Observations of Extreme Wildfire Enhancements of CH3OH, HCOOH, and PAN 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). Three VOCs that are of particular importance are methanol (CH3OH), formic acid (HCOOH), and peroxyacetyl nitrate (PAN). CH3OH is the one of the most abundant VOCs in the atmosphere, and it influences the budgets of many tropospheric species including the hydroxyl radical, carbon monoxide, formaldehyde, and ozone. HCOOH is the most abundant tropospheric carboxylic acid, and thus can have significant impacts on atmospheric acidity, particularly in remote regions such as the Arctic. Lastly, PAN is a key, thermally unstable reservoir species of tropospheric nitrogen radicals (NOx = NO + NO2), controlling the production of tropospheric ozone, and contributing to the ‘Arctic haze’ pollution phenomenon at high latitudes.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 three 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. Initial results of the analysis of this unique biomass burning event will be presented, including comparisons of observations with the GEOS-Chem global chemical transport model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".