The Measured Impact of Wildfires on Ozone in Western Canada From 2001 to 2019
Bibliographic record
Abstract
Abstract The impacts on atmospheric ozone (O 3 ) due to wildfires are difficult to characterize due to the many factors that affect O 3 's formation rate and the episodic nature of fire events. This study uses a very large set of air quality data (518,987 6‐hr data points) collected in Western Canada from 2001 to 2019 to determine the prevalence and severity of fire‐driven increases to measured O 3 values. Wildfire events are identified using the automated Trajectory‐Fire Interception Method (TFIM), looking for interceptions between HYSPLIT back‐trajectories and wildfire hotspots. As with other studies, which have used more restricted sets of measurements, the results from this large‐scale, data‐driven approach indicate increases in the O 3 mixing ratio with wildfire impact, on average ∼2 ppbv across all wildfire time periods. To understand the factors which lead to the largest increases, and to better compare to other studies looking at individual fire events, wildfire events are classified using their distance from the air quality measurement location, time of measurement, and corresponding PM 2.5 value. Increases to O 3 are largest during the daytime, when fires occur close to the air quality measurement, and with corresponding measurements of PM 2.5 > 25 μg/m 3 . When an upper‐limit correction for the bias in UV photometric detection of ozone with MnCl 2 scrubbers is applied, the analysis still yields a persistent increase in O 3 during wildfires except for the highest PM 2.5 levels. However, a more accurate correction to the potential bias is needed to fully understand the magnitude of the impact of wildfires on O 3 .
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".