Seasonal Variation of Air Purifier Effectiveness and Natural Ventilation Behavior: Implications for Sustainable Indoor Air Quality in London Nurseries

Abstract

This study investigates the seasonal effectiveness of high-efficiency particulate air (HEPA) purifiers and window-opening behaviors in three London nurseries, using continuous indoor and outdoor PM_2.5 monitoring, window state and air purifier use, and occupant questionnaire data collected from March 2021 to February 2022. Of the approximately 40–50 nurseries contacted, only three agreed to participate. Results show that HEPA purifiers substantially reduced indoor particulate matter (PM_2.5), with the greatest effect observed during the heating season when windows remained closed for longer periods. Seasonal and behavioral analysis indicated more frequent and longer window opening in the non-heating season (windows were open 41.5% of the time on average, compared to 34.2% during the heating season) driven by both ventilation needs and heightened COVID-19 concerns. Predictive modeling identified indoor temperature as the main driver of window opening, while carbon dioxide (CO₂) had a limited effect. In addition, window opening often increased indoor PM_2.5 under prevailing outdoor air quality conditions, with mean concentrations rising from 2.73 µg/m³ (closed) to 3.45 µg/m³ (open), thus reducing the apparent benefit of air purifiers. These findings underscore the complex interplay between mechanical purification and occupant-controlled ventilation, highlighting the need to adapt indoor air quality (IAQ) strategies to both seasonal and behavioral factors in educational settings.

1. Introduction

Particulate matter (PM) refers to a complex mixture of solid and liquid particles suspended in air, including dust, soot, smoke, and droplets. Fine particulate matter with a diameter of 2.5 μm or less (PM_2.5) is of particular concern due to its ability to penetrate deep into the respiratory tract [1]. Exposure to fine particulate matter (PM_2.5) poses significant health risks to children, who are particularly vulnerable due to their developing respiratory and immune systems [2]. Even low levels of PM_2.5 can result in adverse health effects, including respiratory and cognitive impairments [1,3]. In 2021, the WHO revised the annual PM_2.5 limit from 15 to 5 µg/m³, emphasizing that no level of PM_2.5 exposure is entirely safe [4]. Many national standards are less strict; for example, the UK has set a target of 10 µg/m³, while China’s standard is 35 µg/m³ [5]. Chronic exposure above these levels is associated with increased health risks, making effective IAQ management particularly critical for children [6]. Nurseries are primary environments where young children spend significant time indoors, making IAQ in these settings a public health priority.

In response, high-efficiency particulate air (HEPA) purifiers have been widely implemented in indoor environments as an effective strategy to reduce airborne pollutants [7,8]. HEPA filters are designed to capture at least 99.97% of airborne particles with a diameter of 0.3 microns or larger, making them highly effective against fine particulate matter such as PM_2.5. These filters have been shown to significantly reduce indoor concentrations of PMs when operated in appropriately sized rooms [9,10]. The effectiveness of different purifiers depends on filter grade, airflow rate, and operational conditions. Some units combine HEPA filters with pre-filters or activated carbon for broader pollutant removal.

Previous studies have reported that mean indoor PM_2.5 concentrations in nurseries worldwide range from 19.7 to 69.5 µg/m³, which often exceeds recommended guidelines, posing ongoing health risks. [11,12,13,14,15]. Research in educational settings has demonstrated that the use of air purifiers can help reduce indoor PM_2.5 levels and is associated with health benefits for children, including decreased incidence of asthma and nasal symptoms [16,17,18]. In a South Korean study, PM₁₀, PM_2.5, airborne bacteria, and fungi were measured in 10 nurseries both before and during air cleaner operation. Over 3 weeks of use, the air cleaners substantially reduced indoor pollutants: PM_2.5 concentrations dropped from 39.9 µg/m³ to 5.6 µg/m³, and PM₁₀ from 81.3 µg/m³ to 15.0 µg/m³ [19]. However, most published evidence has focused on homes, offices, or laboratory/short-term interventions [8,20,21]. Data on long-term performance of air purifiers in nurseries, especially considering seasonal variation, remain scarce. [22].

