Investigating Effects of Meteorology, Built Environment, and Traffic Composition on Near Road Air Pollution in Dense Urban Areas
Bibliographic record
Abstract
This study aimed at capturing the determinants of near-road concentrations of ultrafine particles (UFP) using linear mixed-effects models, investigating the effects of meteorology, built environment, and traffic. In addition, the differences in the levels of UFP between both sides of the road were investigated. To reach these objectives, field measurements were conducted on 16 weekdays in the months of March and April 2015, along Papineau Avenue, a high-volume street in Montreal, Canada. Four sites were identified varying in land use, building height, and road characteristics. Air quality measurements were conducted at each location (on both sides of the road) for two consecutive hours, at four different times during the day and repeated four times, leading to a total of 16 visits per location. Traffic volume and composition was also recorded. On-site meteorological variables including wind speed, wind direction, temperature and relative humidity were collected using a portable weather station. Linear mixed-effects models with random intercept were developed for both dependent variables: the natural logarithm of the mean UFP concentration and the difference in UFP concentrations between two sides of the road. Lower temperatures and wind speeds were associated with increased UFP concentrations. Winds orthogonal to the road tended to increase UFP concentrations as well as the differences between both sides of the road. Finally, built environment variables such as the presence of open areas and buildings on both sides of the road, had a positive influence on the difference between UFP on the two sides.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".