................................................................................................2
1.3 Open-plan Office......................................................................................................................7
1.4 Visual Environment..................................................................................................................9
1.5 Acoustic Environment.............................................................................................................10
1.6 Thermal Environment.............................................................................................................11
1.7 Research Objective.................................................................................................................12
1.8 Research Significance and Originality....................................................................................121.8.1 Mathematical model between environment parameters and research performance........121.8.2 Influence of combinational effect on occupants’ perception and satisfaction.................13
1.8.3 Fatigue Effect.................................................................................................................13
1.8.4 Controlled research office with radian floor heating system...........................................13
1.9 Thesis Structure.......................................................................................................................14
Chapter II: Experimental design and research methodology........................................................16
2.1 Data Collection.......................................................................................................................16
2.2 Controlled Research Office.....................................................................................................16
2.3 Experiment Sequence..............................................................................................................18
2.4 Experiment Procedure.............................................................................................................19
2.5 Environment Physical Parameters...........................................................................................21
2.6 Subjective Questionnaire........................................................................................................22
2.7 Research Performance.............................................................................................................24
2.8 Evaluation of research performance........................................................................................25
2.9 Sample Size Calculation..........................................................................................................26II2.10 Data Analysis Method...........................................................................................................27
2.10.1 Paired Sample T-test...................................................................................................27
2.10.2 One-way ANOVA......................................................................................................28
2.10.3 Correlation Analysis...................................................................................................28
2.10.4 Factor Analysis...........................................................................................................28
2.10.5 Regression Analysis...................................................................................................29
Chapter III: Influence mechanism of thermal environment on environmental perception,satisfaction and research performance............................................................................................30
3.1 Influence mechanism of thermal environment on thermal perception.....................................30
3.2 Influence mechanism of thermal environment on thermal satisfaction....................................33
3.3 Influence mechanism of thermal environment on acoustic perception....................................36
3.4 Influence mechanism of thermal environment on acoustic satisfaction...................................37
3.5 Influence mechanism of thermal environment on visual perception........................................38
3.6 Influence mechanism of thermal environment on visual satisfaction......................................39
3.7 Influence mechanism of thermal environment on research performance................................40
3.7.1 The effect of thermal environment on answer accuracy of performance test..................40
3.7.2 The effect of thermal environment on response time of performance test.......................41
3.7.3 The effect of thermal environment on RPI of performance test......................................42
Chapter IV: Influence mechanism of acoustic environment on environmental perception,satisfaction, and research performance...........................................................................................44
4.1 The influence of acoustic environment on acoustic perception and satisfaction......................44
4.2 The influence of acoustic environment on thermal perception and satisfaction.......................47
4.3 The influence of acoustic environment on visual perception and satisfaction..........................48
4.4 The influence of acoustic environment on research performance............................................50
Chapter V: Influence mechanism of visual environment on environmental perception,satisfaction and research performance............................................................................................54
5.1 Influence mechanism of visual environment on visual perception and satisfaction.................54
5.2 Influence mechanism of visual environment on thermal perception and satisfaction..............56
5.3 Influence mechanism of visual environment on acoustic perception and satisfaction.............59
5.4 Influence mechanism of visual environment on research performance...................................61III

5.4.1 The effect of visual environment on answer accuracy.....................................................61
5.4.2 The effect of visual environment on response time of performance test.........................62
5.4.3 The effect of visual environment on relative research performance................................63
Chapter VI: Indoor Environmental Index Equation......................................................................65
6.1 Prediction equation of environmental comfort index..............................................................68
6.1.1 Prediction equation of thermal comfort index................................................................68
6.1.2 Prediction equation of acoustic comfort index...............................................................71
6.1.3 Prediction equation of visual comfort index...................................................................74
6.1.4 Summary........................................................................................................................77
6.2 Correlation Analysis................................................................................................................78
6.3 Improvement of prediction equation.......................................................................................79
6.3.1 Improvement of thermal prediction equation.................................................................79
6.3.2 Improvement of acoustic prediction equation................................................................82
6.3.3 Improvement of visual prediction equation....................................................................85
6.3.4 Summary........................................................................................................................89
Chapter VII: Relationship between environmental comfort index and research performance..90
7.1 Indoor environment quality equation......................................................................................90
7.2 Relationship between indoor environment quality and research performance.........................93
7.2.1 Curvilinear regression....................................................................................................95
7.2.2 Quadratic regression......................................................................................................97
7.2.3 Cubic regression.............................................................................................................99
7.2.4 Performance prediction equation.................................................................................101
Chapter VIII: Conclusion and Future Work................................................................................103
8.1 Conclusion............................................................................................................................103
8.1.1 Answer for main objective...........................................................................................103
8.1.2 Answer to Sub objective 1............................................................................................104
8.1.3 Answer to Sub objective 2............................................................................................105
8.1.4 Answer to Sub objective 3............................................................................................105
8.1.5 Answer to Sub objective 4............................................................................................105
8.2 Limitation and Future Work..................................................................................................106IV

8.2.1 Amount of environment conditions..............................................................................106
8.2.2 Short-term stimulation.................................................................................................106
8.2.3 Performance test type...................................................................................................106
Acknowledgement...........................................................................................................................108
Reference.........................................................................................................................................109
Appendix..........................................................................................................................................117

V

Table List
Table 2-1 Illustration of 12 different environmental conditions.
Table 2-2 Experiment sequence of each experiment group.
Table 2-3 Summary of measured environment physical parameters (mean value ± standard deviation) for different
environmental conditions
Table 3-1 Results of thermal perception and thermal satisfaction for different thermal groups
Table 3-2 Test of homogeneity of variances and ANOVA results about thermal perception for each thermal group
Table 3-3 Test of homogeneity of variances and ANOVA results about thermal satisfaction for each thermal group
Table 3-4 Test of homogeneity of variances and ANOVA results about acoustic perception for each thermal group
Table 3-5 Test of homogeneity of variances and ANOVA results about acoustic satisfaction for each thermal group,and comparison between adjacent temperatures for Dark and Noisy group
Table 3-6 Test of homogeneity of variances and ANOVA results about visual perception for each thermal group,and comparison between adjacent temperatures for Bright and Noisy group
Table 3-7 Test of homogeneity of variances and ANOVA results about visual satisfaction for each thermal group
Table 3-8 Test of homogeneity of variances and ANOVA results about answer accuracy of performance test for eachthermal group
Table 3-9 Test of homogeneity of variances and ANOVA results about response time of performance test for eachthermal group
Table 3-10 Test of homogeneity of variances and ANOVA results about RPI of performance test for each thermalgroup
Table 4-1 Results of thermal perception and paired sample t-test results about influence of acoustic environment on
thermal perception for each acoustic group
Table 4-2 Results of thermal satisfaction and paired sample t-test results about influence of acoustic environmenton thermal satisfaction for each acoustic group
Table 4-3 Results of visual perception and paired sample t-test results about influence of acoustic environment on
visual perception for each acoustic group
Table 4-4 Results of visual satisfaction and paired sample t-test results about influence of acoustic environment on
visual satisfaction for each acoustic group
Table 4-5 Results of answer accuracy of performance test and paired sample t-test results about influence of acoustic
VIenvironment on answer accuracy for each acoustic group
Table 5-1 Results of thermal perception and paired sample t-test results about influence of visual environment on
thermal perception for each visual group
Table 5-2 Results of thermal satisfaction and paired sample t-test results about influence of visual environment on
thermal satisfaction for each visual group
Table 5-3 Results of acoustic perception and paired sample t-test results about influence of visual environment on
acoustic perception for each visual group
Table 5-4 Results of acoustic satisfaction and paired sample t-test results about influence of visual environment on
acoustic satisfaction for each visual group
Table 5-5 Paired sample t-test results about influence of visual environment on answer accuracy of performance
test for each visual group
Table 6-1 Summary of thermal comfort indexes calculated from three mathematic models (mean value ± standard
deviation) and actual thermal comfort for different environmental conditions
Table 6-2 Summary of thermal physical parameters (mean value ± standard deviation) for different environmental
conditions
Table 6-3 Summary of acoustic physical parameters (mean value±standard deviation) for different environmental
conditions
Table 6-4 Summary of acoustic comfort indexes calculated from three mathematic models (mean value±standard
deviation) and actual acoustic comfort for different environmental conditions
Table 6-5 Summary of visual physical parameters (mean value ± standard deviation) for different environmental
conditions
Table 6-6 Summary of visual comfort indexes calculated from three mathematic models (mean value ± standard
deviation) and actual visual comfort for different environmental conditions
Table 6-7 Correlation analysis results of thermal, acoustic and visual comfort
Table 6-8 Regression analysis results of thermal prediction equation
Table 6-9 Regression analysis results of acoustic prediction equation
Table 6-10 Regression analysis results of visual prediction equation
Table 7-1 The results of KMO and Bartlett’s test of sphericity
Table 7-2 The results of communalities list and total variance explained list
Table 7-3 Results of component score, normalized component score and standardized component scoreVII

Table7-4 Results of thermal comfort index, visual comfort index, acoustic comfort index, IEQ index andperformance index for each experiment group under 12 environment conditions
Table 7-5 Summary of regression results of ten fitting models
Table 7-6 Iteration results of quadratic regression analysis
Table 7-7 Variance analysis of quadratic regression
Table 7-8 Iteration results of cubic regression analysis
Table 7-9 Variance analysis of cubic regression

VIII

Figure List
Fig. 1-1. Cool Biz Poster by Ministry of Environment
Fig. 1-2. Hive type open-plan office
Fig. 1-3. Nest type open-plan office
Fig. 1-4. Club type open-plan office
Fig. 1-5. Thesis structure of this paper
Fig. 2-1. Plan view of controlled research office
Fig. 2-2. Daily experiment process for each experiment group
Fig. 2-3. Photos of participants during the experimentFig. 2-4. Sample of subjective questionnaire.
Fig. 2-5. Sample of performance test
Fig. 2-6. Sample size calculation using online analysis software.
Fig. 3-1. Thermal perception votes and linear regression of the mean votes of four different visual and acoustic
combinations: (A) 200lux and 45dBA, (B) 200lux and 70dBA, (C) 500lux and 45dBA and (D) 500lux
and 70dBA
Fig. 3-2. Results of comparison between adjacent temperatures about thermal perception for each thermal group
Fig. 3-3. Results of comparison between adjacent temperatures about thermal satisfaction for each thermal group
Fig. 3-4. Scatter plot of the actual percentage of dissatisfaction about thermal environment for different environment
conditions
Fig. 3-5. Results of comparison between adjacent temperatures about response time of performance test for eachthermal group
Fig. 3-6. Results of comparison between adjacent temperatures about RPI of performance test for each thermalgroupFig. 4-1. Results of acoustic perception and paired sample t-test results about influence of acoustic environment on
acoustic perception for each acoustic group
Fig. 4-2. Results of acoustic satisfaction and paired sample t-test results about influence of acoustic environment on
acoustic satisfaction for each acoustic group
Fig. 4-3. Results of response time of performance test and paired sample t-test results about influence of acoustic
environment on response time for each acoustic group
IX

Fig. 4-4. Results of RPI of performance test and paired sample t-test results about influence of acoustic environmenton RPI for each acoustic group
Fig. 5-1. Results of visual perception and paired sample t-test results about influence of visual environment on
visual perception for each visual group
Fig. 5-2. Results of visual satisfaction and paired sample t-test results about influence of visual environment on
visual satisfaction for each visual group
Fig. 5-3. Paired sample t-test results about influence of visual environment on response time of performance testfor each visual group
Fig. 5-4 Paired sample t-test results about influence of visual environment on RPI for each visual group
Fig. 6-1. Paired sample t-test results between thermal comfort indexes from three mathematical models and actual
thermal comfort
Fig. 6-2. Paired sample t-test results between acoustic comfort indexes from three mathematical models and actual
acoustic comfort
Fig. 6-3. Paired sample t-test results between visual comfort indexes from three mathematical models and actual
visual comfort
Fig. 6-4. (A) Histogram and normal distribution line of residuals and from thermal regression (B) P-P plot forstandardized residual from thermal regression
Fig. 6-5. Scatter plot between the thermal comfort predicted using improved thermal comfort equation and the actual
thermal comfort
Fig. 6-6. (A) Histogram and normal distribution line of residuals from acoustic regression and (B) P-P plot forstandardized residual from acoustic regression
Fig. 6-7. Scatter plot between the acoustic comfort predicted using improved acoustic comfort equation and theactual acoustic comfort
Fig. 6-8. (A) Histogram and normal distribution line of residuals from visual regression and (B) P-P plot forstandardized residual from visual regression
Fig. 6-9. Scatter plot between the visual comfort predicted using improved visual comfort equation and the actual
visual comfort
Fig. 7-1. Regression plot of ten fitting models
Fig. 7-2. (A) Histogram and normal distribution line of residuals from quadratic regression and (B) P-P plot forstandardized residual from quadratic regression
XFig. 7-3. (A) Histogram and normal distribution line of residuals from cubic regression and (B) P-P plot forstandardized residual from cubic regression
Fig.7-4. Scatter diagram of PI-IEQ and cubic regression curve of performance prediction equation
Fig.7-5. Derivative of performance prediction equation

XIChapter I
Introduction
1.1 Research BackgroundNowadays, with the increasing pressure of work and life, overtime work has become the normal
phenomenon in Japan and China. According to the “2018 Beijing residents’ time usage survey report”,
an office worker needs to work around 60 hours per week on average [1]. Many companies andinstitutions regard overtime as one of the core indicators to evaluate the employees’ KPI and give theemployees who work overtime the priority to promotion and salary increase.
But can overtime really bring higher output? The answer depends on the nature of the work. For
the traditional manufacturing industry, because work is a mechanical repetition of simple operation
processes, overtime can indeed bring higher output [2]. For example, on the factory assembly line, aworker works 8 hours to produce 800 pieces and 10 hours to produce 950 pieces. Although theefficiency has reduced during overtime, the extended working hours can lead to the production of anadditional 150 products. However, for mental workers, their job is not to mechanically repeat thesame operation process, but to think and create. Long working hours will make employees physicallyand mentally exhausted, which hence reduce work quality, and even increase the probability ofmaking mistakes [3].
At the same time, overtime will create a vicious circle. If an employee actively extends his
working hours in order to get a promotion or salary increase, other employees have no choice but towork overtime in order not to be eliminated. However, as all employees generally work overtime, theextra hours spent on overtime will become ordinary working hours. If that employee still wants toobtain the promotion chance, he needs to extend working hours again. However, because too long
working hours will reduce work efficiency and quality, employees actually create little additional
benefits for the company. This also makes the enterprise fall into an embarrassing situation, where
employees either refuse to work hard or seem to work hard overtime. But in the end, it does not bringreal benefits to the enterprise, but only brings gradually higher employee overtime compensation andutility bills [4].
Overtime will also have a negative impact on employees' subjective enthusiasm. Some employees
1will conduct performative overtime in order to meet the KPI requirements, but do not want to conduct
more tasks [5]. For work tasks that can be completed during core working hours, employees will
actively reduce their work efficiency to ensure that there is still work needed to be completed during
the overtime.
Based on this situation, how to improve employees' work efficiency within the standard working
hours has become an urgent problem to be solved. The research of Wargocki et al. showed that the
impacts on employees' work efficiency can be summarized into the following four categories: social
environment (relationship between people), personal characteristic (career, commitment to work andhome/work relationship), organizations (leadership and organizational structure) and indoor
environment [6]. Among them, the first three factors vary greatly due to different individuals and thenature of the company, which belongs to subjective factors. The indoor environmental factor is the
only objective factor among the four factors, and it is also the factor that has the greatest impact onwork efficiency [7].
At the same time, with the development of society, the core of modern work has gradually shiftedfrom the manufacturing industry to the knowledge-based department in the indoor office environment.
Its main feature is mental work, which requires high cognitive, judgment, reaction, and creative
abilities [8, 9]. ASHRAE's survey also shows that 80% to 90% of the work time is spent in the indoor
environment [10]. Therefore, it is increasingly important to understand the indoor office environment
and its impact on occupants’ comfort and productivity.
1.2 Current Research Tendency
For a long time, the design methodology of buildings tends to pursue energy conservation andemission reduction to reduce the global energy burden. For example, the activity called the “COOLBIZ campaign” was launched by the Japanese government in the summer of 2005. Through increasing
the pre-set cooling temperature of the air conditioner from 26℃ to 28℃ and modifying the summer
work dress code, this activity reduced energy consumption of the commercial building by 1.2% persquare meter per year [11]. The Chinese government also issued a similar policy in 2007, requiringthat the indoor air conditioning temperature should not be lower than 26℃ in summer and not higher
than 20℃ in winter, and windows should not be opened for ventilation during air conditioning
operation. This reduced the peak power load of air conditioning by 10–15% [12]. However, the relevant2policies do not take into account the impact of the influence of indoor environment change on indoor
personnel’s’ comfort and work efficiency.

Fig. 1-1. Cool Biz Poster by Ministry of Environment [13]Similar to the policy concerns on building energy conservation and emission reduction, in existing
research, energy conservation and environmental protection are still the main research directions forindoor environment optimization design. For example, Awadh conducted objective research on four
different energy-based green building rating systems (LEED, BREEAM, Estidama and GSAS) in
order to compare the different emphases of the four rating systems [14]. Through the energy
performance evaluation of 321 non-certified multi-family housing complexes, 126 multi-family
housing which obtained certificate of Green Standards for Energy and Environmental Design (GSEED) and 8 multi-family housing which were certificated by Leadership in Energy & Environmental
Design, Jeong et al. developed an evaluation process for evaluating the energy performance of both3

green and non-green residence buildings [15]. Rashid and Yusoff compared and summarized theexisting life cycle assessment methods based on energy consumption standards according to different
evaluation objects [16]. A six-week empirical study was conducted by Jain et al. with 43 participants
to compare the impact of five established design components in different eco feedback interface
design on saving building energy consumption [17]. Li et al. compared the disadvantages andadvantages of the two indoor thermal environment optimization control methods (thermal sensationbased control and set point-based control method). The resulted showed that the thermal sensationbased control can create a more comfortable thermal environment than the set point-based control
method, but the daily energy consumption was increased by 13.8% [18]. Merabet et al. compared theinfluence of 20 AI tools developed for energy consumption and comfort control on energy saving andcomfort. The results showed that with the application of artificial intelligence technology andpersonalized comfort model, it can make an average energy saving of 21.81% to 44.36% and an
average comfort improvement of 21.67% to 85.77% [19].
However, for scientific research facilities, it is not enough to focus only on energy conservationand environmental protection. Since the research institution is the structure with a specific purpose,
how to increase researchers' research performance is another goal that must be considered. In orderto study the relationship between research performance and indoor environment, it is necessary to
introduce a variable for evaluating indoor environment, which is indoor environment quality (IEQ),
and an intermediate concept which is indoor environment comfort.
IEQ is an important indicator of the sustainable development of buildings, and it can reflect the
comfort levels of residents in buildings. Comfort is the comprehensive psychological response ofresidents to the indoor physical environment based on their physiological and psychological state [20].
Comfort can be divided into several parts, four of which are thermal, indoor air quality, acoustic, andvisual comfort. Mui et al. pointed out that the comfort of occupants in buildings depends on manyenvironmental parameters, such as indoor air temperature and relative humidity [21]. When high IEQ
condition is achieved, occupants will have high level of comfort [22]. Relevant literature has shownthat the indoor environment has a direct impact on the health and work efficiency of occupants. For
example, a bad indoor environment will lead to long-term health problems and reduce the happinessindex and work efficiency [23-28].
Many previous studies focused on the impact of single environmental factors (such as thermal
4parameters) on occupants’ environment comfort or academic performance. In terms of thermal
environment, Mishra et al. conducted a mixed methods thermal comfort study in a classroom at
Eindhoven University of Technology to better understand students’ thermal perceptions whenentering and adapting to the classroom environment [29]. Pereira et al. took the classroom ofPortuguese middle school as an example and conducted a field study using the methods of physicalparameter monitoring and a questionnaire survey, in order to compare the judgment of the thermal
environment with the field measurement results [30]. A 24-day questionnaire survey was conducted onchildren aged 9-11 in non-air-conditioned classrooms in three different schools in the Netherlands totest the thermal comfort of children in primary school classrooms. The results showed that the thermal
perception predicted by Fanger's predicted mean votes model was 1.5 levels lower than that of actual
thermal perception of children [31]. Akimoto et al. measured the immediate thermal environment andworkers’ behavior, investigated the thermal comfort and fatigue of occupants to determine theinfluence of workers’ behavior and working conditions on workers’ thermal comfort and work
efficiency [32].
In terms of visual environment, the effect of three lighting arrangements (general lighting, wall
washing and cove lighting) and two illuminance levels (320lux and 500lux) on the visual perception
about indoor space was studied [33]. Four different visual environments were tested on 15 participants
by Zhang et al. to explore the effects of dynamic lighting with daily changes in illuminance andcorrelated color temperature level on participants' visual perception, comfort and work efficiency [34].
Chraibi et al. studied the effects of different lighting control conditions on individuals' visual
perception and visual satisfaction in an open-plan office environment [35]. Konis studied the change
of indoor personnel's perception of indoor visual environment after introducing sunlight and reducing
electrical lighting for open-plan office conditions [36]. Juslén et al. studied in detail how lighting
conditions affect employees' work efficiency by affecting visual perception and visual comfort. At
the same time, Juslén et al. also proposed a new evaluation method to study the impact of lightingchanges on work performance at workplace [37].
In terms of acoustic environment, Haapakangas et al. studied employees' acoustic perception of
office and its relationship with work performance [38]. The study also showed that background noise
was the main problem for indoor environment of open-plan office. Through interfering theconversations and tasks which depend on working memory and language processes, background noise5