The literature suggests that window-opening practices significantly influence indoor pollutant dynamics; for instance, in UK primary schools, mean window open duration increased from 13 min per hour in winter to 43 min per hour in summer, leading to significantly lower CO₂ and PM_2.5 levels during the warmer season. Specifically, classrooms with frequent window opening maintained average PM_2.5 concentrations below 10 µg/m³, while rooms with limited ventilation showed higher levels, often exceeding 15 µg/m³ [23]. Yet, few studies have systematically integrated the effects of air purification and occupant-controlled ventilation in nurseries over extended periods and across different seasons [23,24,25]. Studies reported that air purifiers were most effective at reducing indoor PM_2.5 concentrations when windows were kept closed [26,27]. A recent study reported that the mean reduction rate of PM_2.5 achieved by using air purifiers was 63% when windows were closed, compared to 46% when windows were open [24]. Both window operation and the use of air purifiers have significant impacts on indoor air quality [28]. This highlights the need to understand the combined and potentially competing effects of air purification and ventilation in real-world settings.

Due to these complexities, there is a pressing need to systematically explore how air purification technologies and ventilation practices interact in real-world nursery settings across different seasons. The adoption and use of air purifiers should be informed, targeted, and integrated with other IAQ strategies to ensure both efficacy and sustainability [29].

This work addresses a research gap by investigating the interplay between seasonal air purifier performance and window-opening behavior in nurseries under real-world conditions. Accordingly, this study addresses the following research questions: 1. How does seasonal variation affect the performance of HEPA air purifiers in real-world nursery settings? 2. What are the main environmental and behavioral drivers of window opening, and how do these vary by season? 3. How do window opening and air purifier operation jointly affect indoor PM_2.5 concentrations?

Based on these questions, we hypothesize that: 1. the effectiveness of air purifiers is greater during the heating season when windows remain closed for longer periods; 2. window opening behavior is predominantly driven by indoor temperature but also influenced by occupant perception and external events such as COVID-19; 3. window opening may, under certain outdoor air pollution conditions, reduce the apparent benefit of air purification. The findings are expected to contribute new evidence to the field, supporting both healthy and sustainable IAQ management strategies for nurseries and other educational buildings.

2. Materials and Methods

2.1. Site Selection

The Greater London Authority (GLA) and Transport for London (TfL) jointly compiled the London Atmospheric Emissions Inventory (LAEI 2019), a comprehensive air quality dataset for London [30]. This inventory provides estimates of pollutant emissions from domestic, transport, industrial, and commercial sources and presents modelled 2019 ground-level annual mean concentrations of NO_X, NO₂, PM₁₀, and PM_2.5 (in µg/m³) at a 20 m grid resolution. Based on this dataset, predicted pollutant exposure levels in London nurseries were evaluated using ArcGIS spatial analysis. The LAEI 2019 data were imported into ArcGIS. Then, nursery locations were geocoded and overlaid onto the pollutant concentration layers, allowing for the extraction and analysis of exposure estimates at each site.

The nurseries selected for this study were chosen based on the exposure risks mentioned above. Priority was given to contacting nurseries with higher exposure risks. After contacting approximately 40–50 nurseries, three nurseries agreed to participate in the experiment. The locations of these three sites can be found in Figure 1. All three nurseries were located in urban areas of London: one in Haringey (north) and two in Southwark (south). Site 1 was near a busy road and adjacent to a police station car park. Site 2 was in a quieter residential area with less traffic. Site 3 was next to two major roads, a garage, and a tire shop with frequent roadside activity. In each nursery, three to five rooms and one outdoor monitoring site were chosen for environmental monitoring. The monitoring equipment was installed according to each school’s specific safety requirements. A summary of the main characteristics of each studied room is shown in

Table 1. Summary of the main characteristics of each studied room.

