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A method to conduct longitudinal studies on indoor environmental quality and perceived occupant comfort
Accepted Manuscript A method to conduct longitudinal studies on indoor environmental quality and perceived occupant comfort Justin Berquist, Mohamed Ouf, William O'Brien PII:
S0360-1323(18)30822-9
DOI:
https://doi.org/10.1016/j.buildenv.2018.12.064
Reference:
BAE 5898
To appear in:
Building and Environment
Received Date: 4 October 2018 Revised Date:
29 December 2018
Accepted Date: 31 December 2018
Please cite this article as: Berquist J, Ouf M, O'Brien W, A method to conduct longitudinal studies on indoor environmental quality and perceived occupant comfort, Building and Environment (2019), doi: https://doi.org/10.1016/j.buildenv.2018.12.064. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Justin Berquista, Mohamed Oufb*, William O'Brienb a
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A method to conduct longitudinal studies on Indoor Environmental Quality and Perceived Occupant Comfort
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National Research Council Canada, 1200 Montreal Road, Ottawa, Ontario, K1A 0R6, Canada. Department of Civil and Environmental Engineering, Carleton University, 3432 Mackenzie Building, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada
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Corresponding author Email address: [email protected] Phone: +1 613 520-5784 Fax: +1 613 520-3951 Department of Civil and Environmental Engineering, Carleton University, 3432 Mackenzie Building, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada
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Abstract
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Buildings with transient occupancy such as malls and sport facilities pose a particular challenge for evaluating the effect of indoor environmental quality (IEQ) on occupant comfort due to 1) the difficulties in communicating with their occupants and 2) significant daily variations in their occupancy profiles. To this end, this research presents a method to conduct longitudinal studies in which IEQ and occupant comfort are continuously and simultaneously evaluated. The proposed method relies on using electronic occupant survey devices to enable data collection on a large scale and over longer durations. To validate the proposed method, it was implemented in a gymnastics center in eastern Ontario, Canada between September 2017 and February 2018. Over 1000 survey responses were collected and analyzed relative to prevailing IEQ conditions in the hour preceding each survey response. Analyses showed a stronger correlation between measured temperature and occupants’ thermal comfort in comparison to the relationship between carbon dioxide concentrations and occupants’ perception of the indoor air quality. The case study demonstrated the feasibility of the proposed research method which can be replicated on a wider scale in different building types, especially those with transient occupancy, to further investigate the effect of IEQ on occupant comfort.
1. Introduction
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Evaluating the effect of indoor environmental quality (IEQ) on occupant comfort relies on occupant surveys which typically focus on long-term, one-time evaluations of occupant comfort (e.g. over a week, month(s), or a year) [1]. These types of surveys are more suitable for regular building occupants who are exposed to the same building’s IEQ conditions, daily. Therefore, more research on the acceptable ranges of IEQ conditions and occupant comfort was conducted in commercial, institutional, or residential buildings, relative to buildings with transient occupancy, such as airports, malls, and sport facilities [2]– [6]. To investigate the relationship between IEQ and occupant comfort in these buildings, longitudinal and simultaneous evaluations of both aspects should be conducted to ensure the variations in indoor conditions and occupant demographics are captured.
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Due to the transient nature of occupancy in some buildings, it is more challenging to administer surveys to evaluate satisfaction with the indoor environment for longitudinal studies. Occupant demographics (age, physical condition, and sex) and occupant densities can drastically change within a single day, from day to day within a given building, as well as from one building to another. Therefore, longitudinal studies on the effect of IEQ on occupant comfort are required. However, many of the common occupant survey tools are not geared towards continuous evaluations combined with simultaneous IEQ measurements [1]. Furthermore, previous studies in which simultaneous IEQ measurements and occupant satisfaction surveys were conducted, covered relatively short durations and surveyed a relatively small sample of occupants [7], [8]. To this end, the purpose of this paper is to present a novel research method utilizing electronic survey devices to continuously evaluate occupant satisfaction with simultaneous IEQ measurements for longitudinal studies. The proposed method can be applied in any building type, especially those with transient occupancy. The objectives of this paper are to present the proposed method and demonstrate its main advantages and disadvantages. To validate this method, a case study is also presented where the electronic survey devices, as well as temperature, relative humidity (RH), and carbon dioxide sensors were installed in a gymnastics center in eastern Ontario, Canada between September 2017 and February 2018. Over 1000 occupant responses regarding satisfaction with the thermal environment and indoor air
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quality (IAQ) after each training session were collected and correlated to measured IEQ parameters. Results of this case study demonstrate the feasibility of the proposed method to conduct longitudinal studies with simultaneous IEQ and occupant comfort measurements in similar settings. First a brief review is provided covering the different methodological approaches used to conduct research on IEQ and occupant comfort. This is followed by a description of the proposed approach, its implementation in the case study, and finally a discussion of the proposed method’s advantages and disadvantages.
2. Background
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This section provides a review of the different methodological approaches used to evaluate IEQ and occupant comfort. The final subsection provides an overview of the main differences between sport facilities and other building types, since they were used as a case study representing buildings with transient occupancy.
2.1 Investigating the effect of IEQ on occupant comfort
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To assess occupants’ perception of IEQ, surveys are the most commonly used tool due to their relatively low cost of implementation and simplicity [9]. Peretti and Schiavon [1] provided a review of different IEQ surveys in the literature indicating that the two most widely used are the Building Use Studies Occupant Survey (BUS) [10] and the Center for the Built Environment (CBE) survey [11]. Despite their popularity, several challenges are associated with using surveys to evaluate IEQ, such as the difficulty in selecting representative questions, difficulties in interpreting subjective responses, and finding a representative survey time-frame. For example, Rajagopalan and Jamei [12] analyzed occupant comfort in sports facilities using surveys with a seven-point scale combined with objective IEQ measurements. However, surveys were only completed once by each occupant to evaluate their comfort level throughout the day on which measurements were taken. Conducting multiple simultaneous IEQ measurements and surveys over a longer time-frame may address this issue, although it can potentially lead to “survey fatigue” and reduce response rates [13]. As an alternative method to evaluate thermal comfort, Gunay et al. [14] measured indoor and outdoor temperature in four large office buildings and concurrently monitored thermal complaints and thermostat overrides which represent a proxy for occupant dissatisfaction. However, Schweiker et al. [15] indicated that occupant comfort is also influenced by the number of adaptive control opportunities available, such as the ability to adjust thermostat setpoints or use fans or open windows. Therefore, Schweiker et al. [15] used a test chamber to conduct controlled experiments by exposing occupants to variable IEQ conditions and a different number of adaptive control opportunities. Occupants were asked to complete multiple surveys throughout an eight-day period to evaluate their satisfaction with the indoor environment under these different scenarios. However, the relatively small number of subjects and the test chamber environment posed a challenge to generalizing the results of these laboratory-based studies. Torresin et al. [16] provided in-depth literature review that explored how the interaction between different IEQ parameters (i.e. acoustic, thermal, visual, and air quality factors) influences occupants’ perception and performance. Since occupants are constantly subjected to these multiple IEQ factors simultaneously, their interaction has a strong influence on their perceived comfort. Furthermore, reducing discomfort for one aspect (e.g. opening a window to improve air quality), may negatively affect other aspects such as acoustic quality. Previous studies indicated that the interaction between thermal conditions and IAQ,
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influences occupants’ perceived comfort. For example, Fang et al. [17] showed that above about 28°C and 70% RH, air quality was perceived negatively regardless of its actual pollution levels. In general, the indoor air quality of hot and humid air was perceived negatively, while cold and dry air was perceived as fresher, less stuffy, and acceptable [18]–[20]. On the other hand, IAQ was not found to affect occupants’ thermal assessment [21], [22], except in one study by Melikov and Kaczmarczyk [23], where occupants felt slightly warmer under higher pollutant concentrations, although temperature and RH were the same. Due to the large variations in potential interactions between IEQ parameters, studies quantifying these interactions’ effect on occupant comfort require more simultaneous measurements of both aspects, which would not be possible using “one-time” occupant surveys.
2.2 Investigating IEQ parameters
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The increased availability of wireless sensing technologies and data-logging capabilities facilitates longitudinal IEQ measurements at more granular temporal resolutions [24]. Researchers have developed mobile measurement carts, combining different sensors to simultaneously measure several IEQ parameters, which are useful for quickly moving multiple sensors around a space and keeping sensors steady for the measurement period [25], [26]. However, the bulkiness of these carts can make it difficult to move them between buildings and to conduct measurements during occupants’ presence, especially in buildings with transient occupancy and higher occupant traffic. Revel and Arnesano [27] demonstrated a monitoring methodology for IEQ parameters in sport facilities which were used to estimate thermal comfort through the predicted mean vote (PMV). The study outlined preferred fixed sensor locations to collect measurements on four thermal-related IEQ variables which can be used to calculate PMV. In another study, Revel and Arnesano [6] validated the proposed method using occupant surveys for subjective measurements. However, they were only administered once per day over a four-day investigation period to evaluate occupants’ perception of IEQ. Despite the study’s relatively short duration, results verified the proposed method and showed a strong correlation between the thermal sensation vote (TSV) calculated from survey responses, and PMV calculated using IEQ measurements.
