Impact of Architectural Details on the Transmission of Airborne Pollutants between Flats in Residential High-Rise Buildings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it