Investigating the Effects of Occupancy and Natural Ventilation on the Indoor Air Quality of Dormitories in Cold Regions

Abstract

Indoor air quality (IAQ) in higher education institutions’ dormitories, without mechanical ventilation, is a significant concern for students’ health due to prolonged occupancy in cold regions. The present investigation assessed IAQ by measuring two dormitories’ CO₂, temperature, and relative humidity with the presence of one, two, three, and four occupants in the United Kingdom. Considering the possibility of natural ventilation by opening the windows in the summer, IAQ was monitored using two sensors located at 1 m and 2 m heights from the floor level of the dormitories in July. The tracer mass balance model showed close agreement with the monitored IAQ levels, with a direct relationship observed between occupant numbers and CO₂ build-up. CO₂ levels exceeded 1000 ppm within an hour during occupancy and closed ventilation, with air exchange rates between 0.12 and 0.2 h⁻¹, increasing to 1334, 1259, 1884, and 2064 ppm after 30 min with one, two, three, and four occupants, respectively. Desired IAQ standards (1000 ppm) were achieved in 13, 33, 80, and 86 min for one, two, three, and four occupants after starting natural ventilation by opening 20% of the windows. The analysis of variance affirmed the effect of occupancy on IAQ, while the impact of height (1 m and 2 m) on CO₂ levels was insignificant. This study underscores the need to effectively ventilate the partial opening of windows in dormitories to mitigate CO₂ build-up, ensuring the desired ambient environment within dormitory rooms during summers in cold regions.

1. Introduction

Humans residing in urban areas of developed countries spend most (~90%) of their time in homes, offices, and public buildings, highlighting the significance of indoor air quality (IAQ) [1,2]. Over the last two decades, a shift from outdoor air quality to IAQ assessment has been observed due to extensive urbanization [3]. After realizing the benefits of economic and energy saving, many organizations allow their employees to work from home, even after the COVID-19 pandemic [4]. In the UK, around 25 to 40 percent of people preferred working from home in 2022 [5]. This lifestyle change indicates the need to develop new techniques to monitor IAQ and to ensure a desired indoor CO₂ level [6].

As a healthy and adequate lifestyle requires a clean and green environment, indoor air pollution has been recognized as a significant human health issue worldwide. The World Health Organization (WHO) reported 3.2 million deaths yearly due to indoor air pollution [7]. In classrooms and office buildings, carbon dioxide (CO₂) is generally regarded as an indicator of IAQ and ventilation, since elevated levels of CO₂ are linked to poor ventilation [8,9,10]. The maximum allowable concentration of CO₂ is an essential parameter in determining IAQ, since humans are the primary source of CO₂ generation through the natural exhalation process. CO₂ levels exceeding the limit of 1000 ppm may cause diseases such as sick building syndrome, which affects the decision-making ability of a human being and may cause dizziness, eye and throat irritation, headaches, allergies, and fatigue [11,12,13,14]. Furthermore, CO₂ levels higher than 1500 ppm indicate poor ventilation quality [15]. In addition, higher CO₂ levels can indirectly contribute to indoor humidity, fostering the growth of bacteria and dust mites and incomplete combustion in gas stoves and fireplaces, increasing carbon monoxide (CO), nitrogen oxide (NO_x), and particulate matter (PM) [16]. As relative humidity (RH) and temperature are vital for creating a comfortable indoor environment, RH should lie within 40% to 70% for adequate IAQ [17,18].

Educational institutions and dormitories need more attention regarding ventilation rate (VR) and IAQ due to their high occupancy rates and specific character associated with human health and working efficiency [10]. Excluding combustion from stoves and heaters, human respiration is the main contributor to indoor CO₂, which directly increases with the number of occupants in residential, commercial, and public buildings [1,2]. The primary source of indoor CO₂ is human respiration. Each person exhales approximately 1 kg of CO₂ per day, which can significantly increase indoor CO₂ concentrations, particularly in enclosed spaces with limited ventilation.