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Measuring IAQ specifically can be more challenging than thermal-related IEQ parameters due to its dependence on several physical and chemical parameters [28]. Although standards and guidelines for acceptable IAQ exist [29], [30], the diversity of potential indoor pollutants and their characteristics makes it difficult to standardize sampling and measurement methods. The most commonly used methods for indoor air sampling and analysis are TO (Toxic Organic) methods (U.S. EPA Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air) and indoor pollutant methods (Compendium of Methods for the Determination of Air Pollutants in Indoor Air) [31], [32]. However, previous studies typically used carbon dioxide concentration as an indicator for IAQ to evaluate its effect on occupant comfort as indicated by Lan et al. [19].
2.3 Case studies on IEQ in sport facilities Studies focusing on sport facilities are generally limited relative to other building types such as offices and homes [33]. However, sporting activities can result in a higher rate of carbon dioxide, odor, and moisture generation by occupants [34]. Therefore, standards for indoor ventilation, such as ASHRAE Standard 62.1 [29] specify higher ventilation rates for sport facilities compared to other building types such as office buildings. For example, the required ventilation rate for gymnasiums is 10 L/s.person + 0.9 L/s.m2, whereas the required ventilation rate for office spaces is 2.5 L/s.person + 0.3 L/s.m2. Zitnik et al. [34] also indicated that sporting activities contribute more to the increase in temperature and relative
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humidity (RH) in indoor spaces, unlike other activities such as office work. By monitoring IEQ in a 12,500 m3 gymnasium for a month, Zitnik et al. [34] estimated that a single person evaporated 0.94 kg of water into the air and contributed 0.03 K to air temperature increase, during a 90-minute exercise session. These characteristics of sport facilities suggest that the relationship between IEQ and occupant comfort in these buildings may follow different patterns than other building types.
3. Methodology
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The proposed method relies on using electronic survey devices to administer occupant surveys, rather than the common, paper- or web-based platforms. Simultaneously, fixed sensors are installed in the monitored spaces to measure key IEQ parameters at relatively short intervals throughout the study period. Because survey responses can be collected at any time during the study period, continuous measurements of IEQ data is necessary to ensure the IEQ conditions relating to each survey response are captured. To investigate whether survey responses increase during unfavorable indoor conditions, occupancy sensing infrastructure should also be used. Figure 1 provides an overview of the proposed research method and the main aspects to consider when implementing it. Define the research question(s)
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• Identify specific IEQ parameters to focus on • Identify relevant occupant comfort parameters • Determine the study period to maximize data collection and ensure temporal and seasonal variations are captured
Occupant surveys
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• Identify relevant survey questions based on the selected comfort parameters • Maximize response rates by selecting only 1 or 2 questions at most to be administered using electronic survey devices • Strategically install devices in locations with higher occupant traffic to maximize response rates • Ensure responses are collected after occupants have experienced IEQ by strategically locating the survey devices near exit doors and providing clear instructions • Monitor occupants’ use of the survey devices to avoid miscommunication regarding the survey intent and expected responses
IEQ measurements • Identify the types of sensors required based on the selected IEQ parameters • Calibrate sensors prior to installation • Identify the temporal and spatial resolution for each sensor type based on the measured IEQ parameter, space type, size and layout, budget and space limitations
Occupancy detection • Identify the type of occupancy sensors to be used (Preference should be given to occupant counting technologies) • Calibrate sensors prior to installation • Consider placing occupancy sensors close to the electronic survey devices
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• Identify the analysis methods to address the identified research questions • Identify specific statistical tests and ensure all relevant data is collected
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• Evaluate the relationship between measured IEQ parameters and comfort • Identify action items to improve occupant comfort in the monitored space
Figure 1 Overview of the proposed research method and major points to consider
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To the authors’ knowledge, the proposed method specifically relying on electronic survey devices to collect occupant responses has not been used in previous studies for evaluating occupant satisfaction with IEQ. These devices provide several advantages over paper-based and online surveys due to their ease of use, which facilitates faster data collection on a large scale. These features are especially useful for buildings with transient occupancy, where other communication forms with occupants (e.g. through email or paper-based surveys) may not be feasible. They require minimal effort for participants and are thus less likely to suffer from participant fatigue. Following the proposed implementation and data-collection approach, each survey response can be correlated to prevailing IEQ conditions at that time.
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One of the possible drawbacks of the proposed method is increasing the potential for receiving biased survey responses if occupants are more likely to respond during unfavorable conditions. To investigate this issue, the frequency distributions of measured IEQ parameters throughout occupancy periods can be compared to the frequency distribution of IEQ parameters corresponding to survey responses. This investigation can be improved using occupancy data to normalize the frequency distribution of measured IEQ parameters using recorded occupancy counts.
3.1 Case Study
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To validate the proposed research method, it was implemented in a gymnastics center, located in eastern Ontario between September 2017 and February 2018 to capture both heating and cooling periods. The center is open for approximately 12 hours per day, 7 days a week, however, the number of occupants during the hours of operation varies significantly. According to the gymnastics center's management, up to 40 occupants can be present in the gymnastics area during weekends, with approximately 20 parents in the lobby and viewing area. In contrast, the number of occupants can drop to two or three staff members on weekdays. Figure 2 shows the floor plan of the gymnastics center which is approximately 1,580 m2 (17,000 ft2), as well as its mechanical system layout. Two rooftop units serve the gymnastics area, both of which are located above the entry location of the supply duct, which is noted in Figure 2.
Figure 2 Gymnastics center's mechanical system
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The overall goal of this case study is to demonstrate the feasibility of the proposed research method for collecting large scale occupant survey responses and correlating them to corresponding IEQ measurements. Specific research questions focused on 1) identifying potential bias to determine if occupants tend to respond to the surveys more frequently during unfavorable conditions, 2) evaluating the relationship between thermal comfort and temperature, RH measurements, 3) evaluating the relationship between IAQ comfort and measured carbon dioxide concentration, and 4) evaluating the interaction between thermal comfort and carbon dioxide concentration, as well as IAQ comfort and temperature measurements.
3.1.1 IEQ measurements
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Six temperature and RH sensors (E-348-UX100-011) were installed to evaluate the thermal environment inside the gymnastics center, while an additional data logger (E-348-MX1102) that measured carbon dioxide concentration, temperature and relative humidity was installed to evaluate the thermal comfort and IAQ of the gymnastics center. The temperature sensors had a measurement range between 0°C and 50°C, with accuracy of ±0.21°C and resolution of 0.024°C, while the RH measurements had a range between 0% and 90%, with an accuracy of ±2.5% and resolution of 0.05%. The Temperature and RH sensors were installed at a height of 1.5 m above the floor, while the carbon dioxide sensor was installed at a 3-m height to avoid potential occupant tampering. The measurement accuracy of the carbon dioxide concentration was ±50 ppm. All devices were factory calibrated and, upon researcher testing, were immediately installed after purchase. Figure 3 shows the placement of these sensors within the gymnastics center, where “T” denotes temperature and RH measurement locations, and “C” denotes the carbon dioxide concentration measurement location. All of the sensors took measurements every 10 minutes over the six-month period between September 2017 and February 2018. To correlate thermal conditions to occupants’ satisfaction with the thermal environment, which was evaluated after training sessions, the average readings of the six temperature and RH sensors over one hour prior to each survey response were calculated. Similarly, the carbon dioxide concentration readings over one hour were recorded to be correlated to occupant satisfaction with the IAQ. In addition to temperature, RH and carbon dioxide sensors, motion sensors, denoted by “O”, were installed next to the entrance door of the gymnastics center to detect motion in that area, which provides a proxy representing occupancy.
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Figure 3 Sensor layout for the gymnastics center
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3.1.2 Electronic survey devices installation
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To simultaneously evaluate occupant satisfaction with IEQ, electronic occupant survey devices, manufactured by MMCall ltd., were installed next to the exit door as shown in Figure 3 and denoted by “S”. These devices were intentionally placed in that location to solicit feedback after occupants have completed their training sessions, which typically lasted for one hour, and experienced the indoor environment. A custom machined case was created for each device to allow for wall mounting. The surveys solicited occupants’ feedback regarding the IAQ and thermal environment in the space using a five-point scale as shown in Figure 4. They automatically filtered out rapid button pushes to avoid double counting occupants’ responses.
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Figure 4 Electronic occupant survey devices
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To evaluate thermal comfort, two occupant survey devices were installed; one for males and one for females which asked occupants to provide feedback regarding their thermal comfort. Large signs were placed above each device to indicate the respective gender. The following question was asked to both groups: “How satisfied were you with the temperature in the gymnastics center today? Were you…”. Possible responses were as follows: “warm, slightly warm, neutral, slightly cool, cool”. The timestamp for each response was recorded and the data was sent to a centralized database. The temperature and RH values associated with each button-push were obtained by averaging the measurements over the preceding hour. This allowed for the approximate environmental conditions that the occupant experienced to be compared to their response.
