Development of longitudinal datasets (2000–2020) with high spatiotemporal resolution for air pollution exposure assessment in Canada
Bibliographic record
Abstract
We developed datasets intended to aid and inform, health and epidemiological studies in Canada by providing highly resolved spatiotemporal concentrations for regulated air pollutants (fine particulate matter, nitrogen dioxide, and ozone) across Canada. Daily estimates were generated at various spatial resolutions for the years 2000 through 2020. The datasets are based on simulations of the US EPA’s Community Multiscale (CMAQ) model at 12 km horizontal resolution. In an effort to increase the accuracy and spatial resolution of the exposure estimates, especially in complex urban environments, we used statistical and machine learning methods to downscale CMAQ outputs to finer resolutions. Downscaling relies on raw CMAQ results, high resolution land-use datasets, existing concentrations datasets, and observations from National Air Pollution Surveillance (NAPS) network. Widely used machine learning (ML) algorithms like random forest and gradient boosting were chosen and proved to be promising. Generated datasets at various spatial resolutions (census divisions, postal codes, gridded 12 km, or 1 km) showed adequate statistical performance and clear representations of spatial features associated with pollutant emissions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".