Detection of the long-range transport of wildfire pollution to the Arctic using a network of ground-based FTIR spectrometers, satellite observations and model results
Bibliographic record
Abstract
We present a multi-year time series of the total columns of carbon monoxide (CO), hydrogen cyanide (HCN) and ethane (C2H6) obtained by Fourier Transform Infrared (FTIR) spectrometer measurements at nine sites. Six are high-latitude sites: Eureka, Nunavut; Ny Alesund, Norway; Thule, Greenland; Kiruna, Sweden; Poker Flat, Alaska and St. Petersburg, Russia and three are mid-latitude sites; Zugspitze, Germany; Jungfraujoch, Switzerland and Toronto, Ontario. For each site, the inter-annual trends and seasonal variabilities of the CO total column time series are accounted for allowing for 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 species emitted in a wildfire event is specific to the type of vegetation burned and the burning phase, correlations of CO to the other long-lived wildfire tracers HCN and C2H6 allow for further confirmation of the detection of wildfire pollution. Back-trajectories from HYSPLIT and FLEXPART as well as fire detections from the Moderate Resolution Spectroradiometer (MODIS) allow the source regions of the detected enhancements to be determined while satellite observations of CO from the Measurement of Pollution in the Troposphere (MOPITT) and Infrared Atmospheric Sounding Interferometer (IASI) instruments can be used to track the transport of the smoke plume. Differences in travel times between sites allows ageing of biomass burning plumes to be determined, providing a means to infer the physical and chemical processes affecting the loss of each species during transport. Comparisons of ground-based FTIR measurements to GEOS-Chem chemical transport model results are used to investigate these processes, evaluate wildfire emission inventories and infer the influence of wildfire emissions on the Arctic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".