The Impact of the 2023 Canadian Forest Fires on Air Quality in Southern Ontario
Bibliographic record
Abstract
Abstract The record‐breaking 2023 Canadian wildfire season had large‐scale burning that resulted in wide‐reaching long‐range transport of smoke plumes and their associated trace gases. This paper examines three events (May 16‐23, June 3‐9 and June 17‐30, 2023) during which the composition of smoke was measured over Toronto and Egbert, Ontario. Tropospheric columns (0–10 km) of CO, C 2 H 6 , CH 3 OH, HCN, HCOOH, NH 3 and O 3 were measured using high‐resolution Fourier transform infrared spectrometers. Coincident enhancements of CO and other gases during the events were used to calculate enhancement ratios. Correlations with CO were observed for C 2 H 6 , CH 3 OH, HCN and HCOOH, but not for NH 3 and O 3 . Plume transport was investigated with the Hybrid Single‐Particle Lagrangian Integrated Trajectory model, the GEM‐MACH‐FireWork (GM‐FW) air quality model, and Measurements of Pollution in the Troposphere (MOPITT) CO satellite data. Additional measurements examined were surface CO, O 3 , and PM 2.5 , plume height from a Mini Micro Pulse Lidar, and EM27/SUN XCO columns. GM‐FW model output was compared with ground‐based surface and 0–10 km column measurements, and MOPITT CO maps. Over the 2023 forest fire season (May‐September), the model underestimated background tropospheric columns of CO, NH 3 and O 3 , but generally overestimated enhancements during smoke events. Relative to surface in situ measurements, GM‐FW seasonal averages overestimated CO and underestimated O 3 (which was not generally enhanced during smoke events), while PM 2.5 fluctuated between a positive and negative bias. Compared to MOPITT, the GM‐FW event‐averaged CO columns appropriately represent plume dispersion across the country, with some offsets on the scale of the ground‐based locations that are consistent with the discussed findings.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".