The effect of occupancy on the concentration of CO₂ has been a subject of interest for years [19,20]. To understand the impact of human exhalation and its relationship with air movement and different ventilation setups, Mahyuddin and Awbi [21] monitored horizontal and vertical CO₂ concentrations in test chambers with two occupants and evaluated the impact of ventilation strategies on CO₂ distribution patterns in a room. Liu et al. [22] monitored CO₂ in a university classroom (7.9 m width × 9.1 m length × 3 m height) with 30 students during the summer and winter. They reported CO₂ concentration exceeding 1500 ppm after 30 min of occupancy in the respiration region near the floor. Interestingly, no significant change in CO₂ levels was observed across the seasons. Their findings coincide with the proposition Mahyuddin and Awbi [21] gave that buoyancy, a non-uniform airflow field, and gravitational settling are responsible for the change in the vertical profile of CO₂. Studies by Mohamed et al. [23] and Lama et al. [24] observed CO₂ in classrooms with 30 to 60 students and reported levels higher than 1000 ppm. Recently, Sakamoto et al. [25] studied the emission rates of individuals in a controlled chamber and reported the direct effect of participants’ temperature on CO₂ generation. McLeod et al. [26] and Zuhaib et al. [27] showed increased monitored CO₂ concentrations from 600 with 50 students to over 1000 with 83 students, although the room size was larger in the latter case.

Along with occupancy, other factors that contribute to increased CO₂ are occupant behavior (activity level), the rate at which the air is being renewed (ventilation), the amount of time the occupants spend in a room, and the ambient concentration of CO₂ [14,28]. Other studies reported that the size of the room and ceiling height are also essential factors for IAQ assessment [21].

In an indoor environment, thermal stratification forms layers of air (with temperature differences) that influence CO₂ distribution by raising the warmer air toward the ceiling and keeping the cooler air closer to the floor. Huang et al. [29] identified a 300 ppm difference in CO₂ concentration between two points one meter vertically apart in a heavily occupied classroom. CO₂ accumulates at the ceiling if the space is naturally ventilated, even though its density is 1.5 times higher than fresh air [30]. This phenomenon predominantly occurs in large spaces with high ceilings, as observed by Wang et al. [31]. They found that thermal stratification in five warehouse buildings, ranging from 6.6 m to 9.2 m in height, is attributed to different temperature zones. Seduikyte et al. [32] assessed ventilation performance in a mechanically ventilated sports hall 6.9 m in height. Data loggers measured CO₂, temperature, and RH at six different heights between 0.1 and 6.9 m and reported elevated temperatures above 3.9 m. The above discussion concludes that most studies relate thermal stratification to large indoor spaces, while studies on small indoor spaces with low ceiling heights are not frequently reported.

IAQ in educational institutions has obtained prime importance since the COVID-19 outbreak, with indoor activities rapidly increasing because of online teaching. Consequently, more studies have taken CO₂ as a proxy to determine the IAQ in educational institutions [33,34]. Higher CO₂ levels in classrooms are related to the decreased cognitive performance of students; therefore, adequate CO₂ monitoring in classrooms can help reduce the transmission of air-induced pollutants [35,36]. Awang et al. [37] investigated IAQ in dormitories in the absence of occupants by measuring background concentrations of CO₂, total volatile organic compounds (TVOCs), RH, carbon monoxide (CO), and ozone (O₃). The results indicated increased TVOC concentrations when the windows were open. Korsavi et al. [38] explained the discrepancy in CO₂ levels in educational institutions in the UK, considering the occupancy ratios, behaviors, and generation rates of the occupants. Although most of the variation in CO₂ was linked to the open area (m²), occupant behaviors tend to change CO₂ levels more than other factors. The study recommended that the occupant density of 2.3 m² per person and a 7.76 m³ space volume can maintain CO₂ levels below 1000 ppm. Although some studies have linked students’ performance to IAQ [39,40,41], occupancy as a primary function has yet to be further investigated in dormitories in educational institutions.

Low-cost sensors are practical for indoor monitoring because they provide real-time data with high spatial frequency. Non-dispersive Infrared (NDIR) is the most commonly used sensor for CO₂ measurement. TVOC sensors indirectly measure CO₂ by providing equivalent CO₂ levels; consequently, their use is limited [42]. A survey by Mahyuddin and Awbi [43] identified the current techniques of IAQ monitoring and the suitable locations of monitoring sensors and found that 1 to 1.2 m from the floor level is the most preferred height for CO₂ measurements. The International Well Building standard also specifies measuring CO₂ levels 1.2 to 1.8 m above the floor level [44]. Palmisani et al. [45] measured CO₂, particulate matter-2.5 (PM_2.5), and TVOCs in oncology wards at two European hospitals using low-cost sensors installed at 1.5 to 2 m heights. Pei et al. [46] investigated the effect of sensor location on the energy performance and demand control ventilation of a building. They found variations in monitoring results due to the sensors’ placement and thermal plumes generated by occupants. The study recommended attaching the sensor to a wall, as sensors placed near the occupants recorded higher concentrations. Mahyuddin and Awbi [43] also reported higher concentrations of pollutants in rooms with low localized air movement.

