Indoor Air Quality in Children Daycare Centers with Baby Beds

Keywords (5): Indoor air quality; Children; Baby Beds; Daycare center; Sensor technology; Ventilation control; Volatile organic compound

Introduction

Humans, in a sense, act as natural air filters, engaging continuously with the air around them through the simple yet vital act of breathing. Adults typically inhale and exhale around 23,000 times a day, but this frequency is notably higher in infants and toddlers, who take about 50,000 breaths daily [1]. This increased rate of breathing in young children, coupled with their proportionally larger breathing volume relative to body weight, not only highlights their more intense interaction with their immediate atmospheric environment but also underscores the critical importance of the quality of air they are exposed to. This aspect is particularly crucial in settings like daycare centers (DCCs), which, in addition to homes, are the primary indoor environments frequented by young children [2].

DCCs, serving as early childhood education institutions, are the first integral program for the cognitive, physical, and social development of young children ranging from infants to primary-school-aged kids [3]. Young children in DCCs are notably susceptible to the adverse impacts of environmental contaminants due to their still-developing immune, physiological, and neurological systems [4-6]. Therefore, maintaining and improving indoor air quality (IAQ) in DCCs is essential for protecting the health, comfort, and well-being of these young children. This goal forms the core focus of the current study, a PhD research, underlining the need for robust measures to ensure safe and healthy environments for early development [7].

Part One: IAQ Monitoring Evaluation [8]

Recognizing the importance of IAQ, this study acknowledges, as recently indicated by the World Health Organization (WHO), that air quality monitoring is the first step in understanding and addressing children’s exposure to air pollutants. Therefore, the research first assessed the performance of low-cost air quality monitors and single sensors (without casings) within the unique context of DCCs to ensure that real-time IAQ monitoring would be effectively tailored to detect typical indoor emission events. This study involved testing the responses of five low-cost air quality monitors and five types of single sensors, and these devices were evaluated against research-grade instruments (RGIs). The evaluation was focused on multiple key indoor air quality parameters: particulate matter (PM), carbon dioxide (CO₂), and total volatile organic compounds (TVOC), along with temperature and relative humidity. Through experiments conducted in a climate chamber (see Figure 1), the study simulated typical daycare activities (background activities, arts-and-crafts, and cleaning) under two typical indoor climatic conditions (cool and dry [20 ± 1°C & 40 ± 10%], warm and humid [26 ± 1°C & 70 ± 5%]).

For PM monitoring, this study revealed that low-cost PM devices, including both sensors and monitors, under-reported PM concentrations when compared to research-grade instruments. Furthermore, the low-cost devices were found to exhibit strong correlation and moderate quantitative agreement with research-grade instruments in scenarios that produced significant particle emissions, such as cleaning activities, where PM2.5 levels were recorded in the range of 22-455 µg/m³. However, the correlation diminished or even disappeared in events characterized by lower particle emissions, including arts-and-crafts and background activities, where PM2.5 levels were less than 15 µg/m³. Moreover, the performance of these low-cost devices was not consistent across different cleaning events; their sensitivity and response were influenced by the concentration and composition of particulate matter in emission sources.

Regarding CO₂ monitoring, all tested low-cost devices exhibited strong correlation with the reference data across all simulated events, except in the "no occupancy" background test. The variation in baseline values of these devices affected their performance. These devices showed capability in responding to CO₂ emissions and effectively returning to baseline levels after emissions ceased. A sensor based on photoacoustic principle technology demonstrated superior performance over traditional NDIR-based CO₂ sensors.

The study highlighted that the TVOC measurements are complex relative to other gas-phase sensors tested due to the diversity of organic gases they cover. The study suggested that although the tested TVOC devices showed considerable differences in quantitative responses to simulated events, they maintained a strong correlation with research-grade instruments and were capable of detecting emission events in DCCs. The study also observed that most low-cost TVOC devices often reported concentrations outside their specified detection range, indicating uncertainties in the performance of low-cost TVOC devices.

Overall, the study confirms the effectiveness of low-cost air quality devices in capturing specific air quality parameters during various indoor activities under different climatic conditions in DCCs. It further demonstrates that these devices, when used to monitor multiple IAQ parameters simultaneously, are capable of detecting typical activities in DCCs, including background, arts-and-crafts, and cleaning events, albeit with differing levels of accuracy.