level could influence the personal productivity. There are similar findings in the research of Lenne etal., in which background noise was the main source of employees’ troubles and would cause theperception and satisfaction decrease about acoustic perception [39]. In order to solve the impact ofnoise on acoustic perception and satisfaction, Hongisto et al. used principle of masking sound tocontrol the interference caused by speech, and compared the effects of background noise on acoustic
perception before and after sound masking [40].
However, according to the work of Zomorodian et al., acceptable indoor conditions cannot be
achieved unless overall acceptance of IAQ, thermal, acoustic, and visual comfort is obtained at thesame time [9]. But in the actual environment, it is difficult to evaluate the impact of each physicalenvironment parameter on the comfort of occupants separately. This is mainly due to the coexistenceof these parameters and their interactions with each other [41].
The number of research studies about the relationship between IEQ and occupants’ productivity
which focus on educational and research institution are also limited. Moreover, in the limited numberof literature reviews, most of the research was based on experiments conducted in university
classrooms.
For example, Ishii conducted a one-year experiment with 40 college students to explore theinfluence of the indoor thermal environment on the thermal comfort of college students in Japan [42].
Catalina and Iordache conducted a questionnaire survey on the indoor environment of 174 students
and 20 professors in university classrooms in Romania, and established a multiple nonlinear
regression model of the IEQ index of university classrooms based on the questionnaire data [43].
Almeida and Freitas evaluated the indoor environment quality of 24 classrooms in Portugal andformulated optimized solutions according to the evaluation results [44]. Bajc et al. analyzed therelationship between local thermal comfort and productivity loss under four different indoor comfort
conditions [45]. Jowkar et al. conducted environmental measurement and questionnaire survey in eight
university buildings of Edinburgh and Coventry universities in order to study acclimatization, age
and gender related differences on thermal perception [46].
Since the research institution is the structure with a specific purpose, besides environmental
protection and energy-saving requirements, improving researchers’ performance is another goal thatmust be considered. The population density, the metabolism levels, and study pressure of researchers
are quite different from those of ordinary office workers, it is impossible to directly apply theories
6from other building uses to the research institution [47].
1.3 Open-plan Office
In the 1950s, F.W. Taylor and H. Ford put forward the concept of "open-plan office". The openplan office is a large office or similar space that can accommodate a large number of employees towork together at the same time. In the open-plan office, the employees are centrally arranged invarious unitary workstations without partitions. Therefore, different from the traditional office,colleagues can talk and interact with others [48]. Now, the open-plan office can be divided into thefollowing types according to the function and structure [49,50]:(1) Hive type: Hive type is common in open-plan office. As the name suggests, it is like ahoneycomb. Three or four tables are assembled together with a certain distance between each other.
Because of this, the communication space between employees is reduced, which increases thecommunication between employees, but also reduces employees' autonomy.

Fig. 1-2. Hive type open-plan office [103](2) Nest type: In nest type open-plan office, all the tables are assembled together, and all theemployees are working together. Because the communication space is even shorter than the hive type,the interaction between colleagues is much more frequent. This can not only increase the cooperative
relationship between employees, but also improve the negotiation efficiency of work, which issuitable for cooperation tasks.

7

Fig. 1-3. Nest type open-plan office [104](3) Club type: Club type is not common in open-plan office. It is designed following the patternof club, which provide relative independent space for the occupants.

Fig. 1-4. Club type open-plan office [104]Nowadays, open-plan office is more and more widely used because of its low construction cost,high employee density and relatively low rent, which can better realize natural ventilation and lighting.
It can also promote communication, cooperation and knowledge sharing among employees [51].
However, the research of Kang et al. showed that open-plan office design revealed contradictorycharacteristics. In addition to promoting communication, it also reduced employees' work efficiency.
The results showed that the work efficiency of open-plan office was 15% lower than that of closed
office, and people's attention was difficult to concentrate [52].
Relevant research showed that the indoor environment has a significant impact on the comfort of8

open office environment [53]. The research of Lai et al. showed that the acoustic environment quality
was most closely related to the overall comfort of office workers, followed by indoor air quality,
visual environment and thermal environment [54]. Therefore, for the open-plan research office, it isnecessary to study how to improve the research performance of the researchers through adjusting thephysical parameters of the acoustic, visual and thermal environment.
1.4 Visual Environment
Vision relies on the response of visual organs to the stimulation of external light sources, which
excite sensory cells and produce visual perception through the later processing of visual nerve.
Through vision, human beings can perceive the shape, brightness, dynamic status, distance and otherinformation which has great significance to survival [55]. Also, vision is the main source of humanperception of external things. For an adult, at least 80% of the external information is obtained throughvision [56].
Visual environment is very important for the transmission of visual information. On the one hand,the brain can more efficiently and accurately receive the information carried by external visual signals
in a good visual environment. On the other hand, in addition to transmitting information, light will
also transmit external brightness and other perceptual information to the brain, which will have apositive impact on the mental state and psychological feeling of the human body [57]. For example,
for work and study places, a good visual environment can inspire spirit and improve work efficiency.
While for places of rest and entertainment, soft and dim visual environment can create a comfortable,
elegant, lively or solemn atmosphere.
For modern architectural design, indoor visual environment design is an indispensable part.
Illuminance is an important indicator of indoor visual environment. It refers to the luminous flux ofvisible light received per unit area (Eq.1-1), where 𝑑∅ is the luminous flux incident on the point
panel and 𝑑𝐴 represents the area of the point panel. [58]. Illuminance is used to indicate the quantityof light at the object surface. It is generally believed that illuminance is one of the important factors
affecting visual perceptions and work efficiency [59,60]. The research of Candas and Dufour showedthat at low illuminance environment (50lux), the probability of indoor personnel having the sickbuilding symptoms such as eye irritation, irritability and difficulty in focusing was increased. The
learning efficiency and work efficiency of the occupants also declined [61].
9𝐼𝑙𝑙𝑢𝑚𝑖𝑛𝑎𝑛𝑐𝑒 =𝑑∅(𝐸𝑞. 1 − 1)𝑑𝐴

Glare is another important indicator, which is used to reflect the uncomfortable feeling caused byuneven illumination distribution, which can reduce the visual perception about details. Therefore, inthis experiment, illuminance and glare are the environment physical parameters used to detect indoor
visual environment.
1.5 Acoustic Environment
When the sound wave acts on the auditory organ, the vibration of sound wave excites the sensory
cells of auditory organ and causes the impulse of auditory nerve. And then through analyzing by theauditory centers at all levels, the feeling is generated, which is called hearing [62]. If the sound wave
carries information, the information is transferred through this process and reached the auditory
cortex [63]. Because the waveform and frequency of different information is distinct, when the brain
processing the related sound waves, different waveforms and frequencies are recognized andconverted into corresponding information according to the rule of decoding. This rule of decoding is
the knowledge which brain already own [64-66].
For human beings, hearing is not only an important sensory channel, but also the second important
long-distance information acquisition channel of human body. The threshold of human hearing is
generally between 0dB(A) and 130dB(A). When the sound intensity exceeds 140dB(A), the soundwave no longer causes hearing, but tenderness [67]. However, the auditory threshold is also affectedby individual differences, environment, and other factors with a little fluctuation.
In modern office buildings, employees generally work in a semi enclosed area without partition,
which is so-called open-plan office. Such office conditions have the characteristics of low cost andconvenient communication, so they are favored by more and more companies [68]. However, therelevant studies showed that employees' environmental perception in an open-plan office wassignificantly lower than that in a traditional office, and the acoustic perception was the most affected[60,70]. This is because that, compared with traditional offices, open-plan offices often have somebackground noise, like telephone ringing, conversation, walking, typing and machine noise. These
noises are often useless, irrelevant and unpredictable, which causes subjective interference and has anegative impact on employees' acoustic environment perception [71].
10Previous studies only focused on the impact of high-intensity noise above 80dB(A) on the
acoustic perception and occupants’ productivity. However, for open-plan office, except for somespecial cases, the indoor background noise intensity is generally between 40dB(A) (quiet open-plan
office conditions) and 75dB(A) (normal busy open-plan office conditions). Moreover, the change ofsound intensity not only affects acoustic perception, but also exerts influence on thermal perception
and visual perception. However, the number of research about the influence of sound intensity on
indoor perception within this sound intensity region is quite limited. In addition, because researchtask needs more calculation and logistic activity than the ordinary office task, there exists an obvious
difference about the work difficulties between the research task and the ordinary office task. Therefore,
it is necessary to conduct research about the impact of acoustic environment changes on research
performance separately.
Some studies also showed that background noise and background music had different effects on
employees' work productivity under the same sound intensity. Sengupta and Jiang's research showedthat under the background music, subjects' hand movement during typing task was more stable [72].
Lesiuk's research showed that the learning curve was positively changed when studying withbackground music, which improved the studying performance of subjects [73]. However, some studies
believed that the background music would reduce the work efficiency [74].
1.6 Thermal Environment
Thermal perception is the skin feeling which stimulated by different temperatures. It includes two
different sensory systems: warmth perception and cold perception. Warmth perception and cold
perception are determined by relationship between stimulation temperature and the skin surface
temperature. If the stimulation temperature is above the skin surface temperature, it will cause warmth
perception. On the contrary, if the stimulation temperature is below the skin surface temperature, itwill cause cold perception [75]. For the indoor environment, the stimulation temperature can be
regarded as the average indoor air temperature.
Although thermal perception cannot carry information like vision and hearing, thermal perception
is still an important perception used to maintain the functional balance of the body. For the building
environment, thermal environment is an important indicator for indoor environment. Bad thermal
environment, like overheating or overcooling, will have a negative impact on indoor personnel.
11Living and working in such an environment for a long time will not only greatly reduce the work
performance, but also affect health, and even produce sick building syndrome [76,77]. Sick building
syndrome is closely related to the indoor environment. It refers to the adverse reactions to theoccupants’ health or comfort after a long residence time in the building. It is not caused by disease ordefinite pathology and limited to a specific space of the building. Most of the discomfort is eliminatedafter leaving the building [78].
1.7 Research Objectives
Main objective:
To develop a mathematical model which predicts the researcher’s performance based on theenvironment physical parameters for research institution.
Sub objectives:
In order to solve this main objective, three sub objectives were put forward:
1. To identify the influence mechanism of indoor environments on researchers’ environmental
perception and satisfaction in an open-plan office of research institution by considering the
combinational effect of different aspects of indoor environments.
2. To identify the influence mechanism of indoor environments on researchers’ research performancefor research institution by considering the combinational effect of different aspects of indoor
environments.
3. To identify the correlation effect between different environmental comforts.
4. To identify the weight of each environment indexes for predicting indoor environment quality
1.8 Research Significance and Originality
1.8.1 Mathematical model between environment parameters and research performance
Most of the existing literature used qualitative analysis when studying the impact of indoor
environment on occupants’ productivity. Since the coexistence of indoor environment parameters andtheir interaction with each other [79], it is quite difficult to develop a mathematical model between
occupants’ performance and combinations of different environment conditions. In this research, a12mathematical model about the influence of indoor environment on researchers’ research performance
were established, which filled the gap in this field.
1.8.2 Influence of combinational effect on occupants’ perception and satisfaction
For indoor environment, most of the research mainly focused on the influence of single
environment parameter on the researchers’ comfort [80-85]. According to work by Zomorodian et al.,acceptable indoor condition would not be achieved unless a holistic acceptance in air quality, thermal,
acoustic and visual comfort at the same time [9]. For this research, besides the research of influenceof individual parameter on corresponding comfort, the combinational effect of thermal, visual andacoustic parameters on researchers’ comfort were also analyzed, which filled the blank of the existing
research.
1.8.3 Fatigue Effect
The researchers need to conduct similar work day by day, where fatigue is a quite important factor
when evaluating the researchers’ performance. However, most of the research only used the task
accuracy as the only indicator to evaluate the occupants’ performance [11]. As fatigue will lead to thedecline of efficiency, it often takes extra time to complete the same work. For this research, in orderto balance the effect of fatigue on researchers' research performance, the accuracy and response timeshared the equal weight when calculating the performance index. Meanwhile, in order to prevent thefatigue influence caused by the sequence of experiments on the experimental results, Latin square
design was used in this research.
1.8.4 Controlled research office with radian floor heating system
In the existing research, the number of the research studied the relationship between IEQ andoccupants’ comfort and productivity in research institution are limited. In addition, most of theexperiments were conducted in a controlled office using air conditioner to adjust the thermal
environment in summer [43, 47]. But this is not in line with the winter heating condition in northern partof China, where radiant floor heating system are widely used instead of air conditioner. Therefore,
under the radiant floor heating system in winter, the relationship between IEQ and occupants’ comfort
and productivity need to be studied separately. Therefore, this research was based on the data froman experiment carried in a controlled research office in a pharmaceutical research institution in13

Northeast of China in winter. And the controlled research office was equipped with radian floor
heating system which supply heating in winter.
1.9 Thesis Structure
The thesis structure of this paper is shown in Fig. 1-5. Firstly, this paper qualitatively analyzed
the impact of indoor environment on researchers' environmental perception (Chapter 3, 4 and 5),which solved the sub objective 1. Secondly, this paper qualitatively analyzed the impact of indoor
environment on researchers' research performance (Chapter 3, 4 and 5), which solved the sub
objective 2. Thirdly, Chapter 6 of this paper analyzed the correlation among the three environmental
comforts, and identified the interaction among the three environmental comforts, which solved thesub objective 3. Fourth, this paper compared the existing three mathematical models of comfort
prediction, and improved the existing model equations through linear regression, so as to obtain theenvironmental comfort index. Through factor analysis of environmental comfort index, the weight ofeach environmental comfort index in indoor environment quality equation was obtained (Chapter 7),
which solved the sub objective 4. Finally, through the nonlinear regression analysis of IEQ index andresearch performance index, the relationship between IEQ index and Performance Index was obtained.
Through the series connection between performance prediction equation, indoor environment quality
equation and improved prediction equation of environmental comfort, the relationship between
research performance and environment physical parameters in research facilities was established. And
this mathematical model was the main objective of this research.

14

Fig. 1-5. Thesis structure of this paper

15Chapter II
Experimental design and research methodology
2.1 Data Collection
The participants of this experiment were researchers from a pharmaceutical research company innortheast of China. In total, 32 participants were recruited, including 16 males and 16 females. The
equal number of male and female recruited was to eliminate the interference of gender to theexperiment. The age of the participants was between 25 and 40 years old, such age was classified as
young people [86]. By just recruiting the young people, the interference of age to the experiment canbe eliminated. In this experiment, all the participants were voluntary to participate, and they were
healthy, without any history of serious diseases such as hypertension, asthma, diabetes and heartdisease. All participants lived in Northeast China for more than two years and adapted to the winterclimate conditions of the experimental site, so as to avoid the impact of climate adaptability on theexperimental results.
Before participating in the experiment, the subjects were told to keep enough sleep and had agood diet to reduce the impact of fatigue and physical sub-health on the experimental results. At thesame time, alcohol and smoking were strictly prohibited within 12 hours before the beginning of theexperiment. Drink tea, coffee, other functional drinks that would stimulate human function was also
forbidden within four hours before the experiment. In addition, strenuous exercise, like running or
jumping were not allowed within four hours before the experiment. Through requiring participants tocomply with these regulations, it can avoid the interference of short-term stimuli to the experiment.
It can also ensure that the simulated research scene was highly consistent with the real scene when
the subjects participated in the experiment.
2.2 Controlled Research OfficeThis research was conducted in a controlled research office in a pharmaceutical research company
in Northeast of China. The controlled office was transformed from an ordinary office, with a total
floor area of 64.8m2 and the clear ceiling height of 3m. The exterior wall of the controlled office wascovered by heat-insulating materials, and the six window shadings were installed to prevent the16interference of the nature light. The entire controlled office was heated using a radiant floor heating
system, with a total of six water inlet pipes. The amount of hot water flowing into the office wascontrolled through six valves, which was used to change the indoor air temperature. In winter, theindoor environment was relatively dry; therefore, the room was equipped with a humidifier to keep
the average indoor humidity at around 40%. The ceiling of the office was equipped with four groupsof eight 40W ceiling fluorescent lamps, which provided about 200lux of illumination on the desk ofthe participants. Each desk was equipped with an LED desk lamp, providing an additional 300lux
illumination. An 80-minute interview program was prepared on a computer for the noisy session tosimulate the situation of discussion in the research office. By adjusting the volume of the video, thenoise intensity near the desk was maintained around 70dB(A). The plan view of the controlled officeand the location of each environmental sensor are shown in Fig. 2-1.

Fig. 2-1. Plan view of controlled research office

Normally, experiments about indoor environment were usually carried out in a in a climate
chamber [11]. However, for this experiment, it was conducted in an existing office. The reason is thatthe traditional climate chamber is generally surrounded by concrete walls with heat insulation panels
and no window is installed in the room. This will give people in the climate chamber a cold anddepressing feeling [87]. In addition, the traditional climate chamber has a small indoor area, which can
only accommodate one participant at one time. Such structure cannot simulate the interaction of17

indoor personnel in the open-plan office environment. But the experiment conducted in the existing
office can well simulate the actual research environment and avoid the impact of difference ofstructures on the experimental data.
2.3 Experiment Sequence
In this experiment, three environment physical parameters were selected as the independent
variables: indoor air temperature (21℃, 24℃, 27℃), the illuminance level of the participants’desktop (200lux, 500lux), and the average background noise level of the research office (45dB(A),
70dB(A)). In total, there were 12 different thermal, visual and acoustic combinations, which wererecorded as environment conditions A-L. The description of each environment condition is shown in
Table 2-1.
Table 2-1
Illustration of 12 different environmental conditions.

The environmental comfort and research performance of participants are not only affected byindoor environment parameters but are also closely related to personal factors. Because of individual
differences, everyone has a different tolerance to fatigue. Therefore, to prevent the fatigue influencecaused by the sequence of experiments on the experimental results, a Latin square design was usedin this research. The principle of Latin square design is that every independent variable has the samechance to appear at every position of the experimental sequence. The specific experiment sequencesof the four experiment groups are shown in Table 2-2.
Table 2-2
Experiment sequence of each experiment group.

18

2.4 Experiment Procedure
In this experiment, the participants were divided into four experiment groups. Each experimentgroup included eight participants, where the number of male and female were equal. Each experimentgroup needed to participate in the experiment for three days. In each day’s experiment, the visual andacoustic conditions were changed four times, while the temperature remained unchanged. Eachenvironmental condition lasted for 90 minutes. The participants needed to carry out daily scientificresearch activities in the first 80 minutes and complete the subjective questionnaire as well as the
productivity performance test in the last 10 minutes. In each condition, the participants needed toremain seated to minimize the influence of activity level on perception. After each condition, theparticipants had a 20-minute break to eliminate the fatigue impact. In noisy conditions, participants
were allowed to discuss their research with others, while this behavior was strictly forbidden in quiet
conditions. The corresponding flow chart is shown in Fig. 2-2.

