In a recent article published in the journal Indoor Air, researchers investigated the sources of indoor air pollution and the exposure levels of students within school classrooms using optical particle counter (OPC) sensors. By focusing on particulate matter (PM) concentrations, the research aims to identify how classroom occupancy influences air quality and to propose potential interventions for improving IAQ in schools.
Study: Investigating indoor air pollution sources and student’s exposure within school classrooms: Using a low-cost sensor and source apportionment approach. Image Credit: Chim/Shutterstock.com
Background
Indoor air quality (IAQ) is increasingly recognized as a significant factor influencing public health, particularly in environments where individuals, especially children, spend extended periods, such as schools. Research has shown that poor IAQ can lead to a range of health issues, including respiratory problems, allergies, and decreased cognitive function.
Given that children are more vulnerable to the effects of air pollution due to their developing bodies and higher respiratory rates, understanding the sources and levels of indoor pollutants in educational settings is crucial.
Previous research has established that indoor environments, especially classrooms, can harbor various pollutants from both external and internal sources. Factors such as students' presence, classroom activities, and building materials contribute to the accumulation of particulate matter.
The World Health Organization (WHO) has set guidelines for acceptable PM levels, emphasizing the need for continuous monitoring and management of air quality in schools. This study builds on existing literature by employing a systematic approach to assess PM concentrations in different classroom settings, thereby providing a clearer understanding of the dynamics of indoor air pollution in educational institutions.
The Current Study
The research was conducted over a two-week period in a school located in South Wales. Three OPC sensors (Alphasense OPC-N3) were utilized to collect data on particle number size distribution and PM mass concentrations across three distinct classrooms. The classrooms were selected based on their varying activities: religious studies, English studies, and cooking studies. The study employed a source apportionment (SA) analysis using non-negative matrix factorization (NMF) to identify and characterize the sources of PM.
Data collection involved continuous monitoring of PM levels, with the sensors calibrated to ensure accuracy. The measurements were recorded at 1-minute intervals, allowing for detailed analysis of diurnal variations in PM concentrations. To assess the sources of indoor air pollution, a non-negative matrix factorization (NMF) algorithm was applied for source apportionment, enabling the identification of contributions from various factors, including indoor activities and outdoor air infiltration.
The study also included a comparative analysis of PM levels during occupied and unoccupied periods to evaluate the impact of student presence on air quality. Statistical analyses were performed to determine the significance of differences in PM concentrations across classrooms and time periods. The findings were contextualized against the World Health Organization's guidelines for PM levels, providing a framework for understanding the implications of the results for indoor air quality management in educational settings.
Results and Discussion
The findings revealed significant variations in PM concentrations based on classroom occupancy. During school hours, when students were present, the mean PM2.5 levels ranged from 8.1 to 14.4 µg/m³, remaining below the WHO's 24-hour guideline. However, PM10 concentrations varied more widely, with measurements between 13.3 and 51.0 µg/m³.
Notably, the English studies classroom was the only one to exceed the WHO guideline for PM10 during occupied hours. The analysis identified three distinct factors contributing to PM levels: indoor activity, external sources, and background pollution. Factor 1, associated with indoor activities, showed a low PM1/PM10 ratio, indicating that student presence and associated activities were substantial sources of coarse indoor particles.
The study also highlighted the diurnal variation of PM concentrations, with peaks observed during school hours, suggesting that student activities significantly influence air quality. The presence of carpets in certain classrooms and the cooking activities in others were noted as potential contributors to increased PM levels. The research underscores the importance of understanding the specific sources of indoor air pollution to develop targeted interventions.
Conclusion
This study provides a comprehensive assessment of indoor air pollution sources and student exposure in school classrooms. By employing low-cost monitoring techniques and source apportionment analysis, the research offers a detailed understanding of how classroom occupancy affects air quality. The findings indicate that while PM2.5 levels generally remain within acceptable limits, PM10 concentrations can exceed guidelines, particularly in specific classroom settings.
The research emphasizes the need for ongoing monitoring and the development of effective air quality management strategies in schools. By addressing both external and internal pollution sources, schools can improve indoor air quality, thereby enhancing the health and well-being of students and staff. The insights gained from this study serve as a foundation for future research and practical applications aimed at fostering healthier indoor environments in educational institutions.
Journal Reference
Rose O. G., Bousiotis D., et al. (2024). Investigating indoor air pollution sources and student’s exposure within school classrooms: Using a low-cost sensor and source apportionment approach. Indoor Air 34(1), 1-17. DOI: 10.25500/edata.bham.00001050, https://onlinelibrary.wiley.com/doi/10.1155/2024/5544298