Exploring high-rise preventive ventilation: Experimental investigation of inter-zone air pressurization with tracer gas analysis
Bibliographic record
Abstract
Over the past four years, five major variants of the SARS-CoV-2 virus have circulated globally, causing seven million deaths. Meanwhile, specific preventative ventilation measures were recommended to minimize exposure to the aerosol infection risk, such as opening doors, and disabling ventilation systems. Given more attention to high-rise buildings, where quite a few outbreaks have been reported, there is a notable lack of reported studies assessing preventive ventilation measures. Therefore, this study focused on practicing several ventilation measures in a 16-story high-rise building located in Montreal, Canada. A variety of inter-zonal tracer measurements, including room-to-floor and inter-floor tests, were carried out considering controlled airflow/pressure across zones. An automated data acquisition system was developed for real-time monitoring of tracer/CO2 spatial concentrations. Findings show that enabled ventilation with opened doors reduces CO2 concentrations by up to 82% in nearby source locations and positively pressurized zones; however, negatively pressurized zones experience a 40% increase in tracer exposure. With ventilation active, a corridor source leads to a higher risk of tracer exposure (up to 86%) than the in-room source. Disabled ventilation with closed doors is a recommended ventilation measure, for corridors with infection sources, to minimize tracer transport across zones. Zones with infection sources follow the latter ventilation measure; however, ventilation with open doors offers a conditional ventilation measure if careful airflow is designed particularly at return and exhaust points within the ventilation system. This study has implications for practicing preventative ventilation measures for post-COVID-19 building practices and design considerations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".