Fig. 2-2. Daily experiment process for each experiment group19

In order to avoid the influence of weekly cycle on participants, each experiment group participatedin the experiment at the same time every week. For example, the first experiment group participatedin the experiment every Monday, the second experiment group participated in the experiment every
Tuesday, and so on. In the weekly experiment, the indoor thermal environment of each experimentgroup remained unchanged. Because the change of indoor thermal environment took a certain time
to take effect and maintain stability, the valve of radiant floor heating system was adjusted every
Friday, and the controlled research office is left vacant for two days to make the indoor temperaturereach the temperature required for the next week's experiment.
One week before the experiment, each participant was asked to be familiar with the entire
experiment procedure and understand how to fill in the subjective questionnaire and complete theproductivity performance test. The experiment photos are shown in Fig. 2-3.

20

Fig. 2-3. Photos of participants during the experiment

2.5 Environment Physical Parameters
Four environment physical parameters were recorded during the experiment process: indoor air
temperature, relative humidity, the illuminance level of the participants’ desktop, and the averagebackground noise level of the research office. The temperature/RH sensor and sound meter werelocated 1.2 m above the floor at the center of the office, while the luxmeter was set on each desktop.
Office temperature and relative humidity were recorded three times in each environmental condition.
The illuminance level was measured twice, one was at the beginning and the other was at the end of
the experiment session. The noise intensity was measured every 20 minutes during the experiment.
The measured environment physical parameters of each experiment group under various environment
conditions are shown in Table 2-3.
Table 2-3
Summary of measured environment physical parameters (mean value ± standard deviation) for different
environmental conditions

The temperature/RH sensor used HT618 digital thermo-hygrometer humidity temperature meter(Dongguan Habotest Instrument Technology Co., Ltd, China). The temperature range is from -20°C
to 60°C with an accuracy of ±0.1°C. And the humidity range is 0% to 100%, with the accuracy of
0.1%.
Luxmeter adopted HT620l digital LED light meter (Dongguan Habotest Instrument Technology
21Co., Ltd, China), with a range of 0 to 200000lux and an accuracy of ±3%.
The sound meter adopted HT622A digital sound level meter/digital noise meter (Dongguan
Habotest Instrument Technology Co., Ltd, China), with a range of 30dB to 130dB and an accuracy of±1.5dB. At the same time, the sound meter can calculate the A-weighted sound level so that the output
value is the weighted average background noise intensity.
2.6 Subjective Questionnaire
At present, subjective questionnaire is the main evaluation method to study the perception andcomfort of indoor environment to indoor personnel. A specially designed questionnaire wasdistributed to the participants to assess their environmental perceptions and satisfactions about theenvironment of the research office. It included three sections: general information, environment
preference, and environment perception and satisfaction. General information collected theinformation of participants’ name, gender, age and clothing insulation level when tested. Environment
preference collected the information about the participants' preference for thermal environment,
visual environment and acoustic environment. Environmental perceptions and satisfactions recorded
the participants' perception and satisfaction about indoor thermal environment, visual environment
and acoustic environment under different environment conditions.
7-point ASHRAE scale was used for rating the participants’ perceptions in this experiment [9, 10].
For each environmental perception, the meaning of the seven scores is listed as following:(1) For thermal perception, the seven scores corresponded to Very Cold: -3，Cold: -2, Cool: -1,
Neutral: 0, Warm: 1, Hot: 2, Very Hot: 3.
(2) For visual perception, the seven scores corresponded to Very Dark: -3，Dark: -2, Slightly
Dark: -1, Neutral: 0, Slightly Bright: 1, Bright: 2, Very Bright: 3.
(3) For acoustic perception, the seven scores corresponded to Very Noisy: -3，Noisy: -2, Slightly
Noisy: -1, Neutral: 0, Slightly Quiet: 1, Quiet: 2, Very Quiet: 3.
Participants' satisfaction was also scored by 7-point ASHRAE scale. The meaning of the sevenscores corresponded to Very Dissatisfied: -3，Dissatisfied: -2, Slightly Dissatisfied: -1, Neutral: 0,
Slightly Satisfied: 1, Satisfied: 2, Very Satisfied: 3.

22

Fig. 2-4. Sample of subjective questionnaire.

23

3-point McIntyre scale was adopted for choosing the participants’ clothing level whenparticipating in the experiment. 2-point scale was used to collect the participants’ preference about
the environment.
2.7 Research Performance
In daily work, we will encounter a common phenomenon: when completing tasks with the same
workload, if we pursue the speed of completing tasks, there will be more errors and the quality oftask completion will decline; On the contrary, if we pursue the quality of completing tasks, we arebound to spend more working time to improve the quality of the work. The inverse relationshipbetween speed and completion quality is speed – accuracy trade-off [88]. For research work, thissituation is more obvious. Therefore, both speed and accuracy need to be considered when evaluating
researchers' research performance.

Fig. 2-5. Sample of performance test

In this experiment, a specially designed numerical calculations program was used to evaluate the24research performance of participants under various environmental conditions. The program included
a 10 × 11 matrix, each component was an integer from 0 to 10. During the experiment, the subjects
were asked to calculate the difference between two adjacent numbers in the same row (former minus
latter). If the difference was greater than 0, a positive value needed to be typed; if the difference wasless than 0, a negative value needed to typed; if the two numbers are equal, the difference wasrepresented by 0. The participants needed to answer the questions from the first line in order to thelast line, and each line needed to be completed from left to right. Correcting the previous mistakes
was strictly prohibited. At the same time, the participants were required to complete the test within
300s. The sample of performance test is in Fig. 2-5.
2.8 Evaluation of research performance
After the participants completed the performance test, the completed answers were manually
brought into the accuracy evaluation program, and the program automatically calculated theparticipants’ answer accuracy under this environment condition. The statistical method is shown in
Eq. 2-1, where 𝑁𝐶 represents the number of answers correctly completed by the participants, 𝑁𝑇stands for the total number of test questions. The range of accuracy is from 0 to 100.
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =𝑁𝐶
× 100 (𝐸𝑞. 2 − 1)𝑁𝑇After the participants completed the performance test, the numerical calculations programautomatically counted the participants' response time, and the unit was in seconds. According to thepilot tests, the average response time for the participants to complete the performance test under allenvironment conditions was 213s, and the standard deviation is 23s. According to the principle oftriple standard deviation, the probability of participants completing the performance test within𝑚𝑒𝑎𝑛 − 3𝜎 = 213 − 3 × 23 = 144s to 𝑚𝑒𝑎𝑛 + 3𝜎 = 213 + 3 × 23 = 282𝑠 was 99.73%. Therefore,

the effect range of response time was between 144s and 282s. Considering the individual differences,
the response time range was expanded to 100 to 300s.
Because speed and accuracy needed to be considered at the same time when evaluating
researchers' research performance. Therefore, in this experiment, geometric weighted average wasadopted to obtain performance indexes, in which the accuracy and speed shared the equal weight,
both were 0.5. The performance index was expressed as Eq. 2-2. Because the range of accuracy of
25

the participants' answers was from 0 to 100, and the range of the response time was from 100 to 300s.
Through calculating using Eq. 2-2, the range of actual performance index was from 0 to 100.
To express the productivity performance of the participants more intuitively, the performanceindexes in each environment condition needed to be standardized. The formula is shown in Eq. 2-3,
where 𝑃𝐼𝑖𝑗 represents the performance index of participant i under environmental condition j, n
represents the total environment conditions and equals to 12, and 𝑅𝑃𝐼𝑖𝑗 represents the percentage ofparticipant i’s performance under environmental condition j compared with the average performance
under all 12 conditions.
𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 (𝑃𝐼) = 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 0.5 × 𝑆𝑝𝑒𝑒𝑑 0.5 × 100 =𝑅𝑃𝐼𝑖𝑗 =

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 0.5
× 100 (𝐸𝑞. 2 − 2)
𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑇𝑖𝑚𝑒 0.5

𝑛 × 𝑃𝐼𝑖𝑗
× 100% (𝐸𝑞. 2 − 3)
∑𝑛𝑗=1 𝑃𝐼𝑖𝑗

2.9 Sample Size Calculation
According to the principle of statistics, there exists a relationship between the level of significanceα, statistical power (1-β), the effect size ES and the sample size N. Once three of the above variables
are determined, the fourth variable is also determined. Therefore, the sample size N can be estimatedusing the significance level α, statistical power (1-β) and the effect size ES.
Among them, the significance level α refers to the probability or risk that H0 is rejected incorrectlyin the analysis when the original assumption H0 is correct, that is also called type I error. In thisexperiment, the confidence interval was 95%, so the level of significance α was set as 0.05.
Statistical power is related to beta error probability. Beta error probability refers to the probability
or risk that H0 is wrongly accepted in the analysis when the original assumption is wrong, that is also
called type II error. Statistical power refers to the probability that H0 is correctly rejected in theanalysis when the original hypothesis H0 itself is wrong. Therefore, the 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙 𝑝𝑜𝑤𝑒𝑟 = 1 − 𝛽 .
In the experimental analysis, the statistical power is generally greater than 80%, so when estimating
the number of samples, the statistical power was set as 0.8.
Effect size ES refers to the difference caused by factors, and it is an index to measure the size oftreatment effect. Different from the significance test, effect size is not affected by the sample size.
When estimating the number of samples, the effect size is calculated using Cohen's d, generally the26value of d was set as 0.5.
Because the relationship among significance level α, statistical power (1-β), effect size ES andsample size N is complex, so the sample size was estimated with the help of online analysis software"understanding statistical power and significance testing" [89]. Fig. 2-6 shows the results of sample
size calculation. It can be seen from the results that the total number of samples needed to be at least
31.4. Therefore, in this experiment, the total number of samples was set to 32.

Fig. 2-6. Sample size calculation using online analysis software.

2.10 Data Analysis Method
The data of this experiment were statistically analyzed by SPSS 21.0. Related analysis methods
included paired sample t-test, one-way ANOVA, correlation analysis, factor analysis and regression
analysis2.10.1 Paired Sample T-test

27

Paired sample t-test refers to calculating the difference between the results of two different
processing methods for the same sample, and testing the significance of the difference and zero. If
there is no difference between two treatments, the overall mean value of the difference should be zero,and the difference should fluctuate around zero. On the contrary, if there is difference between twotreatments, the overall mean value of the difference should be far away from zero.
One of the requirements for the paired sample t-test is that the difference between two groups
should be normally distributed. In this experiment, paired sample t-test was used to examine theeffects of different visual and acoustic environments on environmental perception, satisfaction andresearch performance.
2.10.2 One-way ANOVA
Analysis of variance, abbreviated as ANOVA, refers to the determination of whether the overallmean value is equal by analyzing the variance of multiple samples. Before analyzing the variance ofdata samples, the samples need to meet three conditions:
1. The samples need to meet the normal distribution,
2. The samples are independent of each other, and3. The samples are homogeneous in variance.
In this experiment, analysis of variance was used to examine the qualitative relationship between
different thermal environments and environmental perception, satisfaction and research performance.
It was also used to examine the significance of the regression equation.
2.10.3 Correlation Analysis
Correlation analysis refers to the analysis of two or more variable elements with correlation, so
as to measure the correlation degree of two or more factors. Correlation analysis can be carried out
only when there is a certain connection or probability between the relevant elements.
In this experiment, correlation analysis was used to analyze the correlation among participants'
actual thermal satisfaction, visual satisfaction and acoustic satisfaction. The correlation coefficient
between any two variables was calculated by Pearson’s correlation coefficient.
2.10.4 Factor Analysis
In this experiment, factor analysis was used to calculate the weight of each environmental comfort
28

index in IEQ index equation. The principle is to use the information concentration principle, throughstudying the internal dependence between many variables, explore the basic structure of observation
data and calculate the weight by using the variance interpretation rate.
2.10.5 Regression Analysis
Regression analysis is a predictive modeling technique. The method is to determine thequantitative relationship between dependent variables and independent variables from a set of data.
This is also regarded as establishing a mathematical model and estimating the unknown parameters.
The common method of estimating parameters is the least square method. The samples of regression
analysis should meet the following requirements:
1. There is no collinearity between independent variables, which is detected in the collinearity
analysis of independent variables;
2. Residuals should be independent of each other. And there is no correlation among residuals. In
addition, the residuals must obey normal distribution. Analysis of residuals was conducted to examine
the above three requirement for residuals.
In this experiment, multiple linear regression was needed to improve the three environmental
index equations. Nonlinear regression was needed to study the relationship between performanceindex and IEQ index.

29

Chapter III
Influence mechanism of thermal environment on environmental perception,satisfaction and research performance
Sick building syndrome is caused by bad indoor environment. It refers to the adverse reactions to
the occupants’ health or comfort after a long residence time in the building. How to improve theindoor thermal environment and reduce sick building syndrome is an important research direction. In
order to answer this question, it is necessary to clearly study the impact mechanism of indoor thermal
environment on indoor environmental perception and satisfaction of occupants, which is one of thepoints to be discussed in this chapter. At the same time, the thermal environment also affects theresearch efficiency. Therefore, another point to be discussed in this chapter is the influencemechanism of indoor thermal environment on research performance.
3.1 Influence mechanism of thermal environment on thermal perception
In order to compare the effects of different thermal environments on environmental perception
and satisfaction, 12 environment conditions were divided into four thermal groups according todifferent visual and acoustic environment combinations. For each thermal group, the visualenvironment and acoustic environment remained the same, but the thermal environment was different.
Therefore, the average indoor air temperature was the only variable for the analysis within thermalgroup in this chapter.
The results of subjective evaluation of the participants’ thermal perception are shown in Table 31 under different combinations of visual and acoustic condition. According to the results, theparticipants felt cool at 21℃ thermal environment, since the average thermal perception at 21℃ for
all four thermal groups were in the range between -0.75 and -0.50. When the temperature increased
to 24℃, the average thermal perception value changed to the range between 0.25 and 0.59. This
indicated that at this temperature, no matter how the visual and acoustic environment changed, theparticipants’ perceptions of the thermal environment were warm. For the temperature of 27℃, theresults showed that the participants' feelings about thermal environment were concentrated around
1.5, which was regarded as hot.
30

Table 3-1
Results of thermal perception and thermal satisfaction for different thermal groups

In each thermal group, the tendency of thermal perception with temperature for each thermalgroup is visually displayed in the bubble plot Fig. 3-1. The size of the bubble in the figure reflects thenumber of votes by the participants at that score. And the straight line represents the linear regression
line based on the average thermal perceptions of three different thermal environments. It can be seenfrom the regression line that the goodness of fit of mean values for four thermal groups were larger
than 0.99, which were close to 1. This showed that there existed a linear relationship between theindoor air temperature and the mean values of thermal perception for each thermal group. And in thetemperature range of 21℃ to 27℃, the thermal perception increased linearly with the increase ofindoor air temperature.
In order to analyze whether there existed a significant difference between thermal perception indifferent temperature environments, one-way ANOVA was used in this experiment. The results of
ANOVA for each thermal group are shown in Table 3-2.
According to the results of the test of homogeneity of variables, the significance of Levenevariance homogeneity test was greater than the significance level of 0.05. Therefore, it could beconsidered that the variance between the sample data of each thermal group was homogeneous. In
the post hoc analysis, because of the Homogeneity of variance, Tukey honestly significant difference(Tukey HSD) test was used for multiple comparison procedure.

31

Fig. 3-1. Thermal perception votes and linear regression of the mean votes of four different visual and acoustic
combinations: (A) 200lux and 45dBA, (B) 200lux and 70dBA, (C) 500lux and 45dBA and (D) 500lux and 70dBA

According to the results of one-way ANOVA, the significance of the sum of squares between
groups was 0.000, less than the significance level of 0.05. Therefore, it could be concluded thatdifferent temperatures had a significant influence on the participants' perception of the thermal
environment.
Fig. 3-2. demonstrates the results of multiple comparisons between different temperatures withineach thermal group. The red mark indicated that the mean difference of this pair was significant.
Therefore, it could be seen that the mean difference of thermal perception of 21℃, 24℃ and 27℃32was obvious. In addition, it could also prove that the subject's thermal perception would increasesignificantly with the increase of temperature.
Table 3-2
Test of homogeneity of variances and ANOVA results about thermal perception for each thermal group

Fig. 3-2. Results of comparison between adjacent temperatures about thermal perception for each thermal group

3.2 Influence mechanism of thermal environment on thermal satisfaction
The results of the participants’ thermal satisfaction for different thermal groups are shown in Table
3-1. According to the results, the average thermal satisfaction at 21℃ for all four thermal groups werein the range between -0.59 and -0.31, which indicated that the participants were slightly dissatisfied
33

about this thermal environment. For the temperature of 24℃, the results showed that the participants'satisfaction about thermal environment were around 0.5, which can be interpreted as slightly satisfied.
When the temperature increased from 24℃ to 27℃, the average thermal satisfaction dropped toaround -0.5, which represented that the occupants were slightly dissatisfied about the thermal
environment of 27℃. Through analyzing the tendency of average values of thermal satisfaction under
different acoustic and visual environment combinations, it can be found that the relationship between
thermal satisfaction of the participants and indoor air temperature can be regarded as a parabola. And
the peak satisfaction rate appeared around 24℃.
In order to analyze whether there existed a significant difference about the thermal satisfaction
between the pair of adjacent temperature, one-way ANOVA was conducted. The results of ANOVA
for each thermal group are shown in Table 3-3.
According to the results of the test of homogeneity of variables, the significance of Levenevariance homogeneity test was greater than the significance level of 0.05 only for thermal group of
dark and noisy. Therefore, for dark and noise thermal group, it could be considered that the variance
between the sample data was homogeneous. In the post hoc analysis, because of the homogeneity ofvariance, Tukey HSD test was used for multiple comparison procedure. However, for the other threethermal groups, the significance value of Levene variance homogeneity test was less than thesignificance level of 0.05. Therefore, the variance between the sample data was unequal and Dunnett's
all-pairs comparison test (Dunnett’s T3) was used to analyze the difference between adjacent pair oftemperatures within thermal group.
According to the results of one-way variance analysis, the significance of the sum of squares
between groups was less than the significance level of 0.05. Therefore, it can be concluded thatdifferent temperatures had a significant influence on the participants' satisfaction of the thermal
environment.
For the results of multiple comparisons between adjacent pair of temperatures within thermalgroup (Fig. 3-3), the red mark indicated that the mean difference of this pair was significant. Therefore,
it can be seen that with the increase of the temperature from 21℃ to 24℃, the thermal satisfaction
score significantly increased. But opposite trend was found when the temperature continued toincrease from 24℃ to 27℃.

34

Table 3-3
Test of homogeneity of variances and ANOVA results about thermal satisfaction for each thermal group

Fig. 3-3. Results of comparison between adjacent temperatures about thermal satisfaction for each thermal group

When the thermal comfort was calculated in this experiment, the concept of actual percentage ofdissatisfaction, which was calculated as the proportion of people who chose the “very dissatisfied” or“dissatisfied” options about the thermal satisfaction voting to the total number of the participants.
Therefore, it was necessary to understand the relationship between actual percentage of dissatisfaction
and thermal environment.
Fig. 3-4 demonstrates the scatter plot of the actual percentage of dissatisfaction about thermal
environment for different environment conditions. The regression line was drawn for each thermal
35

group with the same visual and acoustic environment. From the figure, it can be seen that the actual
percentage of dissatisfaction was the lowest when the temperature was 24℃, while the mostdissatisfaction occurred when the temperature was 27℃. The optimal temperature of the lowest points
for the above four parabolic curve were between 22℃ and 23℃. When the temperature increased
from 21℃ to the optimal temperature, the actual percentage of dissatisfaction decreased. Once thetemperature exceeded the optimal temperature, the actual percentage of dissatisfaction increased
significantly.