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To evaluate occupants’ satisfaction with IAQ, a third electronic occupant survey device was installed to be used by all occupants (i.e. not based on gender). The following question was asked “How was the air quality in the gymnastics center today (i.e. stuffy/stale air, cleanliness, odors)? Was it…”, to which possible responses were as follows “very poor, poor, neutral, good, very good”. The timestamp for each response was also automatically recorded, and the carbon dioxide concentration measurements were averaged over the previous hour. The relationship between the occupants’ thermal comfort feedback and the sensor measurements could then be analyzed to determine the relationship between these two aspects.
3.1.3 Data analysis
The first step in analyzing the data entailed using descriptive statistics to characterize measured IEQ conditions by identifying averages, minima, maxima, and frequency distributions. As part of this analysis, the relationship between occupants’ survey responses (i.e. perceived comfort) and averaged IEQ measurements in the hour prior to each response was also plotted. For temperature and RH measurements, the average of the six measurements was taken for each time interval. The appropriateness of utilizing the average of the six sensor measurements was verified by evaluating the “momentary” differences between each sensor reading. The average “momentary” standard deviation was approximately 0.35 °C and the
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maximum standard deviation was approximately 1.02 °C. The low fluctuation between sensor measurements implied that the space was well mixed and that the sensors were not interfered with. Therefore, utilizing an average of the six measurements was deemed acceptable for this application. Furthermore, occupancy sensor data was used to normalize the frequency distributions of measured IEQ parameters by using the number of motion detections as a proxy for the gymnastics center’s occupancy. In this case, the number of motion detections at each time interval was used as the corresponding weight for IEQ measurements at that time interval to produce an occupancy-weighted frequency distribution.
4. Results and Analysis
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The second step of the analysis entailed using statistical tests to evaluate these relationships between occupants’ perceived comfort and measured IEQ parameters. Measured temperatures, RH, and carbon dioxide concentrations corresponding to each comfort vote category (e.g. too cool, too warm, or very poor, very good…etc.) were not normally distributed as assessed by Shapiro-Wilk's test (p <.05), thus non-parametric tests were used for the statistical analysis. To determine if there were statistically significant differences between measured IEQ parameters corresponding to each of the comfort vote categories, a non-parametric Kruskal-Wallis H test was used. Whenever a statistically significant difference was detected, pair-wise comparisons were performed using Dunn's procedure with a Bonferroni correction. To measure the strength of the relationship between measured IEQ parameters and the corresponding comfort vote, a Spearman's rank-order correlation test was used since comfort votes were not measured on a continuous scale (i.e. ordinal variables). Since thermal comfort votes were collected separately for males and females, a Mann-Whitney U test was used to compare measured IEQ parameters corresponding to each thermal vote category between both groups.
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This section presents the case study results to demonstrate some examples for analyzing data collected following the proposed research method. The first subsection focuses on comparing the frequency distributions of IEQ measurements during occupied periods to measurements corresponding to survey responses, for investigating potential biases. The following three subsections focus on analyzing the effect of IEQ on occupant comfort within the investigated gymnastics center. The final subsection provides an analysis of the variation in occupants’ perception of the indoor conditions.
4.1 Investigating potential bias in survey responses
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Figure 5 shows the relative frequency distribution of temperature measurements during occupied periods, which was also normalized to occupancy. Normalization to occupancy was achieved using the number of motion detections collected from the installed occupancy sensor every 10 minutes. These numbers were used as a proxy for occupancy in the space, although we acknowledge the limitations of this approach as it does not provide exact occupant counts. The frequency distributions of measured temperature corresponding to thermal comfort responses for males and females are also shown. These results suggest there was limited bias in occupants’ thermal comfort responses. Only minor peaks in survey responses at cooler and hotter temperatures can be observed, suggesting a slight increase in responses during unfavorable conditions (i.e. relatively colder and warmer temperatures). Females were more likely to respond to the survey at colder temperatures, while males were more likely to respond to the survey at warmer temperatures as shown in Figure 5. Interestingly, the frequency distribution of combined male and female survey responses closely followed the distribution of occupancy-normalized measured temperature, which suggests limited bias in overall temperature responses.
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Frequency
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Figure 5 Relative frequency distribution of measured temperature, with occupancy-weighted distribution and the distribution of temperature at which survey responses were received for males and females
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In contrast to temperature measurements, the frequency distribution of carbon dioxide measurements during occupied periods suggested there was more bias in occupants’ IAQ comfort responses. The frequency distribution of measurements corresponding to survey responses did not closely match overall carbon dioxide measurements during occupied periods, even after normalizing this distribution to occupancy as shown in Figure 6. Note that IAQ comfort responses were not collected separately for males and females. Results showed that IAQ comfort responses were received at higher carbon dioxide concentrations relative to typical conditions. Approximately 90% of the recorded carbon dioxide concentration measurements were below 1000 ppm. However, more than 50% of the survey responses were received when carbon dioxide concentration exceeded that range.
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Frequency
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Figure 6 Relative frequency distribution of measured carbon dioxide concentration, with occupancy-weighted distribution and the distribution of carbon dioxide concentration at which survey responses were received
4.2 Temperature, RH measurements and thermal comfort
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Figure 7 shows all temperature and RH measurements taken every 10 minutes during occupied hours in the gymnastics center, throughout the six-month study period. Results indicated that the average temperature was 21.1°C ± 1.2°C, while the average RH was 37.3% ± 16%. The maximum and minimum recorded temperature during occupied hours were 25.4°C, and 15.1°C, respectively, while the maximum and minimum recorded RH were 71%, and 7% respectively. Approximately 90% of the recorded temperature measurements were lower than 22°C, and RH measurements were lower than 57.5%.
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Figure 7 Psychrometric chart showing the frequency of global temperature and RH measurements during occupied hours in the gymnastics center
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Analysis of thermal comfort results indicated that 55% of males and 55% of females felt warm or very warm. However, 16% of females felt cool or very cool, relative to 22% of males. Note that 1598 survey responses were received from female subjects, relative to 801 responses from male subjects. Figure 8 and Figure 9 show the relationship between perceived thermal comfort for males and females, respectively and the average temperature and RH in the hour preceding each thermal comfort response.
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Figure 8 Psychrometric chart with gymnastics center air temperature and RH and corresponding male survey response. The asterisks represent the mean conditions for each vote category.
Figure 9 Psychrometric chart with gymnastics center air temperature and corresponding female survey response. The asterisks represent the mean conditions for each vote category.
Figure 10 shows the median temperatures corresponding to thermal comfort vote categories, which were significantly different based on the Kruskal-Wallis H tests for males (χ2(4) = 111.48, p < 0.05) and females (χ2(4) = 102.83, p < 0.05). However, pair-wise comparisons showed that statistically significant differences were only found between the median temperatures corresponding to feeling “too cool” and
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“too warm” for both males and females. In other words, the differences in median temperature corresponding to feeling “cool”, “neutral” or “warm” were not statistically significant.
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Temperature
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Temperatures that led to reporting discomfort (e.g. very cool or very warm) did not vary significantly between males and females in this sample, according to the Mann-Whitney U test results. However, the median temperature corresponding to neutral votes was slightly higher for males (21.82°C) than for females (21.7°C). Note that the median temperatures corresponding to different thermal comfort votes only varied within less than 0.5°C, which suggests that identifying an optimal temperature to be perceived as comfortable by the majority of occupants is difficult due to individual differences. Results of the Spearman's rank-order correlation indicate there was a statistically significant correlation between measured temperatures and thermal comfort votes for both males and females (rs = 0.24, 0.25, respectively, P < 0.005).
Figure 10 Measured temperature corresponding to thermal comfort responses for males and females
The relationship between occupants’ thermal comfort and measured RH was investigated following a similar approach. Although the relationship between thermal comfort votes for both males and females and measured RH was statistically significant (rs = 0.18, 0.12, respectively, P < 0.005), it was relatively weaker than their correlation with temperature. The Kruskal-Wallis H tests showed statistically significant differences in the median RH corresponding to thermal vote categories for males (χ2(4) = 57.5, p < 0.05) and females (χ2(4) = 27.11, p < 0.05). However, pair-wise comparisons also showed that statistically significant differences were only found between the median RH corresponding to feeling “too cool” and “too warm” for both males and females.
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4.3 Carbon dioxide measurements and perceived comfort with IAQ
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Results indicated that the average recorded carbon dioxide concentration during occupied hours was 671±258 ppm, while the maximum and minimum recorded carbon dioxide concentrations were 1864, and 282 ppm, respectively. Analysis of the survey results (1,892 responses) indicated that 42% of respondents rated the gymnasium’s IAQ as good or very good, while 35% rated it as poor or very poor. Mui and Wong [35] found that approximately 70-80% of the occupants were satisfied with carbon dioxide concentrations between 1080 and 1500 ppm. However, people began to experience headaches, dizziness, heavy head, fatigue, difficulty concentrating and unpleasant odor at concentration levels between 1500 and 4000 ppm. Therefore, the measured carbon dioxide concentration in the gymnastics center would not be expected to cause severe discomfort.
