Spatial Variations of Atmospheric Alkylated Polycyclic Aromatic Hydrocarbons across the Western Pacific to the Southern Ocean: Unexpected Increasing Deposition
Bibliographic record
Abstract
Spatial variations of atmospheric alkylated polycyclic aromatic hydrocarbons (Alk-PAHs) are key to understanding their long-range atmospheric transport (LRAT). However, limited Alk-PAHs data have hindered their LRAT characterizations on a global scale. In this study, 49 Alk-PAHs were measured in the atmospheric samples collected across the Western Pacific to the Southern Ocean. The summed concentration of 39 frequently detected Alk-PAHs (Σ 39 Alk-PAHs) was 25.8 ± 25.3 ng m –3 . The concentrations of Σ 39 Alk-PAHs significantly declined with the decrease in latitude (°N). Higher concentrations (55.8 ± 33.8 ng m –3 ) were linked to continental air mass compared to oceanic/Antarctica air mass (17.0 ± 13.6 ng m –3 ), highlighting continental emissions as the primary source of marine atmospheric Alk-PAHs. An unexpected increase in the G/P partitioning ratio ( K P ) was found in samples farther away from the continent, which cannot be explained by the influence of temperature on the partitioning process. Deposition analysis suggested that gaseous concentrations and the G/P partitioning largely influenced deposition patterns. Hypothetical scenario analysis indicated that increased K P under snowy conditions could enhance the total Alk-PAH deposition. These findings emphasize the need for accurate characterization of partitioning and deposition processes when studying the global fate of Alk-PAHs, particularly in remote and polar regions.
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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 | 0.000 | 0.001 |
| 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 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".