Site	Room 1	Abbr.	Occupancy 4	Area (m2)	Volume (m3)	Air Purifier
Site 1	classroom 1	N1_class1	33	35	105	No
	classroom 2	N1_class2	33	41	160	No
	classroom 3	N1_class3	20	48	190	No
	staffroom 1 2	N1_staff1	6	45	188	Yes (1 unit)
	staffroom 2 3	N1_staff2	4	48	193	Yes (1 unit)
		N1_class5	20
Site 2	classroom 1	N2_class1	22	55	210	No
	classroom 2	N2_class2	12	36	85	No
	staffroom 1 2	N2_staff1	5	55	215	Yes (1 unit)
	staffroom 2	N2_staff2	3	49	170	Yes (1 unit)
Site 3	classroom 1	N3_class1	50–60	165	530	Yes (2 units)
	classroom 2	N3_class2	20–30	55	180	No
	classroom 3	N3_class3	25	71	140	No

¹ Natural ventilation with openable windows and exterior doors. ² Both rooms had been previously used as classrooms but were used as staffrooms during COVID-19 period. ³ Staffroom 2 was used as a staffroom during non-heating season and a classroom (classroom 5) during heating season. ⁴ Occupancy refers to the number of children enrolled in each classroom during standard operating hours (9:00–16:00) on school days.

.

2.2. Data Collection

Direct-reading monitors (Eltek TU1082-AQ110) were installed in each classroom and at outdoor locations within the nursery grounds from March 2021 to February 2022, providing approximately 12 months of continuous measurement. One monitor was installed in each room at a height of 1.5–2.0 m above the floor, positioned to minimize disruption to daily activities and kept out of reach of children. These monitors recorded temperature, relative humidity, particulate matter (PM₁, PM_2.5, PM₁₀), and total volatile organic compounds (TVOCs) at 5 min intervals (see Figure 2). For analysis, only PM_2.5 data were used in this study. For detailed sensor specifications, please refer to Appendix A. The recorded data were uploaded automatically to a cloud server via Eltek Squirrel SRV250 data loggers. Sensors operated continuously, recording 24 h data. However, for analysis, only data from occupied hours (9:00–16:00) on regular school days were retained; data collected during holidays and outside working hours were excluded. Plots of the monitored nurseries, including the studied rooms, locations of indoor air quality sensors (red dots), outdoor air quality sensors (green dots), air purifiers (yellow boxes), and window labels, are shown in Figure 3 (detailed window areas can be found in Appendix B). Due to privacy requirements, some parts of the floor plans are displayed in grey as placeholders and cannot be fully disclosed.

Window status (open/closed) was monitored in each room using Eltek GS34 window sensors with magnetic reed switches installed on all external doors and windows (Figure 3).

Table 2. List of Valid Windows Monitored in Each Classroom.

Site	Classroom	Windows
Nursery 1	N1_class1	c1w4
(N1)	N1_class2	c2w3, c2w5, c2w6
	N1_class3	c3w1–8
	N1_staff1	c4w5
	N1_staff2	c5w1–3, c5w6–8
Nursery 2	N2_class1	c1w1, c1w2
(N2)	N2_class2	c2w1
	N2_staff1	-
	N2_staff2	c3w1, c3w2
Nursery 3	N3_class1	-
(N3)	N3_class2	c2w1–4
	N3_class3	c3w4

presents the distribution of valid windows monitored in each classroom. For each room, only windows with valid sensor data and more than 5% usage were included in the analysis and are listed. The operation status of air purifiers was tracked using an optical pulse meter placed over the purifier’s LED indicator, which flashes when the unit is running. This setup enabled assessment of both indoor air quality and the effectiveness of air purifiers under varying ventilation conditions.

HEPA air purifiers (CleanZone SLS, IQAir) were installed in selected rooms. Each unit is equipped with H13-grade HEPA filters. The manufacturer does not specify the Clean Air Delivery Rate (CADR); however, the maximum airflow rate (480 m³/h) was used during all monitoring periods in this study. Given the certified high filtration efficiency of the device, this value can be considered a reasonable proxy for CADR under the tested conditions. The main specifications of the unit are listed in

Table 3. Main specifications of the air purifier used in this study.