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Carbon dioxide concentration (PPM)
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Figure 11 shows the median carbon dioxide concentration corresponding to IAQ comfort vote categories. Based on the results of the Kruskal-Wallis H test and subsequent pair-wise comparisons, the only significant differences were found between carbon dioxide measurements corresponding to “very poor” votes, compared to other vote categories (χ2(4) = 29.8, p < 0.005). The relationship between IAQ comfort and measured carbon dioxide concentration, assessed using Spearman's rank-order correlation, was also weak (rs = 0.1, P < 0.005) relative to the relationship between thermal comfort and measured temperature. These findings may be explained by the fact that occupants were more likely to respond to the IAQ comfort survey under higher carbon dioxide concentration as shown in Figure 6Figure 11, resulting in smaller differences between the different vote categories.
Figure 11 Measured carbon dioxide concentration corresponding to IAQ comfort responses
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4.4 Interaction between IAQ and Temperature and perceived comfort
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Carbon dioxide concentration
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Statistical tests were also used to investigate the effect of carbon dioxide concentration on thermal comfort, and the effect of temperature and RH on IAQ comfort. The relationship between measured carbon dioxide concentration and thermal comfort for both males and females was statistically significant based on the Spearman's rank-order correlation (rs = 0.22, 0.19, respectively, P < 0.005), which is equivalent to the correlation between temperature and thermal comfort. Figure 12 shows the distribution of measured carbon dioxide concentration corresponding to each thermal comfort vote category for both males and females. However, results of the Mann-Whitney U test indicated that the differences between both groups were not statistically significant. Based on the results of Kruskal-Wallis H tests, statistically significant differences were found between the median carbon dioxide concentrations corresponding to thermal comfort vote categories for males (χ2(4) = 44.83, p < 0.05) and females (χ2(4) = 58.8, p < 0.05). Pair-wise comparisons showed that these statistically significant differences were mainly between carbon dioxide concentration corresponding to “very warm” and other votes. However, it should be noted that occupants’ thermal comfort is also influenced by several other factors that were not measured within the scope of this study. For example, air speed, metabolic rates, and clothing levels are factors that influence thermal comfort [36] and may have influenced these observations. In particular, higher metabolic rates would increase “warmer” sensation and simultaneously increase carbon dioxide concentration. This was confirmed using the Spearman's rank-order correlation test which showed a statistically significant moderate correlation between temperature and carbon dioxide measurements (rs = 0.54, P < 0.005).
Figure 12 Measured carbon dioxide concentration corresponding to thermal comfort responses for males and females
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In contrast, the relationship between IAQ comfort and measured temperature and RH was relatively weak based on the results of the Spearman's rank-order correlation (rs = 0.07, 0.02, respectively P < 0.005). Based on the results of Kruskal-Wallis H test and subsequent pair-wise comparisons, the only significant differences were found between temperature measurements corresponding to “very poor” and “very good” IAQ comfort (χ2(4) = 12.4, p < 0.005). These findings were in line with previous studies which also showed that temperature did not influence occupants’ IAQ comfort [21], [22].
4.5 Variation of Occupants’ Interpretation of the Indoor Conditions
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Thermal comfort vote
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Occupants’ perception of comfort can vary even under the same indoor conditions. To demonstrate this observation, Figure 13 shows the range of thermal comfort responses at different temperature ranges. For example, although the median thermal comfort vote at 20°C was “neutral” for both males and females, several occupants reported feeling cool or warm, which highlights individual differences in occupants’ perception of indoor temperature. Note that these temperature ranges were rounded to the nearest integer and the corresponding distribution of thermal comfort votes was considered.
Figure 13: Variation of the male occupants' interpretation of temperature in the gymnastics centre
5. Discussion The presented case study demonstrated the feasibility of the proposed research method to collect occupant survey responses on a large scale (over 1000 responses) to be correlated to IEQ conditions in a building with transient occupancy. Future implementation of this research method could cover longer durations to further investigate temporal and seasonal variations in IEQ conditions and corresponding occupant
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comfort. Despite the advantages of the proposed method, it also poses some limitations which are summarized in Table 1. Table 1 Advantages and disadvantages of the proposed method
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Disadvantages • Prevents open-ended questions and using different Likert scales (restricted to available models) • Potential miscommunication about questions’ intent due to the lack of interaction between researchers and subjects • Only feasible for short surveys (1 or 2 questions) thus not suitable for administering longer surveys • Potential for biases if response rates are higher during unfavourable conditions
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Advantages • Facilitates large-scale data collection (thousands of potential survey responses) • Minimizes the “Hawthorne” effect by limiting interaction between researchers and subjects • Enables remote data collection and storage to minimize required research labor • Easy and quick use by occupants, which could lead to higher response rates • Enables administering surveys over relatively long-term durations
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The case study also provided some insights regarding the effect of IEQ conditions on occupant comfort in sport facilities. The analysis showed that temperature had a stronger correlation to occupant’s perceived thermal comfort in comparison to the correlation between carbon dioxide concentration and IAQ comfort. However, contrary to assumptions that suggest females are more likely to prefer warmer temperature [37], the median temperature corresponding to neutral votes (i.e. neither feeling hot nor cold) was higher for males than females. A higher percentage of males also reported feeling cool or very cool relative to females in this case study. These findings may have resulted from focusing on a gymnastics center where occupants’ activities lead to different metabolic rates and thermal sensation than office-based activities, which were the basis for many previous thermal comfort studies [2], [4]. Furthermore, a larger proportion of females used the case study facility which was also reflected in the number of survey responses received from female subjects (double the number of responses received from males).
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Although the analysis showed a weaker correlation between carbon dioxide concentration and IAQ comfort, many other factors influence IAQ conditions which may not have been captured in this study. In particular, the presence of chalk dust in the gymnastics center, which provides a medium for bacteria, can negatively affect occupants’ health. Furthermore, it may have influenced occupants’ perception of IAQ in the facility and their corresponding survey responses. The types of equipment and floor mats used in the space may have also increased volatile organic compounds (VOCs) and adversely affected IAQ. However, VOC measurements were outside the scope of this study. Other limitations of this case study included collecting carbon dioxide measurements in only one location which may not be representative of the entire space. Furthermore, this sensor was installed at a 3 m height to avoid occupant tampering, which is higher than the occupant breathing zone. Additionally, the indoor air quality survey did not separate male and female votes. This is an aspect that is worth consideration in future studies as a comparison of the males’ and females’ interpretation of the indoor air quality could result in valuable insight on occupant comfort. The survey questions were only written in English as shown in Figure 4, but given the diversity of occupants using this sport facility, language barriers or misinterpretation could have posed an issue, especially since the researchers were absent when occupants responded to the surveys.
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Although devices were clearly labeled for males and females to collect thermal comfort responses separately for each gender, no additional mechanisms ensured that occupants followed these instructions and prevented multiple repetitive responses by the same occupant (i.e. user tampering). Moreover, a method for approximating the number of occupants that participated in the occupant survey could be implemented in future studies. An additional survey device that gives several ranges pertaining to the number of times that the occupant has completed the survey has multiple benefits. Firstly, the additional questionnaire would allow for the determination of whether the sampled votes can be taken as representative of the population for the gymnastic center. The questionnaire would also yield a better understanding of the occupants’ interest in the survey, i.e. were the occupants ever interested in the survey and at what point do they typically lose interest (after one vote, 5 votes, etc). Despite these limitations, it should be noted that this case study mainly aimed to provide a proof of concept for the proposed research method. Future research and implementation of this research approach should address these limitations and ensure all relevant IEQ parameters are quantified.
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6. Conclusion
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This research presented a novel method to conduct longitudinal studies on the relationship between IEQ and occupant comfort. Relying on continuous IEQ measurements over an extended period and using electronic survey devices provided hundreds of unique data points to assess occupant comfort and its relationship with IEQ simultaneously. Results of the case study presented in this paper, which focused on a sport facility, also suggested that some assumptions about occupant comfort and its relationship to IEQ may not be applicable for different building types. For example, more males reported feeling cool or very cool than females, and the median temperature corresponding to neutral or thermally comfortable votes was higher for male respondents.
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Although simultaneous IEQ measurements and occupant surveys were used in previous studies [7], [16], [38], several challenges prevented data collection on a larger scale and restricted these studies to shorter durations and smaller samples. Electronic survey devices which are integral to the proposed research method can address some of these challenges due to their ease of use by both occupants and researchers, which also significantly reduces the required researcher labor. Although using these survey devices is relatively more expensive than traditional approaches, technological advances can lead to reducing their costs and improving their functionality. To address potential bias due to higher response rates under unfavorable conditions, incentives could be used to encourage occupants to respond to the surveys irrespective of indoor conditions. Future research should build on the proposed research method by including additional IEQ measurements that can be correlated to occupant comfort in different building types, especially those with transient occupancy.
Acknowledgments
The financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged. The assistance of Homesol and the gymnastics center manager and participation from the occupants is also appreciated.