Students in cold regions can only open dormitory windows for natural ventilation during the summer. The above studies on higher education institutions primarily shed light on occupant behaviors and their relationship with increasing CO₂ levels in large rooms with higher occupancy and shorter durations or controlled conditions with mechanical ventilation. The impact of occupancy and natural ventilation on the IAQ of dormitories without HVAC in cold regions with longer student residence time needs further attention.

The primary aim of the present work is to find the impact of occupancy and natural ventilation on IAQ in dormitories of a higher education institution (HEI). The specific objectives were (i) to monitor CO₂, temperature, and relative humidity in real dormitories under varying graduate student occupancy states using sensors; (ii) to estimate the air exchange rate, the CO₂ generation rates under varying occupancies, and estimate CO₂ levels using the tracer gas mass balance model; and (iii) to evaluate the impact of natural ventilation on CO₂ decline. This study’s results will help assess the natural ventilation intervals needed to maintain the desired IAQ in dormitory rooms.

2. Materials and Methods

2.1. Methodological Approach

The current study achieved its objectives by conducting indoor air quality monitoring experiments with natural ventilation in two dormitories of an HEI located outside the main city center in Birmingham, UK. First, air quality monitoring sensors were installed on the walls of dormitories. Experiments were performed with one, two, three, and four occupants inside the rooms to evaluate the impact of occupancy. Sensors monitored CO₂, temperature, and RH in airtight conditions until the CO₂ levels exceeded the maximum allowable concentration of 1000 ppm, followed by natural ventilation by opening the windows. This study’s second phase estimated the air exchange rate, theoretical CO₂ levels using the tracer gas mass balance model, and CO₂ generation rates per person for varying occupancies. The tracer gas decay approach has been used in various studies to model air exchange rates and natural ventilation efficiency in residential and commercial buildings [47,48]. The data obtained from sensors were compared with the results of the mass balance model. Third, an analysis of variance (ANOVA) evaluated the hypothesis on the impact of occupancy on CO₂ levels in respiration zones with varying occupancy levels. Finally, this study recommends guidelines regarding using sensors and their accuracy in determining CO₂ profiles indoors and strategies for natural ventilation in dormitories for graduate students in HEIs.

2.2. Study Area

The current study focuses on student accommodation named “The Spinney”, located in Pritchatts Park Village near the University of Birmingham, UK. This accommodation has single bedrooms for university students and can hold 104 students. This study selected two dormitory rooms, namely Room A and Room B, in the same building for CO₂ monitoring. One flat is on the first floor, and the other is on the second. The second room can hold more occupants and has a higher ceiling than the first room. Figure 1 shows a location map and photographs of the room, whereas Figure 2 illustrates the dimensions of the rooms. Room B, with a higher ceiling and capacity for more occupants than Room A, was used to assess CO₂ levels with 2, 3, and 4 occupants, whereas Room A was monitored with 1 occupant. Although Room B also contains one resident on regular days, this study was performed with more occupants during the monitoring period to evaluate the impact of occupancy on CO₂. Both rooms feature single-sided windows with a maximum 20% opening allowance.

Two non-dispersive infrared (NDIR) sensors were placed at two heights, 1 m and 2 m (see Figure 1 and Figure 2). The sensors were placed based on the location specified by Pei et al. [46], according to the standard set by ASHRAE [49]. Mahyuddin and Awbi [21] also conducted an extensive study on the placement of sensors to monitor IAQ and reported that 1 m to 1.2 m was the preferred CO₂ measurement height. Hence, 1 m was selected as the first point, and 2 m was selected to capture the spatial distribution of CO₂.

Table 1. Room size and characteristics of the tested dormitory.

