Developing a Modelling Approach for Predicting PM<inf>2.5</inf> Concentrations in Classrooms for School Children
2
Issued Date
2025-05-01
Resource Type
ISSN
16808584
eISSN
20711409
Scopus ID
2-s2.0-105004759032
Journal Title
Aerosol and Air Quality Research
Volume
25
Issue
5
Rights Holder(s)
SCOPUS
Bibliographic Citation
Aerosol and Air Quality Research Vol.25 No.5 (2025)
Suggested Citation
Chen C.Y., Hsu C.Y., Ramachandran G., Khajonklin T., Yoon C., Tsai P.J. Developing a Modelling Approach for Predicting PM<inf>2.5</inf> Concentrations in Classrooms for School Children. Aerosol and Air Quality Research Vol.25 No.5 (2025). doi:10.1007/s44408-025-00028-8 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/110197
Title
Developing a Modelling Approach for Predicting PM<inf>2.5</inf> Concentrations in Classrooms for School Children
Corresponding Author(s)
Other Contributor(s)
Abstract
Purpose: Given the fluctuations in ambient PM2.5 and the varying classroom activities, traditional monitoring methods may be impractical for assessing long-term indoor PM2.5 exposures among schoolchildren due to the substantial sample size required. Although the well-mixed room (WMR) model offers a potential solution, it first requires quantifying the indoor PM2.5 generation rate (G) and the non-ventilation removal rate (K). This study presents a technique to simultaneously estimate both G and K using a combination of numerical and iterative methods and applies the results to WMR for predicting PM2.5 concentrations inside the classroom. Methods: The whole study was conducted in a typical elementary school classroom equipped with two fresh air units (FAUs) and four electric fans. PM2.5 and CO2 concentrations were measured simultaneously inside and outside the classroom. Results: The PM2.5 generation rate, G (GM (GSD)), was estimated at 10.7 (2.1) µg min⁻1 during lecture sessions and 19.2 (1.2) µg min⁻1 during the noon rest period. However, the non-ventilation removal rate, K, was consistently found to be 0. Activity intensity significantly influenced the magnitude of G. Higher-intensity activities, such as extensive chalk usage, resulted in elevated G values compared to lower-intensity activities, such as quizzes. Noon rest periods exhibited notably higher G values, likely due to diverse student activities, including eating and walking. The consistent absence of K may be attributed to the small particle size of PM2.5. No significant differences were observed between measured and model-predicted PM2.5 levels inside the classroom. Conclusions: These findings suggest that the developed technique is practical and effective for quantifying both G and K, supporting the use of the WMR modeling approach for estimating long-term classroom PM2.5 concentrations for schoolchildren.