An investigation of the impact of Canadian wildfires on US air quality using model, satellite, and ground measurements
Bibliographic record
Abstract
Canadian wildfires transport large concentrations of particulate matter into the US, leading to various effects on the surface temperature, the radiation balance, and visibility and exacerbating pollution-related respiratory conditions. Using a combination of surface, satellite, and numerical models, this study quantifies the increase in surface fine particulate matter (PM 2.5 ) in the continental US due to long-range transported smoke from Canadian wildfires during a wildfire episode from 9–25 August 2018. As a widely used indicator of surface pollution levels, satellite-retrieved aerosol optical depth (AOD) can provide crucial information on columnar pollution mass. However, the daily spatial coverage of satellite AOD is restricted due to cloud cover. In order to quantify the daily changes in surface pollution, we fill in the AOD gaps by utilizing simulated 10 km spatial resolution AOD from a chemistry transport model (CTM). Meteorological variables influencing smoke transport were also integrated alongside the gap-filled AOD product to estimate surface PM 2.5 using geographically weighted regression (GWR) and random forest (RF) models. The model with better performance was subsequently applied to quantify PM 2.5 changes due to Canadian wildfires. To isolate the impact of Canadian wildfires, we calculate the surface PM 2.5 ratio with and without Canadian fire sources by conducting two CTM simulations: one with Canadian wildfire emissions enabled and another with these emissions turned off. Our results show that Canadian wildfires caused a significant increase in surface PM 2.5 , contributing up to 28 µg m −3 (a 69 % increase) across different US Environmental Protection Agency (EPA) regions during the August 2018 wildfire event.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".