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 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 | 0.000 | 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".