Air Quality Data Approach for Defining Wildfire Influence: Impacts on PM<sub>2.5</sub>, NO<sub>2</sub>, CO, and O<sub>3</sub> in Western Canadian Cities
Bibliographic record
Abstract
As the climate warms, it is recognized that wildfires are increasing in size and frequency. The negative effects of wildfires on air quality are well documented, especially on commonly monitored atmospheric pollutants such as PM 2.5, NO 2, CO, and O 3 . However, it is not clear how frequently wildfires influence urban air quality and the size of that influence relative to traffic and industrial pollutants. To understand the impact of wildfires on air quality, we have established an automated method to identify wildfire-influenced ambient air measurements. The trajectory-fire interception method (TFIM) compares hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) back-trajectories from an air quality monitoring station to satellite imagery of fire “hot-spots” to determine the number of trajectory-fire interceptions that occur. From the number of interceptions and local PM 2.5 measurements, we have defined a wildfire-influenced period to occur if the interception count is ≥20. TFIM wildfire identification compares favorably with Environment and Climate Change Canada’s smoke forecast, FireWork, and with the BlueSky trajectory-based forecast. Using TFIM, we studied the impact of wildfire-influenced periods on PM 2.5, NO 2, CO, and O 3 from 2001 to 2019 in Western Canadian urban areas. We show that wildfire-influenced periods have elevated concentrations of PM 2.5, NO 2, and CO but not O 3 . We show that a decreasing urban baseline of CO and NO 2 over time results in a relatively greater impact of wildfires on these pollutants, which emphasizes the changing relative importance of wildfires on air quality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".