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Highlights
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A novel research method to conduct longitudinal studies of IEQ and comfort is presented It relies on electronic survey devices to collect occupant responses on a large scale The method was validated in a sport facility as a case study Over 1000 survey responses were correlated to simultaneous IEQ measurements Results show the method’s feasibility especially for buildings with transient occupancy
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S0360-1323(18)30822-9
DOI:
https://doi.org/10.1016/j.buildenv.2018.12.064
Reference:
BAE 5898
To appear in:
Building and Environment
Received Date: 4 October 2018 Revised Date:
29 December 2018
Accepted Date: 31 December 2018
Please cite this article as: Berquist J, Ouf M, O'Brien W, A method to conduct longitudinal studies on indoor environmental quality and perceived occupant comfort, Building and Environment (2019), doi: https://doi.org/10.1016/j.buildenv.2018.12.064. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Justin Berquista, Mohamed Oufb*, William O'Brienb a
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A method to conduct longitudinal studies on Indoor Environmental Quality and Perceived Occupant Comfort
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National Research Council Canada, 1200 Montreal Road, Ottawa, Ontario, K1A 0R6, Canada. Department of Civil and Environmental Engineering, Carleton University, 3432 Mackenzie Building, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada
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Corresponding author Email address: [email protected] Phone: +1 613 520-5784 Fax: +1 613 520-3951 Department of Civil and Environmental Engineering, Carleton University, 3432 Mackenzie Building, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada
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Abstract
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Buildings with transient occupancy such as malls and sport facilities pose a particular challenge for evaluating the effect of indoor environmental quality (IEQ) on occupant comfort due to 1) the difficulties in communicating with their occupants and 2) significant daily variations in their occupancy profiles. To this end, this research presents a method to conduct longitudinal studies in which IEQ and occupant comfort are continuously and simultaneously evaluated. The proposed method relies on using electronic occupant survey devices to enable data collection on a large scale and over longer durations. To validate the proposed method, it was implemented in a gymnastics center in eastern Ontario, Canada between September 2017 and February 2018. Over 1000 survey responses were collected and analyzed relative to prevailing IEQ conditions in the hour preceding each survey response. Analyses showed a stronger correlation between measured temperature and occupants’ thermal comfort in comparison to the relationship between carbon dioxide concentrations and occupants’ perception of the indoor air quality. The case study demonstrated the feasibility of the proposed research method which can be replicated on a wider scale in different building types, especially those with transient occupancy, to further investigate the effect of IEQ on occupant comfort.
1. Introduction
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Evaluating the effect of indoor environmental quality (IEQ) on occupant comfort relies on occupant surveys which typically focus on long-term, one-time evaluations of occupant comfort (e.g. over a week, month(s), or a year) [1]. These types of surveys are more suitable for regular building occupants who are exposed to the same building’s IEQ conditions, daily. Therefore, more research on the acceptable ranges of IEQ conditions and occupant comfort was conducted in commercial, institutional, or residential buildings, relative to buildings with transient occupancy, such as airports, malls, and sport facilities [2]– [6]. To investigate the relationship between IEQ and occupant comfort in these buildings, longitudinal and simultaneous evaluations of both aspects should be conducted to ensure the variations in indoor conditions and occupant demographics are captured.
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Due to the transient nature of occupancy in some buildings, it is more challenging to administer surveys to evaluate satisfaction with the indoor environment for longitudinal studies. Occupant demographics (age, physical condition, and sex) and occupant densities can drastically change within a single day, from day to day within a given building, as well as from one building to another. Therefore, longitudinal studies on the effect of IEQ on occupant comfort are required. However, many of the common occupant survey tools are not geared towards continuous evaluations combined with simultaneous IEQ measurements [1]. Furthermore, previous studies in which simultaneous IEQ measurements and occupant satisfaction surveys were conducted, covered relatively short durations and surveyed a relatively small sample of occupants [7], [8]. To this end, the purpose of this paper is to present a novel research method utilizing electronic survey devices to continuously evaluate occupant satisfaction with simultaneous IEQ measurements for longitudinal studies. The proposed method can be applied in any building type, especially those with transient occupancy. The objectives of this paper are to present the proposed method and demonstrate its main advantages and disadvantages. To validate this method, a case study is also presented where the electronic survey devices, as well as temperature, relative humidity (RH), and carbon dioxide sensors were installed in a gymnastics center in eastern Ontario, Canada between September 2017 and February 2018. Over 1000 occupant responses regarding satisfaction with the thermal environment and indoor air
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quality (IAQ) after each training session were collected and correlated to measured IEQ parameters. Results of this case study demonstrate the feasibility of the proposed method to conduct longitudinal studies with simultaneous IEQ and occupant comfort measurements in similar settings. First a brief review is provided covering the different methodological approaches used to conduct research on IEQ and occupant comfort. This is followed by a description of the proposed approach, its implementation in the case study, and finally a discussion of the proposed method’s advantages and disadvantages.
2. Background
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This section provides a review of the different methodological approaches used to evaluate IEQ and occupant comfort. The final subsection provides an overview of the main differences between sport facilities and other building types, since they were used as a case study representing buildings with transient occupancy.
2.1 Investigating the effect of IEQ on occupant comfort
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To assess occupants’ perception of IEQ, surveys are the most commonly used tool due to their relatively low cost of implementation and simplicity [9]. Peretti and Schiavon [1] provided a review of different IEQ surveys in the literature indicating that the two most widely used are the Building Use Studies Occupant Survey (BUS) [10] and the Center for the Built Environment (CBE) survey [11]. Despite their popularity, several challenges are associated with using surveys to evaluate IEQ, such as the difficulty in selecting representative questions, difficulties in interpreting subjective responses, and finding a representative survey time-frame. For example, Rajagopalan and Jamei [12] analyzed occupant comfort in sports facilities using surveys with a seven-point scale combined with objective IEQ measurements. However, surveys were only completed once by each occupant to evaluate their comfort level throughout the day on which measurements were taken. Conducting multiple simultaneous IEQ measurements and surveys over a longer time-frame may address this issue, although it can potentially lead to “survey fatigue” and reduce response rates [13]. As an alternative method to evaluate thermal comfort, Gunay et al. [14] measured indoor and outdoor temperature in four large office buildings and concurrently monitored thermal complaints and thermostat overrides which represent a proxy for occupant dissatisfaction. However, Schweiker et al. [15] indicated that occupant comfort is also influenced by the number of adaptive control opportunities available, such as the ability to adjust thermostat setpoints or use fans or open windows. Therefore, Schweiker et al. [15] used a test chamber to conduct controlled experiments by exposing occupants to variable IEQ conditions and a different number of adaptive control opportunities. Occupants were asked to complete multiple surveys throughout an eight-day period to evaluate their satisfaction with the indoor environment under these different scenarios. However, the relatively small number of subjects and the test chamber environment posed a challenge to generalizing the results of these laboratory-based studies. Torresin et al. [16] provided in-depth literature review that explored how the interaction between different IEQ parameters (i.e. acoustic, thermal, visual, and air quality factors) influences occupants’ perception and performance. Since occupants are constantly subjected to these multiple IEQ factors simultaneously, their interaction has a strong influence on their perceived comfort. Furthermore, reducing discomfort for one aspect (e.g. opening a window to improve air quality), may negatively affect other aspects such as acoustic quality. Previous studies indicated that the interaction between thermal conditions and IAQ,
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influences occupants’ perceived comfort. For example, Fang et al. [17] showed that above about 28°C and 70% RH, air quality was perceived negatively regardless of its actual pollution levels. In general, the indoor air quality of hot and humid air was perceived negatively, while cold and dry air was perceived as fresher, less stuffy, and acceptable [18]–[20]. On the other hand, IAQ was not found to affect occupants’ thermal assessment [21], [22], except in one study by Melikov and Kaczmarczyk [23], where occupants felt slightly warmer under higher pollutant concentrations, although temperature and RH were the same. Due to the large variations in potential interactions between IEQ parameters, studies quantifying these interactions’ effect on occupant comfort require more simultaneous measurements of both aspects, which would not be possible using “one-time” occupant surveys.
2.2 Investigating IEQ parameters
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The increased availability of wireless sensing technologies and data-logging capabilities facilitates longitudinal IEQ measurements at more granular temporal resolutions [24]. Researchers have developed mobile measurement carts, combining different sensors to simultaneously measure several IEQ parameters, which are useful for quickly moving multiple sensors around a space and keeping sensors steady for the measurement period [25], [26]. However, the bulkiness of these carts can make it difficult to move them between buildings and to conduct measurements during occupants’ presence, especially in buildings with transient occupancy and higher occupant traffic. Revel and Arnesano [27] demonstrated a monitoring methodology for IEQ parameters in sport facilities which were used to estimate thermal comfort through the predicted mean vote (PMV). The study outlined preferred fixed sensor locations to collect measurements on four thermal-related IEQ variables which can be used to calculate PMV. In another study, Revel and Arnesano [6] validated the proposed method using occupant surveys for subjective measurements. However, they were only administered once per day over a four-day investigation period to evaluate occupants’ perception of IEQ. Despite the study’s relatively short duration, results verified the proposed method and showed a strong correlation between the thermal sensation vote (TSV) calculated from survey responses, and PMV calculated using IEQ measurements.
