Observations of Extreme Wildfire VOC Enhancements over the Canadian High Arctic
Bibliographic record
Abstract
Wildfires are a common occurrence in many parts of the globe and can emit significant quantities of trace gases and particulate matter, negatively impacting air quality on large spatial scales. Among the various trace gases emitted by wildfires are volatile organic compounds (VOCs). Four VOCs that are of particular importance are methanol (CH3OH), formic acid (HCOOH), peroxyacetyl nitrate (PAN), and ethylene (C2H4). These reactive VOCs can have a variety of negative impacts on the atmospheric chemistry and environment of remote regions including influencing trace gas budgets, impacting atmospheric acidity, and contributing to the ‘Arctic haze’ pollution phenomenon.During August 2017, two independent large-scale wildfires in British Columbia and the Northwest Territories of Canada generated vast smoke plumes that merged and were subsequently transported to the high Arctic. Simultaneous observations by a high-resolution ground-based Fourier transform infrared (FTIR) spectrometer at the Polar Environment Research Laboratory (PEARL) in Eureka, Nunavut (80.05°N, 86.42°W), and the Infrared Atmospheric Sounding Interferometer (IASI) satellite instruments display extreme enhancements in these species relative to background concentrations during the fire-affected period in late August 2017, demonstrating the long-range transport and secondary formation of these typically short-lived species. Comparisons of observations with the GEOS-Chem global chemical transport model illustrate that this exceptional wildfire event contributed to a substantial perturbation to the VOC budget of the high-Arctic atmosphere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".