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Record W7010730707

Investigating the effects of the urban environment on cyclist exposure to near-roadway air pollution

2014· dissertation· en· W7010730707 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2014
Typedissertation
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAir pollutionContext (archaeology)PollutionPollutantAir pollutantsUrban areaAir quality indexUrban environment
DOInot available

Abstract

fetched live from OpenAlex

This thesis seeks to understand how the built environment and surrounding land use affect variations in near-roadway air pollution. In particular, two air pollutants are investigated: ultrafine particles (UFP) and black carbon (BC). Specifically, the study explores this question in the context of urban cycling. That is, how do factors such as traffic, buildings, and cycling infrastructure affect the concentration of air pollution to which cyclists are exposed?These answers are sought by way of a large-scale environmental monitoring campaign on the Island of Montreal during the summer of 2012. The campaign is comprised of two components: a mobile measurement portion whereby bicycles equipped with air pollution monitoring equipment cycle across roughly 500 km of unique roadway collecting UFP and BC concentrations, paired with global positioning system (GPS) data which allowed air pollution levels to be associated with the street on which they were collected—and a fixed site monitoring portion whereby research assistants measured pollution and counted traffic volumes and composition for set intervals of time at 73 locations. The overarching objective was to explain the variations in air pollution concentration by meteorological and built environment data, using land-use regression (LUR) analysis techniques. The mobile analysis relies primarily on geographic information systems (GIS) based land-use data while the fixed site analysis relies primarily on field measurements.Several investigations follow from this general data collection campaign. From the fixed site data a LUR model is developed based on meteorological factors, vehicular volumes and compositions, and built environment characteristics of the roadway corridor. These data also form the basis of a secondary investigation which explains the differences in UFP levels on opposite sides of the same street using wind, urban canyon, and traffic characteristics. Notable findings include support for some meteorological and urban canyon effects on air pollution, the relevance of both vehicular volumes as well as the truck component thereof.Two investigations arise from the mobile data collection campaign. The first attempts to explain the variations in air pollution using meteorological, land-use, and roadway characteristics, including results from a mesoscopic traffic simulation. The second seeks to understand how the nature of cycling infrastructure and cycling network design affect cyclists’ exposure to pollution. In addition to the effects captured in the fixed site analysis, relevant observations include the strong effects of nearby highways, especially for BC, and nearby restaurants, especially for UFP. The latter investigation from the mobile campaign shows that cycling facilities along major roads tend to have higher levels of pollution, however separated cycling infrastructure did reduce exposure to BC, perhaps owing in part from their greater distance from the street centerline. However the strongest reductions in air pollution were observed on multi-use trails, which typically run through parks and are located at substantial distances from the street.Together, these investigations use a novel methodological framework to unravel the interactions between cycling, the built environment, land use, traffic, and air pollution.Keywords: black carbon; cycling infrastructure; cycling facilities; cyclist exposure; environmental monitoring; land use regression; air pollution exposure; ultrafine particles

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.202
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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