Occupancy	Test Location	Room Size (m × m × m)	Number of Windows	Window Size (m2)	Room Area (m2)	Room Volume (m3)	Sensor Location
1	Room A	3.47 × 2.17 × 2.38	2	1.67 $\times$ 1	7.52	17.92	Wall mounted
2	Room B	3.30 × 2.97 × 2.97	1	1.67 $\times$ 1	9.80	29.10	Wall mounted
3	Room B	3.30 × 2.97 × 2.97	1	1.67 $\times$ 1	9.80	29.10	Wall mounted
4	Room B	3.30 × 2.97 × 2.97	1	1.67 $\times$ 1	9.80	29.10	Wall mounted

describes the conditions of the four tests, including occupancy, room number (as explained in Figure 1 and Figure 2), room size, the number and size of windows, and room volume.

2.3. Carbon Dioxide Monitoring

Two non-dispersive infrared (NDIR) ARANET sensors were installed to measure the CO₂ concentrations at the selected locations in the room. The rates of measurement error of ARANET are as follows: ±30 ppm (±3%) for CO₂, ±0.4 °C for temperature, and ±3% for RH. ARANET 4 provides wireless data collection; subsequently, the recorded CO₂ concentrations can be extracted from the device directly using the ARANET 4 phone application. The sensor gives accurate readings up to 5000 ppm from the ARANET datasheet. This instrument can also measure additional features like RH and atmospheric pressure. The sensor’s maximum range is 9999 ppm and can operate from 0 °C to 50 °C [50].

The measurement of CO₂ indoors and its relation to IAQ and ventilation have been the subject of many research studies [10,51,52]. Three techniques using CO₂ as a tracer gas to measure the indoor air change rate are build-up, steady-state, and decay [53,54]. With technological advances, the use of sensors has become the more convenient and affordable method of CO₂ measurement. ARANET is a low-cost and durable sensor that provides real-time coverage of CO₂, RH, and atmospheric data and easy access to recorded data using the Internet of Things (IoT). Conversely, a short life span of <2 years and calibration and standardization issues are some reported limitations [45]. Due to their ease of use and affordability, most researchers and organizations use NDIR sensors to ensure ambient IAQ, owing to their advantage. According to Mahyuddin and Awbi [43], one CO₂ sensor installed at a 1 m or 2 m height is enough for measuring a floor area of <100 m². The current study used multiple sensors to find the spatial distribution of CO₂. Two sensors were wall-mounted at 1 m and 2 m, and the measurements were recorded at 1 min intervals. All the experiments were carefully conducted under controlled ventilation, and the rooms were kept airtight to minimize the impact of zonal infiltration or air.

2.4. Air Exchange Rate

Exhaling CO₂ as a tracer gas has been used in many studies to find the room’s air exchange rate (AER) [55,56]. The present study assessed the AER before the start of the monitoring period. The measurements were obtained in the empty room as per the method prescribed by Fan et al. [57], stating that AER can be calculated by air infiltration using the decay method when the initial CO₂ is >1000 ppm. Subsequently, the decrease (decay) in CO₂ levels was monitored without any occupant.

AER was estimated by measuring CO₂ in an empty room for 2 h before the experiment with a fitting parameter of R² > 0.99. Equation (1) calculated the AER following first-order kinetics, as follows:

$$N={\displaystyle \frac{\ln \left(\frac{C_{initial}}{C_{final}}\right)}{t}}$$

(1)

where N represents the air exchange rate (AER) h⁻¹, t is the time in minutes, C_initial is the initial CO₂ when the concentration was higher than 1000 ppm, and C_final is the CO₂ when the concentration reached 1000 ppm.

2.5. Mass Balance for CO₂ Estimation

Many studies have used a tracer gas mass balance Equation (2) to predict subjects’ VR, as shown below [57].

$$C_{in}=C_{out}+[C_{in}\left(0\right)-C_{out}\left(\mathrm{t}\right)] e^{-\left(\mathrm{N}\right)t}$$

(2)

where $C_{in}$ is the indoor CO₂ in ppm at time t = 0, C_out is the outdoor concentration, and Equation (1) estimated N for different occupancy levels. The current study used Equation (2) to assess the theoretical concentration of CO₂ and compared it with the sensor data. All the occupants involved in monitoring were male, aged between 21 and 30, and their activity was sedentary.

