Analysis of urban wind conditions and wildfire smoke dispersion for downtown Montréal using computational fluid dynamics
Bibliographic record
Abstract
Urban wind conditions and air quality have the potential to affect the majority of the world's population. Specifically, smoke from wildfires is increasingly posing risks to people living in cities as it is transported by the wind, making a more complete understanding of how atmospheric flow affects air quality in urban environments necessary. Computational fluid dynamics is used to investigate the flow and smoke dispersion characteristics in Montréal on July 17, 2023, between 16:00 and 22:00 UTC. This day exhibited moderate southwest winds carrying significant amounts of wildfire smoke into the city. Reynolds-averaged Navier-Stokes simulations using the standard k-ε and Shear Stress Transport k-ω turbulence models are compared against measurements of wind velocity and PM2.5 concentration from an anemometer, a Doppler lidar , and an air quality monitoring station. While the models are shown to accurately predict the urban boundary layer wind profile , only the k-ε model provides satisfactory predictions of wind speed in comparison with the anemometer, stressing the importance of accurately modeling dynamics on the building scale. The effect of the turbulent Schmidt number is investigated, for which the value of 0.6 most accurately reproduces the dispersion phenomena near the air quality monitoring station. Concentrations of the wildfire smoke are found to vary significantly across areas of the city, as some building morphologies are found to direct pollution to regions where it becomes trapped. Additional discussion of local wind and air quality characteristics is presented to better inform citizens of potential risks.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".