Within-city spatial variations of novel air pollution exposure metrics and their relationship with cardiovascular mortality and brain cancer incidence in the Canadian urban environment
Bibliographic record
Abstract
Outdoor air pollution, including fine particulate matter (PM2.5)air pollution, contributes to a range of adverse health outcomes and has a large population health impact.However, the standard method of measuring exposures to particulate air pollution as a mass concentration has limitations.Recently, emerging measures have been developed that account for the composition and toxicity of particles.The overall aim of this thesis was to describe within-city spatial variations in newly-developed measures of particle composition and toxicity (including multiple measures of particle oxidative potential as well as a measure of exposure to magnetite nanoparticles) across Canadian urban areas and to assess their effects on long-term health outcomes.To accomplish this aim, we completed three objectives that constitute the body of this manuscript-based thesis.In Objective 1, we conducted monitoring campaigns at 124 sites in Montreal and 110 sites in Toronto, Canada to collect pollutant data, and developed land-use regression models to predict the spatial distributions of PM2.5 oxidative potential, production of reactive oxygen species, and magnetite nanoparticles.We used Bayesian lasso regression models with land-use characteristics from Geographic Information Systems databases to predict pollutant measures at unobserved points in order to create high-resolution exposure surfaces.We observed high spatial variability of oxidative potential measures (coefficients of variation 42.0-66.0%)and magnetite (coefficients of variation 69.7-75.4%)within each city relative to PM2.5 mass concentration (coefficients of variation 24.3-30.8%).Multivariable land-use regression models predicted elevated concentrations of oxidative potential, reactive oxygen species generation, and magnetite around highways, railways, and road intersections.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".