According to Ahn et al. [58], Equation (2) holds the following assumptions: (i) the dilution of CO₂ is only possible with natural ventilation, and (ii) the zonal infiltration of air in the room is negligible.

2.6. CO₂ Generation Rates

Occupant emission rates were calculated using Equation (3), assuming the air was well mixed [25,59].

$$nE=Q(C_{ss}-C_{initial})$$

(3)

where n denotes the number of occupants, E shows the average CO₂ generation rate per occupant [mL/h], and C_ss and C_final show the indoor CO₂ mixing ratio at a steady state and the mixing ratio in the supply air and natural ventilation, respectively. At the steady state, initial CO₂ refers to the experiment’s last and first 15 min when the occupants left and entered the room, respectively. Here, Cinital was assumed to be at 420 ppm since no occupant was present in the room.

Q in Equation (3) denotes the volumetric outdoor air change rate given in [m³/h], which was calculated as a product of room volume and renewal rate per hour. The renewal rate for dormitories was assumed to be 4, according to the standard by DIN 1946 [60]. Q was found to be 71.68 m³/h for Room 1 (17.92 m³) and 116.4 m³/h for Room 2 (29.1 m³).

3. Results

3.1. CO₂ Monitoring

Figure 3 presents the monitoring results for CO₂ levels obtained from sensors for different numbers of occupants, showing a clear trend of rising CO₂ concentrations with increasing occupancy. However, the rate of increase varied based on room size, with smaller rooms experiencing a sharper rise in CO₂ levels. In Room A, CO₂ levels rose significantly after one occupant entered at 22:38, reaching 1542 ppm at 1 m and 1614 ppm at 2 m within an hour. Ventilation intervals showed a distinct difference in CO₂ levels between sensors, attributed to the room’s smaller size. In Room B, with two occupants, CO₂ levels steadily increased, peaking at 1932 ppm at 1 m and 2026 ppm at 2 m. Ventilation at 20% for 18–23 min helped reduce CO₂ levels below 1000 ppm. When three occupants were present in Room B, CO₂ concentrations rose rapidly, especially when occupants were standing and talking, reaching 1903 ppm at 1 m and 2039 ppm at 2 m in 35 min. Ventilation at 20% lowered the 1 m sensor reading below 1000 ppm after 48 min, while the 2 m sensor remained slightly higher. During the test with four occupants in Room B, CO₂ spiked to 4192 ppm at 1 m and 4266 ppm at 2 m in around 1.6 h, with fluctuations due to movement and talking. Ventilation at 20% reduced CO₂ levels but took longer to bring them below 1000 ppm, particularly at 2 m.

Figure 3a–d also compare actual data with theoretical data.

Table 2. Estimated AERs and CO₂ generation rates.

Test No.	Room No.	Occupancy (n)	Air Exchange Rate ERs “N” (h−1)	Average CO2 Generation Rate Per Person “E” (L/s)
Test 1	A	1	0.12	0.0106
Test 2	B	2	0.16	0.0035
Test 3	B	3	0.16	0.0017
Test 4	B	4	0.37	0.0020

presents the AER values estimated using the decay method described in Equation (1) for rooms A and B. The AER for tests 2 and 3 was similar despite the higher occupants in test 3. The mass balance Equation (2) obtained theoretical concentrations compared to the sensors’ readings. The modeling results accurately predicted CO₂ concentration, except for Test 1 (Figure 3a). Test 1 (Figure 3a) showed a discrepancy between the model and actual data. The model predicted ventilation onset earlier than observed, causing variations in predicted CO₂ levels from 1860.48 ppm to 1308.70 ppm, causing skewness. The model prediction for Test 3 (Figure 3c) was the most accurate one observed since it readily predicted window opening and closing. Tests 2 and 4 (Figure 3b,d) show that the model had the same prediction rate for these scenarios, as the AER was similar for both cases. The AER results obtained were in the range of 0.12 to 0.37 h⁻¹. The results in Figure 3 indicate that average CO₂ levels throughout the day frequently exceeded the allowable concentration of 1000 ppm in dormitories with higher occupant density. The reasons for the CO₂ profiles in Room B (Figure 3b–d) being slightly less smooth than those in Room A (Figure 3a) can be attributed to the impact of talking and standing by more occupants. The results in Figure 3 show that the mass balance model given in Equation (2) can estimate the time required to initiate natural ventilation with different occupancy levels in dormitories of the sizes shown in Figure 2.