Specifications
Airflow Rate per fan speed *	230/290/350/420/480 m3/h
Sound Power Level per fan speed *	23/29/35/42/48
Total System Efficiency for:
Fine Particles and allergens (≥0.3 µm)	≥99%
Ultrafine Particles, bacteria and viruses (<0.1 µm)	≥99%
Particulate Matter (PM2.5, PM10)	≥99%
Particle Filter	High-efficiency particle filter with high-capacity media surface area (approx. 13.3 m2)

* Tolerance: ±10%.

.

2.3. Questionnaire

The questionnaire was developed to collect information on three key areas: (1) general characteristics of each nursery, including occupant density and hours of operation; (2) patterns of window use; and (3) staff perceptions and self-reported satisfaction with indoor environmental quality. The survey was administered in paper form to nursery staff on two occasions prior to the start of the monitoring period and again upon completion of the study. A total of 40 questionnaires were completed (16 from Site 1, 11 from Site 2, and 13 from Site 3), with at least one respondent from each monitored room. Nearly all on-duty staff participated, as the questionnaire was organized and distributed by the head teacher in each nursery. Survey structure and the content are based on those developed for SINPHONIE study [31]. The full list of questionnaire items can be found in Appendix C. Internal consistency testing was not conducted for Likert-type items, as these were only used descriptively and not as a scale.

2.4. Data Analysis

Descriptive statistics and visualizations (e.g., boxplots, heatmaps) were employed to compare pollutant levels, ventilation patterns, and seasonal variations across different sites and periods (heating vs. non-heating). The heating season was defined as 1 November to 31 March, and the non-heating season as 1 April to 31 October. Data from school holidays were excluded from all analyses to ensure consistency in school occupancy and operation patterns. Only occupied hours (09:00–16:00) were included in the analysis.

A logistic regression approach was employed to model occupants’ window opening and closing behaviors, which is a widely adopted method in building occupant behavior research [32,33,34]. To address the environmental feedback between window state and indoor conditions, the analysis focused on discrete opening and closing actions for each monitored window.

The probability P of a window being opened (or closed) was estimated as a function of multiple indoor and outdoor environmental variables, following the generic mathematical form:

$$\log \left({\displaystyle \frac{\mathrm{P}}{1-\mathrm{P}}}\right)={\mathrm{b}}_0+\sum _{\mathrm{i}=1}^{\mathrm{k}}{\mathrm{b}}_{\mathrm{i}}{\mathrm{x}}_{\mathrm{i}}$$

where P is the probability of window opening/closing, k denotes the total number of predictor variables, b₀ is the intercept, and b_i are regression coefficients for each explanatory variable x_i. All data preprocessing and analyses were performed using R software version 4.5.0 (2025-04-11) within the RStudio environment (version 2023-06-01).

3. Results

3.1. Air Purifier Performance in Different Seasons

3.1.1. PM_2.5 Concentrations Across Seasons

As shown in Figure 4, the median PM_2.5 concentrations were generally lower during the heating season in most rooms. However, in classrooms with limited ventilation, such as Classroom 1 of Nursery 3, the seasonal variation in PM_2.5 was less evident. This pattern may be related to window operation, as windows are typically kept closed during colder months. These results highlight the importance of window opening behavior—including the frequency and duration of opening—when evaluating both indoor air quality and the effectiveness of air purifiers.

3.1.2. Severe Polluted Settings

Figure 5 presents a special case observed during the summer vacation, when a nursery was temporarily closed and construction work in two adjacent classrooms led to elevated PM concentrations (the work finished at around 3 pm). After the workers left, data were collected in both unoccupied rooms with all windows and doors closed. Room 1 was not equipped with an air purifier, while Room 2 had the purifier operating. Despite the high initial PM_2.5 concentrations, levels in Room 2 with the air purifier dropped rapidly from a peak of 165.2 µg/m³ to nearly zero within 45 min. In contrast, Room 1 without a purifier saw a slower decline, with PM_2.5 falling from 112.5 µg/m³ to 41.7 µg/m³ initially and only reaching 8 µg/m³ after 5 h. This result demonstrates the significant effect of air purifiers in accelerating particle removal from indoor air, particularly in poorly ventilated, closed environments following high-pollution episodes.

