Distributions of Polycyclic Aromatic Hydrocarbons, Aromatic Ketones, Carboxylic Acids, and Trace Metals in Arctic Aerosols: Long-Range Atmospheric Transport, Photochemical Degradation/Production at Polar Sunrise
Bibliographic record
Abstract
The distributions, correlations, and source apportionment of aromatic acids, aromatic ketones, polycyclic aromatic hydrocarbons (PAHs), and trace metals were studied in Canadian high Arctic aerosols. Nineteen PAHs including minor sulfur-containing heterocyclic PAH (dibenzothiophene) and major 6 carcinogenic PAHs were detected with a high proportion of fluoranthene followed by benzo[k]fluoranthene, pyrene, and chrysene. However, in the sunlit period of spring, their concentrations significantly declined likely due to photochemical decomposition. During the polar sunrise from mid-March to mid-April, benzo[a]pyrene to benzo[e]pyrene ratios significantly dropped, and the ratios diminished further from late April to May onward. These results suggest that PAHs transported over the Arctic are subjected to strong photochemical degradation at polar sunrise. Although aromatic ketones decreased in spring, concentrations of some aromatic acids such as benzoic and phthalic acids increased during the course of polar sunrise, suggesting that aromatic hydrocarbons are oxidized to result in aromatic acids. However, PAHs do not act as the major source for low molecular weight (LMW) diacids such as oxalic acid that are largely formed at polar sunrise in the arctic atmosphere because PAHs are 1 to 2 orders of magnitude less abundant than LMW diacids. Correlations of trace metals with organics, their sources, and the possible role of trace transition metals are explained.
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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 | 0.000 | 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".