Airborne Measurements of NO2, HONO, and HCHO Emissions from Canadian Wildfires and the Oil Industry
Bibliographic record
Abstract
In August 2023 and September 2024, King’s College London (KCL) conducted airborne campaigns in Canada to investigate emissions from wildfires and the oil industry. The British Antarctic Survey (BAS) Twin Otter aircraft was equipped with an array of in-situ and remote sensing instruments, including the SWING instrument. Developed by the Royal Belgian Institute for Space Aeronomy (BIRA-IASB), SWING is a compact whiskbroom imager designed to map trace gases that absorb in the UV-visible spectral range (300-550 nm).We present the integration of SWING into the BAS Twin Otter and its operations during the airborne campaigns. On 14 and 19 August 2023, the aircraft sampled the plume from a wildfire in Ontario, measuring NO2. On 11 September 2024, the aircraft flew over a fire in Saskatchewan, where we detected NO2 together with HCHO and HONO. In the same 2024 campaign, we observed NO2 emissions from flaring at oil facilities in Alberta: Fort McMurray, Fort McKay, and from chemical plants near Edmonton. These airborne measurements are compared with satellite-based air quality data from TROPOMI and TEMPO, for which we are close to the northern edge of the field of view. We also estimate the HONO/NO2 from the fire and investigate how such measurements may help to quantify the emissions from wildfires and from natural gas flaring.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".