Purpose <p>Given the fluctuations in ambient PM<sub>2.5</sub> and the varying classroom activities, traditional monitoring methods may be impractical for assessing long-term indoor PM<sub>2.5</sub> 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 PM<sub>2.5</sub> 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 PM<sub>2.5</sub> concentrations inside the classroom.</p> Methods <p>The whole study was conducted in a typical elementary school classroom equipped with two fresh air units (FAUs) and four electric fans. PM<sub>2.5</sub> and CO<sub>2</sub> concentrations were measured simultaneously inside and outside the classroom.</p> Results <p>The PM<sub>2.5</sub> generation rate, G (GM (GSD)), was estimated at 10.7 (2.1) µg min⁻<sup>1</sup> during lecture sessions and 19.2 (1.2) µg min⁻<sup>1</sup> during the noon rest period. However, the non-ventilation removal rate, K, was consistently found to be 0.&#xa0;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 PM<sub>2.5</sub>.&#xa0;No significant differences were observed between measured and model-predicted PM<sub>2.5</sub> levels inside the classroom.</p> Conclusions <p>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 PM<sub>2.5</sub> concentrations for schoolchildren.</p> Graphical Abstract <p></p>

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Developing a Modelling Approach for Predicting PM2.5 Concentrations in Classrooms for School Children

  • Chun-Yu Chen,
  • Chien-Yu Hsu,
  • Gurumurthy Ramachandran,
  • Thanawat Khajonklin,
  • Chungsik Yoon,
  • Perng-Jy Tsai

摘要

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.

Graphical Abstract