Impact of wildfires on air quality as seen by IAGOS in-situ measurements
Bibliographic record
Abstract
For thirty years, the European Research Infrastructure IAGOS (In-Service Aircraft for a Global Observing System) has been equipping commercial aircraft with instruments to measure atmospheric composition on long-haul flights around the world. Ten aircraft are currently equipped with IAGOS instruments to measure ozone, and the precursors carbon monoxide and nitrous oxides from the surface to the upper-troposphere during landing and take-off at worldwide airports, and at cruise altitude where we observe the long-range transport of polluted airmasses. We analyse the transport of biomass burning pollutants from the intense Canadian wildfire seasons of 2023 and 2024 which impacted air-quality in North America and in Europe, and the extreme wildfires over the Amazon in 2024 that impacted air quality in South American cities. The significance of these events is interpreted within the context of the 30-year climatology. The events will be compared with forecasts and analyses from the Copernicus Atmosphere Monitoring Service's global and regional models (projects CAMS2_82 and CAMS2_83) and we further highlight the role of IAGOS in developing air-quality networks in susceptible urban areas (project RI-URBANS) and the impacts of heatwaves and wildfires on air-quality in a changing climate (project IRISCC).
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".