3.2. Seasonal Variation of Ventilation Behavior

Figure 6 and

Table 4. Summary of window opening behaviors across classrooms and seasons.

Site	Window	Percentage of Time in the Open State (%)			Median Duration (min)
		Overall	Non-Heating	Heating	Non-Heating	Heating
N1_class3	c3w3	24.5	24.2	18.7	215	145
N1_class4	c4w5	5.1	8.2	2.8	270	185
N1_class5	c5w8	32.3	24.7	49.3	415	365
N2_class1	c1w2	40.3	55.5	25.3	235	125
N2_class2	c2w1	44.7	66.5	25.7	380	317.5
N2_staff2	c4w7	53	36.1	-	335	50
N3_class2	c2w3	60.1	66.9	63.9	395	625
N3_class2	c2w4	48.7	49.8	53.6	420	822.5
Mean			41.5	34.2	333	329

present seasonal variations in window opening behavior across all monitored classrooms. Figure 6 visualizes these variations with a heatmap, highlighting differences in opening rates between heating and non-heating seasons. Table 4 summarizes the percentage of time each selected main window was open and the median duration of opening events in different seasons. Seasonal differences were observed: in most classrooms, certain windows showed a consistent preference for ventilation, particularly during the non-heating season, which could reflect user behavior or room-specific characteristics. However, in a few classrooms, increased window opening was also observed during the heating season. This may be attributed to heightened awareness of indoor air quality during the post-COVID-19 period, when natural ventilation was promoted as a key strategy to reduce airborne transmission risk.

3.3. Drivers of Window Operation (Questionnaire Analysis)

Table 5. Self-reported window opening frequency by season.

Window Opening Frequency	Non-Heating Season	Heating Season
Always	88%	74%
2–3 times a day	9%	21%
Once a day	3%	3%
Never	0%	3%

shows the self-reported window opening frequency. In the non-heating season, 88% of staff reported always opening windows, compared to 74% in the heating season. More staff opened windows only 2–3 times a day or less during the heating season. Only 3% of staff reported never opening windows in winter. Overall, window opening was more frequent in the non-heating season, despite increased ventilation in winter due to COVID-19 concerns.

The questionnaire results (see Figure 7) indicated that ventilation needs were the primary driver for window operation. During the non-heating season, the main reasons selected for opening windows and external doors included obtaining fresh air (32%), concerns about COVID-19 (28%), cooling the room (24%), and enabling children to go outside (16%). The proportion of respondents citing COVID-19 concerns increased to 43% in the heating season, highlighting greater awareness of the importance of ventilation. Additionally, all participants reported in the questionnaire that they opened windows more frequently during the COVID-19 period than before, even in colder months, suggesting that thermal comfort became a secondary consideration. Consequently, 56% of respondents reported thermal discomfort (scoring below 0 on a seven-point comfort scale) during the heating season.

In addition to reasons for opening windows, staff were also asked to indicate their main reasons for closing windows during both seasons. The majority of respondents indicated “None” as their reason for closing windows in both seasons, suggesting that windows were most often closed without a specific motivation. Other reported reasons included satisfactory air quality, thermal discomfort in winter, or external noise, but these were less common (see

Table 6. Reasons for Window Closing Reported by Nursery Staff.

Reason for Closing Windows	Non-Heating Season	Heating Season
In concerns of COVID-19	0%	4%
Concerns about outdoor air pollution	4%	4%
Story time	7%	0%
Air quality is fine, no ventilation needs	21%	14%
Noise outside	4%	4%
Too cold	0%	14%
None	64%	61%

).

3.4. Predictive Modeling of Window Opening Behavior

Figure 8, Figure 9 and Figure 10 present the predicted probability of window opening as a function of indoor temperature, relative humidity, and CO₂ concentration, respectively, for all selected windows across the three measured sites. Each line color corresponds to a unique window, as labeled in the legend. As shown in Figure 8, the window opening probability generally increases with rising indoor temperature for most windows. The response curves vary in steepness and starting probability, indicating heterogeneity among different windows or classrooms. Most windows remain closed at lower temperatures and are increasingly likely to be opened as the temperature approaches or exceeds 25 °C. A small number of windows exhibit weaker or even negative temperature dependence.