Fig. 3-4. Scatter plot of the actual percentage of dissatisfaction about thermal environment for different environment
conditions

3.3 Influence mechanism of thermal environment on acoustic perception
In order to analyze the effect of thermal environment on acoustic perception, one-way ANOVA
was used, and the results are shown in Table 3-4. From the test of homogeneity of variables fordifferent thermal groups, it can be seen that the significance of Levene variance homogeneity test
were greater than the significance level of 0.05 for all four thermal groups. Therefore, it can be36

considered that the variance between the sample data of each thermal group was homogeneous.
According to the results of one-way analysis of variance, the significance of the sum of squares
between groups was far larger than the significance level of 0.05. Therefore, the influence of thermal
environment on acoustic perception was not significant, and subsequent multi comparison analysis
was not required.
Table 3-4
Test of homogeneity of variances and ANOVA results about acoustic perception for each thermal group

3.4 Influence mechanism of thermal environment on acoustic satisfaction
One-way ANOVA was used to analyze the effect of thermal environment on acoustic satisfaction.
The results are shown in Table 3-5. According to the test of homogeneity of variables for different
thermal groups, it can be seen that the significance of Levene variance homogeneity test were larger
than the significance level of 0.05 for all four thermal groups. Therefore, it can be considered that thevariance between the sample data of each thermal group was homogeneous.
According to the results of one-way analysis of variance, only for thermal group of dark and noisy,
the significance of the sum of squares between groups was less than the significance level of 0.05.
Therefore, for thermal group of dark and noisy, post hoc analysis was required. And because of thehomogeneity of variance, Tukey HSD test was used for multiple comparison procedure.
The multiple comparison results showed that only for pair comparison between temperature of
21℃ and 27℃, the significance value of Tukey HSD test was 0.023, which was less than 0.05.
However, since 21℃ and 27℃ were not adjacent temperature, therefore, this result was meaningless.
And for the two adjacent temperature pairs, both of the significance values of Tukey HSD test werelarger than 0.05. This indicated that even though the significance of ANOVA was less than 0.05,
37

through post-hoc analysis, thermal environment had no effect on acoustic satisfaction.
Table 3-5
Test of homogeneity of variances and ANOVA results about acoustic satisfaction for each thermal group, andcomparison between adjacent temperatures for Dark and Noisy group

3.5 Influence mechanism of thermal environment on visual perception
One-way ANOVA was used in this section to analyze the effect of thermal environment on
acoustic satisfaction. The results are shown in Table 3-6. From the test of homogeneity of variables
for different thermal groups, it can be seen that the significance of Levene variance homogeneity test
were larger than the significance level of 0.05 for all different combinations of visual and acoustic
environment.
According to the results of one-way analysis of variance, only for bright and noisy group, thesignificance of the sum of squares between groups was less than the significance level of 0.05.
Therefore, post hoc analysis was required. And because of the homogeneity of variance, Tukey HSD
test was used for multiple comparison procedure. The multiple comparison results showed that when
the temperature increased from 24℃ to 27℃, the visual perception increased 0.719 level withsignificance of 0.011.

38

Table 3-6
Test of homogeneity of variances and ANOVA results about visual perception for each thermal group, andcomparison between adjacent temperatures for Bright and Noisy group

3.6 Influence mechanism of thermal environment on visual satisfaction
In order to analyze the influence of thermal environment on visual satisfaction, ANOVA wasadopted in this experiment. The results of homogeneity test for different groups are in Table 3-7. Onlyfor bright and noisy condition, the significance of Levene variance homogeneity test was less than0.05, where the original hypothesis that the variance was homogeneous should be refused.
Table 3-7
Test of homogeneity of variances and ANOVA results about visual satisfaction for each thermal group

According to the results of one-way analysis of variance, the significance of the sum of squares
39between groups was far larger than the significance level of 0.05. Therefore, the influence of thermal
environment on visual satisfaction was not significant, and subsequent multi comparison analysis was
not required.
3.7 Influence mechanism of thermal environment on research performance
In this experiment, two indicators were used to calculate the research performance, which were
the answering accuracy and response time of the performance test. For this section, these two
indicators together with the relative research performance index were considered as the dependent
variables. And the one-way ANOVA test was used to examine whether the thermal environment
would affect the research performance.
3.7.1 The effect of thermal environment on answer accuracy of performance test
Table 3-8 shows the homogeneity test results and ANOVA of answering accuracy for all four
thermal groups. The results showed that the significance value of Levene variance homogeneity test
was greater than the significance level of 0.05, which proved the variance of answering accuracy were
all homogeneous. The F factor between the groups were all larger than 0.05, which indicated that thedifference of answering accuracy caused by the thermal environment was not significant and there
was no need for further comparison.
Table 3-8
Test of homogeneity of variances and ANOVA results about answer accuracy of performance test for each thermal
group

40

3.7.2 The effect of thermal environment on response time of performance test
Table 3-9 demonstrates the ANOVA results about the response time for different visual andacoustic environment combinations. The results showed that for dark and noisy group, p value ofLevene variance homogeneity test was 0.034 < 0.05. This indicated that the variance of data for dark
and noisy group was unequal. Because of that, Dunnett T3 method was used to conduct the post-hoc
comparison. The variance of data for the other thermal groups were examined as homogeneous, where
Tukey HSD method was selected for post-hoc comparison. The significance values between thegroups of variance analysis were all less than 0.05, except for the bright and quiet thermal group. This
proved the thermal environment had the effect on answering speed of participants for thermal groups
other than bright and quiet group. And the data of these three groups were qualified to conduct the
post-hoc comparison using the corresponding methods mentioned above.
Table 3-9
Test of homogeneity of variances and ANOVA results about response time of performance test for each thermal
group

According to the multi comparison results, for dark and quiet thermal group, the answering speed
decreased with the temperature increased from 21℃ to 27℃. For dark and noisy group, only whentemperature increased from 21℃ to 24℃, the answering speed decreased. For bright and noisy group,
both the p value for two adjacent temperature pairs were larger than 0.05, which meant the influenceof temperature on response time at this situation was not significant.

41

Fig. 3-5. Results of comparison between adjacent temperatures about response time of performance test for eachthermal group

3.7.3 The effect of thermal environment on RPI of performance test
The relative research performance index of each environment condition was calculated according
to Eq. 2-2 and Eq. 2-3. The ANOVA results of relevant RPI within each thermal group are shown in
Table 3-10. According to the results of the test of homogeneity of variables, the significance valuesof Levene variance homogeneity test were greater than the significance level of 0.05 for both dark
quiet and bright quiet conditions. For these two thermal groups, the variance between the sample data
was homogeneous and Tukey HSD test was used for multiple comparison procedure. For dark noisy
and bright noisy groups, the variance of the data was not equal, thus, Dunnett T3 method was used to
conduct the post-hoc comparison. From the F-test results, the p values of all four thermal groups wereless than 0.05, which indicated that the entire sample was qualified for variance analysis.
From the post-hoc analysis results, it can be seen that the p values for adjacent temperature pairs
for dark and quiet thermal group were less than 0.05, which indicated that thermal condition indeed
had affect the researcher’s research performance.

42𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 (𝑃𝐼) = 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 0.5 × 𝑆𝑝𝑒𝑒𝑑 0.5 × 100 =𝑅𝑃𝐼𝑖𝑗 =

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 0.5
× 100 (𝐸𝑞. 2 − 2)
𝑅𝑒𝑎𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 0.5

𝑛 × 𝑃𝐼𝑖𝑗
× 100% (𝐸𝑞. 2 − 3)
∑𝑛𝑗=1 𝑃𝐼𝑖𝑗

Table 3-10
Test of homogeneity of variances and ANOVA results about RPI of performance test for each thermal group

Fig. 3-6. Results of comparison between adjacent temperatures about RPI of performance test for each thermal
group

43

Chapter IV
Influence mechanism of acoustic environment on environmental perception,satisfaction, and research performance
For the open-plan office environment, background noise intensity is one of the main factors
affecting the indoor environmental perception, indoor environmental satisfaction and productivity.
For the research facilities with strict requirements for noise level, the influence on occupants’
perception and performance caused by the change of acoustic environment is more significant.
Therefore, it is necessary to understand the influence mechanism of acoustic environment onenvironmental perception, satisfaction and research performance.
In this chapter, based on the results of the subjective questionnaire, the influence mechanism ofbackground noise intensity on the participants' environmental perception and satisfaction was
qualitatively analyzed. In addition, according to the performance test results of the participants,
whether the change of background noise level affected the researchers' scientific performance wasalso qualitatively analyzed.
4.1 The influence of acoustic environment on acoustic perception and satisfaction
In order to compare the effects of different acoustic environments on acoustic perception andsatisfaction, 12 environment conditions were divided into six acoustic groups according to different
thermal and visual environment combinations. For each acoustic group, the thermal and visual
physical parameters were the same, but the acoustic physical parameter was different. Fig. 4-1 andFig. 4-2 show the voting results of the participants about acoustic perception and acoustic satisfaction
based on different acoustic groups.
The results showed that the mean value of acoustic perception was in the range from 0.31 to 0.78
for acoustic environment of 45dB(A). This indicated that the average feeling of participants aboutbackground noise level 45dB(A) was neutral or slightly quiet. According to the acoustic satisfaction
results, the participants’ average satisfaction about 45dB(A) was between 0.53 and 0.91. This
indicated that the participants were slightly satisfied with this acoustic environment.
There existed some difference between the actual acoustic perception and satisfaction with the44expectations at noise level 45dB(A). One reasonable explanation is that according to the American
National Standards Institute (ANSI) and European standard EN15251, the one-hour steady-statebackground noise levels should not exceed 40dB(A) for the scientific facilities [90, 91]. Because thecontrolled research office in this experiment was classified as open-plan office, where the typing noiseby operating the computer could not be avoided. Therefore, even though all the participants wereasked to keep quiet during the experiment, the minimum background noise intensity can be achievedin controlled research office was around 45dB(A), which was slighter higher than the required noiselevel from standard. Because of that, the average acoustic perception and average acoustic satisfaction
were lower than the expectation.

Fig. 4-1. Results of acoustic perception and paired sample t-test results about influence of acoustic environment on
acoustic perception for each acoustic group

For the background noise intensity of 75dB(A), the mean value of acoustic perception decreased
to the range from -1.88 to -1.59, where the participants generally believed that this acoustic
environment was noisy. And the mean value of acoustic satisfaction was also decreased to the range
from -2.16 to -1.50. This indicated that the participants felt dissatisfied about 75dB(A) acoustic
environment. This was consistent with the expectation of employees’ perception about noisy open45

plan office condition.
The paired sample t-test results about acoustic perception and acoustic satisfaction for eachacoustic group are also shown in Fig. 4-1 and Fig. 4-2 respectively. The results showed that thesignificance value for all six acoustic groups were less than 0.05 with effect size larger than 0.8. The
mean difference between quiet condition and noisy condition were around 2 levels. This indicatedthat the background noise intensity had a significant impact on participants’ acoustic perception.
According to paired sample t-test about acoustic satisfaction, for every combination of thermaland visual environment, the participants’ satisfaction about the acoustic environment increased withthe decrease of the background noise intensity (all p < 0.05). The increase range was between 2.313
and 2.688 levels. This showed that the acoustic environment had a significant impact on acoustic
satisfaction.

Fig. 4-2. Results of acoustic satisfaction and paired sample t-test results about influence of acoustic environment on
acoustic satisfaction for each acoustic group

One possible explanation is that with the increase of background noise intensity, humansympathetic nerve activity also increases. This further affects the irritability and negative emotion ofoccupants, and finally reduces the comfort level of the occupants. Another possible explanation is46

that when the same noise acts on participants for a long time, it will cause a significant reduction inauditory receptivity. This is so-called auditory fatigue. When auditory fatigue happens to theoccupants after a long-time noise, the comfort level about acoustic environment of the participants
would decrease.
4.2 The influence of acoustic environment on thermal perception and satisfaction
Table 4-1 shows the paired sample t-test results of the thermal perception of participants for eachacoustic group. The results showed that only for 21℃ dark and 24℃ dark environment, there existed
significant difference of thermal perception when the average background level changed (t (31) = 2.490, p = 0.018 < 0.05 and t (31) = -2.252, p = 0.032 < 0.05). The effect size for 21℃ dark
environment was -0.328, and for 24℃ dark environment was -0.274. Both absolute values were
within the range of 0.2 to 0.5. This indicated that influence of acoustic environment on thermal
perception at these two specific acoustic groups was small.
One possible reason is that 21℃ and 24℃ achieve cool comfort and warm comfort respectively.
At this time, visual and acoustic parameters become the main factors affecting the thermal perception.
And people tend to be more sensitive in dark environments. Therefore, at 21℃ dark and 24℃ dark
environments, the acoustic environment has a significant influence on thermal perception.
In addition, for all six acoustic groups, the mean difference of thermal perception between quiet
condition and noisy condition was less than zero. This indicated that quiet environment can make theparticipants feel cooler, while the noisy environment can make the participants feel hotter.
The paired sample t-test results of background noise intensity on participants' thermal satisfaction
are shown in Table 4-2. The results showed that although the thermal satisfaction of personnel in quiet
environment was higher than that in noisy environment under all six combinations of thermal andvisual environments, the gap between quiet and noisy environments was not obvious (all p > 0.05).
This showed that background noise intensity was not a secondary factor affecting participants’
thermal satisfaction.

47

Table 4-1
Results of thermal perception and paired sample t-test results about influence of acoustic environment on thermal
perception for each acoustic group

Table 4-2
Results of thermal satisfaction and paired sample t-test results about influence of acoustic environment on thermalsatisfaction for each acoustic group

4.3 The influence of acoustic environment on visual perception and satisfaction
Table 4-3 shows the paired sample t-test results about the influence of acoustic environment on
visual perception between quiet and noisy conditions in all six acoustic groups. The results showedthat the increase of background noise intensity would reduce the visual perception in 24℃ bright (t(31) = 2.339 with p = 0.026 < 0.05) and 27℃ dark environments (t (31) = 2.104 with p = 0.044 <48

0.05). These were two extreme environments. For 24℃ bright environment, the thermal environment
reached warm comfort and visual environment reached visual comfort. At this time, the acoustic
parameter was the only factor affecting visual perception. On the contrary, for 27℃ dark environment,
participants felt discomfort for both thermal and visual aspects. Because of this, the participants
became more sensitive about the environment. Therefore, the change of background noise intensity
would make significant influence on visual perception. The effect size for 24℃ bright environment
was 0.416, and for 27℃ dark environment was 0.278. Both absolute values were within the region of
0.2 to 0.5. This indicated that influence of acoustic environment on visual perception at these two
specific acoustic groups was small.
Table 4-3
Results of visual perception and paired sample t-test results about influence of acoustic environment on visual
perception for each acoustic group

This section also studied the influence of the change of acoustic environment on participants'
visual satisfaction under different thermal and visual environments (Table 4-4). The results of paired
sample t-test showed that, for acoustic group 21℃ bright and 24℃ bright, with the increase of
background noise intensity, the participants' visual satisfaction decreased about 0.5 level (t (31) =2.247 with p = 0.032 < 0.05 and t (31) = 2.79 with p = 0.009 < 0.05). The effect size for 21℃ brightenvironment was 0.280, indicating that for this acoustic group, the influence of acoustic environment
on visual satisfaction was small. And the effect size for 24℃ bright environment was 0.513. This
demonstrated that for 24℃ bright condition, acoustic environment can exert medium influence on
49visual satisfaction.
A reasonable explanation is that participants achieved cool comfort and warm comfort in the 21℃
and 24℃ bright environments respectively, and the visual environment also reaches the optimal
condition, where noise level becomes a secondary factor affecting visual satisfaction.
For other thermal and visual environments, although the significance values of paired sample ttest were greater than 0.05, it was still found that the visual satisfaction decreased with the increaseof background noise intensity.
Table 4-4
Results of visual satisfaction and paired sample t-test results about influence of acoustic environment on visual
satisfaction for each acoustic group

4.4 The influence of acoustic environment on research performance
The section studied whether the change of background noise intensity would affect the researchers’
research performance under each thermal and visual environment combination. According to thepaired sample t-test between acoustic environment and answer accuracy from performance test of theparticipants (Table 4-5), only for 21℃ bright environment, the average answer accuracy which theparticipants accomplished in quiet condition was 1.406 points higher than that accomplished in noisy
condition (t (31) = 2.948 with p = 0.006 < 0.05). The Cohen’s d value of this acoustic group was
0.327, which was in the region between 0.2 and 0.5. This indicated that the effect between acoustic
environment and answer accuracy for acoustic group 21℃ bright was small. For other acoustic groups,
all significance value was greater than 0.05, which meant that the change of background noise
50

intensity had no significant impact on the answer accuracy of the participants.
Table 4-5
Results of answer accuracy of performance test and paired sample t-test results about influence of acoustic
environment on answer accuracy for each acoustic group

According to the paired sample t-test results of influence of acoustic environment on answer
response time of performance test (Fig. 4-3), all the significance value for all six acoustic groups wereless than 0.05 and the absolute value of effect size was all larger than 0.8. Therefore, there existed asignificant difference in answer speed between quiet environment and noisy environment for all six
acoustic groups. It can be seen all the mean value of difference between quiet condition and noisy
condition were less than 0, which indicated that the participants could answer faster in quiet condition
than that in noisy condition.
When the thermal condition was 21℃ and the visual condition was 200lux, the response time ofthe participants was most affected by the background noise intensity, and the response time in quiet
condition was 17.7% shorter than that in noisy condition. When the thermal condition was 27℃ andthe visual condition was 200lux, the participants’ answering speed was least affected by the noiselevel, where the time to complete the test was shortened only 7.6%.

51

Fig. 4-3. Results of response time of performance test and paired sample t-test results about influence of acoustic
environment on response time for each acoustic group

The relative performance index was calculated using Eq. 2-2 and Eq. 2-3. According to the paired
sample t-test (Fig. 4-4), the significance value of all the thermal and visual combinations were lessthan 0.05 with effect size larger than 0.8. And the mean value of difference between quiet and noisy
condition was negative. Therefore, with the increase of background noise intensity, the researchers’
research performance decreased significantly. This was in line with the influence trend of acoustic
environment on answer accuracy and response time.
𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 (𝑃𝐼) = 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 0.5 × 𝑆𝑝𝑒𝑒𝑑 0.5 × 100 =𝑅𝑃𝐼𝑖𝑗 =

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 0.5
× 100 (𝐸𝑞. 2 − 2)
𝑅𝑒𝑎𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 0.5

𝑛 × 𝑃𝐼𝑖𝑗
× 100% (𝐸𝑞. 2 − 3)
∑𝑛𝑗=1 𝑃𝐼𝑖𝑗Same as the condition in paired sample t-test analysis of response time, when the thermal
environment was 27℃ and the visual condition was 200lux, the relative research performance wasleast affected by noise level, and only increase 3.57%. On the contrary, for 21℃ 200lux environment,
the Cohen’s d value was 3.843, which was the largest. This indicated that the impact of acoustic
environment on research performance was the most significant with a 9.70% increase.

52

Fig. 4-4. Results of RPI of performance test and paired sample t-test results about influence of acoustic environmenton RPI for each acoustic group53

Chapter V
Influence mechanism of visual environment on environmental perception,satisfaction and research performance
At present, the research on the influence of visual environment on environment comfort andresearch efficiency is still very limited. Therefore, this chapter qualitatively analyzed the impact ofdifferent visual environments on researchers' indoor environmental perception and satisfaction under
different thermal and acoustic combinations according to the data collected from subjective
questionnaires and physical environment monitoring. At the same time, according to the performance
test results of researchers, the effects of different visual environments on researchers' research
performance were qualitatively analyzed.
5.1 Influence mechanism of visual environment on visual perception and satisfaction
Similar to the analysis of acoustic environment on environmental perception and satisfaction, 12
environment conditions were divided into six visual groups according to different thermal andacoustic environment combinations, so as to compare the effects of different acoustic environments
on acoustic perception and satisfaction. For each visual group, the thermal and acoustic environment
remained same, and the only difference was the visual physical parameters.
The voting results of the visual perception of the controlled research office under different thermal
and acoustic combinations are shown in Fig. 5-1. The voting results showed that under all six thermal
and acoustic combinations, the average visual perception of the participants to 200lux environment
was between -1.50 and -1.03, indicating that the participants' subjective perception of 200lux wasdark to slightly dark. On the contrary, the average visual perception of the participants to 500lux was
between 0.81 and 1.53, indicating that the participants generally believed that the environment of
500lux was slightly bright to bright.
Fig. 5-2 shows the participants’ visual satisfaction of the controlled research office under 12
environment conditions. According to the average value of visual satisfaction, for all six visual groups,
the average distribution of researchers' visual satisfaction about 200lux visual environment was
between -1.25 and -0.81. This indicated that researchers generally hold a slightly dissatisfied attitude
54

towards low illumination environment regardless of the external thermal and acoustic environment.
The average distribution of researchers' visual satisfaction with 500lux visual environment was
between 0.25 and 1.00, which showed that researchers were slightly satisfied with the highillumination level.
The paired sample t-test results of participants' visual perception within each visual group are also
shown in Fig. 5-1. The results clearly showed that under the same thermal and acoustic environment,
the participants' visual perception about the visual environment of 500lux was significantly higher
than that about the environment of 200lux, where all significance values were less than 0.05. Since
all Cohen’s d value were larger than 0.8, this indicated that the influence of visual environment on
visual perception was remarkable. The mean difference of visual perception within each visual group
was between 2.031 and 3.031 levels, which further proved that high illumination level could greatly
increase the visual perception of the participants.

