Multizone Modeling of Airborne Quanta Transmission and CO2-based Ventilation Designs for Assessing Indoor Exposures
Bibliographic record
Abstract
In indoor environments, ventilation is essential for diluting or removing contaminants, pathogens, excess heat, and moisture, thereby ensuring a healthy and comfortable space. The COVID-19 pandemic underscored the critical role of ventilation in controlling airborne respiratory infections indoors. During this period, inadequate ventilation systems and improper operations in densely populated public spaces were frequently linked to outbreaks and superspreading events, heightening concerns over indoor exposure risks for occupants. As COVID-19 restrictions begin to relax globally, the focus is transitioning to long-term management strategies for the virus. This transition necessitates a comprehensive understanding of the specific ventilation requirements for various indoor spaces. It is imperative to swiftly and accurately assess ventilation conditions and consistently ensure an adequate supply of clean air. This study focuses on mitigation strategies to reduce indoor exposure risks and prepare for the post-pandemic era. The multizone CONTAM modeling of aerosol transport under different mechanical mitigation strategies was investigated in five DOE prototype buildings. To utilize field evidence for improving indoor air quality, a novel approach integrating Bayesian inference and stochastic CO2 grey-box models was applied. This approach was used to evaluate the ventilation conditions within two primary school classrooms in Montreal. The Equivalent Clean Airflow Rate (ECAi) was calculated following ASHRAE 241, revealing an insufficient clean air supply in both classrooms. To achieve a sufficient ECAi, an additional 0.38 m3/s of clean air delivery rate (CADR) from air-cleaning devices is recommended. Finally, steady-state CO2 thresholds (Climit, Ctarget, and Cideal) were established to indicate when ECAi requirements could be achieved under various mitigation strategies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".