Impact of Architectural Details on the Transmission of Airborne Pollutants between Flats in Residential High-Rise Buildings
Bibliographic record
Abstract
The interflat cross-contamination of air pollutants such as coronavirus disease 2019 (COVID-19) in built environments has become a growing concern. This study investigates the effect of the geometrical details of building facades on wind-induced airborne pollutant transmission routes in high-rise buildings. Parametric studies of different external-shading elements of buildings, wind speeds, and wind directionality are considered. A high-resolution computational fluid dynamics (CFD) using a realizable k-epsilon turbulence closure model is employed to analyze the airflow field. For the windward single-sided ventilation case, the reentry ratio from the source room to the other unit under prevailing, 45°, and 90° wind directions are quantified, and the possible interflat cross-contamination routes are simulated. The transmission route is highly dependent on a building's architectural features, wind speed, wind directionality, and location of the source room. The result shows that external shading plays a crucial role in mitigating or accelerating airborne pollutant transmissions. A building with horizontal shadings restricts vertical cross-contamination between flats, but it allows horizontal interflat cross-contamination. However, buildings with vertical shadings reduce the risk of horizontal cross-contamination but increase the probability of vertical cross-contamination. The egg-crate shading minimizes the risk of both horizontal and vertical cross-contamination. A smooth facade building is highly susceptible to cross-contamination for a wide range of wind directionality. Therefore, this study is helpful for architecture and building science for the analysis of airborne pollutants, for tracing the routes of cross-contamination in residential buildings, and for reducing the risk of transmission of respiratory diseases such as COVID-19.
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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) | 0.000 | 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".