Fig. 5-1. Results of visual perception and paired sample t-test results about influence of visual environment on
visual perception for each visual group

In order to test whether the indoor visual environment had an impact on the researchers' visual
satisfaction, each group of data with same thermal and acoustic physical parameters was brought into55

paired sample t-test. The pair sample t-test results about influence of visual environment on visual
satisfaction are also shown in Fig. 5-2. The results showed that all significance values of all six visualgroups were less than 0.05, which showed that there was a significant difference in the average visual
satisfaction between 200lux visual environment and 500lux visual environment. The effect size wasalso calculated using Cohen’s d. The results showed that the effect size for all visual groups werelarger than 0.8, which can be regarded as large effect. This indicated that the influence of visualenvironment on visual perception was remarkable. In addition, the mean difference between low
illuminance conditions and high illuminance conditions for all the thermal and visual environment
combinations were between 1.500 and 1.938. This can be interpreted as: with the improvement ofvisual environment from low illumination to high illumination, the participants' visual satisfaction
improved by nearly two levels.

Fig. 5-2. Results of visual satisfaction and paired sample t-test results about influence of visual environment on
visual satisfaction for each visual group

5.2 Influence mechanism of visual environment on thermal perception and satisfaction
In order to study the effect of visual environment on participants' thermal perception under
different thermal and acoustic conditions, paired sample t-test was used (Table 5-1). The results of56

paired t-test showed that the participants' subjective thermal perception increased significantly with
the increase of illumination level under the conditions of 24 centigrade with 45dB(A) and 24
centigrade with 70dB(A) (both significance value p < 0.05). The effect size was calculated using
Cohen’s d, where absolute value of Cohen’s d for the above two visual groups were both within therange of 0.2 to 0.5. This indicated the effect of visual environment on thermal perception for 24
centigrade with 45dB(A) and 24 centigrade with 70dB(A) were small. Under other thermal andacoustic conditions, the effect of illumination on participants' thermal perception was not statisticallysignificant.
This is mainly because under the conditions of 24 centigrade quiet and noisy conditions, thethermal environment reached the warm comfort, so the influence of the visual environment changeon the participants’ thermal perception begins to appear. Under other conditions, the thermal
environment is not optimal. Therefore, the thermal environment is still the major impact on thermal
perception, and the impact of visual environment change is not significant.
Table 5-1
Results of thermal perception and paired sample t-test results about influence of visual environment on thermal
perception for each visual group

In addition, for all six groups of thermal and acoustic environment combinations, the averagevalue of difference about thermal perception between dark condition and bright condition was below
0. This indicated that when the visual environment improved from low illuminance to highilluminance, subjects would feel a little bit hotter.
57

One possible explanation is that the desk lamp was used in this experiment to adjust theilluminance level. When the desk lamp is switched on, the electric current gets through the wire andreaches the semiconductor chip. At this time, the electron and electron hole are pushed into thequantum well, where electron hole recombination happened. Because of the movements of freeelectron, there is a change in the energy level as the voltage drops from the conduction band to thevalance band, with a release of energy [92]. Part of the light energy will transfer to heat, and would
transmit to the participants, which will make the participants feel hotter.
Paired sample t-test was conducted to study the effect of illuminance level on participants’ thermalsatisfaction using the subjective votes of thermal satisfaction for all six groups of different thermal
and acoustic environments. The results are summarized in Table 5-2. From the paired sample t-test
results, it can be seen that the subjects were more satisfied when the illuminance level was 500lux.
This trend only existed when the thermal condition was in warm comfort (24℃ temperature). At thissituation, the significance value for quiet condition was 0.048 with absolute of Cohen’s d of 0.351.
The significance value for noisy condition was 0.016 with absolute of Cohen’s d of 0.389. Based on
the Cohen’s d value for both conditions, the effect size were both small. For other thermal situation,
the influence of visual environment on participants' thermal satisfaction was not statisticallysignificant.
Table 5-2
Results of thermal satisfaction and paired sample t-test results about influence of visual environment on thermalsatisfaction for each visual group58

From the mean difference between dark and bright conditions, it can be seen the satisfaction of
the participants about the dark visual environment was less than that about bright visual environment,even though the difference was not that significant. This indicated that under the same thermal andacoustic environment, high level of illuminance would make the participants more comfort about thetemperature.
The results of thermal satisfaction based on visual environment was consistent with the effect ofvisual environment on thermal perception. When thermal environment reached warm comfort, theinfluence of illuminance level began to play a decisive role.
5.3 Influence mechanism of visual environment on acoustic perception and satisfaction
Table 5-3 shows the paired sample t-test results of visual environment on participants' acoustic
perception under different acoustic and thermal combinations. The results showed that only in thequiet environment of 21 centigrade, the significance value was 0.008, which was far less than 0.05.
Since Cohen’s d value was 0.309, which is in the range of 0.2 to 0.5, the paired sample t-test effectwas small. Therefore, for quiet environment of 21 centigrade, the participants' subjective acoustic
perception was affected by the indoor visual environment. In other acoustic and thermal combinations,the change of illumination level had no significant effect on the participants' acoustic perception.
In addition, from the paired sample t-test results, it can be concluded that the average acoustic
perception of participants in dark environment was lower than that in bright environment for all six
visual groups. This proved that in dark environment, participants believed that the environment was
noisier than in quiet environment with the same thermal and acoustic condition.
One possible reason is that in dark conditions, participants’ ability to obtain external information
through vision is weakened. Therefore, the participants need to rely more on hearing and other senses
to collect information in order to make up for the information loss due to bad visual environment.
When more information is received through auditory organs, more background noise will also becollected. Therefore, the participants would feel noisier when the visual condition is dark. The otherpossible reason is that in the participants is more prone to generate sense of fear to the outside world
in dark environment. This makes the participants more sensitive to the surrounding environment.
When there is some irrelevant sound from the outside, like the background noise, it will cause
excessive perception to the participants.
59

Table 5-3
Results of acoustic perception and paired sample t-test results about influence of visual environment on acoustic
perception for each visual group

This section also studied the effect of the visual environment on participants' acoustic satisfaction.
The relevant results for all six visual groups are in Table 5-4. According to the paired sample t-test,
under the 27 centigrade thermal environment, for both dark and bright conditions, the significancevalue were less than 0 (quiet condition: t (31) = -2.175, p = 0.037 < 0.05 and noisy condition t (31) =-2.881, p = 0.007 < 0.05). This indicated that the increase of illuminance level from 200lux to 500lux
would improve the participants' acoustic satisfaction by 0.5 level when the temperature was 27
centigrade. And at these situations, the absolute value of Cohen’s d for both quiet and noisy conditions
were in the range of 0.2 to 0.5, which indicated that the influence effect was small.
In other thermal and acoustic environmental combinations, although the change of illuminancelevel had limited impact on acoustic satisfaction, it could still be found that with the deterioration ofvisual environment, researchers' acoustic satisfaction also decreased.
This was consistent with the effect of visual environment on acoustic perception. As discussed
earlier, this is because people are more sensitive to sound in a dark environment. Another reasonable
explanation for the obvious impact of illuminance level on acoustic satisfaction in a thermal
environment of 27 degree Celsius is that when the temperature reaches the warm discomfort zone,
the tolerance to the environment of the participants will reduce because the participants had to endure
the thermal discomfort caused by temperature. Therefore, any slight deterioration of the visual60

environment will cause participants to feel extremely uncomfortable, thus reducing the score of
acoustic satisfaction.
Table 5-4
Results of acoustic satisfaction and paired sample t-test results about influence of visual environment on acoustic
satisfaction for each visual group

5.4 Influence mechanism of visual environment on research performance
In order to study the effects of different visual environments on the research performance ofparticipants, the paired sample t-test was conducted for six visual groups based on performance test
results collected under different thermal and acoustic environments. The relevant results are shownin Table 5-5, Fig. 5-3 and Fig. 5-4.
5.4.1 The effect of visual environment on answer accuracy
The results of paired sample t-test (Table 5-5) on the answer accuracy according to illuminancelevel showed that in the thermal environment of 21 degree Celsius, the accuracy of participants
completing the performance test in the 500lux condition was higher than that in the 200lux condition(quiet condition: t (31) = -2.436, p = 0.021 < 0.05 and noisy condition: t (31) = 2.133, p = 0.041 <0.05). For the thermal environment of 24 centigrade and 27 centigrade, there was no significant
difference in the answer accuracy of subjects between low illumination level and high illuminationlevel.
61

This is because the average outdoor temperature was between negative 17.9 centigrade andnegative 7.4 centigrade. Thus, the participants generally wore thick sweaters, thick trousers and thickshoes. The clothing coefficient was 1.05, nearly two times of clothing coefficient for indoor summer
environment. Therefore, in a relative cool environment, the influence of thermal environment
becomes the minimized and the influence of visual environment becomes to appear. This is also
proven in Chapter 7, where the weight of the thermal index is highest, and the weight of the visualindex is a little bit lower than that, but far larger than the weight of acoustic index.
Table 5-5
Paired sample t-test results about influence of visual environment on answer accuracy of performance test for eachvisual group

5.4.2 The effect of visual environment on response time of performance test
According to the results in Fig. 5-3, except the 21℃ quiet environment, the significance value forthe other five visual groups was less than 0.05. This indicated that the change of visual environment
had a significant impact on the participants' response time for these five different thermal and acoustic
environment combinations. And from the mean difference, it can be seen that in dark environment,
the participants needed more time to accomplish the performance test than that in bright environment.
One possible explanation is that participants are eager to collect more information in a short time
because of the desire to master information. If the illuminance is not enough, the informationcollection process will be obstructed. And the participants need to spend more time and energy for62the information collection, which will cause the fatigue. This impact is more significant for thescientific research tasks which needs lots of accurate and complex operations. Therefore, theparticipants need to pay more time to accomplish the same quantity of tasks in dark environment thanthat in bright environment.
For the environment of 21℃ quiet, the thermal environment and acoustic environment reached
the comfort zone respectively. The impact caused by insufficient light source would be reduced
accordingly, so the mean difference between response time in dark environment and brightenvironment was similar.

Fig. 5-3. Paired sample t-test results about influence of visual environment on response time of performance testfor each visual group

5.4.3 The effect of visual environment on relative research performance
The relative research performance index of each environment condition was calculated according
to Eq. 2-2 and Eq. 2-3. The comparison results of relevant RPI within each visual group are shown in
Fig. 5-4. The results showed that under all six thermal and acoustic environment combinations, theparticipants' relative research performance would increase by 1.36% to 7.25% with the increase ofillumination level. And the significance values for all six visual groups were less than 0.05, this63

indicated that the appropriate indoor visual environment played a significant role in improving the
researchers' research efficiency. This was also consistent with the previous analysis about the visualenvironment on the participants' answer accuracy and response time.
𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 (𝑃𝐼) = 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 0.5 × 𝑆𝑝𝑒𝑒𝑑 0.5 × 100 =𝑅𝑃𝐼𝑖𝑗 =

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 0.5
× 100 (𝐸𝑞. 2 − 2)
𝑅𝑒𝑎𝑐𝑡𝑖𝑜𝑛 𝑇𝑖𝑚𝑒 0.5

𝑛 × 𝑃𝐼𝑖𝑗
× 100% (𝐸𝑞. 2 − 3)
∑𝑛𝑗=1 𝑃𝐼𝑖𝑗Fig. 5-4 Paired sample t-test results about influence of visual environment on RPI for each visual group64

Chapter VI
Indoor Environmental Index Equation
With the development of society and the improvement of living standards, the requirements forliving and working environment have changed from only meeting the most basic functions to seeking
a more comfortable environment. How to meet the increasing demand for indoor comfort has become
an important topic in the field of sustainable development of modern architecture. Indoor environment
quality is considered to be an important index for the sustainable development of buildings. It is
calculated based on the subjective voting results of environmental satisfaction about indoor thermal,
visual and acoustic environment, so as to reflect comfort level of occupants to the indoor environment.
Because it is impossible to conduct the subjective test to every indoor environment, and the subjective
voting results are not easy to quantify. Therefore, it is particularly important to develop amathematical model which can directly evaluate the indoor environment quality according to thephysical parameters of the indoor environment.
From Chapter III to Chapter V, this paper qualitatively analyzed the effect of indoor thermal
environment, acoustic environment and visual environment on occupants’ environment satisfaction,
respectively. However, it is relatively difficult to quantitatively analyze the relationship between
physical parameters of indoor environment and occupants’ satisfaction. This is because a single
environmental indicator will be affected by various environmental physical quantities [41]. For
example, the thermal comfort index of indoor environment is not only affected by the physicalparameters of thermal environment such as temperature and humidity, but also affected by physicalparameters of visual and acoustic environment, such as luminance and average background noise
intensity.
Among the existing relevant models, Wong's multivariate logistic model, Iordache's IEQ modeland Ncube's IEQ model are three classis mathematical model for predicting occupants' comfort basedon environment physical parameters [20, 43, 93]. Among them, Wong's multivariate logistic model isdeveloped based on the subjective evaluation about indoor environmental conditions from 293
occupancies in a typical air-conditioned office in Hong Kong. The equation of Wong's multivariate
logistic model is listed from Eq. 6-1 to Eq. 6-5:

65

𝑇ℎ𝑒𝑟𝑚𝑎𝑙 𝐼𝑛𝑑𝑒𝑥: 𝜙1 = 1 −

𝑃𝑃𝐷
100

(𝐸𝑞. 6 − 1)

1

1

1

𝐼𝐴𝑄 𝐼𝑛𝑑𝑒𝑥: 𝜙2 = 1 − 2 (1+exp(3.118−0.00215𝜁 ) − 1+exp(3.230−0.00117𝜁 )) , 500 ≤ 𝜁2 1800 𝑝𝑝𝑚
2

𝑉𝑖𝑠𝑢𝑎𝑙 𝐼𝑛𝑑𝑒𝑥: 𝜙4 = 1 −2

1, 200 ≤ 𝜁4 ≤ 1600 𝑙𝑢𝑥
1+exp(−1.018+0.00558𝜁4 )𝐴𝑐𝑜𝑢𝑠𝑡𝑖𝑐 𝐼𝑛𝑑𝑒𝑥: 𝜙3 = 1 −1, 45 ≤ 𝜁3 ≤ 72 𝑑𝐵𝐴
1+exp(9.540−0.134𝜁3 )1𝑂𝑣𝑒𝑟𝑎𝑙𝑙 𝐼𝐸𝑄: 𝜃 = 1 − 1+exp(−15.02+∑4

𝑖=1 𝐾𝑖 𝜙𝑖 )(𝐸𝑞. 6 − 2)

(𝐸𝑞. 6 − 3)

(𝐸𝑞. 6 − 4)

, 𝐾1 = 6.09, 𝐾2 = 4.88, 𝐾3 = 4.74, 𝐾4 = 3.70

(𝐸𝑞. 6 − 5)

Iordache’s IEQ model is a multiple non-linear regression models developed based on the datafrom university classroom and professors’ office in Romania. The equation of Iordache’s IEQ model
is listed from Eq.6-6 to Eq.6-10:
𝑇ℎ𝑒𝑟𝑚𝑎𝑙 𝐼𝑛𝑑𝑒𝑥: 𝐼𝑡ℎ = {28.57𝜃𝑜 − 514, 𝜃𝑜 ≤ 21.5
−28.57𝜃𝑜 + 800, 𝜃𝑜 ≥ 24.5

𝐼𝐴𝑄 𝐼𝑛𝑑𝑒𝑥: 𝐼𝐼𝐴𝑄 = 3.125𝑄𝑎𝑖𝑟 − 12.5
𝑉𝑖𝑠𝑢𝑎𝑙 𝐼𝑛𝑑𝑒𝑥: 𝐼𝑣 = 0.33𝐸𝑎𝑣(𝐸𝑞. 6 − 6)

(𝐸𝑞. 6 − 7)

(𝐸𝑞. 6 − 8)

𝐴𝑐𝑜𝑢𝑠𝑡𝑖𝑐 𝐼𝑛𝑑𝑒𝑥: 𝐼𝑎 = −3.33𝐿𝑝𝑖 + 200(𝐸𝑞. 6 − 9)𝑂𝑣𝑒𝑟𝑎𝑙𝑙 𝐼𝐸𝑄: 𝐼𝐼𝐸𝑄 = 14(𝐼𝑡ℎ + 𝐼𝑎 + 𝐼𝑣 + 𝐼𝐼𝐴𝑄 )(𝐸𝑞. 6 − 10)

Ncube’s IEQ model is a multiple regression model developed based on the surveyed input datafrom 68 occupants in two selected office buildings in UK. It is used to quick assess the environmental
performance of the air-conditioned office alongside energy performance. The Equation of Ncube’s
IEQ Model is from Eq. 6-11 to Eq. 6-15.
𝑇ℎ𝑒𝑟𝑚𝑎𝑙 𝐼𝑛𝑑𝑒𝑥: 𝑇𝐶𝑖𝑛𝑑𝑒𝑥 = 100 − 𝑃𝑃𝐷(𝐸𝑞. 6 − 11)−0.25
𝐼𝐴𝑄 𝐼𝑛𝑑𝑒𝑥: 𝐼𝐴𝑄𝑖𝑛𝑑𝑒𝑥 = 100 − 𝑃𝐷𝐼𝐴𝑄 , 𝑤ℎ𝑒𝑟𝑒 𝑃𝐷𝐼𝐴𝑄 = 395 × exp (−15.15 × 𝐶𝐶𝑂) (𝐸𝑞. 6 − 12)2

𝑉𝑖𝑠𝑢𝑎𝑙 𝐼𝑛𝑑𝑒𝑥: 𝐿𝑖𝑛𝑑𝑒𝑥 = −176.16𝑥 2 + 738.4𝑥 − 690.29, 𝑥 = ln (ln(𝑙𝑢𝑥)) (𝐸𝑞. 6 − 13)
𝐴𝑐𝑜𝑢𝑠𝑡𝑖𝑐 𝐼𝑛𝑑𝑒𝑥: 𝐴𝐶𝑖𝑛𝑑𝑒𝑥 = 100 − 𝑃𝐷𝐴𝐶𝑐 , 𝑤ℎ𝑒𝑟𝑒
𝑃𝐷𝐴𝐶𝑐 = 2 × (𝐴𝑐𝑡𝑢𝑎𝑙𝑆𝑜𝑢𝑛𝑑𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒𝐿𝑒𝑣𝑒𝑙 − 𝐷𝑒𝑠𝑖𝑔𝑛𝑆𝑜𝑢𝑛𝑑𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒𝐿𝑒𝑣𝑒𝑙 ) (𝐸𝑞. 6 − 14)66

𝐼𝐸𝑄𝑖𝑛𝑑𝑒𝑥 = 0.30𝑇𝐶𝑖𝑛𝑑𝑒𝑥 + 0.36𝐼𝐴𝑄𝑖𝑛𝑑𝑒𝑥 + 0.18𝐴𝐶𝑖𝑛𝑑𝑒𝑥 + 0.16𝐿𝑖𝑛𝑑𝑒𝑥 (𝐸𝑞. 6 − 15)

However, there are several shortcomings for these three mathematical models. On the one hand,when establishing a single environmental index, these three models only considered the impact of its
corresponding environmental physical parameter, ignoring the impact of other environmental
physical parameters. According to the correlation analysis between satisfaction in this chapter, there
existed correlation between different aspects of environmental satisfaction. Therefore, whenestablishing each environmental index equation, it is necessary to comprehensively consider the
impact of all environmental aspects on the index.
On the other hand, Wong's multivariate logistic model, Iordache's IEQ model and Ncube's IEQ
model were established based on the data of working environment, learning environment and living
environment. Their applicability in the research facilities has not been fully tested. In addition, somestudies pointed out that the coefficient of environmental indexes in the IEQ equation were different
for different building usages [94, 95]. For example, Wong et al. and Lee et al. pointed out that theimportance of acoustic environment on working environment was higher than that of thermal or visualenvironment. Therefore, the coefficient of acoustic index in IEQ equation should be higher than theother two indexes [20, 96].
In this experiment, three environmental indexes were used to evaluate the indoor environment
quality of controlled research office, which were thermal comfort index, acoustic comfort index andvisual comfort index. Each comfort index needed to establish the relationship with all the environment
physical parameters. This was accomplished by improving the existing mathematical equations ofenvironmental prediction. Therefore, this chapter first illustrated the comparison between three
existing mathematical models and the actual voting results of environmental satisfaction. And themost appropriate prediction equation for each environmental comfort was selected. Secondly, thecorrelation among three environmental satisfaction was analyzed based on the actual satisfaction
voting results. Finally, the optimal equation corresponding to each environmental index was improvedby adding the parameters of other environmental aspects, so as to make it more in line with the actual
research environment.

