Reactive nitrogen partitioning fuels contribution of Canadian wildfire plumes to U.S. ozone air quality
Bibliographic record
Abstract
Accurately quantifying wildfire impacts on ozone air quality is challenging due to complex physical and chemical processes in wildfire smoke. Here we use measurements from the 2018 WE-CAN aircraft campaign to parameterize emissions of reactive nitrogen (NOy) from wildfires into PAN (37%), NO3- (27%), and NO (36%) in a global chemistry-climate model with 13 km horizontal resolution over the contiguous US. The NOy partitioning, compared with emitting all NOy as NO, reduces model ozone bias in near-fire smoke plumes sampled by the aircraft but significantly enhances ozone downwind when Canadian smoke plumes reach cities in Washington state, Utah, Colorado, and Texas. Using multi-platform observations, we identify the smoke-influenced days with daily maximum 8-h average (MDA8) ozone of 70-85 ppbv in Spokane, Salt Lake City, Denver and Dallas. On these days, mixing of wildfire smoke into urban pollution enhance simulated MDA8 ozone by 10–20 ppbv.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".