Evaluating indoor aerosol concentrations and size distributions under varying individual behaviors, ventilation and air purifier conditions
Bibliographic record
Abstract
Quantifying aerosol concentrations in indoor environments is essential for mitigating airborne transmission of respiratory pathogens. This study aimed to characterize the effects of speaking, mask use, and gender on aerosol concentrations in a controlled chamber, and to assess mitigation strategies in a typical shared space. In a controlled 12 m³ chamber, six participants (three males, three females) completed four 20 min sessions—natural breathing and reading aloud, each with and without a surgical mask. Aerosol size-resolved concentrations (0.25–33 µm) were measured every 6 s using an optical particle counter. A mixed-effects model applied at the 5 % significance level showed that speaking increased concentrations by 26 % ( p < 0.001), while surgical masks reduced it by 63 % ( p < 0.001). Male participants emitted 38 % more aerosols than females, though the difference was not statistically significant ( p = 0.079). A two-mode lognormal model revealed a dominant submicron mode (Count Median Diameters (CMD) = 0.20–0.24 µm) and a secondary mode (CMD = 1.97–2.45 µm), consistent with lower and upper respiratory tract origins. Most particles fell in the submicron range. To evaluate broader implications, a complementary study was conducted in a 31.2 m³ graduate student office with four occupants under ten scenarios combining ventilation, air purification, and mask use. Without ventilation, CO₂ exceeded 1000 ppm within 20 min. The air purifier reduced aerosol concentrations, especially in the submicron range, while ventilation effectively controlled CO₂. Mask use had limited impact in this setting, where movement and surface activity dominated concentrations. These findings highlight the importance of concurrently implementing ventilation and air purification to manage aerosol and CO₂ levels in shared indoor environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".