Figure 9 shows that the influence of indoor relative humidity on window opening probability is less consistent across windows. Some windows display a moderate increase in opening probability with higher humidity, while others show flat or even decreasing trends. Overall, the effect of humidity appears secondary compared to temperature.

The response curves in Figure 10 suggest a mixed relationship between indoor CO₂ levels and window opening probability. While a subset of windows shows higher opening probability at elevated CO₂ concentrations (indicative of occupants’ adaptive behavior to perceived air stuffiness), many windows present a weak or negative association. This result implies that, in practice, window operation is not solely or primarily triggered by CO₂ levels, but rather by a combination of thermal and air quality factors, possibly moderated by occupant perception and habits.

It should be noted that a very small number of windows showed atypical or illogical operation patterns in the temperature and relative humidity models, likely due to their low frequency of use—a phenomenon also observed in previous studies [34,35]. In our analysis, we included only windows with a usage rate above 5%, and for completeness and transparency, these observations were retained.

3.5. Effect of Window Opening on Indoor PM_2.5 Concentration

Figure 11 presents the distribution of indoor PM_2.5 concentrations under different window opening conditions (Closed vs. Open) for three measurement periods (N1, N2, N3) across all monitored classrooms. Overall, indoor PM_2.5 concentrations were consistently higher during periods when at least one window was open, compared to when all windows were closed. This trend was observed in almost all classrooms and for all three measurement periods. For instance (see

Table 7. Median indoor PM_2.5 (µg/m³) by window status in each classroom.

Nursery	Classroom	Window		Diff Rate (%)
		Closed	Open
N1	N1_class1	4.0	5.3	32.5
	N1_class2	3.4	4.8	41.2
	N1_class3	4.0	4.6	15.0
	N1_staff1	0.3	0.3	0.0
	N1_staff2	1.9	2.1	10.5
N2	N2_class1	3.3	4.3	30.3
	N2_class2	2.5	3.9	56.0
	N2_staff1	0.8	1.6	100.0
	N2_staff2	0.6	0.6	0.0
N3	N3_class1	2.2	2.3	4.5
	N3_class2	5.2	5.6	7.7
	N3_class3	4.5	6.0	33.3
Mean		2.7	3.5	27.6

), the median PM_2.5 concentration increased from 4.0 μg/m³ (closed) to 5.3 μg/m³ (open) in N1_class1, the median PM_2.5 in N2_class1 rose from 3.3 μg/m³ (closed) to 4.3 μg/m³ (open), and from 0.8 to 1.6 μg/m³ in N2_staff1; and similar increases were observed in other classrooms. The magnitude of the increase varied by classroom and measurement period, but the general pattern indicates that window opening was associated with an elevation in indoor PM_2.5 levels. This is likely due to outdoor air infiltration, especially when outdoor PM_2.5 concentrations were elevated. These findings suggest that, under the monitored conditions, window opening may not always improve indoor air quality with respect to PM_2.5, and in some cases, it may even increase occupants’ exposure to fine particulate matter. This also helps to explain why air purifiers were more effective during the winter or in periods when windows remained closed, as reduced ventilation limited the entry of outdoor pollutants and allowed the purifiers to better control indoor PM_2.5 levels.

4. Discussion

This study provides new insights into the interplay between air purifier effectiveness, natural ventilation behavior, and seasonal factors in nursery environments. While previous studies have confirmed the utility of HEPA-filtered air purifiers in reducing indoor PM_2.5, our findings demonstrate that their real-world performance is modulated by occupant window operation patterns and seasonal variation.