Equation (3) calculated occupants’ average CO₂ generation rates relating to metabolic activity [61]. CO₂ generation rates were estimated to be between 0.0017 and 0.01062 L/s. The generation rates measured in Tests 2, 3, and 4 are slightly lower than those of Persily and Polidoro [62], between 0.0049 and 0.0051 L/s for lecture rooms and conference halls with 50 to 65 occupants. The generation rate estimated for one occupant in Test 1 (0.012 L/s) is well above the ranges reported by Presily and Polidoro [62].

An analysis of variance (ANOVA) was performed to evaluate the impact of occupancy on CO₂ levels in the respiration zone at 1 m height from the floor of Room B, with two, three, and four occupants, for the following hypothesis:

H01. 

Occupancy does not affect CO₂ levels in the respiration zone.

Ha1. 

Occupancy affects CO₂ levels in the respiration zone.

H02. 

Occupancy does not affect CO₂ levels above the respiration zone.

Ha2. 

Occupancy affects CO₂ levels above the respiration zone.

The ANOVA results with an F-value of 138.8, significantly higher than the F-critical value of 3.012, reject H01 for a 95% confidence level. Thus, occupancy significantly affects CO₂ levels in dormitories. Also, for the case of CO₂ measurements 2 m from the floor level, the F-value of 145.8 is higher than the F-critical value of 3.012, rejecting H02. These results affirm the significant effect of occupancy on indoor CO₂ levels in dormitories, while the impact of height (1 m and 2 m) on CO₂ levels was insignificant with F-values < Fcritical. ANOVA results are given in Appendix A.

3.2. Temperature

Figure 4a–d show the results of four tests for temperature measurements obtained at 1 m and 2 m heights. The figure illustrates that the temperature increases by increasing the number of people inside the room. Room A (18 m³) was used in Test 1, while the larger Room B (29 m³) was used in Tests 2, 3, and 4. Figure 4e displays a whisker diagram for temperature over the measurement period, showing consistent readings across measurements taken at 1 m and 2 m heights; as a result, absolute values were used in the box and whisker plots. Test 1 showed a peak temperature of 24 °C, while tests 2–4 show lower temperatures due to a larger room size.

3.3. Relative Humidity

Figure 5a–d show the RH obtained in both rooms during the occupied period. The figures show increased RH with the increase in the number of people inside the room. The relative humidity levels in all four tests were between 52% and 65%, which aligned with the guidelines set by Health and Safety UK, ranging between 40% and 70% [63]. Figure 5e displays whisker diagrams for RH over the measurement period. The highest value of RH, 62.5%, was observed in Test 4. Evaluating the impact of occupancy in Room B shows that RH remained between 58.5 and 61.5% with two occupants, which decreased to 48.5–60% in the case of three occupants and then again increased to 52–62% with four occupants. These variations can be attributed to the dilution effect in a large room that can mitigate RH’s rise. Overall, the RH remained between 30% and 60% in Room B, the optimal range desired for occupants’ comfort and health of the occupants [61,64]. The findings also showed that RH and temperature exhibited minimal changes, suggesting that occupancy levels did not significantly impact these parameters.

3.4. Impact of Natural Ventilation

In Test 1, Room A’s CO₂ levels varied between 727 ppm and 1551 ppm (at 1 m height from the floor) before the ventilation opening with one occupant in one hour (Figure 6a). Natural ventilation by opening 20% of windows reduced the CO₂ levels to less than 1000 ppm (IAQ standards) in 13 min and 500 ppm at the end of Test 1, around one hour after opening the window (Figure 6b).

Tests 2, 3, and 4 were performed in Room B (3.30 m wide × 2.97 m long × 2.97 m high). In one hour, CO₂ increased from 507 ppm to 1932 ppm in 80 min with two occupants (Test 2), substantially higher than the level found for one occupant. Natural ventilation initiated at 80 minutes reduced CO₂ levels to 1000 ppm in around half an hour (33 min) and 500 ppm in one hour (Figure 6a). Interestingly, the desired IAQ was achieved in more than double the natural ventilation time (NVT) required in Test 1 due to higher CO₂ levels, while the overall time to reach 500 ppm remained the same in both tests (Figure 6b).

