Detection and Attribution of Wildfire Pollution in the Arctic
Bibliographic record
Abstract
The Arctic experiences poor air quality due to transport of pollutants from mid-latitudes, with wildfires providing an episodic source of trace gases and particulates. We present a multi-year time series of the total columns of CO, HCN, and C2H6 measured using Fourier transform infrared spectrometers at ten sites affiliated with the Network for Detection of Atmospheric Composition Change (NDACC). Six are high-latitude sites: Eureka, Ny Alesund, Thule, Kiruna, Poker Flat, and St. Petersburg, and four are mid-latitude sites: Zugspitze, Jungfraujoch, Toronto, and Rikubetsu. For each site, the inter-annual trends and seasonal variabilities of the CO total column time series are accounted for, allowing ambient concentrations to be determined. Enhancements above ambient levels are then used to identify possible wildfire pollution events. Since the abundance of each trace gas emitted in a wildfire event is specific to the type of vegetation burned and the burning phase, correlations of CO to the long-lived wildfire tracers HCN and C2H6 allow for further confirmation of the detection of wildfire pollution, while complementary measurements of aerosol optical depth from nearby AERONET sites confirms the presence of wildfire smoke. GEOS-Chem tagged CO simulations with Global Fire Assimilation System (GFAS) biomass burning emissions were used to determine the source attribution of CO concentrations at each site from 2003-2017. The influence of the various wildfire sources is found to differ between sites; however, North American and Eurasian boreal wildfires fires are found to be the greatest contributors to episodic CO enhancements in the summertime at all sites.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".