A Fire‐Driven Shift in Canadian Air Quality Concerns Mirrors Trends in the US
Bibliographic record
Abstract
Abstract The summer of 2023 was the most significant wildfire and smoke season on record in Canada. Data from five different satellite instruments going back to 2001 show that Canada and most provinces and territories experienced peak visible‐wavelength aerosol optical depth and ultraviolet aerosol index values in 2023. Longer‐term, 2023 had the highest number of “smoke” or “haze” reports in weather records by a factor of two compared with the previous record in 1981, and by a factor of seven compared with the 1953–2022 average. These reports show an east‐to‐west shift in Canada's summer air pollution patterns. Smoke and haze in eastern Canada have decreased since the 1980s because of pollution control measures domestically and in the US. On the other hand, wildfire smoke has increased in the Northwest Territories, British Columbia, Alberta, and Saskatchewan since the 2010s, and is now the main air quality concern in western Canada. Interpreting the analysis here for Canada alongside previous work over the US, there was a shift over North America in summer air quality concerns from the east to the west. Climate model projections suggest more wildfire‐driven smoke in the future throughout North America, particularly in the west. In contrast to air pollution from smokestacks and tailpipes that can be addressed at the source through government regulation, a future with more wildfire smoke will require downwind mitigation and will be the responsibility of public health officials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".