Seasonal Cycle of Isotope‐Based Source Apportionment of Elemental Carbon in Airborne Particulate Matter and Snow at Alert, Canada
Bibliographic record
Abstract
Abstract Elemental carbon (EC) is a major light‐absorbing component of atmospheric aerosol particles. Here, we report the seasonal variation in EC concentrations and sources in airborne particulate matter (PM) and snow at Alert, Canada, from March 2014 to June 2015. We isolated the EC fraction with the EnCan‐Total‐900 (ECT9) protocol and quantified its stable carbon isotope composition (δ13C) and radiocarbon content (∆14C) to apportion EC into contributions from fossil fuel combustion and biomass burning (wildfires and biofuel combustion). Ten‐day backward trajectories show EC aerosols reaching Alert by traveling over the Arctic Ocean from the Russian Arctic during winter and from North America (>40°N) during summer. EC concentrations range from 1.8–135.3 ng C m−3 air (1.9–41.2% of total carbon [TC], n = 48), with lowest values in summer (1.8–44.5 ng C m−3 air, n = 9). EC in PM (Δ14C = ‐532 ± 114‰ [ave. ± SD, n = 20]) and snow (−257 ± 131‰, n = 7) was depleted in 14C relative to current ambient CO2 year‐round. EC in PM mainly originated from liquid and solid fossil fuels from fall to spring (47–70% fossil), but had greater contributions from biomass burning in summer (48–80% modern carbon). EC in snow was mostly from biomass burning (53–88%). Our data show that biomass burning EC is preferentially incorporated into snow because of scavenging processes within the Arctic atmosphere or long‐range transport in storm systems. This work provides a comprehensive view of EC particles captured in the High Arctic through wet and dry deposition and demonstrates that surface stations monitoring EC in PM might underestimate biomass burning and transport.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".