4.1. Seasonal Variation in Air Purifier Effectiveness

Air purifiers consistently lowered indoor PM_2.5 concentrations, with rapid removal observed even during high-pollution episodes when windows and doors were closed. In nursery settings, significant reductions in particulate matter have been observed. For instance, in South Korea, PM_2.5 concentrations decreased from 39.9 µg/m³ to 5.6 µg/m³, and PM₁₀ from 81.3 µg/m³ to 15.0 µg/m³, following the implementation of air purifiers [19]. This effect was most pronounced in the heating season, when limited ventilation helped maintain low particle concentrations. For example, a study observed that in school gyms, the I/O ratio for PM_1–10 decreased by 95% with purifiers in operation and windows closed, but only by 6% when windows were open [26]. Another study found that, in an aged-care center in China, the mean removal efficiency for PM_2.5 was 24.5% higher in rooms with windows closed compared to those with windows open [27]. During the non-heating season, increased window opening (driven by the desire for fresh air, thermal comfort, or post-COVID-19 infection control) led to higher indoor PM_2.5 concentrations, potentially offsetting the gains from air purification. Our findings are consistent with this international literature and reinforce the message that air purifier effectiveness is highly context-dependent in real-world nurseries. In our study, median indoor PM_2.5 concentrations were generally lower during the heating season in most rooms, a pattern likely attributable to more frequent window closure and the greater effectiveness of air purifiers under these conditions. This suggests a trade-off: while natural ventilation is crucial for indoor air quality and infection risk reduction, it may introduce more outdoor PM_2.5 indoors, particularly in urban environments. Thus, investigating window-opening behavior and its interaction with air purifier use is essential for developing effective strategies to optimize both health protection and indoor air quality. Notably, outdoor PM_2.5 concentrations near urban roads in London can substantially exceed WHO guidelines, especially during rush hours [36]. Thus, classroom windows facing busy streets could exhibit greater PM_2.5 exposure when opened, highlighting the importance of considering local outdoor air quality when using natural ventilation. These findings demonstrate that optimizing air purifier effectiveness requires a holistic approach, accounting for both operational context and external pollution sources.

4.2. Drivers of Window Operation

Evidence suggests that COVID-19 response measures may significantly impact behavior, including alterations in energy-related behaviors, decision-making processes, and daily routines [37,38]. Previous studies reported that the operation of windows was mainly related to temperature, and people tend to open windows more frequently in higher temperatures (during warmer periods) [23,34,39,40]. However, in this study, COVID-19 concerns motivated occupants to increase window opening even in the heating season. Despite this, overall window opening remained lower in the heating season than in the non-heating season. Questionnaire results showed that 88% of respondents reported “always” opening windows in the non-heating season, compared to 74% in the heating season, with 21% of staff reporting 2–3 times daily opening in the heating season. These self-reported patterns were consistent with monitored data, which indicated that windows were open on average 41.5% of the time in the non-heating season and 34.2% in the heating season. Notably, the increased window opening during the heating season, driven by COVID-19 concerns, may have resulted in thermal discomfort for occupants. A study in Sevilla mentioned that, during the pandemic, over 60% of hours were outside of thermal comfort conditions in primary school classrooms [41]. In this study, the questionnaire confirmed that many staff prioritized ventilation over thermal comfort in the heating season.

Window operation was found to be highly variable across rooms and seasons, influenced not only by environmental factors such as temperature and humidity, but also by user perception, school policies, and heightened awareness following the COVID-19 pandemic [42]. For example, some windows, such as c3w3 in N1_class3, showed only minor seasonal differences in opening frequency (24.2% in non-heating vs. 18.7% in heating season), while others, such as c5w8 in N1_class5, exhibited much greater differences (24.7% in non-heating vs. 49.3% in heating season). When respondents were asked about their reasons for closing windows, the majority selected “None” (64% in the non-heating season and 61% in the heating season), suggesting that most windows were closed without a clear or conscious motivation.

