1. Introduction
In the design of the built environment, it is often a conventional practice to only consider the physical features of spaces whilst there are many other aspects largely influencing the indoor environmental quality (IEQ). For example, outdoor air ventilation rates are determined according to the conditioned area served by the Heating, Ventilation, Air Conditioning (HVAC) system and a default occupant density value defined according to building type [
1]. This design method fails to take into account the effect of occupant density changing over time and their interaction with the immediate indoor environment. Therefore, researchers shift their focus to the adaptive design method, in which occupants are considered as integral parts of the whole comfort system of the building [
2]. The number of occupants is modelled as a stochastic variable that can have an influence either by actively improving the thermal environment through occupant comfort control [
2,
3] or passively being the source causing discomfort in the space [
4,
5]. For example, an adult around 21–50 years old releases approximately 0.005 L/s [
6] of carbon dioxide (CO
2) as a by-product of bodily function and heat of 150 W through convection, radiation, vapour, and sweat [
7].
Table 1 lists the various IEQ measurement thresholds in non-residential buildings. Through measuring CO
2 concentration and indoor air temperature, this study aims to evaluate the contribution of high occupant density to undesirable indoor air quality and thermal conditions in a typical classroom.
Multiple research studies concern the IEQ and thermal comfort of educational buildings. CO
2 is often used as one of the metrics for evaluating the IEQ since the presence of CO
2 at its threshold level is often an indication of an area of indoor pollutant concerns with poor ventilation [
13]. Asif et al. [
14] conducted an assessment of IEQ in four university buildings and investigated on the impact of different HVAC systems on building IEQ. They concluded that IEQ is heavily dependent on the type of ventilation system used. CO
2 levels were found to be highest in the university building utilizing non-centralized HVAC system. Krawczyk et al. [
12] measured the CO
2 concentrations in two school buildings located in different climate and developed a model for estimating the concentration level. The study noted that the CO
2 concentration threshold is often exceeded within the first hour of occupancy. They suggested using air change rates of 2.5-5 to reduce the concentration. Zomorodian, Tahsildoost, and Hafezi [
15] conducted a review on the thermal comfort in educational buildings and noted that most studies emphasize ventilation as a significant determining factor of IEQ and thermal comfort in classrooms. Ventilation demand increases with higher occupancy in classrooms [
16]. University lecture halls are of particular interest for indoor air quality and thermal condition studies because students spend most of their time in lecture halls and energy savings are of importance to institutions [
17]. This is also due to its occupancy pattern, usually with high occupant density that may dramatically vary throughout a day as students enter and leave the classroom in groups. If the HVAC system is not operated sufficiently, the heat and CO
2 accumulated during one lecture session may adversely affect the students in the following lecture session [
18]. Architects and engineers use the thermal environment condition standards of ASHRAE 55, European Committee for Standardization CEN 15251:
Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings, and International Organization for Standardization ISO-7730:
Ergonomics of the Thermal Environment as reference documents in their designs, but largely ignored (knowingly or unknowingly) the impact of transient occupancy pattern to the IEQ of classrooms. Studies [
18,
19,
20,
21,
22] have expressed the inappropriate application of current standards, which are based on office buildings with a steady number of occupants, to the classroom environment due to different occupancy schedules.
Seppänen, Fisk, and Lei [
23] conducted 24 case studies and concluded that a 2% decrease in productivity is observed for a 1 °C increase in air temperature above 25 °C. Charzidiakou et al. [
24] suggest that IEQ assessments be mandatory as part of building regulations due to the interrelationship between thermal condition, indoor pollutant levels, ventilation rates, and CO
2 concentration. In their studies focusing on educational buildings, it is observed that keeping temperatures below 26 °C in summer and 22 °C in winter by outdoor air ventilation can limit the amount of Volatile Organic Compounds (VOC) below the threshold, above which sensory irritation is likely to occur. There is a correlation between high indoor air temperature and occupants’ productivity as noted by Singh, Ooka, and Rijal [
18]. Indoor air temperature and CO
2 concentration may be related because there is a higher likelihood of a space overheating above 25 °C when CO
2 concentration are above 1500 ppm [
24]. Persily and de Jonge [
5] identify that CO
2 accumulation in the space from occupants can cause indoor air quality concerns. CO
2 is reported to affect students’ decision making and performance starting from 1000 ppm, and more significant effects when exposed to 2500 ppm [
25]. CO
2 concentration of 1000 ppm is the threshold of safety defined by relevant standards and design guidelines [
1,
5], due to studies showing a correlation between cognitive function scores and CO
2 concentration [
2]. Sick Building Syndrome (SBS), or the health effects caused by long term exposure to pollutants in the built environment, is prevalent in the presence of degraded indoor air quality [
26]. The symptoms may include nasal congestion, dryness of eyes and skin, and headaches [
27]. Norback et al. [
28] conclude that indoor air temperature and CO
2 concentration are important considerations in indoor environment assessments. CO
2 concentration measurements are to be used to calculate accurate outdoor air ventilation rates.
The HVAC systems should be operated pre-actively in response to uncertain occupancy patterns anticipated in classrooms. Jaakkola et al. [
27] relate SBS with inadequate mechanical ventilation rates as the primary cause. They identified that a reduction in ventilation rate caused a slight but significant increase in the occurrence of SBS symptoms. Due to the growing awareness of energy efficiency, research has been conducted over the past 25 years to investigate alternative HVAC controls for addressing the issues of internal heat gain without excessive energy consumption [
11]. Kleiminger et al. [
11] mentioned that occupancy prediction algorithms which control heating systems and temperature setpoint are effective in adjusting heating output throughout the day for the purpose of saving energy. The above-mentioned studies focus mainly on using ventilation as the strategy to remove excessive internal heat gain while lowering its corresponding energy consumption. CO
2 concentration, as another source of discomfort in indoor environments with high variable occupancy, is discussed in studies targeting CO
2 based ventilation control [
29].