Part Two: VOCs Emission and Health Risk Assessment [9]

Further delving into the issue of indoor pollutants, the thesis explored the presence and health risk of volatile organic compounds (VOCs) on young children and pedagogical staff in DCCs. As described in Part One, the study conducted full-scale climate-controlled experiments (see Figure 1) to investigate VOC emissions from typical activities in DCCs. These activities included arts-and-crafts (painting, gluing, and modeling) and cleaning. Additionally, this study expanded its scope to simulate sleeping activities, with a particular focus on mattress usage. The use of a controlled environment allowed for the isolated measurement of VOCs emitted during these activities, leading to a detailed profiling of these emissions. This approach resulted in identifying a complex array of 96 VOC species, categorized into twelve groups including alcohols, aldehydes, alkanes, and others.

Table 1 presents an overview of the specific types of simulated activities, along with the measured TVOC levels and selected individual VOC emissions. The results reveals distinctive chemical emissions for each event, with significant variations in TVOC emissions across events depending on the products used. For instance, in the gluing activities, “Liquid Gluing” usage resulted in a concentration of 420 μg/m³, with isopropyl alcohol as a major component. “Stick Gluing”, however, had a substantially lower concentration of 96 μg/m³, dominated by dodecane.

Arts-and-crafts activities exhibited distinct VOC profiles based on the specific activity. Acrylic painting, for example, showed a high TVOC concentration of 808 μg/m³, mainly consisting of ethers, esters, and alcohols. In contrast, poster painting presented a much lower TVOC level at 58 μg/m³, with a chemical makeup primarily of ketones and alkanes. Gluing activities also varied in TVOC levels; liquid glue usage resulted in a concentration of 420 μg/m³, dominated by alcohols, aldehydes, and ketones, with isopropyl alcohol as a major component. Stick gluing, however, had a substantially lower concentration of 96 μg/m³, characterized by a mix of alkanes, aldehydes, and esters. Modeling activities further demonstrated this variability; plasticine modeling under cool and dry conditions produced a TVOC concentration of 78 μg/m³, largely comprised of ethers, alcohols, and aldehydes, while sand modeling under the same conditions showed a slightly higher TVOC level of 136 μg/m³, with aldehydes like nonanal and aromatics such as styrene being prominent.

Cleaning activities in DCCs also resulted in varied TVOC concentrations depending on the products used. For instance, cleaning with Dettol disinfectants led to a TVOC concentration of 348 μg/m³, mainly due to alcohols like 1-phenoxy-2-propanol. Conversely, cleaning with Suma disinfectants showed a higher TVOC level of 896 μg/m³, with alkenes such as propene being predominant.

The study also found that sleeping activities involving mattresses are notable sources of VOC emissions. New mattresses in cool and dry conditions exhibited a significant TVOC concentration of 574 µg/m³, with a chemical profile dominated by alkanes and esters. In contrast, used mattresses under the same conditions showed an elevated TVOC concentration of 1,711 µg/m³, indicating a change in emissions over time and with usage.

The health risk assessment on VOC emissions from DCCs indicated that the majority of measured chemical concentrations remained within the safe limits prescribed for non-carcinogenic risks. However, for carcinogenic risks, the study identified specific events and conditions that presented elevated risks. Particularly alarming were the findings from specific activities such as "Sand Modeling" and the use of "New Mattresses." In these scenarios, certain VOCs like styrene in sand modeling and ethylene oxide from new mattresses exhibited Risk Quotient (RQ) values that approached or exceeded the critical threshold for carcinogenic risks. This was especially pronounced for infants and toddlers, highlighting their increased vulnerability to these emissions. In contrast, adults generally showed lower RQ values, indicating minimal risk. This discrepancy in risk based on age highlights the need for targeted interventions and protective measures for the more vulnerable young population in DCCs.

In general, from a practical perspective, this part of the study emphasizes the critical need for carefully selecting arts-and-crafts materials and cleaning agents in DCCs to effectively reduce VOC emissions and thus exposure. Likewise, the study suggests that regular mattress check-ups and replacements further mitigate VOCs exposure during naps. Certain events revealed VOC levels exceeding safety thresholds, emphasizing the need for age-specific health risk assessments, especially for younger children.