In Test 3 with three occupants, the concentration increased from 536 ppm to 1903 ppm in the first 35 minutes, much earlier than the first two tests (Figure 6a). Test 3 shows a rapid increase in levels, compared to the first two cases, in less time due to excessive CO₂ generation in the presence of three occupants. In this case, natural ventilation took 80 minutes (more than two times in Test 2) to achieve the desired IAQ standards of 1000 ppm and over two hours (80 minutes) to 500 ppm (Figure 6b).

Exceeding IAQ standards within 13 minutes, Test 4 with four occupants showed increased CO₂ from 570 ppm to 4192 ppm (more than two times with three occupants) in ~1.5 h (Figure 6a). After 1 hour and 26 minutes (slightly more than Test 3) of the window opening, the CO₂ level reduced to 1000 ppm, complying with IAQ standards. The results clearly show the impact of occupancy on IAQ. A significant increase in CO₂ levels was observed in dormitory rooms by changing occupancy from two to three persons (Test 3 and Test 4).

4. Discussion

Due to the extensive nature of their studies, graduate students in HEIs spend extended hours in dormitories. In the case of group work, dormitories may house several occupants in a relatively small space, resulting in increased CO₂ levels through human respiration and other academic activities. Such a situation may pose a unique challenge for maintaining acceptable air quality [65]. Diverse ventilation habits due to graduate students’ personal preferences bring about another challenge, particularly in dormitories without HVAC [48]. Most studies in the past assessed IAQ in large classrooms with many occupants in HEIs [23,24,25,26,27]. The present study investigated the impact of occupancy and natural ventilation in real dormitories of graduate students in HEIs.

CO₂ levels varied between 727 ppm and 1542 ppm for Room A (17.92 m³) before the ventilation opening with one occupant in one hour. In the case of Room B (29.1 m³), in which Tests 2, 3, and 4 were performed, CO₂ varied between 507 ppm and 1653 ppm with two occupants in one hour, substantially higher than the level found for one occupant. In this case, ventilation was initiated after 1 h and 20 min when the concentration was 1932 ppm. The concentration varied from 536 ppm to 1903 ppm in the first 35 min before the ventilation opening for three occupants. Test 3 shows a rapid increase in levels in less time, related to excessive CO₂ generation. Test 4 with four occupants showed increased CO₂ from 570 ppm to 4192 ppm after 1 h and 36 min. Batog and Badura [66] conducted a test in rooms of a similar size, with a volume of 21 m³ to 23.6 m³, and found CO₂ levels within the range of 700 ppm to 1800 ppm in one hour, adjacent to the current study.

The average CO₂ generation rate per person assessed in the present study for one occupant was higher, and for two, three, and four occupants, it was slightly lower than 0.0049 and 0.0051 L/s, reported by Persily and Polidoro [62] for lecture rooms and conference halls with 50 to 65 occupants. These variations can be attributed to confined dormitories where even a minor increase in occupants can accumulate higher CO₂ levels [67]. Nusseck et al. [68] reported a significant increase in CO₂ levels with an increase in occupants in an unventilated environment, akin to the present study.

The temperature difference remained below one degree Celsius in all scenarios, indicating no stratification conditions within the room. These results align with the previous study by Liu et al. [22], which reported that low vertical temperature differences suggest the effective insulation performance of vertical elements such as doors and windows, contributing to occupant comfort. The RH monitored of 52 to 65% lies within the prescribed humidity levels by HSE UK [63]. The temperature was also almost consistent at the two monitored depths. This stability in temperature and RH throughout the test period further emphasizes the adequacy of the room’s insulation and ventilation in maintaining a consistent indoor environment.

A close agreement between monitored CO₂ levels and calculated values shows the suitability of using the mass balance approach for IAQ modeling in dormitory rooms in the HEI of cold regions and its utility in planning and managing ventilation in such settings. Due to the absence of heating, ventilation, and air conditioning (HVAC) systems, CO₂ increases rapidly, negatively impacting the student’s well-being. The findings showed that adequate ventilation is necessary at regular intervals to reduce the accumulation of CO₂ in these rooms. CO₂ levels before the occupancy were below 1000 ppm, which agrees with the previous study performed by Pereira et al. [69]. Because of its overall condensed size, CO₂ accumulates much faster in these rooms. After 30 min of occupancy, IAQ in these rooms decreases due to increased CO₂ levels, akin to what Lama et al. [24] found in identical rooms. Future dormitory designs could benefit from incorporating hybrid ventilation systems, as discussed by Tognon et al. [41], to optimize air exchange without compromising thermal comfort, especially during seasonal extremes.

