Eighteen years of daily PM2.5 predictions (2005–2022) for a region of western Canada: Machine learning and satellite inputs for applications in rural health
Bibliographic record
Abstract
ABSTRACT Northeastern British Columbia is a rural and remote region in Western Canada that is experiencing rapid growth in unconventional oil and gas development (UOGD) and increasingly severe wildfire impacts. Air quality is a concern for the communities in the region, but there is very limited air pollution monitoring. To address this gap, this study explores the application of machine learning to the satellite-based estimates of aerosols and meteorology from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) to estimate daily fine particulate matter (PM 2.5 ) at a spatial and temporal resolution relevant for ongoing health studies. The random forest model was trained and validated on the ground-level air quality monitoring network from 2013 to 2022 and then used to predict and backcast daily concentrations in the study area at a 50-kilometer resolution from 2005 to 2022. The predictions were then compared to global and provincial health guidelines and analyzed for annual trends. Our model achieved a 10-fold cross validation root mean square error (RMSE) and R 2 of 3.89 μg/m 3 and 0.77, and test scores of 3.02 μg/m 3 and 0.78. Between 2006 and 2021, the number of days exceeding PM 2.5 guidelines increased by 122%, and the person-days exceeding the guideline increased by 166%. At our spatial resolution, we find that wildfire is a more important variable in predicting daily PM 2.5 concentrations compared to UOGD. We demonstrate the application of satellite reanalysis products to estimate ground-level PM 2.5 in a rural area of Canada with minimal ground monitoring stations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".