The analysis of window opening predictors in this study suggests that temperature remains the most influential environmental driver. This is consistent with the main findings from previous studies [33,35,43,44]. CO₂, often assumed as a trigger for ventilation, showed inconsistent effects. The relatively weak link between indoor CO₂ and window opening in this setting might reflect the dominance of scheduled or policy-driven ventilation over direct occupant perception. This is also consistent with previous research, which found that although high indoor CO₂ levels can be associated with discomfort and serve as an indicator of poor air quality, occupants are generally less sensitive to CO₂ compared to factors like temperature [45]. A study found that higher indoor CO₂ levels were associated with a reduced likelihood of window opening in residential settings [46]. Similarly, another concluded that CO₂ was not a significant driver of window operation in schools [40]. This underlines a potential research gap in many international IAQ studies, where the role of automated or policy-driven ventilation is not always distinguished from user-responsive behaviors. Incorporating air purifier operation as an additional predictor may help to refine window opening models in future studies and help better quantify the potential benefits of air purifier use in real-world settings. Overall, these findings underscore that window-opening behavior is highly dynamic, shaped by both environmental and psychosocial factors, and merits further in-depth investigation to inform more effective indoor air quality management strategies in educational settings.

4.3. Implications of Window Operation and Air Purifier Performance

In this study, the difference in indoor PM_2.5 concentrations between window open and closed states was substantial, with most cases showing significantly higher PM_2.5 levels when windows were open. Across all measured rooms, the mean indoor PM_2.5 concentration increased from 2.7 µg/m³ (windows closed) to 3.5 µg/m³ (windows open), representing an average increase of 27.6%. In some classrooms, such as N2_class2 and N2_staff1, the difference rate exceeded 50%. This finding directly explains why air purifiers demonstrated better performance during the heating season, or in periods when windows remained closed more frequently, as window opening allows more outdoor pollutants to enter the indoor environment and can counteract the purifier’s effect. Window opening can either improve indoor air quality or introduce more pollutants, depending on various factors such as rural versus urban settings, rush hours versus periods of low outdoor pollution, and other local conditions [47,48]. Therefore, the relationship between air purifier usage and window operation is complex—effective improvement of indoor air quality requires careful coordination of both strategies, particularly in locations with persistently high outdoor PM_2.5.

Overall, the findings highlight the need for a balanced and sustainable approach to IAQ management, where both mechanical air purification and ventilation are optimized according to outdoor air quality, seasonal needs, and occupant health priorities [49]. Real-time IAQ feedback and adaptive ventilation strategies may help reconcile the trade-off between fresh air supply and PM_2.5 exposure while also supporting environmental sustainability in nurseries.

4.4. Limitations

It should be noted that this study has several limitations. Despite efforts to recruit a larger sample, only three nurseries ultimately participated in the study, which may limit the generalizability of the findings. The analysis was limited to a relatively small sample of nursery rooms and may not represent other educational or residential environments. The monitoring methods captured window open/closed states but did not record the degree of window opening or the contribution of door ventilation. In addition, the results primarily reflect PM_2.5 levels; other pollutants or longer-term health outcomes were beyond the scope of this research. Future studies involving a broader range of buildings, more detailed ventilation measurements, and multiple indoor air quality indicators are recommended, including potential impacts on cognition [50]. Further, cross-country studies comparing nurseries in different policy and pollution contexts would be valuable for developing globally relevant guidelines and identifying best practices.

5. Conclusions

This study demonstrates that HEPA air purifiers are effective in reducing indoor PM_2.5 concentrations in nursery classrooms, particularly when windows remain closed for longer periods during the heating season. Although COVID-19 concerns led to increased window opening in winter, overall window opening rates were still higher in the non-heating season (41.5%) than in the heating season (34.2%), resulting in elevated indoor PM_2.5 during warmer months and highlighting the trade-off between natural ventilation and air purification. Analysis of window operation confirmed that indoor temperature was the primary driver, consistent with previous studies. However, occupant perception, especially concerns about COVID-19, and school policy also contributed to increased window opening. Importantly, window opening had a clear impact on indoor air quality: the median PM_2.5 concentration was 2.7 µg/m³ with windows closed, rising to 3.5 µg/m³ when open. These findings suggest that IAQ strategies in nurseries should combine HEPA air purification with context-aware ventilation management, fully considering seasonal and behavioral factors to optimize children’s health protection. Such approaches also contribute to the broader goal of creating sustainable, healthy indoor environments in educational settings.