鑒於環境 PM2.5濃度的變化以及教室內活動的多樣性，傳統監測方法在評估學童長期室內 PM2.5暴露時常因所需樣本數龐大而難以實施。雖然完全混合室（well-mixed room, WMR）模式提供了一項可行的替代方案，但其應用仰賴對室內 PM2.5產生率（G）與非通風移除率（K）的精確量化。本研究提出一種可同時估算G與K的數值迭代方法，並將其應用於 WMR 模型以預測教室內 PM2.5濃度，進一步結合學生的時間－活動模式，透過蒙地卡羅模擬法估算其長期 PM2.5 暴露分布。
本研究於台灣典型小學教室中進行，其中一間教室設有兩部新風機（FAUs），另一間使用空氣清淨機（AP）。於室內外同步監測PM2.5及CO2濃度用以推估G及K，並評估模式準確性。結果顯示，使用新風機之教室在講課期間與午休期間的幾何平均數（GM（GSD））分別為 10.7（2.1）與 19.2（1.2）µg/min。使用空氣清淨機之教室在講課期間與午休期間的幾何平均數（GM（GSD））分別為 8.7（3.1）與 23.2（1.7）µg/min。G的峰值與高強度活動（如使用粉筆、清潔與走動）有關。K則皆趨近於零，推測與細懸浮微粒粒徑小以及 FAU及AP提供的高換氣率有關。模型預測值與實測值之間無顯著差異，證實所提出方法的準確性與可靠性。在FAUs正常運作的情境下，學生的長期 PM2.5暴露分布呈現對數常態分布，其 GM（GSD）為 8.5（1.4）µg/m3，95 百分位為 15.1 µg/m3，略高於世界衛生組織（WHO）建議的每日PM2.5暴露限值（15 µg/m3）。在AP正常運作的情境下，學生的長期 PM2.5暴露其 GM（GSD）為 5.0（1.3）µg/m3，95 百分位為 8.4 µg/m3，低於WHO建議之每日PM2.5暴露限值。為避免暴露於過高的CO2濃度，模擬分析使用AP且加強通風下，若要使學生的 95 百分位暴露值維持在 WHO 指引以下，AP所需流率需增加到9 m3/min。
本研究所提出的方法為估算學童室內PM2.5暴露提供了一項實用且具通用性的技術基礎，並有助於強化環境衛生領域對PM2.5暴露健康影響之評估與相應控制策略的制定。

Given the fluctuations in ambient PM2.5 concentrations and the variability of classroom activities, traditional monitoring approaches are often 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 promising alternative, its application relies on accurate quantification of the indoor PM2.5 generation rate (G) and 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 the WMR model for predicting PM2.5 concentrations inside classrooms. Students' time-activity patterns were further integrated to estimate long-term PM2.5 exposure distributions using Monte Carlo simulations.
The study was conducted in two typical elementary school classrooms in Taiwan—one equipped with two fresh air units (FAUs), and the other with an air purifier (AP). PM2.5 and CO2 concentrations were simultaneously measured inside and outside the classrooms to estimate G and K and evaluate model accuracy. In the FAU-equipped classroom, the geometric means (GM (GSD)) of G were 10.7 (2.1) µg/min and 19.2 (1.2) µg/min during lecture sessions and noon rest periods, respectively. In the AP-equipped classroom, the GM (GSD) of G were 8.7 (3.1) and 23.2 (1.7) µg/min for the respective periods. Higher G values were associated with intensive activities such as chalk usage, cleaning, and student movement.
The K was consistently zero (K = 0) in both classrooms, likely due to the small particle size of PM2.5 and the high air exchange rates provided by both FAUs and the AP. No significant differences were found between the measured and model-predicted PM2.5 concentrations, confirming the accuracy and reliability of the proposed approach.
With FAUs in operation, students’ long-term PM2.5 exposure distribution followed a log-normal distribution, with a GM (GSD) of 8.5 (1.4) µg/m3 and a 95th percentile of 15.1 µg/m3—slightly exceeding the WHO daily guideline of 15 µg/m3. Sensitivity analysis highlighted that outdoor PM2.5 concentrations had the greatest influence on students’ exposure. Scenario-based simulation further showed that to maintain students' 95th percentile exposure below the WHO guideline, the GM of outdoor PM2.5 during school hours must not exceed 15.9 µg/m3.
Under AP operation, the GM (GSD) of student exposure was 5.0 (1.3) µg/m3, and the 95th percentile was 8.4 µg/m3, remaining well below the WHO guideline. Simulation analysis also showed that to maintain students’ 95th percentile exposure below the WHO limit while avoiding excessive CO2 accumulation, the AP would need to be supplemented with a ventilation rate of at least 9 m3/min.
In conclusion, the proposed method offers a practical and generalizable approach for estimating indoor PM2.5 exposures among schoolchildren. The techniques developed in this study will contribute to the field of environmental health science by enabling more effective assessment of health impacts associated with PM2.5 exposures

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