Validation of a PUFF Dispersion Model: Air Quality Simulation for New Highway Infrastructure
Bibliographic record
Abstract
This study focuses on the validation of the CALMET-CALPUFF modelling system to be ultimately used as a tool for evaluating the potential air quality impacts of a new highway extension. The authors describe the development and validation of dispersion modelling along a 3.6 kilometer segment of highway 25 in the city of Montreal, Canada. For this purpose, hourly traffic data were obtained for one week in January 2012 (January 1-6), emissions of PM₂.₅ were modelled while accounting for traffic composition, and hourly PM₂.₅ concentrations were simulated and validated against measurements taken for the same time period at a highway monitoring station. The results show a reasonable performance of the dispersion model with a 0.84 correlation between simulated and observed concentrations. The simulated concentrations are often lower than the observed concentrations partly due to the fact that the emissions of other roads are unaccounted for. The authors also demonstrate the importance of dispersion modelling in evaluating the effects of changes in highway emissions on the resulting concentrations by illustrating the weak relationship between emission changes and the resulting concentration changes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".