Investigating the effects of meteorology, built environment and traffic on near road air pollution
Bibliographic record
Abstract
Metropolitan areas across the world have witnessed serious threats of increased dependence on vehicles as a result of rapid urbanization and changing land use patterns, leading to more people living and working in close proximity to busy highways and roads. As traffic-induced air pollution has been associated with health effects in epidemiological studies, investigations of traffic emissions and their dispersion in urban environments remain of utmost significance. This thesis aims at capturing the determinants of near-road concentrations of ultrafine particles (UFP) in the context of an urban canyon using linear mixed-effects models, and investigates the effects of meteorological conditions, built environment, land use and traffic flow. In addition, the absolute differences in the levels of UFP between both sides of the road were investigated. To reach these objectives, a data collection campaign was conducted on 16 weekdays in the months of March and April of 2015, along Papineau Avenue, a high-volume street in Montreal, Canada which runs north-south and serves as an important arterial road connecting the neighbouring cities of Laval and Longueuil with the Island of Montreal. Four data collection sites varying in land use, building height and road characteristics were identified, and air quality measurements were randomly scheduled to be conducted four days per week on the principle of each segment measured once in a one time period each day. On-site meteorological variables such as street level wind speed, wind direction, temperature and relative humidity were also collected by using a portable weather station, and a set of built environment characteristics were generated with geographical information systems (GIS). In addition, the traffic information regarding vehicle volumes and vehicle composition were counted manually. Two quantitative investigations were conducted. Linear mixed-effects models with random intercept were developed to explain both dependent variables: the natural logarithm of the mean UFP concentration and the absolute difference of UFP concentrations between two sides of the road. Notable findings include the association of lower temperatures and wind speeds with increased UFP concentrations. Furthermore, winds orthogonal to the road and lower relative humidity were shown 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. Together, these investigations of UFP concentrations help explain its interactions with meteorology, site characteristics, and traffic flow. Keywords: ultrafine particles; near-road air pollution; urban canyon; linear mixed-effects regression
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".