This study’s findings underscore the importance of natural ventilation in maintaining acceptable IAQ in dormitories without HVAC in cold regions and mitigating the adverse effects on student health and cognitive performance. Previous studies, such as that by Coley et al. [36], have established a direct link between high CO₂ levels and diminished decision making and cognitive function, particularly concerning educational environments where student productivity is paramount. Natural ventilation through the window opening provided adequate airflow to reduce elevated CO₂ below 1000 ppm within 0.5 h for one and two occupants and around 1.5 h for three and four occupants. This study also suggests natural ventilation after around half an hour in the case of two occupants and 15 min with three or four occupants in a ~10 m² dormitory. This study’s observations highlight the model’s effectiveness in capturing CO₂ variations and ventilation dynamics, although factors like occupant activity and initial ventilation assumptions might explain the discrepancies in Test 1. This analysis emphasizes the model’s potential utility in managing indoor air quality, especially when tailored to specific occupant behaviors and environmental variables. It is important to note that all the observations remained at 5000 ppm, which is the highest reliability of the ARANET sensors. The relatively consistent relative humidity and temperature across occupancy levels suggest that insulation in these dormitories is sufficient; however, the rapid accumulation of CO₂ indicates a need for enhanced ventilation designs or automated systems to regulate air quality in real time.

The negligible vertical temperature differences observed between sensors confirm that thermal stratification does not significantly impact IAQ in dormitory rooms with low ceiling heights. This research highlights the importance of incorporating ventilation strategies into dormitory designs, particularly in cold regions with limited natural ventilation during winter. Partial window openings proved sufficient to maintain acceptable IAQ, suggesting that hybrid ventilation systems could enhance indoor air quality while ensuring thermal comfort. Future research should explore IAQ under varying occupant activities, extended monitoring durations, alternative ventilation strategies, and mechanical systems. Such studies would provide a more comprehensive understanding of IAQ dynamics and inform guidelines for improving student well-being and academic performance in residential settings.

Kiwan et al. [48] reported a precise assessment of AER in naturally ventilated buildings using the tracer gas decay rate method. The study faced limitations, particularly regarding the calculation of AER, which ideally should have been monitored for at least a week to accurately assess the ventilation rate (VR), considering variations in occupancy, weather, and ventilation. Time constraints prevented this extended data collection. In addition, this study did not include other essential air pollutants, such as NO_x and TVOC, due to resource limitations. Future studies can include these and other relevant parameters for more comprehensive indoor air quality assessment.

5. Conclusions

This study evaluated the impact of occupancy and natural ventilation on indoor air quality (IAQ) in dormitory rooms in cold regions, using CO₂ concentration as the primary metric. The CO₂ levels exponentially increased to double with four occupants compared to one occupant’s case in one hour of monitoring. Importantly, desired IAQ standards (1000 ppm) were achieved in 13, 33, 80, and 86 min for one, two, three, and four occupants after starting natural ventilation by opening 20% of the windows. The results demonstrated that occupancy significantly affects CO₂ levels, with concentrations exceeding the IAQ standard of 1000 ppm within 30 min under closed ventilation conditions for all tested scenarios. This study found negligible temperature variations in dormitories with ceiling heights lower than 3 m, ignoring the impact of thermal stratification. Ventilation through partial window openings can be increased in cold regions during summer to minimize the impact of CO₂ on the indoor environment. This study highlighted the negligible effect of occupancy on the relative humidity in condensed dormitory rooms. Natural ventilation achieved desired IAQ levels, with ventilation times varying based on occupancy, underscoring its effectiveness in maintaining acceptable indoor environments. The mass balance model provided reasonable predictions of CO₂ concentrations across all scenarios, validating its applicability for IAQ modeling and ventilation period assessment in similar settings. Partial window openings proved sufficient to maintain acceptable IAQ, suggesting that hybrid ventilation systems could enhance indoor air quality while ensuring thermal comfort.