676.1 Prediction equation of environmental comfort index
6.1.1 Prediction equation of thermal comfort index
In order to obtain the prediction equation of thermal comfort index, the difference between theactual thermal comfort of the subjects and the thermal comfort index obtained from threemathematical models was compared under different environment conditions. According to theresearch of Fanger, for each environment condition, the actual thermal comfort = 100 – actual
percentage of dissatisfaction about thermal environment [97]. And the actual percentage ofdissatisfaction was the calculated as the proportion of people who chose the “very dissatisfied” or“dissatisfied” options in the thermal satisfaction voting to the total number of the subjects. The
calculation results are summarized in Table 6-1.
Table 6-1
Summary of thermal comfort indexes calculated from three mathematic models (mean value ± standard deviation)
and actual thermal comfort for different environmental conditions

The mathematical equations for predicting thermal comfort index in three mathematical models
are shown in Eq. 6-1, Eq. 6-6 and Eq. 6-11 respectively. Both Wong’s multivariate logistic model andNcube’s model used predicted percentage of dissatisfaction (PPD) when predicting thermal comfort.
Essentially they are the same equation. PPD was calculated through Fanger’s thermal comfort model,
and the calculation formula is shown in Appendix 6-1 [97, 98]. When calculating the PPD, someparameters needed to be set. In this experiment, the relevant parameters were set as follows:68(a) In the experiment, there was no external wind source, so the indoor air flow speed was set as
0.1m/s;(b) The participants carried out relevant scientific research during the experiment, including
reading the literature, analyzing the data and typing. Therefore, the metabolic rate was set to
1.1met;(c) According the clothing data in the subjective questionnaire, the subjects generally wore normalindoor clothes for winter, including long sleeved thick sweaters, thick trousers, thermal
underwear, thick stockings and thick shoes. This is because in the whole experimental stage,
the maximum outdoor average temperature was minus 7.4 degrees Celsius, and the minimum
was minus 17.9 degrees Celsius (See Appendix 6-2). The seat was an ordinary office chair.
With the help of “CBE Thermal Comfort Tool”, the clothing level for this experiment wascalculated to be 1.05 [99, 100].
𝜃𝑜 was used to calculate the thermal comfort index in Iordache’s model, which is operative
temperature. It was calculated using Eq. 6-16, where 𝜃𝑖 is the indoor air temperature, 𝜃𝑚𝑟 is themean radiant temperature for indoor environment, ℎ𝑟 is linear radiative heat transfer coefficient andℎ𝑐 is convective heat transfer coefficient. The indoor air temperature was the temperature measured
in this experiment. Because of the window shading and the heat insulating materials around theexterior wall of the controlled research office, the influence of solar radiation during the experience
period was reduced to the minimum. Therefore, the mean radian temperature was approximately
equal to the indoor air temperature in the controlled research office. By bring 𝜃𝑖 and 𝜃𝑚𝑟 into Eq.
6-16, 𝜃𝑜 was calculated equal to 𝜃𝑖 . In addition, when the operative temperature was between 21.5
to 24.5 centigrade, the thermal comfort index was set to 100 in Iordache’s model [43].
𝜃𝑜 =ℎ𝑟 𝜃𝑚𝑟 + ℎ𝑐 𝜃𝑖(𝐸𝑞. 6 − 16)ℎ𝑟 + ℎ𝑐

In this experiment, participants were divided into four experiment groups. The thermal physicalparameters measured for each experiment group under different environmental conditions are shownin Table 6-2. For each mathematical model, under every environment condition, through bringing theenvironment physical parameters into thermal equation, the thermal comfort index for eachexperiment group was obtained. Afterwards, the thermal comfort index for total subjects under eachenvironment condition was calculated by taking the geometric average of the four groups of thermal
69

comfort indexes. The corresponding results are shown in Table 6-1. In order to make the comparison
more intuitive, the index calculated from Wong's multivariate logistic model was enlarged by 100
times in this experiment.
Table 6-2
Summary of thermal physical parameters (mean value ± standard deviation) for different environmental conditions

In order to examine which mathematic model is more suitable for predicting thermal comfort inthis experiment, the thermal comfort indexes calculated from three mathematical models and theactual thermal comfort of the subjects were compared using paired sample T-test. The results areshown in Fig. 6-1. Because there were 12 different environment conditions, the sample size N = 12.
According to the results of pair sample T-test, there was no significant difference between thermal
70

comfort prediction from Wong's multivariate logistic model and Ncube’s model with the actual
thermal comfort of the subjects (both t (11) = 0.168, p = 0.870 > 0.05). However, for the Iordache's
model, the pair sample T-test results showed that t (11) = 2.372 with p = 0.037 < 0.05. This indicatedthat there existed significant difference between thermal comfort index predicted by Iordache's modeland the actual thermal comfort. Because the prediction of thermal comfort index by Wong's
multivariate logistic model and Ncube's model is essentially the same equation, in this experiment,
Ncube’s model was used to calculate the thermal comfort index.

Fig. 6-1. Paired sample t-test results between thermal comfort indexes from three mathematical models and actual
thermal comfort

6.1.2 Prediction equation of acoustic comfort index
The acoustic physical parameters measured for each experimental group under different
environmental conditions are shown in Table 6-3. Through comparing difference between the actual
acoustic comfort of the subjects and the acoustic comfort indexes obtained from three mathematicalmodels under different environment conditions, the equation for calculating the acoustic comfort
index was found out. Same as the thermal part, the actual acoustic comfort was set as 100 minus
71actual percentage of dissatisfaction about acoustic environment. And the actual percentage ofdissatisfaction was set as the proportion of the people who chose “very dissatisfied” or “dissatisfied”
options about acoustic satisfaction to the total number of the subjects. The results are summarized inTable 6-4.
Table 6-3
Summary of acoustic physical parameters (mean value ± standard deviation) for different environmental conditions

Table 6-4
Summary of acoustic comfort indexes calculated from three mathematic models (mean value ± standard deviation)
and actual acoustic comfort for different environmental conditions

For three mathematical models, the equation of the relationship between acoustic comfort and72

environment physical parameters are shown in Eq. 6-4, Eq. 6-9 and Eq. 6-14 respectively. For Wong’s
multivariate logistic model, when calculating the acoustic comfort index, 𝜁3 was used, which was
the average background noise level measured in this experiment. Ncube’s model used 𝑃𝐷𝐴𝐶𝑐 as anintermediate variable when predicting the acoustic thermal index. And 𝑃𝐷𝐴𝐶𝑐 was regarded aspercentage of dissatisfaction with acoustic environment, which needed to be calculated from ActualSound Pressure Level and Design Sound Pressure Level. Actual sound pressure level was themeasured background noise intensity in this experiment. According to the Environmental Quality
Standard for Noise (GB3096) published by Chinese government, the research facility belongs toClass-I environmental functional area, which needed to be particularly quiet. And the daytime noise
limit must below 55dB(A) [101]. American National Standards Institute (ANSI) required that for corelearning spaces, the one-hour steady-state background noise levels should not exceed 40dB(A) [90].
The European standard EN15251 also recommended the average background noise levels should notexceed 40dB(A) for the scientific facilities [91]. Therefore, the design sound pressure level was set to
40dB(A) in this experiment. The interior sound pressure level 𝐿𝑝𝑖 was adopted in Iordache's model
when establishing the equation for predicting acoustic comfort. In order to calculate 𝐿𝑝𝑖 , the officestructure and material properties of wall and window should be considered. The Appendix 6-3 shows
the specific calculation method for 𝐿𝑝𝑖 .
In order to obtain the acoustic comfort indexes of three mathematical models for the total sample,
it was necessary to bring different environment physical parameters of the four experiment groups
into Eq.6-4, Eq.6-9 and Eq.6-14 to obtain the acoustic comfort indexes of each group respectively.
For each mathematical model, the acoustic comfort indexes of each group were geometricallyaveraged to obtain the acoustic comfort indexes of the overall sample under each environment
condition. The corresponding results are shown in Table 6-5. Also, in order to facilitate comparison,
the acoustic comfort index obtained from Wong’s multivariate logistic model was enlarged by 100
times in this experiment.
The paired sample t-test was conducted between the acoustic comfort index predicted by the threemathematical models and the actual acoustic comfort of the subjects, so as to test whether the relevant
mathematical models can accurately predict the actual acoustic comfort. The relevant results areshown in Fig. 6-2. The results indicated that only the significance value of Ncube’s model was greaterthan 0.05 (t (11) = 1.156, p = 0.272 > 0.05). This meant that there was no significant difference73between the acoustic comfort calculated by Ncube’s model and the actual acoustic comfort of thesubject. And Eq. 6-14 can be used as the prediction equation of acoustic comfort in this experiment.
For the other two models, both significance value of paired sample t-test were less than 0.05 (t (11)= -2.202, p = 0.0499 < 0.05 and t (11) = -2.645, p = 0.023 < 0.05). And the mean difference was lessthan zero. This indicated that the acoustic comfort indexes calculated by Wong’s multivariate logistic
model and Iordache's model were significantly greater than the actual indoor acoustic comfort.
Therefore, the acoustic equation in Wong’s multivariate logistic model and Iordache's model cannot
be used in this experiment.

Fig. 6-2. Paired sample t-test results between acoustic comfort indexes from three mathematical models and actual
acoustic comfort

6.1.3 Prediction equation of visual comfort index
This section mainly discussed which mathematical model can accurately evaluate the indoor
visual comfort of the research facilities. This needed to compare the difference between the actual
visual comfort of the subjects and the visual comfort index obtained by the three mathematical models.
Similar to the thermal environment and acoustic environment, the actual visual comfort = 100 – actual74percentage of dissatisfaction about visual environment. And actual percentage of dissatisfaction was
regarded as the proportion of people who chose “very dissatisfied” or “dissatisfied” options aboutvisual satisfaction to the total number of the participants. The relevant results are shown in Table 65.
The mathematical equations describing the relationship between visual comfort and environment
physical parameters in three mathematical models are shown in Eq. 6-3, Eq. 6-8 and Eq. 6-13. In thisexperiment, each experiment group conducted the experiment for 90 minutes under each environment
condition. At the beginning and end of the experiment, the desktop illumination intensity wasmeasured respectively, and the average value of the two measurements was set as the measured
illuminance level of the desktop. The visual physical parameters measured for each experiment group
under different environment conditions are shown in Table 6-6.
When calculating the visual comfort index, the variable 𝜁4 used in Wong’s multivariate logistic
model, the variable 𝑙𝑢𝑥 used in Ncube’s model and the variable 𝐸𝑎𝑣 used in Iordache’s model werethe measured luminance level of the desktop in this experiment.
Under each environment condition, the visual physical parameters for each experiment groupwere brought into the Eq. 6-3, Eq. 6-8 and Eq. 6-13 to get the visual comfort index for this experimentgroup. Through geometric averaging the four-group visual comfort indexes, the visual comfort index
for entire subjects were obtained under each environment condition. The corresponding results areshown in Fig.6-3. The visual comfort index calculated from Wong’s multivariate logistic model
needed to enlarge 100 times in order to make the comparison more intuitive.
The visual comfort index calculated by the three mathematical models were compared with theactual visual comfort of the subjects by paired sample t-test, and the results are shown in Fig. 6-3.
The purpose was to determine which model can more accurately predict the actual visual comfort in
the research facility. According to the paired sample t-test result, there existed significant difference
between the actual visual comfort of the subjects and the visual comfort calculated by thecorresponding equations of Wong’s multivariate logistic model and Ncube's model, both significancevalues were less than 0.05. For both models, the difference of the mean value were above +10, whichindicated that the prediction of visual comfort using Wong’s multivariate logistic model and Ncube'smodel were significantly too small. For the Iordache's model, the paired t-test results showed that t(11) = -0.800 with p = 0.441 > 0.05. Therefore, the null hypothesis that there existed a difference75between the visual comfort index from model and actual visual comfort can be rejected. In thisexperiment, the visual equation in Iordache's model was selected to evaluate the visual comfort ofresearch facilities.
Table 6-5
Summary of visual physical parameters (mean value ± standard deviation) for different environmental conditions

Table 6-6
Summary of visual comfort indexes calculated from three mathematic models (mean value ± standard deviation)
and actual visual comfort for different environmental conditions

76

Fig. 6-3. Paired sample t-test results between visual comfort indexes from three mathematical models and actual
visual comfort

6.1.4 Summary
Section 6.1.1, 6.1.2 and 6.1.3 evaluated the differences between the comfort indexes calculated
from three mathematical models and the actual comfort of the subjects in thermal environment,
acoustic environment and visual environment respectively. At the end of each section, the mostsuitable equation to predict the environmental comfort was selected. Through unifying the symbols
in the equation, the prediction equations are summarized in Eq. 6-17, Eq. 6-18 and Eq. 6-19.
𝑇ℎ𝑒𝑟𝑚𝑎𝑙 𝐶𝑜𝑚𝑓𝑜𝑟𝑡 𝐼𝑛𝑑𝑒𝑥: 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 = 100 − 𝑃𝑃𝐷(𝐸𝑞. 6 − 17)

𝐴𝑐𝑜𝑢𝑠𝑡𝑖𝑐 𝐶𝑜𝑚𝑓𝑜𝑟𝑡 𝐼𝑛𝑑𝑒𝑥: 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 = 100 − 𝑃𝐷𝐴𝐶𝑐 , 𝑤ℎ𝑒𝑟𝑒
𝑃𝐷𝐴𝐶𝑐 = 2 × (𝐴𝑐𝑡𝑢𝑎𝑙𝑆𝑜𝑢𝑛𝑑𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒𝐿𝑒𝑣𝑒𝑙 − 𝐷𝑒𝑠𝑖𝑔𝑛𝑆𝑜𝑢𝑛𝑑𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒𝐿𝑒𝑣𝑒𝑙 ) (𝐸𝑞. 6 − 18)
𝑉𝑖𝑠𝑢𝑎𝑙 𝐼𝑛𝑑𝑒𝑥: 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 = 0.33𝐸𝑎𝑣(𝐸𝑞. 6 − 19)77

6.2 Correlation Analysis
In the subjective questionnaire, thermal satisfaction, acoustic satisfaction and visual satisfaction
were scored from - 3 to 3, where “- 3” represented strongly dissatisfied about the environment and“3” represented that the participants were very satisfied with the environment. In order to verify thecorrelation between the three indoor environmental variables, it was necessary to analyze thecorrelation of thermal satisfaction, acoustic satisfaction and visual satisfaction of the entire samples.
The sample size for correlation analysis was 𝑁 = 32 × 12 = 384. The corresponding results areshown in Table 6-7.
The results showed that the correlation coefficient between thermal comfort and visual comfort
was 0.215 with p value as 0.000. The correlation coefficient between thermal comfort and acoustic
comfort was 0.143 with p value as 0.005. And the correlation coefficient between visual comfort andacoustic comfort was 0.165 and the significance value was 0.001. Because the significance value ofcorrelation analysis among three comforts were all less than 0.01, there existed obvious correlation
between thermal comfort, acoustic comfort and visual comfort under 99% of confidence level. The
Pearson correlation factors all fell in the region 0.1 to 0.4, which indicated that the correlation among
all three environmental comforts were weak and positive.
Table 6-7
Correlation analysis results of thermal, acoustic and visual comfort

Because the environment satisfaction was used to calculate the actual environment comfort of thesubject, and the actual comfort can be predicted using environment physical parameters. When78

establishing the prediction equation of comfort of a single environmental index, it was necessary toconsider not only the corresponding environment physical parameters, but also the influence of otherenvironment physical parameters.
6.3 Improvement of prediction equation
Although Eq. 6-17, Eq. 6-18 and Eq. 6-19 can predict the corresponding environmental comfort
level using environment physical parameters, there was still a certain gap between the predicted
results and actual environment comfort. In addition, according to the analysis of Chapter 6.2, there
existed a correlation between thermal comfort, visual comfort and acoustic comfort. Whenestablishing the environmental comfort prediction equation, the physical parameters from all theenvironment aspects needed to be considered. Therefore, the prediction equation Eq. 6-17, Eq. 6-18
and Eq. 6-19 needed to be improved by adding the other environment physical parameters to make it
more in line with the actual application environment.
6.3.1 Improvement of thermal prediction equation
In order to improve the prediction equation of thermal comfort, multiple linear regression analysis
was conducted. The independent variables were the thermal comfort index, acoustic comfort index
and visual comfort index obtained from Eq. 6-17, Eq. 6-18 and Eq. 6-19 under each environment
condition. The dependent variable was the actual thermal comfort of the subjects. The regression
results are shown in Table 6-8.
According to the regression results, the adjusted R2 = 0.882 showed that the fitting effect ofregression model was very good and can explain 88.2% of the variance of dependent. Varianceanalysis (ANOVA) showed that p = 0.000 < 0.05, indicating that there existed linear relationshipbetween actual thermal comfort with thermal comfort index, acoustic comfort index and visual
comfort index calculated from models. The t-test for regression coefficient showed that thesignificance value for coefficient of thermal comfort index, coefficient of acoustic comfort index andthe constant were less than 0.05. This meant that thermal comfort index, visual comfort index andconstant had a significant impact on predicting the actual thermal comfort. The p value for thecoefficient of visual comfort index was 0.178, which was larger than 0.05. This showed that the
influence of visual comfort index on predicting the actual thermal comfort was not significant.
79

According to the collinearity diagnosis, it can be seen that all three VIF values were less than 5, which
proved that no collinear problem found for all three independent variables.
Table 6-8
Regression analysis results of thermal prediction equation

After the linear regression analysis, the Independence and normality of residual needed to beanalyzed. In order to test the independence between residuals, Durbin-Watson test was used. The
results showed that D-W value was 1.362, which fell into the region between 1 and 3. This indicatedthat the residuals were independent of each other. The normal distribution test results of residuals areshown in Fig. 6-4(A). The mean value of the residuals was 6.18 × 10−15 and the standard derivation
was 0.853. Since the mean value was close to 0 and the standard derivation was close to 1, theresiduals were regarded as normal distribution. Fig. 6-4(B) demonstrated the P-P diagram of normal
distribution test for residuals. The figure showed that all the residual points were located on both sidesof the straight line 𝑦 = 𝑥, and no obvious dispersion appeared. This also indicated that all residual
points were normally distributed with no obvious abnormal points.
Although visual comfort index had no significant contribution on predicting the actual comfort,
it was retained in the improved thermal comfort equation in order to ensure the prediction accuracy.
Therefore, the improved thermal comfort equation is listed in Eq.6-20. And the maximum value theimproved thermal index was set as 100.

80

𝑇𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 = 1.143 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.129 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.134 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 − 31.209(𝐸𝑞. 6 − 20)

Fig. 6-4. (A) Histogram and normal distribution line of residuals and from thermal regression (B) P-P plot forstandardized residual from thermal regression

Fig.6-5 demonstrated the scatter plot between the thermal comfort predicted using improved
thermal comfort equation and the actual thermal comfort of the subjects under each environment
condition. Through comparing the scatter plot with the straight line 𝑦 = 𝑥, it can be found that all81the points fell on both sides of the straight line, which indicated that the improved equation predicted
thermal comfort well.