The above-mentioned literature shows that the IEQ in classrooms is an area of concern and prompted this study’s investigation on the indoor condition during lecture hours. This study investigates how to regulate both the CO2 concentration and indoor air temperature of classrooms via optimizing HVAC operation. The goal of this research is to evaluate the indoor environment of university lecture halls and propose mitigation strategies accordingly. Actual conditions are monitored, and a thermal sensation survey was done to identify and confirm the existing aspects of discomfort present in a classroom. The presence of discomfort as identified from on-site measurements and survey motivated further research. Specifically, strategies to optimally control the outdoor airflow rate and the heating output of the HVAC system will be proposed and evaluated using Building Energy Simulation (BES) to determine if the cause of discomfort is related to the inadequacy of the existing HVAC system. The main objective is to propose an optimal HVAC operation scheme to improve the indoor environment in classrooms.
4. Discussion
The on-site measurements confirmed that the classroom is overheated in the wintertime and CO2 concentration is accumulating with the increase in occupancy density. In addition, the questionnaire confirms that students are not satisfied with the classroom comfort level. It is evident that occupancy is a major heat and CO2 source while the space does not have adequate methods of dissipating such heat and contaminants.
Simulation identified efficient HVAC operation to be dependent on outdoor airflow rate and quantity of heating output determined by schedules. It provided an insight into the importance of occupancy in BES and HVAC operation.
Table 7 and
Table 8 summarizes the main results of the revised cases, showing reduced CO
2 concentration and the indoor air temperature remained closer to the intended indoor air temperature set-point during the heating season. The previously measured CO
2 concentration is exceeding the ASHRAE standard of 1000 ppm as the acceptable range for indoor air. Therefore, university lecture classrooms should include systems for increased outdoor air ventilation such as the use of a heat recovery ventilator (HRV) or an energy-recovery ventilator (ERV).
This research shows that occupant satisfaction is jeopardized when the HVAC system is not adjusting its operation until the occupant behaviour causes a disturbance on indoor air temperature, which is in agreement with Leaman and Bordass’s findings [
3]. Similarly, it is evident through the results of this current research that the constant change in occupancy density throughout the day causes high air temperature and unsatisfactory indoor air quality within the space. Increasing outdoor air ventilation and scheduling the heating availability dynamically according to occupancy density is therefore proven in this study as effective solutions to stabilize the indoor air temperature and CO
2 concentration. It aligns with the values of research related to Model Predictive Control [
41] and supports the development in set-point temperature algorithms tuned with the estimation of upcoming occupancy load.
5. Conclusions and Future Works
The assessment conducted in this study raises the concern of high indoor air temperature and CO2 concentration within university lecture classrooms. The assumption of static occupancy, as adopted in thermal comfort standards such as ASHRAE 55, is not sufficient for classrooms. It is evident that existing HVAC operation strategies must be improved, and the interventions proposed above have been effective in properly controlling indoor air temperature and CO2 concentration. The key changes to HVAC operation are identified as increasing outdoor air ventilation and controlling the system according to dynamic occupancy density. If institutional buildings adopt these interventions, the benefit would be improved thermal condition and indoor air quality. It will be a stride towards improving students’ productivity and satisfaction towards their learning environment.
Ongoing research includes updating measurements of air temperature in various classrooms over a longer period of time and considering more thermal comfort parameters. Measured data from a winter school term will help calibrate the model (baseline case) more accurately, as well as provide more occupancy data over a longer period of time throughout the year. Furthermore, only the wintertime overheating effect has been analyzed in this study, while summertime subcooling may also be an issue present in the classrooms. In addition to hourly indoor air temperature and CO
2 concentration comparison between measured and simulated data, other parameters to assess thermal comfort such as relative humidity and mean radiant temperature may also be compared using BES, similar to what has been done in Ahmad et al. and Chenari et al.’s research [
49,
50]. Chenari et al. [
50] simulated occupancy and CO
2 based demand-controlled mechanical ventilation strategies using EnergyPlus to explore and arrive at a conclusion regarding the impact that schedule and ventilation strategies have on energy consumption and indoor air quality. In this way, there can be more insight into optimal ventilation strategies that controls a wider range of indoor air quality and thermal condition parameters.
Moreover, note that the current HVAC operation changes implemented in EnergyPlus and eQUEST are theoretical and are to be tested in actual practice to determine its validity in influencing the indoor environment. Changing the heating schedules based on occupancy is a variable that is changed within energy simulation, but the actual implementation of these strategies to the classroom is not within this research’s current scope. By observing the positive effects that resulted from these variations, it indicates a possibility of improving HVAC operational strategies in similar ways, but with practical and tangible methods such as through the use of sensors. This study has demonstrated that BES can identify overheating and high CO
2 concentration to develop possible solutions. However, an overarching practical issue that most energy modelers face is the time and effort required to collect adequate data and develop reliable energy models. Detailed energy modelling using building simulation programs requires many inputs, and modelers may not have full knowledge of each input’s relative importance to simulation outcomes, level of uncertainty, and the appropriate default values to use. This issue is exacerbated when actual or realistic data (i.e., occupancy, operational schedules, infiltration) are not available while the use of typical input values or assumptions is not appropriate for the application [
51]. In the future, BES will provide unprecedented value in assisting the design and operation of low energy buildings that address occupancy comfort. It is hopeful that more HVAC operation schemes can be tested for effectiveness in improving the thermal condition and indoor air quality through building simulation, which in turn can provide better living and working spaces for occupants.