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Measuring IAQ specifically can be more challenging than thermal-related IEQ parameters due to its dependence on several physical and chemical parameters [28]. Although standards and guidelines for acceptable IAQ exist [29], [30], the diversity of potential indoor pollutants and their characteristics makes it difficult to standardize sampling and measurement methods. The most commonly used methods for indoor air sampling and analysis are TO (Toxic Organic) methods (U.S. EPA Compendium of Methods for the Determination of Toxic Organic Compounds in Ambient Air) and indoor pollutant methods (Compendium of Methods for the Determination of Air Pollutants in Indoor Air) [31], [32]. However, previous studies typically used carbon dioxide concentration as an indicator for IAQ to evaluate its effect on occupant comfort as indicated by Lan et al. [19].
2.3 Case studies on IEQ in sport facilities Studies focusing on sport facilities are generally limited relative to other building types such as offices and homes [33]. However, sporting activities can result in a higher rate of carbon dioxide, odor, and moisture generation by occupants [34]. Therefore, standards for indoor ventilation, such as ASHRAE Standard 62.1 [29] specify higher ventilation rates for sport facilities compared to other building types such as office buildings. For example, the required ventilation rate for gymnasiums is 10 L/s.person + 0.9 L/s.m2, whereas the required ventilation rate for office spaces is 2.5 L/s.person + 0.3 L/s.m2. Zitnik et al. [34] also indicated that sporting activities contribute more to the increase in temperature and relative
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humidity (RH) in indoor spaces, unlike other activities such as office work. By monitoring IEQ in a 12,500 m3 gymnasium for a month, Zitnik et al. [34] estimated that a single person evaporated 0.94 kg of water into the air and contributed 0.03 K to air temperature increase, during a 90-minute exercise session. These characteristics of sport facilities suggest that the relationship between IEQ and occupant comfort in these buildings may follow different patterns than other building types.
3. Methodology
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The proposed method relies on using electronic survey devices to administer occupant surveys, rather than the common, paper- or web-based platforms. Simultaneously, fixed sensors are installed in the monitored spaces to measure key IEQ parameters at relatively short intervals throughout the study period. Because survey responses can be collected at any time during the study period, continuous measurements of IEQ data is necessary to ensure the IEQ conditions relating to each survey response are captured. To investigate whether survey responses increase during unfavorable indoor conditions, occupancy sensing infrastructure should also be used. Figure 1 provides an overview of the proposed research method and the main aspects to consider when implementing it. Define the research question(s)
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• Identify specific IEQ parameters to focus on • Identify relevant occupant comfort parameters • Determine the study period to maximize data collection and ensure temporal and seasonal variations are captured
Occupant surveys
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• Identify relevant survey questions based on the selected comfort parameters • Maximize response rates by selecting only 1 or 2 questions at most to be administered using electronic survey devices • Strategically install devices in locations with higher occupant traffic to maximize response rates • Ensure responses are collected after occupants have experienced IEQ by strategically locating the survey devices near exit doors and providing clear instructions • Monitor occupants’ use of the survey devices to avoid miscommunication regarding the survey intent and expected responses
IEQ measurements • Identify the types of sensors required based on the selected IEQ parameters • Calibrate sensors prior to installation • Identify the temporal and spatial resolution for each sensor type based on the measured IEQ parameter, space type, size and layout, budget and space limitations
Occupancy detection • Identify the type of occupancy sensors to be used (Preference should be given to occupant counting technologies) • Calibrate sensors prior to installation • Consider placing occupancy sensors close to the electronic survey devices
Data analysis
• Identify the analysis methods to address the identified research questions • Identify specific statistical tests and ensure all relevant data is collected
Results
• Evaluate the relationship between measured IEQ parameters and comfort • Identify action items to improve occupant comfort in the monitored space
Figure 1 Overview of the proposed research method and major points to consider
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To the authors’ knowledge, the proposed method specifically relying on electronic survey devices to collect occupant responses has not been used in previous studies for evaluating occupant satisfaction with IEQ. These devices provide several advantages over paper-based and online surveys due to their ease of use, which facilitates faster data collection on a large scale. These features are especially useful for buildings with transient occupancy, where other communication forms with occupants (e.g. through email or paper-based surveys) may not be feasible. They require minimal effort for participants and are thus less likely to suffer from participant fatigue. Following the proposed implementation and data-collection approach, each survey response can be correlated to prevailing IEQ conditions at that time.
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One of the possible drawbacks of the proposed method is increasing the potential for receiving biased survey responses if occupants are more likely to respond during unfavorable conditions. To investigate this issue, the frequency distributions of measured IEQ parameters throughout occupancy periods can be compared to the frequency distribution of IEQ parameters corresponding to survey responses. This investigation can be improved using occupancy data to normalize the frequency distribution of measured IEQ parameters using recorded occupancy counts.
3.1 Case Study
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To validate the proposed research method, it was implemented in a gymnastics center, located in eastern Ontario between September 2017 and February 2018 to capture both heating and cooling periods. The center is open for approximately 12 hours per day, 7 days a week, however, the number of occupants during the hours of operation varies significantly. According to the gymnastics center's management, up to 40 occupants can be present in the gymnastics area during weekends, with approximately 20 parents in the lobby and viewing area. In contrast, the number of occupants can drop to two or three staff members on weekdays. Figure 2 shows the floor plan of the gymnastics center which is approximately 1,580 m2 (17,000 ft2), as well as its mechanical system layout. Two rooftop units serve the gymnastics area, both of which are located above the entry location of the supply duct, which is noted in Figure 2.
Figure 2 Gymnastics center's mechanical system
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The overall goal of this case study is to demonstrate the feasibility of the proposed research method for collecting large scale occupant survey responses and correlating them to corresponding IEQ measurements. Specific research questions focused on 1) identifying potential bias to determine if occupants tend to respond to the surveys more frequently during unfavorable conditions, 2) evaluating the relationship between thermal comfort and temperature, RH measurements, 3) evaluating the relationship between IAQ comfort and measured carbon dioxide concentration, and 4) evaluating the interaction between thermal comfort and carbon dioxide concentration, as well as IAQ comfort and temperature measurements.
3.1.1 IEQ measurements
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Six temperature and RH sensors (E-348-UX100-011) were installed to evaluate the thermal environment inside the gymnastics center, while an additional data logger (E-348-MX1102) that measured carbon dioxide concentration, temperature and relative humidity was installed to evaluate the thermal comfort and IAQ of the gymnastics center. The temperature sensors had a measurement range between 0°C and 50°C, with accuracy of ±0.21°C and resolution of 0.024°C, while the RH measurements had a range between 0% and 90%, with an accuracy of ±2.5% and resolution of 0.05%. The Temperature and RH sensors were installed at a height of 1.5 m above the floor, while the carbon dioxide sensor was installed at a 3-m height to avoid potential occupant tampering. The measurement accuracy of the carbon dioxide concentration was ±50 ppm. All devices were factory calibrated and, upon researcher testing, were immediately installed after purchase. Figure 3 shows the placement of these sensors within the gymnastics center, where “T” denotes temperature and RH measurement locations, and “C” denotes the carbon dioxide concentration measurement location. All of the sensors took measurements every 10 minutes over the six-month period between September 2017 and February 2018. To correlate thermal conditions to occupants’ satisfaction with the thermal environment, which was evaluated after training sessions, the average readings of the six temperature and RH sensors over one hour prior to each survey response were calculated. Similarly, the carbon dioxide concentration readings over one hour were recorded to be correlated to occupant satisfaction with the IAQ. In addition to temperature, RH and carbon dioxide sensors, motion sensors, denoted by “O”, were installed next to the entrance door of the gymnastics center to detect motion in that area, which provides a proxy representing occupancy.
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Figure 3 Sensor layout for the gymnastics center
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3.1.2 Electronic survey devices installation
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To simultaneously evaluate occupant satisfaction with IEQ, electronic occupant survey devices, manufactured by MMCall ltd., were installed next to the exit door as shown in Figure 3 and denoted by “S”. These devices were intentionally placed in that location to solicit feedback after occupants have completed their training sessions, which typically lasted for one hour, and experienced the indoor environment. A custom machined case was created for each device to allow for wall mounting. The surveys solicited occupants’ feedback regarding the IAQ and thermal environment in the space using a five-point scale as shown in Figure 4. They automatically filtered out rapid button pushes to avoid double counting occupants’ responses.
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Figure 4 Electronic occupant survey devices
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To evaluate thermal comfort, two occupant survey devices were installed; one for males and one for females which asked occupants to provide feedback regarding their thermal comfort. Large signs were placed above each device to indicate the respective gender. The following question was asked to both groups: “How satisfied were you with the temperature in the gymnastics center today? Were you…”. Possible responses were as follows: “warm, slightly warm, neutral, slightly cool, cool”. The timestamp for each response was recorded and the data was sent to a centralized database. The temperature and RH values associated with each button-push were obtained by averaging the measurements over the preceding hour. This allowed for the approximate environmental conditions that the occupant experienced to be compared to their response.