Fig. 6-5. Scatter plot between the thermal comfort predicted using improved thermal comfort equation and the actual
thermal comfort

6.3.2 Improvement of acoustic prediction equation
Multiple linear regression analysis was conducted in order to improve the prediction equation of
acoustic comfort. Same as Section 6.3.1, the independent variables were the thermal comfort index,
acoustic comfort index and visual comfort index calculated from Eq. 6-17, Eq. 6-18 and Eq. 6-19.
The dependent variable changed to the actual acoustic comfort of the subjects. Table 6-9 shows therelevant regression analysis results.
The resulted showed that the goodness of fit R = 0.990 and the adjusted R2 = 0.974. This indicatedthat the fitting degree of regression model to the observed values was very high, and all the
independent variables of the regression equation can explain 97.4% variance of actual acoustic
comfort change. Variance analysis results showed that significance value was 0.000, which was lessthan 0.05. This further proved that the thermal comfort index, acoustic comfort index and visual82

comfort index can explain the actual acoustic comfort through linear fitting.
The t-test results of the regression coefficient showed only the coefficient of the acoustic comfort
index had a significance value less than 0.05, which indicated that the acoustic comfort index played
an important role when predicting the actual acoustic comfort. The t-test significance value of thecoefficient of thermal comfort and visual comfort, as well as the constant were all greater than 0.05.
This indicated that these two variables and constant had no statistical significance in predicting theactual acoustic comfort in the regression equation. The collinearity diagnosis results showed that nocollinear problem existed among all three environmental comfort indexes, since the VIF values for
all three indexes were less than 5.
Table 6-9
Regression analysis results of acoustic prediction equation

In order to verify the reliability of the regression model, it was necessary to analyze theindependence and normality of the residuals. Durbin Watson test was used to test the independence
between residuals. The results showed that the D-W value is 2.445, which was between 1 and 3. This
indicated that the residuals were independent of each other. The normal distribution test results ofresiduals were shown in Fig.6-6(A). The mean value of residuals was 3.10 × 10−15 , which was close
to 0. And the standard derivation was 0.853, which was close to 1. From the mean value and standard
derivation of residuals, the residuals can be regarded as normally distributed. The P-P diagram in Fig.
6-6(B) can also be used for the normal distribution test of residuals. It can be seen from the figurethat the residual values of 12 samples fell near the straight line 𝑦 = 𝑥 without obvious abnormal
83

points. This also indicated that all residual values were normally distributed.

Fig. 6-6. (A) Histogram and normal distribution line of residuals from acoustic regression and (B) P-P plot forstandardized residual from acoustic regression

For the improved acoustic comfort equation, the acoustic comfort index, thermal comfort index,
visual comfort index and constant were considered in order to ensure the accuracy. The improved
acoustic comfort equation is demonstrated in Eq. 6-21. And the maximum value the improved
acoustic comfort index was set as 100.
84

𝐴𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 = 1.063 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.233 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.085 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 − 28.914(𝐸𝑞. 6 − 21)Fig. 6-7 shows the scatter diagram between the improved acoustic comfort index calculated by
the above equation and the actual acoustic comfort of the subjects under each environment condition.
By comparing the scatter plot with the straight line 𝑦 = 𝑥, it can be found that all points fall on bothsides of the straight line, indicating well-fit of the improved acoustic equation.

Fig. 6-7. Scatter plot between the acoustic comfort predicted using improved acoustic comfort equation and theactual acoustic comfort

6.3.3 Improvement of visual prediction equation
Similar to Section 6.3.1 and Section 6.3.2, in order to improve the visual comfort equation,
multiple linear regression analysis was adopted in this section. The independent variables were still
the thermal comfort index, acoustic comfort index and visual comfort index obtained from Eq. 6-17,
Eq. 6-18 and Eq. 6-19. The dependent variable was the actual comfort of the subjects about the visualenvironment. The relevant regression analysis results are shown in Table 6-10.
85

According to the fitting results of the regression analysis, the goodness of fit R = 0.965, indicatingthat the improved regression model of visual comfort index had a high fitting degree to the observed
values. Adjusted R2 = 0.905 showed that thermal comfort index, acoustic comfort index and visual
comfort index can explain the variance change of 90.5% of the actual visual comfort. The analysis ofvariance showed that significance value p = 0.000 < 0.05, which further proved that the actual visual
comfort can be evaluated by linear fitting of thermal comfort index, acoustic comfort index and visual
comfort index.
The t-test results of regression coefficients showed that the visual comfort index and acoustic
comfort index had a significant impact on the prediction of actual visual comfort in the regression
equation (t (11) = 9.690 with p = 0.000 < 0.05 and t (11) = 2.576 with p = 0.033 < 0.05). For thethermal comfort index, t (11) = 1.702, where the significance value equaled to 0.127, larger than 0.05,
so the thermal comfort index had no statistical significance on the prediction of actual visual comfort
in the regression equation. Similarly, the significance value of the constant was also greater than 0.05,
which had no statistical significance on the prediction of actual visual comfort. The collinearity
diagnosis results showed all VIF values of three comfort indexes were less than 0.05. This indicatedthat thermal comfort index, acoustic comfort index and visual comfort index did not have the problem
of collinearity.
Table 6-10
Regression analysis results of visual prediction equation

After regression analysis between environmental comfort indexes and actual visual comfort,86residuals analysis was conducted in order to verify the reliability of the regression model. Durbin
Watson test was used to examine the independence between residuals, and the results showed that theD-W value was 1.698. Because the D-W value was in the region between 1 and 3, it indicated thatthe residuals were independent of each other.

Fig. 6-8. (A) Histogram and normal distribution line of residuals from visual regression and (B) P-P plot forstandardized residual from visual regression

The histogram of standardized residual in Fig. 6-8(A) and the P-P diagram of standardized
87residuals in Fig. 6-8(B) can be used to test whether the residuals obey the normal distribution.
According to the results of histogram, the mean value of residuals was 1.51 × 10−15 and thestandard deviation of residuals was 0.853. Since the mean value was close to 0, and the standard
deviation was around 1, the residuals were judged as normally distributed. According to the P-P
diagram, the residual values of 12 samples fell near the straight line 𝑦 = 𝑥 , and there were no
obvious abnormal points, indicating that all residual values obeyed the normal distribution law.
Although the significance values of the coefficient of thermal comfort index and constant weregreater than 0.05, in order to ensure the fitting accuracy, the thermal comfort index and constant wereretained in the improved prediction equation of visual comfort. The improved prediction equation ofvisual comfort is demonstrated in Eq.6-22. In order to avoid the situation that visual comfort index
was larger than 100, the max score for visual comfort index was set as 100.
𝑉𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 = 0.793 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.124 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.253 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 − 14.266(𝐸𝑞. 6 − 22)

Fig. 6-9. Scatter plot between the visual comfort predicted using improved visual comfort equation and the actual
visual comfort
88According to the improved prediction equation of visual comfort, the improve visual comfort
index in each environment condition was calculated. Taking the improve visual comfort index as thex-axis, and the actual visual comfort as the y-axis, a scatter diagram was plotted (Fig.6-9). By
comparing the difference between the scatter plot and the straight line 𝑦 = 𝑥, it was found that all
points located on both sides of the straight line, which indicated that the improved prediction equation
predicted visual comfort well.
6.3.4 Summary
Section 6.3.1, 6.3.2 and 6.3.3 improve the prediction equation for thermal comfort, acoustic
comfort and visual comfort respectively. And the fitting degree of each environment equation withthe actual environmental comfort had been examined. The improved equations for environmental
comfort are summarized as the following:
𝑇𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 = 1.143 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.129 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.134 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 − 31.209(𝐸𝑞. 6 − 20)
𝐴𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 = 1.063 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.233 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.085 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 − 28.914(𝐸𝑞. 6 − 21)
𝑉𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 = 0.793 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.124 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.253 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 − 14.266(𝐸𝑞. 6 − 22)

The improved thermal comfort index, improved acoustic comfort index and improved visual
comfort index were treated as the bridge to analyze the relationship between environment physicalparameters and research productivity.

89Chapter VII
Relationship between environmental comfort index and research
performance
Chapter 6 analyzed and compared the difference between the environment comfort indexes
obtained from Wong’s multivariate-logistic model, Ncube’s model and Iordache's model with theactual environmental comfort of the participants. Based on the results from the comparison, the mostappropriate prediction equation for each environment was selected. Due to the correlation among
three environmental comforts and the facts that the three models were not specially developed forresearch facilities, the prediction equation of each environmental comfort was improved using multivariable linear regression analysis respectively.
The analysis of this chapter was based on the prediction equation of environmental comfort
obtained in Chapter 6. Firstly, the factor analysis was used to calculate the weight of environmental
comfort index. Based on the weight, the indoor environment quality equation was established.
Secondly, nonlinear regression analysis was carried out, so as to establish the nonlinear equation
between the indoor environment quality index and research performance index, which was named as
performance prediction equation. Thirdly, through the series connection between performance
prediction equation, indoor environment quality equation and improved prediction equation ofenvironmental comfort, the relationship between research performance and environment physicalparameters in research facilities was established. And this mathematical model was the main objectiveof this research.
7.1 Indoor environment quality equation
In order to calculate the weight of thermal comfort index, acoustic comfort index and visual
comfort index in indoor environment quality equation, factor analysis was carried out. Since thecalculated indexes of 12 different environment condition for 32 participants were used in the factor
analysis, the total sample size 𝑁 = 32 × 12 = 384. Factor analysis required that the sample sizeneed to be five times more than the number of variables and larger than 100. For the factor analysisof this experiment, three variables were analyzed. The sample size was 384, which was larger than90

15 (five times the number of variables). The sample size was also larger than 100. Therefore, thesample size satisfied the requirements of factor analysis.
Factor analysis also required sample quality, where correlation must exist among the tested
variables. According to the discussion in Chapter 6.2, there existed weak and positive correlation
among thermal comfort, acoustic comfort and visual comfort. In this section, KMO and Bartlett wereadopted to double check whether there existed correlation among the three environmental comfort
indexes. According to Table 7-1, the KMO value was 0.572, which was greater than 0.5. And thesignificance value from Bartlett’s test of sphericity was 0.000, which was less than 0.05. From theresults of KMO and Bartlett’s test, there existed a meaningful correlation among thermal comfort
index, acoustic comfort index and visual comfort index. Therefore, the entire sample set met the
sample quality requirement, and was appropriate for factor analysis.
Table 7-1
The results of KMO and Bartlett’s test of sphericity

From the communalities list, the extracted variance of all three variables were larger than 0.4,
which indicated that it was acceptable to conduct factor analysis and majority information can beremained with acceptable loss. According to total variance explained list shown in Table 7-2, it canbe found that only one initial eigenvalue was larger than 1. This demonstrated that there was only one
common factor used to summarize all three environmental comfort indexes, which was the indoor
environment quality index.
Table 7-2
The results of communalities list and total variance explained list91

Table 7-2 continue
The results of communalities list and total variance explained list

Table 7-3
Results of component score, normalized component score and standardized component score

The component scores of the three environmental comfort indexes are shown in Table 7-3. Inorder to obtain the weight of the three environmental comfort indexes in indoor environment quality
equation, it was necessary to normalize and standardize the component scores. The equations fornormalization are shown in Eq. 7-1 and Eq. 7-2.
𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑆𝑐𝑜𝑟𝑒 𝑉𝑒𝑐𝑡𝑜𝑟 =𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑆𝑐𝑜𝑟𝑒 𝑉𝑒𝑐𝑡𝑜𝑟
𝑁𝑜𝑟𝑚 𝑜𝑓 𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑆𝑐𝑜𝑟𝑒 𝑉𝑒𝑐𝑡𝑜𝑟

𝑁𝑜𝑟𝑚 𝑜𝑓 𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑆𝑐𝑜𝑟𝑒 𝑉𝑒𝑐𝑡𝑜𝑟 = √ ∑ 𝐶𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝑆𝑐𝑜𝑟𝑒 2(𝐸𝑞. 7 − 1)

(𝐸𝑞. 7 − 2)Standardization was to unify the normalized component score so that the sum of the threenormalized component score equaled to one. The results deduced after standardization are shown in
Table 7-3. Therefore, the weight for thermal comfort index was 0.363, the weight for visual comfort
index was 0.329 and the weight for acoustic comfort index was 0.297. And the equation to predict
indoor environment quality was set as Eq.7-3.

92

𝐼𝐸𝑄 𝐼𝑛𝑑𝑒𝑥 = 0.363 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 + 0.339 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 + 0.297 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑(𝐸𝑞. 7 − 3)

From the weight results of environmental comfort index, it can be seen that, for research facilities,
thermal comfort index contributed the most impact to the indoor environment quality, followed byvisual comfort index. While the weight of the influence caused by acoustic comfort index on indoor
environment quality was the smallest.
7.2 Relationship between indoor environment quality and research performance
In order to obtain the relationship between indoor environment quality and research performance,on the one hand, it was necessary to calculate the IEQ index of the participants under different
environment conditions. For this experiment, the participants were divided into four experimentgroups, each experiment group needed to experience 12 different environment conditions. For thesame environment condition, the environment physical parameters experienced by different
experiment groups were also different. Therefore, in total, 4 × 12 = 48 sets of different
environment physical parameters needed to be considered when calculating the IEQ index. For eachexperiment group, under each environment condition, the thermal comfort index, acoustic comfort
index and visual comfort index were calculated through bringing the corresponding set ofenvironment physical parameters into Eq. 6-20, Eq. 6-21 and Eq. 6-22. Afterwards, based on thecalculated thermal comfort index, acoustic comfort index and visual comfort index, the IEQ index
was obtained for this experiment group under this environment condition using Eq.7-3. The relevant
results are shown in Table 7-4.
On the other hand, it was also necessary to calculate the performance index of the participants.
Corresponding to the calculation of IEQ index, the experiment group was treated as the base unit
when calculating performance index. For one experiment group, under each environment condition,
the personal performance index of eight participants in this group should be arithmetically averaged
to obtain the average performance index of this experiment group under this environment condition.
The corresponding results are shown in Table 7-4.

93

Table7-4
Results of thermal comfort index, visual comfort index, acoustic comfort index, IEQ index and performance index
for each experiment group under 12 environment conditions

94

Table7-4 continue
Results of thermal comfort index, visual comfort index, acoustic comfort index, IEQ index and performance index
for each experiment group under 12 environment conditions

After obtaining the IEQ index and the corresponding average performance index of eachexperiment group under each environment condition, the nonlinear regression analysis was adopted
to obtain the performance prediction equation. In the nonlinear regression analysis, IEQ index was
the independent variable and average performance index was the dependent variable. The total sample
size was equal to the product of number of environment conditions and number of experiment groups,
which was calculated as 48.
7.2.1 Curvilinear regression
Because it was not clear which nonlinear model can best fit the relationship between IEQ index
and average performance index. One linear fitting models and nine nonlinear fitting models shouldbe tested using curvilinear regression in this experiment with the help of SPSS. The relevant results
are summarized in Table 7-5 and figure plot of ten fitting models are in Fig. 7-1. According to varianceanalysis results, the significance value of all ten fitting methods were all less than 0.05. This indicatedthat all ten fitting models can well explain relationship between IEQ index and average performanceindex.
In order to compare the goodness of fit of ten regression models, R2 of each regression model was
calculated. The results showed that the R2 of quadratic regression model and cubic regression model
95

were the largest among all ten models, both R2 were 0.813. It needed further analysis to examine
which regression model can best predict performance index.
Table 7-5
Summary of regression results of ten fitting models

Fig. 7-1. Regression plot of ten fitting models

When conducting the nonlinear regression analysis using curvilinear regression function in SPSS,
the nonlinear fitting model needs to be linear processed first. After the linear processing, the linearized
fitting model is solved by the least square method. Because of the linearization processing, theaccuracy of fitting using curvilinear regression declines. However, for the actual nonlinear regression,
96

the nonlinear fitting model uses Gauss-Newton method to estimate the variables. Afterwards, theexpected function is expanded using Taylor series and the coefficient of independent variable issolved iteratively. Therefore, quadratic regression analysis and cubic regression analysis needs to be
conducted respectively.
7.2.2 Quadratic regression
According to the results of quadratic regression analysis, after four iterations, the sum of squares
due to error had reached the minimum value 94.495. Because the difference between consecutive sumof squares due to error was reduced to the level of 10−8, the iteration was terminated. At this time,
the optimal solution for quadratic regression was found. The coefficient of quadratic term was
0.001337, coefficient of linear term was -0.004304 and constant term was 58.8621. The results areshown in Table 7-6. The results of variance analysis of quadratic regression are shown in Table 7-7.
The R2 was 0.813, which showed that the quadratic regression equation can explain 81.3% of thevariance of performance index.
Table 7-6
Iteration results of quadratic regression analysis

Table 7-7
Variance analysis of quadratic regression

Residuals should be analyzed in order to verify the reliability of the quadratic regression model.
The normal distribution test results of residuals are shown in Fig. 7-2 (A). The mean value of residuals
97was 1.01 × 10−10 , and the standard deviation was 1.418. Since the mean value was close to 0 andthe standard derivation was close to 1, the residuals were regarded as normal distribution. Fig 7-2(B)demonstrated the P-P diagram of normal distribution test for residuals. The figure showed that all theresidual points were located on both sides of the straight line 𝑦 = 𝑥 , and no obvious dispersion
appeared. This also indicated that all residual points were normally distributed with no obvious
abnormal points.

Fig. 7-2. (A) Histogram and normal distribution line of residuals from quadratic regression and (B) P-P plot forstandardized residual from quadratic regression
987.2.3 Cubic regression
The cubic regression results are shown in Table 7-8. After five iterations, the sum of squares due
to error converged to minimum value of 94.495. The difference between consecutive sum of squares
due to error was reduced to the level of 10−8, therefore, the iteration was terminated and the optimum
solution was obtained. The coefficient of cubic term was −1.923 × 10−4 , coefficient of quadraticterm was 0.0446, coefficient of linear term was -3.161 and constant term was 133.290. Table 7-9
showed the variance analysis of cubic regression, where R2 = 0.851. This indicated that the cubic
regression equation can explain 85.1% of the variance of performance index.
Table 7-8
Iteration results of cubic regression analysis

Table 7-9
Variance analysis of cubic regression

After the nonlinear regression between IEQ index and average performance index using cubicfitting model, residuals analysis was conducted in order to verify the reliability of the regression
model. The histogram of standardized residual in Fig. 7-3(A) and the P-P diagram of standardized
residuals in Fig. 7-3(B) can be used to test whether the residuals obey the normal distribution.
According to the results of histogram, the mean value of residuals was −1.29 × 10−9 and thestandard deviation of residuals was 1.264. Since the mean value was close to 0, and the standard
99

deviation was around 1, the residuals were judged as normally distributed. According to the P-P
diagram, the residual values of 48 samples were located near the straight line 𝑦 = 𝑥, and there were
no obvious abnormal points, indicating that all residual values obeyed the normal distribution law.

Fig. 7-3. (A) Histogram and normal distribution line of residuals from cubic regression and (B) P-P plot forstandardized residual from cubic regression
100

7.2.4 Performance prediction equation
Through comparing the R2 value between quadratic regression model (R2 = 0.813) and cubic
regression model (R2 = 0.851), it can be seen that the fitting degree for cubic regression model wasmuch better than that for quadratic regression model. Therefore, cubic regression model was adopted
to obtain the relationship between performance index and IEQ index in this research. The
performance prediction equation was set as Eq. 7-4.
3
2
𝑃𝐼 = −1.923 × 10−4 × 𝐼𝐸𝑄𝐼𝑛𝑑𝑒𝑥+ 0.0446 × 𝐼𝐸𝑄𝐼𝑛𝑑𝑒𝑥− 3.161 × 𝐼𝐸𝑄𝐼𝑛𝑑𝑒𝑥 + 133.290 (𝐸𝑞. 7 − 4)

Fig. 7-4 showed the scatter diagram of PI-IEQ, in which the X-coordinate of the scatter plot was
the IEQ index of each experiment group under each environment condition, and the Y-coordinate wasthe average performance index of the corresponding group. The dotted line in the figure is the cubic
regression curve based on Eq. 7-4. It can be seen from the figure that all points located on both sidesof the cubic regression curve, and the cubic regression curve was consistent with the trend of scatteredpoints. In addition, there was no obvious abnormal point in the figure. Therefore, the cubic regression
curve can well predict the performance index using IEQ index.

