Flow physics of air pollutants dispersion: A case study in an urban street canyon in Baghbazar, Kathmandu
Bibliographic record
Abstract
The alarming increase of hazardous pollutants in South Asian cities such as Kathmandu, Delhi, Mumbai, Dhaka, etc. risks the life of every individual there. A major source in the production of such harmful pollutants is vehicles and industries. A Computational Fluid Dynamics (CFD) approach is proposed to model the pollutants emitted by vehicles using different Reynolds Averaged Navier Stokes (RANS) turbulence models in the street canyon setup. The primary aim of the study is to understand the effect of turbulence on the transport of gaseous pollutants in the street canyon. RNG k − ϵ model performed best among other turbulence models with mean FB -0.105, NMSE 0.045, FAC2 0.915 and R 0.93 which is validated with the experimental data. Higher wind speeds allowed the concentration to disperse more effectively in Baghbazar’s street canyon, resulting in lower concentrations of pollutants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".