Photochemical and Other Sources of Organic Compounds in the Canadian High Arctic Aerosol Pollution during Winter−Spring
Bibliographic record
Abstract
Total suspended particles collected at Alert in the Canadian high Arctic (February-June) were analyzed for solvent extractable organic compounds using gas chromatography-mass spectrometry to better understand the sources and source apportionment of aerosol pollution that can affect the Arctic climate. More than 100 organic species were detected in the aerosols and were grouped into different compound classes based on the functional groups. Polyacids were found to be the most abundant compound class, followed by phthalates, aromatic acids, fatty acids, fatty alcohols, sugars/sugar alcohols, and n-alkanes, while polycyclic aromatic hydrocarbons, sterols, and lignin and resin acids were minor. Concentrations of total quantified organics seemed slightly higher in darkwinter aerosols (13.2-16.6 ng m(-3), average 14.5 ng m(-3)) than those after polar sunrise (6.70-17.7 ng m(-3), average 11.8 ng m(-3)). During dark winter, fossil fuel combustion products (30-51%), secondary oxidation products, as well as higher plant emissions were found as major contributors to the Arctic aerosols. However, after polar sunrise on 5 March, secondary oxidation products (5-53%) and plasticizer-derived phthalates became the dominant compound classes, followed by fossil fuel combustion and microbial/marine sources. Biomass burning emissions were found to contribute only 0.4-6% of the total identified organics, although they maximized in dark winter. This study demonstrates that long-range atmospheric transport, changes in the solar irradiance, and ambient temperature can significantly control the chemical composition of organic aerosols in the Arctic region.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".