Fig.7-4. Scatter diagram of PI-IEQ and cubic regression curve of performance prediction equation

Eq.7-5 was the derivative function of performance prediction equation, and it was used to judge
the increase and decrease trend between performance index and IEQ index. The characteristic roots101

of the derivative function were 55.0 and 99.6. Fig.7-5 showed the curve of the derivative function. It
can be seen from the figure that when the indoor IEQ index increased from 55.0 to 99.6, theresearcher's research performance gradually increased from 62.46 to 77.99. This was in line with the
expected influence of indoor environment quality on research performance in a comfortableenvironment zone. When the IEQ index was between 99.6 and 100, the researchers' research
performance decreased by 0.002, which was very small and negligible. It was expected that when the
IEQ index reached maximum score (100), the researcher's research performance should also reachthe best. There were slight differences with the actual situation, which was caused by the regression
accuracy. When the IEQ index fell below 55.0, the derivative value is negative. Within this range, theresearcher's research performance decreased with the increase of IEQ index, and the minimum value
was 62.46. Because the IEQ index was already in the uncomfortable range at this time, the reason
why performance index decreased with the increase of IEQ needed to be explored in future
experiments.
2
𝐷𝑒𝑟𝑖𝑣𝑎𝑡𝑖𝑣𝑒 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑃𝐼 ′ = −5.769 × 10−4 × 𝐼𝐸𝑄𝐼𝑛𝑑𝑒𝑥+ 0.0892 × 𝐼𝐸𝑄𝐼𝑛𝑑𝑒𝑥 − 3.161 (𝐸𝑞. 7 − 5)

Fig.7-5. Derivative of performance prediction equation

102

Chapter VIII
Conclusion and Future Work
8.1 Conclusion
8.1.1 Answer for main objective
In order to solve the main objective, this experiment developed a mathematical model between
indoor environment physical parameters and researchers’ research performance. The model issummarized as the following:𝑇ℎ𝑒𝑟𝑚𝑎𝑙 𝐶𝑜𝑚𝑓𝑜𝑟𝑡 𝐼𝑛𝑑𝑒𝑥: 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 = 100 − 𝑃𝑃𝐷
𝐴𝑐𝑜𝑢𝑠𝑡𝑖𝑐 𝐶𝑜𝑚𝑓𝑜𝑟𝑡 𝐼𝑛𝑑𝑒𝑥: 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 = 100 − 𝑃𝐷𝐴𝐶𝑐
𝑤ℎ𝑒𝑟𝑒 𝑃𝐷𝐴𝐶𝑐 = 2 × (𝐴𝑐𝑡𝑢𝑎𝑙𝑆𝑜𝑢𝑛𝑑𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒𝐿𝑒𝑣𝑒𝑙 − 𝐷𝑒𝑠𝑖𝑔𝑛𝑆𝑜𝑢𝑛𝑑𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒𝐿𝑒𝑣𝑒𝑙 )𝑉𝑖𝑠𝑢𝑎𝑙 𝐶𝑜𝑚𝑓𝑜𝑟𝑡 𝐼𝑛𝑑𝑒𝑥:

𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 = 0.33𝐸𝑎𝑣

Improved Thermal Comfort Index:
𝑇𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 = 1.143 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.129 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.134 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 − 31.209

Improved Acoustic Comfort Index:
𝐴𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 = 1.063 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.233 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.085 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 − 28.914

Improved Visual Comfort Index:
𝑉𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 = 0.793 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.124 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 + 0.253 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑜𝑟𝑖 − 14.266

Indoor Environment Quality Index:
𝐼𝐸𝑄𝑖𝑛𝑑𝑒𝑥 = 0.363 × 𝑇𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 + 0.339 × 𝑉𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑 + 0.297 × 𝐴𝑖𝑛𝑑𝑒𝑥−𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑑103Performance Index:
3
2
𝑃𝐼 = −1.923 × 10−4 × 𝐼𝐸𝑄𝐼𝑛𝑑𝑒𝑥+ 0.0446 × 𝐼𝐸𝑄𝐼𝑛𝑑𝑒𝑥− 3.161 × 𝐼𝐸𝑄𝐼𝑛𝑑𝑒𝑥 + 133.290

In the model, the relevant parameters are explained as follows:
1. PPD is calculated using indoor air temperature(℃) and relative humidity(%) according to Fanger’s
thermal comfort prediction model.
2. 𝐴𝑐𝑡𝑢𝑎𝑙𝑆𝑜𝑢𝑛𝑑𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒𝐿𝑒𝑣𝑒𝑙 is the measured average background noise intensity(dB(A)) and
𝐷𝑒𝑠𝑖𝑔𝑛𝑆𝑜𝑢𝑛𝑑𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒𝐿𝑒𝑣𝑒𝑙 is set to 40dB(A) for research institution.
3. 𝐸𝑎𝑣 is the average illuminance level (lux) of the measured point.
8.1.2 Answer to Sub objective 1
From Chapter 3 to Chapter 5, this experiment discussed the influence mechanism of thermal
environment, acoustic environment and visual environment on environmental perceptions andsatisfactions, respectively.
For thermal environment, with the increase of temperature from 21℃ to 24℃, the thermal
perception changes from slightly cool to slightly warm, and the thermal satisfaction also improves.
When the temperature continues to increase from 24℃ to 27℃, the participants start to feel hot, andthe thermal satisfaction start to deteriorate. The tendency of actual comfort is a parabolic curve, where
the optimal point is within the range between 22℃ and 23℃.
As for the influence of thermal environment on environmental perception and satisfaction otherthan thermal aspects, the result can be concluded that the thermal environment has no significant
effect on acoustic perception and satisfaction. Thermal environment also has no effect on visual
satisfaction, but for bright and noisy condition, when the temperature increases from 24℃ to 27℃,
participants feel brighter about visual environment of the office.
For visual environment, with the increase of illuminance level from 200lux to 500lux, theparticipants’ visual perception changes from dark level to bright level, and the satisfaction aboutvisual environment improves about two level. Under the temperature condition of 24℃, with theincrease of illuminance level, the participants feel warmer and the thermal satisfaction score of
subjects increased significantly. As for the influence of visual environment on acoustic satisfaction,
only for the 27℃ condition, the satisfaction about acoustic environment improves with the104improvement of visual environment.
For acoustic environment, when the average background noise intensity changes from 45dB(A)to 70dB(A), the participants’ feeling about acoustic environment decreases from quiet level to noisy
level. And the satisfaction about acoustic environment also decreases about two levels from slightly
satisfied to dissatisfied. As for the influence of acoustic environment on thermal perception, only for
21℃ dark and 24℃ dark condition, the participants feel hotter when the acoustic environmentchanges from 45dB(A) to 70dB(A).
8.1.3 Answer to Sub objective 2
From Chapter 3 to Chapter 5, this experiment discussed the influence mechanism of thermal
environment, acoustic environment and visual environment on researchers’ research performance,
respectively.
For all three environment aspects, there is no significant influence of environment on theanswering accuracy of performance test. However, with the deterioration of the environment, theresponse time has increased significantly. Because the research performance is calculated using
equation where answering accuracy and reciprocal of response time of performance test share thesame weight, and according to the influence tendency of indoor environment on answering accuracy
and response time, the research performance has decreased significantly with the deterioration of theenvironment.
8.1.4 Answer to Sub objective 3
According to the results from Chapter 6, there exists positive and weak correlation among thermal,
visual and acoustic comfort. Even though the correlation level is weak, when establishing theprediction equation of a single environmental comfort index, it was necessary to consider not only
the corresponding environment physical parameters, but also the influence of other environment
physical parameters.
8.1.5 Answer to Sub objective 4
Chapter 7.1 discussed the weight of the thermal comfort, visual comfort and acoustic comfort inpredicting the indoor environment quality for research institution. Through normalization andstandardization, thermal comfort contributes the most impact to the indoor environment quality105(0.363), followed by the visual comfort (0.329), while the weight of the influence caused by acoustic
comfort on indoor environment quality is the smallest (0.297).
8.2 Limitation and Future Work8.2.1 Amount of environment conditions
In this experiment, three different temperature, two different illuminance levels and two different
background noise levels were tested. In total, 12 different environment combinations were considered.
However, the value range of each environment variable is not enough to quantitatively analyze theimpact trend of each environmental variable on the research performance. At the same time, extreme
indoor environment conditions are not considered. Although the possibility of appearance of extreme
indoor environment is very small, the probability is not zero.
Therefore, in the subsequent research, it is necessary to increase the value range and reduce thevalue interval of each environmental variable, so as to quantitatively analyze the impact trend of eachenvironmental variable on the research performance, as well as the impact of extreme environment
conditions on research performance.
Recommended value range and interval for different environmental variables:(1) Temperature: 20℃, 22℃, 24℃, 26℃, 28℃, 30℃(2) Illuminance level: 50lux, 200lux, 500lux, 1000lux(3) Background noise intensity: 45dB(A), 60dB(A), 70dB(A)8.2.2 Short-term stimulation
In this experiment, each environmental condition was carried out for 90 minutes. However,
several researches reported that the effects of occupants’ performance could be highly motivatedduring short-term test and it was difficult to reflect the actual occupants’ performance [102].
Therefore, in the subsequent research, it is necessary to increase the duration of each environment
condition. The recommended duration of each environment condition is 4 hours or 8 hours.
8.2.3 Performance test typeIn this experiment, calculation performance test was used to evaluate researchers' scientificresearch performance. But for research activities, it needs not only computational ability, but also106

logical thinking, creative ability and language ability. Therefore, it is not enough to evaluate scientificresearch performance only by the calculation performance test.
Therefore, in future experiments, other performance tests need to be designed to evaluate logicalthinking, creativity and language ability. In this way, the scientific research level can be evaluatedmore accurately together with the calculation performance test107

Acknowledgement
Foremost, I would like to express my special thanks of gratitude to my supervisor, Prof. Kim
Eunyoung, who gave me golden opportunity and valuable suggestions in my Ph.D study and research.
Her immense knowledge and motivation helped me all the time in writing this thesis.
Besides my supervisor, I would like to thank my minor research project advisor Prof. Nagai Yukari
and the second supervisor Prof. Yuizono Takaya for their encouragements and patient instructions.
My sincere thanks also go to all the professors in School of Knowledge Science in JAIST. Fromtheir dedicated teaching and enlightening lectures, I have benefited a lot, which provides a goodacademic foundation for this academic research.
Last but not the least, I would like to express my gratitude to my beloved parents and my pretty
girlfriend who have always been helping me out of difficulties and supporting me without a word ofcomplaint.

108

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116

Reference:Appendix 6-1
This appendix demonstrated the calculation steps for Fanger’s PPD. The predicted percentage
dissatisfied (PPD) in Fanger’s model is calculated as follows:
𝑃𝑃𝐷 = 100 − 95 × exp(−0.03553 × 𝑃𝑀𝑉 4 + 0.2179 × 𝑃𝑀𝑉 2 ) − 2 ≤ 𝑃𝑀𝑉 ≤ 2
Predicted Mean Votes (PMV) is:
𝑃𝑀𝑉 = [0.303 × exp (−0.036 ×

𝑀+ 0.028)]
𝐴𝐷𝑢

𝑀
𝑀
× {(
− 𝐸𝑊 ) − 3.05 × 10−3 × [5733 − 6.99 × (− 𝐸𝑊 ) − 𝑝𝑎 ]
𝐴𝐷𝑢
𝐴𝐷𝑢
𝑀
𝑀
− 0.42 × [(− 𝐸𝑊 ) − 58.15] − 1.7 × 10−5 ×× (5867 − 𝑝𝑎 )
𝐴𝐷𝑢
𝐴𝐷𝑢
𝑀
− 0.0014 ×× (34 − 𝑇𝑎 )𝐴𝐷𝑢
− 3.96 × 10−8 × 𝑓𝑐𝑙 × [(𝑇𝑐𝑙 + 273)4 − (𝑇𝑚𝑟𝑡 + 273)4 ] − 𝑓𝑐𝑙 × ℎ𝑐𝑜𝑛𝑣 × (𝑇𝑐𝑙 − 𝑇𝑎 )}The surface temperature of clothing 𝑇𝑐𝑙 (℃) is:𝑀
𝑇𝑐𝑙 = 35.7 − 0.028 × (− 𝐸𝑊 )
𝐴𝐷𝑢− 𝐼𝑐𝑟 {3.96 × 10−8 × 𝑓𝑐𝑙 × [(𝑇𝑐𝑙 + 273)4 − (𝑇𝑚𝑟𝑡 + 273)4 ] + 𝑓𝑐𝑙 × ℎ𝑐𝑜𝑛𝑣 × (𝑇𝑐𝑙
− 𝑇𝑎 )}
The convective heat transfer coefficient ℎ𝑐𝑜𝑛𝑣 (𝑊 × 𝑚−2 × ℃) is:
2.38 × (𝑇𝑐𝑙 − 𝑇𝑎 )0.25ℎ𝑐𝑜𝑛𝑣 = 𝑚𝑎𝑥 {}12.1 × √𝑈𝑎𝑟
The ratio of clothed surface area to nude surface area of occupant 𝑓𝑐𝑙 is:
1.290,
𝑓𝑐𝑙 = 𝑓(𝑥) = {𝐼𝑐𝑟
1.05 + 0.645 × 𝐼𝑐𝑟 ,1.00 +

𝐼𝑐𝑟 ≤ 0.078
𝐼𝑐𝑟 > 0.078

𝐴𝐷𝑢 (𝑚2 ) is the surface area of occupant
𝐸𝑊 (𝑊 × 𝑚−2 ) is the external work by activity of occupant𝐼𝑐𝑟 (𝑊 −1 × 𝑚2 × ℃) is the thermal resistance of cloting
𝑀 (𝑊) is the metabolic rate117

𝑝𝑎 (𝑃𝑎) is the water vapor pressure
𝑇𝑎 (℃) is the indoor air temperature
𝑇𝑚𝑟𝑡 (℃) is the mean radian temperature
𝑈𝑎𝑟 (𝑚⁄𝑠) is the indoor air velocity

118

Appendix 6-2
This appendix recorded the outdoor temperature during the experiment
DATE

MIN

MAX

2021/1/13

-20

-11

2021/1/14

-19

-12

2021/1/15

-24

-14

2021/1/18

-27

-18

2021/1/19-22-62021/1/20-702021/1/21-16-12021/1/22-16-6

2021/1/25-9

2

2021/1/26-18-52021/1/27-18-82021/1/28

-25

-14

2021/1/29

-23

-15

2021/2/1-26-15

2021/2/2-25-16

2021/2/3-21-122021/2/4-22-82021/2/5-1402021/2/22-17-82021/2/23-16-7

2021/2/24-13

12021/2/25-11-12021/2/26-542021/3/1

-15

-7

MEAN

-17.9

-7.4

SD

6.0

6.4

119

Appendix 6-3
This appendix demonstrates the calculation step for 𝐿𝑝𝑖 in Iordache’s IEQ model.
Indoor sound pressure level 𝐿𝑝𝑖 is
𝐿𝑝𝑖 = 𝐿𝑝𝑜 − 𝐷𝑏
The sound attenuation 𝐷𝑏 is calculated as:
𝐷𝑏 = 𝑅𝑓 + 10 × log (0.161 × 𝑉)
𝑇𝑟 × ∑ 𝐴𝑖

The sound attenuation of the façade 𝑅𝑓 is calculated as:
𝑅𝑓 = 10 × 𝑙𝑜𝑔 ×

𝐴𝑤𝑎𝑙𝑙 + 𝐴𝑤𝑖𝑛𝑑𝑜𝑤
𝑅𝑤𝑖𝑛𝑑𝑜𝑤𝑅𝑤𝑎𝑙𝑙
10− 10 × 𝐴𝑤𝑎𝑙𝑙 + 10− 10 × 𝐴𝑤𝑖𝑛𝑑𝑜𝑤

The sound attenuation of the opaque structure 𝑅𝑤𝑎𝑙𝑙 is calculated as:
𝑅𝑤𝑎𝑙𝑙 = 13.5 × log(𝜌𝑤𝑎𝑙𝑙 ) + 13.5 log(𝑓) − 22.5

𝐿𝑝𝑜 is outdoor sound pressure level∑ 𝐴𝑖 is sum of all surface area
𝑇𝑟 is the reverberation time of the room
𝑉 is the room volume of the room
𝜌𝑤𝑎𝑙𝑙 is the wall density120Appendix A:

Informed consent(English Translation of Chinese Original Version)

You are invited to participate in a research survey. This informed consent provides you with some
information to help you decide whether to participate in this study. Please read carefully. If you haveany questions, please ask the researcher.
1. This survey is to study the impact of indoor environment on environmental perception, satisfaction
and research efficiency
2. This questionnaire is used to collect the participants' perception, satisfaction and subjective
performance evaluation of the indoor environment
3. If you agree to participate in this study, you will be asked to answer the questionnaire, which
consists of 3 parts and 16 questions. During the research, the researcher will adjust the temperature,illumination and background noise intensity of the office. Please answer the questionnaire
according to your subjective feelings. Your questionnaire is only used for this study.
4. Risk and discomfort: This study will not have any adverse effects on your health.
5. Benefit: Scientific analysis of your questionnaire results will help to improve the indoor officeenvironment.
6. As a research subject, please truly express your subjective feelings and fill in the questionnaire. If
you have any discomfort, please inform the researcher in time.
7. Privacy: If you decide to participate in this study, all the personal information is confidential.
You can learn about the information related to this study at any time. If you have any questions related
to this study, please contact with the researcher, LI ZHIHENG at (+86) 13039009427.

□ I have read this informed consent form.
□ I have the opportunity to ask questions and all the questions have been answered.
□ I understand that participation in this study is voluntary.
□ I can choose not to participate in this study or discontinue this study at any time under any

circumstances.
Signature:
Date:
121

Appendix B:

Subjective Questionnaire(English Translation of Chinese Original Version)

This appendix demonstrates the sample of subjective questionnaire in the experiment (English
Translation of Chinese Original Version)

Indoor Environment Assessment
The questionnaire is used to collect the perception and satisfaction of the participantsPart 1: General Information
Name

GenderClothing

Age

SweaterThick

MediumThin

Trousers

ThickMediumThin

Long

Medium

Short

Socks

Thick

Medium

Thin

Shoes

Thick

MediumThin

Thermal Underwear

Yes

No

Part 2: Preference Information
1. Do you prefer cool or warm thermal condition?

Cool

Warm

2. Do you prefer listening to music when you study?

Yes

No

3. Do you prefer slightly dark or slightly bright condition when you

Slightly

Slightly

study?

Dark

BrightIndoor Environment Assessment
The questionnaire is used to collect the perception and satisfaction of the participants
Environment Condition

A: 21 centigrade, 200lux, 45dBA

Part 3: Indoor Environment Assessment
Q1: What is your feeling about the current thermal condition?
-3

-2

-1

0

1

2

3

Very Cold

Cold

Cool

NeutralWarm

Hot

Very Hot122

Q2: Do you satisfy with the current thermal condition?
-3-2Strongly-10

1Slightly
Dissatisfied

Dissatisfied

2

Slightly
NeutralDissatisfied

3
Strongly

SatisfiedSatisfiedSatisfied

Q3: What is your feeling about the current visual condition?
-3-2

Very Dark

Dark

-1

0

1

Slightly2

3

BrightVery Bright2

3

Slightly
Neutral

Dark

Bright

Q4: Do you satisfy with the current visual condition?
-3-2Strongly-10

1Slightly
Dissatisfied

Dissatisfied

Slightly
NeutralDissatisfied

Strongly
SatisfiedSatisfiedSatisfied

Q5: What is your feeling about the current acoustic condition?
-3-2

Very Noisy

Noisy

-1

0

Slightly

1

2

3

Quiet

Very Quiet

2

3

Slightly
Neutral

Noisy

Quiet

Q6: Do you satisfy with the current acoustic condition?
-3-2Strongly-10

Slightly
Dissatisfied

Dissatisfied

1
Slightly

NeutralDissatisfied

Strongly
SatisfiedSatisfiedSatisfied

Q7: What is your feeling about the current indoor air quality condition?
-3

-2

-1

0

Very Bad

Bad

Slightly Bad

Neutral

1

2

3

Good

Very Good

SlightlyGood
Q8: Do you satisfy with the current Indoor air quality condition?
123

-3

-2

Strongly-10

Slightly
Dissatisfied

Dissatisfied

1

2

Slightly
NeutralDissatisfied

3
Strongly

SatisfiedSatisfiedSatisfied

Q9: Do you think you completed the work efficiently during the last session?
-3

-2

Very

-1
Slightly

InsufficientInsufficient

0

1

2

Slightly
NeutralInsufficient

Very
Efficient

Efficient

124

3

EfficientAppendix C:

Performance Test
(English Translation of Chinese Original Version)

This appendix demonstrates the sample of the performance test in the experiment (English Translationof Chinese Original Version).

Performance Test
Environment Condition A: 21 centigrade, 200lux, 45dBA
Please calculate the difference between the left and right numbers of each line
1. If the number on the left is less than the number on the right, please fill in"-" value "in the space
2. If the number on the left is equal to the number on the right, please fill in"0" in the space
3. If the number on the left is greater than that on the right, please fill in "+value" in the space
3

1

8

3

7

10

9

6

5

10

5

4

2

1

9

2

4

1

5

0

7

8

1

1

7

4

8

0

1

5

6

2

5

4

1

5

9

10

8

3

8

9

4

7

8

9

9

1

3

9

0

4

10

9

10

6

9

8

8

4

2

6

3

9

3

6

0

4

5

6

6

0

10

6

9

3

5

6

9

4

9

2

0

7

3

9

2

10

7

3

4

0

6

1

6

2

6

10

8

7

10

5

4

2

4

5

6

5

3

7

Duration125