Summer PM<sub>2.5</sub> Pollution Extremes Caused by Wildfires Over the Western United States During 2017–2018
Bibliographic record
Abstract
Abstract Using observations and model simulations (ESM4.1) during 1988–2018, we show large year‐to‐year variability in western U.S. PM2.5 pollution caused by regional and distant fires. Widespread wildfires, combined with stagnation, caused summer PM2.5 pollution in 2017 and 2018 to exceed 2 standard deviations over long‐term averages. ESM4.1 with a fire emission inventory constrained by satellite‐derived fire radiative energy and aerosol optical depth captures the observed surface PM2.5 means and extremes above the 35 μg/m3 U.S. air quality standard. However, aerosol emissions from the widely used Global Fire Emissions Database (GFED) must be increased by 5 times for ESM4.1 to match observations. On days when observed PM2.5 reached 35–175 μg/m3, wildfire emissions can explain 90% of total PM2.5, with smoke transported from Canada contributing 25–50% in northern states, according to model sensitivity simulations. Fire emission uncertainties pose challenges to accurately assessing the impacts of fire smoke on air quality, radiation, and climate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".