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To evaluate occupants’ satisfaction with IAQ, a third electronic occupant survey device was installed to be used by all occupants (i.e. not based on gender). The following question was asked “How was the air quality in the gymnastics center today (i.e. stuffy/stale air, cleanliness, odors)? Was it…”, to which possible responses were as follows “very poor, poor, neutral, good, very good”. The timestamp for each response was also automatically recorded, and the carbon dioxide concentration measurements were averaged over the previous hour. The relationship between the occupants’ thermal comfort feedback and the sensor measurements could then be analyzed to determine the relationship between these two aspects.
3.1.3 Data analysis
The first step in analyzing the data entailed using descriptive statistics to characterize measured IEQ conditions by identifying averages, minima, maxima, and frequency distributions. As part of this analysis, the relationship between occupants’ survey responses (i.e. perceived comfort) and averaged IEQ measurements in the hour prior to each response was also plotted. For temperature and RH measurements, the average of the six measurements was taken for each time interval. The appropriateness of utilizing the average of the six sensor measurements was verified by evaluating the “momentary” differences between each sensor reading. The average “momentary” standard deviation was approximately 0.35 °C and the
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maximum standard deviation was approximately 1.02 °C. The low fluctuation between sensor measurements implied that the space was well mixed and that the sensors were not interfered with. Therefore, utilizing an average of the six measurements was deemed acceptable for this application. Furthermore, occupancy sensor data was used to normalize the frequency distributions of measured IEQ parameters by using the number of motion detections as a proxy for the gymnastics center’s occupancy. In this case, the number of motion detections at each time interval was used as the corresponding weight for IEQ measurements at that time interval to produce an occupancy-weighted frequency distribution.
4. Results and Analysis
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The second step of the analysis entailed using statistical tests to evaluate these relationships between occupants’ perceived comfort and measured IEQ parameters. Measured temperatures, RH, and carbon dioxide concentrations corresponding to each comfort vote category (e.g. too cool, too warm, or very poor, very good…etc.) were not normally distributed as assessed by Shapiro-Wilk's test (p <.05), thus non-parametric tests were used for the statistical analysis. To determine if there were statistically significant differences between measured IEQ parameters corresponding to each of the comfort vote categories, a non-parametric Kruskal-Wallis H test was used. Whenever a statistically significant difference was detected, pair-wise comparisons were performed using Dunn's procedure with a Bonferroni correction. To measure the strength of the relationship between measured IEQ parameters and the corresponding comfort vote, a Spearman's rank-order correlation test was used since comfort votes were not measured on a continuous scale (i.e. ordinal variables). Since thermal comfort votes were collected separately for males and females, a Mann-Whitney U test was used to compare measured IEQ parameters corresponding to each thermal vote category between both groups.
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This section presents the case study results to demonstrate some examples for analyzing data collected following the proposed research method. The first subsection focuses on comparing the frequency distributions of IEQ measurements during occupied periods to measurements corresponding to survey responses, for investigating potential biases. The following three subsections focus on analyzing the effect of IEQ on occupant comfort within the investigated gymnastics center. The final subsection provides an analysis of the variation in occupants’ perception of the indoor conditions.
4.1 Investigating potential bias in survey responses
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Figure 5 shows the relative frequency distribution of temperature measurements during occupied periods, which was also normalized to occupancy. Normalization to occupancy was achieved using the number of motion detections collected from the installed occupancy sensor every 10 minutes. These numbers were used as a proxy for occupancy in the space, although we acknowledge the limitations of this approach as it does not provide exact occupant counts. The frequency distributions of measured temperature corresponding to thermal comfort responses for males and females are also shown. These results suggest there was limited bias in occupants’ thermal comfort responses. Only minor peaks in survey responses at cooler and hotter temperatures can be observed, suggesting a slight increase in responses during unfavorable conditions (i.e. relatively colder and warmer temperatures). Females were more likely to respond to the survey at colder temperatures, while males were more likely to respond to the survey at warmer temperatures as shown in Figure 5. Interestingly, the frequency distribution of combined male and female survey responses closely followed the distribution of occupancy-normalized measured temperature, which suggests limited bias in overall temperature responses.
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Figure 5 Relative frequency distribution of measured temperature, with occupancy-weighted distribution and the distribution of temperature at which survey responses were received for males and females
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In contrast to temperature measurements, the frequency distribution of carbon dioxide measurements during occupied periods suggested there was more bias in occupants’ IAQ comfort responses. The frequency distribution of measurements corresponding to survey responses did not closely match overall carbon dioxide measurements during occupied periods, even after normalizing this distribution to occupancy as shown in Figure 6. Note that IAQ comfort responses were not collected separately for males and females. Results showed that IAQ comfort responses were received at higher carbon dioxide concentrations relative to typical conditions. Approximately 90% of the recorded carbon dioxide concentration measurements were below 1000 ppm. However, more than 50% of the survey responses were received when carbon dioxide concentration exceeded that range.
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Figure 6 Relative frequency distribution of measured carbon dioxide concentration, with occupancy-weighted distribution and the distribution of carbon dioxide concentration at which survey responses were received
4.2 Temperature, RH measurements and thermal comfort
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Figure 7 shows all temperature and RH measurements taken every 10 minutes during occupied hours in the gymnastics center, throughout the six-month study period. Results indicated that the average temperature was 21.1°C ± 1.2°C, while the average RH was 37.3% ± 16%. The maximum and minimum recorded temperature during occupied hours were 25.4°C, and 15.1°C, respectively, while the maximum and minimum recorded RH were 71%, and 7% respectively. Approximately 90% of the recorded temperature measurements were lower than 22°C, and RH measurements were lower than 57.5%.
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Figure 7 Psychrometric chart showing the frequency of global temperature and RH measurements during occupied hours in the gymnastics center
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Analysis of thermal comfort results indicated that 55% of males and 55% of females felt warm or very warm. However, 16% of females felt cool or very cool, relative to 22% of males. Note that 1598 survey responses were received from female subjects, relative to 801 responses from male subjects. Figure 8 and Figure 9 show the relationship between perceived thermal comfort for males and females, respectively and the average temperature and RH in the hour preceding each thermal comfort response.
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Figure 8 Psychrometric chart with gymnastics center air temperature and RH and corresponding male survey response. The asterisks represent the mean conditions for each vote category.
Figure 9 Psychrometric chart with gymnastics center air temperature and corresponding female survey response. The asterisks represent the mean conditions for each vote category.
Figure 10 shows the median temperatures corresponding to thermal comfort vote categories, which were significantly different based on the Kruskal-Wallis H tests for males (χ2(4) = 111.48, p < 0.05) and females (χ2(4) = 102.83, p < 0.05). However, pair-wise comparisons showed that statistically significant differences were only found between the median temperatures corresponding to feeling “too cool” and
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“too warm” for both males and females. In other words, the differences in median temperature corresponding to feeling “cool”, “neutral” or “warm” were not statistically significant.
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Temperature
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Temperatures that led to reporting discomfort (e.g. very cool or very warm) did not vary significantly between males and females in this sample, according to the Mann-Whitney U test results. However, the median temperature corresponding to neutral votes was slightly higher for males (21.82°C) than for females (21.7°C). Note that the median temperatures corresponding to different thermal comfort votes only varied within less than 0.5°C, which suggests that identifying an optimal temperature to be perceived as comfortable by the majority of occupants is difficult due to individual differences. Results of the Spearman's rank-order correlation indicate there was a statistically significant correlation between measured temperatures and thermal comfort votes for both males and females (rs = 0.24, 0.25, respectively, P < 0.005).
Figure 10 Measured temperature corresponding to thermal comfort responses for males and females
The relationship between occupants’ thermal comfort and measured RH was investigated following a similar approach. Although the relationship between thermal comfort votes for both males and females and measured RH was statistically significant (rs = 0.18, 0.12, respectively, P < 0.005), it was relatively weaker than their correlation with temperature. The Kruskal-Wallis H tests showed statistically significant differences in the median RH corresponding to thermal vote categories for males (χ2(4) = 57.5, p < 0.05) and females (χ2(4) = 27.11, p < 0.05). However, pair-wise comparisons also showed that statistically significant differences were only found between the median RH corresponding to feeling “too cool” and “too warm” for both males and females.
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4.3 Carbon dioxide measurements and perceived comfort with IAQ
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Results indicated that the average recorded carbon dioxide concentration during occupied hours was 671±258 ppm, while the maximum and minimum recorded carbon dioxide concentrations were 1864, and 282 ppm, respectively. Analysis of the survey results (1,892 responses) indicated that 42% of respondents rated the gymnasium’s IAQ as good or very good, while 35% rated it as poor or very poor. Mui and Wong [35] found that approximately 70-80% of the occupants were satisfied with carbon dioxide concentrations between 1080 and 1500 ppm. However, people began to experience headaches, dizziness, heavy head, fatigue, difficulty concentrating and unpleasant odor at concentration levels between 1500 and 4000 ppm. Therefore, the measured carbon dioxide concentration in the gymnastics center would not be expected to cause severe discomfort.