Part Three: Bed-level Ventilation Conditions [10]

Moving from air quality assessment and source mitigation, the research next explored the bed-level ventilation conditions in DCCs to address mitigation of exposure via ventilation at bed level, with a specific focus on Dutch DCCs where semi-enclosed baby bunk beds are common. The study started with a field survey in 17 Dutch DCCs that covered 68 bedrooms. This survey uncovered a significant concern: approximately 38% of these bedrooms equipped with mechanical ventilation systems failed to meet the Dutch Building Code's ventilation rate requirements for the existing building. To further investigate, a full-scale bedroom setup (see Figure 2) was constructed to replicate typical DCC environments, focusing on the bed-level ventilation conditions. The experiment involved studying the dispersion and inhalation of CO₂ gas in semi-enclosed baby beds using a breathing thermal baby model, taking into account variables, i.e., sleep positions (supine, lateral-to-wall, lateral-to-corridor), baby ages (12- and 30-month-old), and ventilation rates (55 and 250 m³/h).

The key findings of the study highlighted a critical issue: the ventilation at the bed level in semi-enclosed beds was notably inadequate. The inhomogeneity in air distribution between the bed- and bedroom-level was evident despite the presence of sufficient room-level mixing ventilation. This resulted in inadequate replacement of air inside the beds with cleaner room air. More specifically, excess exhaled CO₂ concentration was found to accumulate inside a semi-enclosed bed in most cases (see Figure 3). Besides, the average CO₂ concentration inhaled by infants was three times that of the room's exhaust levels, with mean values reaching 1,647 ppm. In some cases, the inhaled CO₂ concentration surpassed 4,300 ppm, raising serious health concerns for infants.

Additionally, it is observed that sleep position, baby age, and ventilation rate significantly influenced CO₂ distribution and inhalation. Lateral-to-wall sleep positions led to the highest CO₂ accumulation, followed by lateral-to-corridor and supine positions. Higher ventilation rates (250 m³/h), although effective in removing CO₂ emissions, paradoxically increased CO₂ inhalation compared to lower rates (55 m³/h), when sleeping in a lateral-to-corridor position. Relative to infants (12-month-old), older babies (30-month-old) emit more CO₂, which causes more CO₂ to be inhaled and accumulated inside the bed.

Part Four: Bedroom Ventilation [11]

Given the insufficient ventilation conditions observed in semi-enclosed beds, the work further explored the effectiveness of various ventilation strategies (see Figure 4), namely, mixing ventilation (MV), displacement ventilation (DV), and personalized ventilation (PV). This part of the study utilized a full-scale bedroom setup, featuring six baby bunk beds arranged in a bilateral layout and twelve baby mock-ups. These mock-ups simulated the respiration and heat generation of 30-month-old babies. The study compared the performance of three strategies across two types of sleep positions (supine and lateral-to-wall) and two types of ventilation rates (55 and 250 m³/h) considered. 58 CO₂ sensors strategically placed in the room and the beds provided detailed data on CO₂ concentration levels.

The findings revealed the superior performance of PV, followed by DV and MV, with significantly different inhaled CO₂ concentrations per baby, though the mean in-bed values (over a plane) across twelve beds for three strategies under the same sleep position and ventilation rates were similar. Thus, assessing ventilation performance of various ventilation strategies necessitates examining inhaled air quality. Sleep positions and ventilation rates significantly influenced MV and DV modes' performance. Importantly, PV demonstrated energy-saving potential by achieving comparable inhaled air quality at lower ventilation rates. These findings have practical implications for designing occupant-centric ventilation systems in DCC bedrooms.

Part Five: Personalized Ventilation [12]

Building upon Part Four, this study delved deeper into the effectiveness of PV strategies in semi-enclosed babe beds in DCCs, with a particular focus on enhancing inhaled air quality. Addressing the limitations identified in Part Four, including the use of a singular PV system size and a single air supply direction facing the wall, this follow-up study adopted a more comprehensive approach. It employed varied airflow directions and rates through a PV setup provided by AirTulip Company. Specifically, the study investigated three supply airflow directions (wall-side, head-side, cover-side), five ventilation rates (21, 37, 55, 65, 75 m³/h), and three sleeping positions (supine, lateral-to-wall, lateral-to-corridor). Utilizing the same bedroom and baby model setup as in Part Three, this study introduced modifications with 23 CO₂ sensor placements to meet its unique investigative needs. To contextualize the findings, a comparative analysis featuring a control scenario using MV mode was also conducted.

