Influential parameters of near-roadway ultrafine particles along open patio spaces
Bibliographic record
Abstract
The objective of this thesis is to identify factors related to meteorology and the built environment and its influence on near-roadway concentrations of ultrafine particles (UFP) in the context of restaurants and bars located within public open air spaces. The question that must be posed is how are variations in weather, traffic, on-site building characteristics impacting levels of UFP concentration, which in turn may produce adverse health effects for restaurant patrons as well as individuals working within hospitality industries. To facilitate this study, a UFP monitoring campaign was organized and conducted during the months of May to July to measure at eight selected sites situated in Montreal, Canada. The eight study sites, chosen to reflect varying land use compositions and building characteristics, were each visited four times during lunch and dinner hours, where a site would be visited a total of two times (once per time period) during the weekday, and two times (once per time period) during the weekend. This campaign used meteorological information collected at the Montreal-Pierre Elliott Trudeau International Airport and at an automated weather station reporting from downtown Montreal. Traffic-related variables were derived from manual vehicle counts carried out at each study location, and the analysis of land use and other built characteristics relied on geographic information systems (GIS) data extracted from Transportation Research at McGill (TRAM), a transportation research group based at McGill University. The investigation following the conclusion of the data collection campaign began by developing linear mixed effect models centred on variables relating to meteorology, traffic, site characteristics, and temporal factors. This data enables the validation of trends already defined in previous academic literature as well as to identify key findings that are contrary to intuitive hypotheses in the context of this study. Of note, meteorological effects such as temperature, wind speed, and wind direction relative to the orientation of the street are proven to possess inverse relationships with UFP. Homogeneity of land use is also an influential parameter and of significance with respect to this study of mixed use microenvironments. Furthermore, traffic-based predictors, most notably that of total diesel vehicles, contributed marginally to their concentrations. The conclusions reached from this study, along with what is already known about ultrafine particles, allows for dialogue pertaining to optimizing the effectiveness of mixed use neighbourhoods from a health perspective in the context of open air patios. Provisional recommendations to improve the decision-making processes involved with planning and engineering such neighbourhoods are discussed and is critical for understanding the health and well-being of urban residents. Keywords: air pollution exposure, environmental monitoring, land use, public health, restaurant patios, ultrafine particles
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".