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Carbon dioxide concentration (PPM)
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Figure 11 shows the median carbon dioxide concentration corresponding to IAQ comfort vote categories. Based on the results of the Kruskal-Wallis H test and subsequent pair-wise comparisons, the only significant differences were found between carbon dioxide measurements corresponding to “very poor” votes, compared to other vote categories (χ2(4) = 29.8, p < 0.005). The relationship between IAQ comfort and measured carbon dioxide concentration, assessed using Spearman's rank-order correlation, was also weak (rs = 0.1, P < 0.005) relative to the relationship between thermal comfort and measured temperature. These findings may be explained by the fact that occupants were more likely to respond to the IAQ comfort survey under higher carbon dioxide concentration as shown in Figure 6Figure 11, resulting in smaller differences between the different vote categories.
Figure 11 Measured carbon dioxide concentration corresponding to IAQ comfort responses
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4.4 Interaction between IAQ and Temperature and perceived comfort
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Carbon dioxide concentration
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Statistical tests were also used to investigate the effect of carbon dioxide concentration on thermal comfort, and the effect of temperature and RH on IAQ comfort. The relationship between measured carbon dioxide concentration and thermal comfort for both males and females was statistically significant based on the Spearman's rank-order correlation (rs = 0.22, 0.19, respectively, P < 0.005), which is equivalent to the correlation between temperature and thermal comfort. Figure 12 shows the distribution of measured carbon dioxide concentration corresponding to each thermal comfort vote category for both males and females. However, results of the Mann-Whitney U test indicated that the differences between both groups were not statistically significant. Based on the results of Kruskal-Wallis H tests, statistically significant differences were found between the median carbon dioxide concentrations corresponding to thermal comfort vote categories for males (χ2(4) = 44.83, p < 0.05) and females (χ2(4) = 58.8, p < 0.05). Pair-wise comparisons showed that these statistically significant differences were mainly between carbon dioxide concentration corresponding to “very warm” and other votes. However, it should be noted that occupants’ thermal comfort is also influenced by several other factors that were not measured within the scope of this study. For example, air speed, metabolic rates, and clothing levels are factors that influence thermal comfort [36] and may have influenced these observations. In particular, higher metabolic rates would increase “warmer” sensation and simultaneously increase carbon dioxide concentration. This was confirmed using the Spearman's rank-order correlation test which showed a statistically significant moderate correlation between temperature and carbon dioxide measurements (rs = 0.54, P < 0.005).
Figure 12 Measured carbon dioxide concentration corresponding to thermal comfort responses for males and females
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In contrast, the relationship between IAQ comfort and measured temperature and RH was relatively weak based on the results of the Spearman's rank-order correlation (rs = 0.07, 0.02, respectively P < 0.005). Based on the results of Kruskal-Wallis H test and subsequent pair-wise comparisons, the only significant differences were found between temperature measurements corresponding to “very poor” and “very good” IAQ comfort (χ2(4) = 12.4, p < 0.005). These findings were in line with previous studies which also showed that temperature did not influence occupants’ IAQ comfort [21], [22].
4.5 Variation of Occupants’ Interpretation of the Indoor Conditions
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Thermal comfort vote
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Occupants’ perception of comfort can vary even under the same indoor conditions. To demonstrate this observation, Figure 13 shows the range of thermal comfort responses at different temperature ranges. For example, although the median thermal comfort vote at 20°C was “neutral” for both males and females, several occupants reported feeling cool or warm, which highlights individual differences in occupants’ perception of indoor temperature. Note that these temperature ranges were rounded to the nearest integer and the corresponding distribution of thermal comfort votes was considered.
Figure 13: Variation of the male occupants' interpretation of temperature in the gymnastics centre
5. Discussion The presented case study demonstrated the feasibility of the proposed research method to collect occupant survey responses on a large scale (over 1000 responses) to be correlated to IEQ conditions in a building with transient occupancy. Future implementation of this research method could cover longer durations to further investigate temporal and seasonal variations in IEQ conditions and corresponding occupant
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comfort. Despite the advantages of the proposed method, it also poses some limitations which are summarized in Table 1. Table 1 Advantages and disadvantages of the proposed method
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Disadvantages • Prevents open-ended questions and using different Likert scales (restricted to available models) • Potential miscommunication about questions’ intent due to the lack of interaction between researchers and subjects • Only feasible for short surveys (1 or 2 questions) thus not suitable for administering longer surveys • Potential for biases if response rates are higher during unfavourable conditions
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Advantages • Facilitates large-scale data collection (thousands of potential survey responses) • Minimizes the “Hawthorne” effect by limiting interaction between researchers and subjects • Enables remote data collection and storage to minimize required research labor • Easy and quick use by occupants, which could lead to higher response rates • Enables administering surveys over relatively long-term durations
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The case study also provided some insights regarding the effect of IEQ conditions on occupant comfort in sport facilities. The analysis showed that temperature had a stronger correlation to occupant’s perceived thermal comfort in comparison to the correlation between carbon dioxide concentration and IAQ comfort. However, contrary to assumptions that suggest females are more likely to prefer warmer temperature [37], the median temperature corresponding to neutral votes (i.e. neither feeling hot nor cold) was higher for males than females. A higher percentage of males also reported feeling cool or very cool relative to females in this case study. These findings may have resulted from focusing on a gymnastics center where occupants’ activities lead to different metabolic rates and thermal sensation than office-based activities, which were the basis for many previous thermal comfort studies [2], [4]. Furthermore, a larger proportion of females used the case study facility which was also reflected in the number of survey responses received from female subjects (double the number of responses received from males).
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Although the analysis showed a weaker correlation between carbon dioxide concentration and IAQ comfort, many other factors influence IAQ conditions which may not have been captured in this study. In particular, the presence of chalk dust in the gymnastics center, which provides a medium for bacteria, can negatively affect occupants’ health. Furthermore, it may have influenced occupants’ perception of IAQ in the facility and their corresponding survey responses. The types of equipment and floor mats used in the space may have also increased volatile organic compounds (VOCs) and adversely affected IAQ. However, VOC measurements were outside the scope of this study. Other limitations of this case study included collecting carbon dioxide measurements in only one location which may not be representative of the entire space. Furthermore, this sensor was installed at a 3 m height to avoid occupant tampering, which is higher than the occupant breathing zone. Additionally, the indoor air quality survey did not separate male and female votes. This is an aspect that is worth consideration in future studies as a comparison of the males’ and females’ interpretation of the indoor air quality could result in valuable insight on occupant comfort. The survey questions were only written in English as shown in Figure 4, but given the diversity of occupants using this sport facility, language barriers or misinterpretation could have posed an issue, especially since the researchers were absent when occupants responded to the surveys.
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Although devices were clearly labeled for males and females to collect thermal comfort responses separately for each gender, no additional mechanisms ensured that occupants followed these instructions and prevented multiple repetitive responses by the same occupant (i.e. user tampering). Moreover, a method for approximating the number of occupants that participated in the occupant survey could be implemented in future studies. An additional survey device that gives several ranges pertaining to the number of times that the occupant has completed the survey has multiple benefits. Firstly, the additional questionnaire would allow for the determination of whether the sampled votes can be taken as representative of the population for the gymnastic center. The questionnaire would also yield a better understanding of the occupants’ interest in the survey, i.e. were the occupants ever interested in the survey and at what point do they typically lose interest (after one vote, 5 votes, etc). Despite these limitations, it should be noted that this case study mainly aimed to provide a proof of concept for the proposed research method. Future research and implementation of this research approach should address these limitations and ensure all relevant IEQ parameters are quantified.
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This research presented a novel method to conduct longitudinal studies on the relationship between IEQ and occupant comfort. Relying on continuous IEQ measurements over an extended period and using electronic survey devices provided hundreds of unique data points to assess occupant comfort and its relationship with IEQ simultaneously. Results of the case study presented in this paper, which focused on a sport facility, also suggested that some assumptions about occupant comfort and its relationship to IEQ may not be applicable for different building types. For example, more males reported feeling cool or very cool than females, and the median temperature corresponding to neutral or thermally comfortable votes was higher for male respondents.
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Although simultaneous IEQ measurements and occupant surveys were used in previous studies [7], [16], [38], several challenges prevented data collection on a larger scale and restricted these studies to shorter durations and smaller samples. Electronic survey devices which are integral to the proposed research method can address some of these challenges due to their ease of use by both occupants and researchers, which also significantly reduces the required researcher labor. Although using these survey devices is relatively more expensive than traditional approaches, technological advances can lead to reducing their costs and improving their functionality. To address potential bias due to higher response rates under unfavorable conditions, incentives could be used to encourage occupants to respond to the surveys irrespective of indoor conditions. Future research should build on the proposed research method by including additional IEQ measurements that can be correlated to occupant comfort in different building types, especially those with transient occupancy.
Acknowledgments
The financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged. The assistance of Homesol and the gymnastics center manager and participation from the occupants is also appreciated.
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Highlights
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A novel research method to conduct longitudinal studies of IEQ and comfort is presented It relies on electronic survey devices to collect occupant responses on a large scale The method was validated in a sport facility as a case study Over 1000 survey responses were correlated to simultaneous IEQ measurements Results show the method’s feasibility especially for buildings with transient occupancy
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