This study confirms that PV can better enhance inhaled and bed-level air quality for infants compared to traditional MV methods. The results showed that, the PV head-side strategy, in particular, proved to be most efficient at lower rates, optimizing inhaled air quality and suggesting potential for energy savings. The results also indicate that while increased ventilation rates typically improve bed-level air quality, the optimal ventilation rate is influenced by the specific strategy and sleeping position. This emphasizes the need for tailored ventilation solutions in DCCs occupied by infants.

Conclusion [7]

Overall, this study contributes to the body of knowledge on IAQ in DCCs by providing a detailed assessment of IAQ conditions and monitoring technologies, and by developing effective mitigation strategies. It shows that, in general, the IAQ in Dutch DCCs is not optimal as a result of the activities performed in DCCs and the design of the sleeping environment in such centers. To improve this situation, a thoughtful and comprehensive approach is needed that integrates various monitoring tools, careful selection of materials, and tailored ventilation strategies focused on inhaled air quality. By providing detailed analysis and practical recommendations, this PhD thesis offers a roadmap for creating healthier and safer indoor environments in DCCs for the youngest, who are among the most vulnerable members of society.

References

[1]     U.S. Environmental Protection Agency (EPA), Exposure Factors Handbook 2011 Edition (Final Report). 2011: U.S. Environmental Protection Agency, Washington, DC.

[2]     Zheng, H., S. Walker, and W. Zeiler. IAQ Aspects of Daycare Centers: A Systematic Review of Exposure to Particular Matter. in ASHRAE Topical Conference Proceedings. 2022. American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc. DOI: https://www.aivc.org/sites/default/files/2_C3.pdf.

[3]     Zhang, Y., P.K. Hopke, and C. Mandin, Handbook of Indoor Air Quality. 2022: Springer Nature Singapore Pte Ltd. DOI: https://doi.org/10.1007/978-981-16-7680-2.

[4]     Bearer, C.F., How are children different from adults? Environmental health perspectives, 1995. 103(suppl 6): p. 7-12. DOI: https://doi.org/10.2307/3432337.

[5]     Schwartz, J., Air pollution and children's health. Pediatrics, 2004. 113(4): p. 1037-1043. DOI: https://doi.org/10.1542/peds.113.S3.1037.

[6]     WHO, WHO global air quality guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. 2021, License: CC BY-NC-SA 3.0 IGO: World Health Organization. DOI: https://iris.who.int/handle/10665/345329.

[7]     Zheng, H., Indoor Air Quality in Daycare Centers: Assessing and Mitigating Indoor Exposure on Young Children. 2024, Eindhoven University of Technology, Built Environment: Eindhoven. DOI: https://research.tue.nl/files/322463579/20240507_Zheng_H._hf.pdf

[8]     Zheng, H., et al., Laboratory evaluation of low-cost air quality monitors and single sensors for monitoring typical indoor emission events in Dutch daycare centers. Environment International, 2022. 166: p. 107372. DOI: https://doi.org/10.1016/j.envint.2022.107372.

[9]     Zheng, H., et al., Species profile of volatile organic compounds emission and health risk assessment from typical indoor events in daycare centers. Science of The Total Environment, 2024. 918: p. 170734. DOI: https://doi.org/10.1016/j.scitotenv.2024.170734.

[10]   Zheng, H., et al., Bed-level ventilation conditions in daycare centers. Building and Environment, 2023. 243: p. 110638. DOI: https://doi.org/10.1016/j.buildenv.2023.110638.

[11]   Zheng, H., et al., Bedroom ventilation performance in daycare centers under three typical ventilation strategies. Building and Environment, 2023. 243: p. 110634. DOI: https://doi.org/10.1016/j.buildenv.2023.110634.

[12]   Zheng, H., et al. Breathing Better: Evaluating the Impact of Personalized Ventilation in Daycare Baby Beds. in 44th AIVC Conference, 12th TightVent Conference, 10th venticool Conference: Retrofitting the Building Stock: Challenges and Opportunities for Indoor Environmental Quality. 2024. INIVE (International Network for Information on Ventilation and Energy. DOI: https://pure.tue.nl/ws/portalfiles/portal/342088087/AIVC_Conference_Paper_2024-April-18_Hailin_Zheng_Submission_Version.pdf.