Polycyclic Aromatic Hydrocarbons in the Marine Atmosphere from the Western Pacific to the Southern Ocean: Spatial Variability, Gas/Particle Partitioning, and Source Apportionment
Bibliographic record
Abstract
The spatial variability of polycyclic aromatic hydrocarbons (PAHs) in the marine atmosphere contributes to the understanding of the global sources, fate, and impact of this contaminant. Few studies conducted to measure PAHs in the oceanic atmosphere have covered a large scale, especially in the Southern Ocean. In this study, high-volume air samples were taken along a cross-section from China to Antarctica and analyzed for gaseous and particulate PAHs. The data revealed the spatial distribution, gas–particle partitioning, and source contributions of PAHs in the Pacific, Indian, and Southern Oceans. The median concentration (gaseous + particulate) of ∑ 24 PAHs was 3900 pg/m 3 in the Pacific Ocean, 2000 pg/m 3 in the Indian Ocean, and 1200 pg/m 3 in the Southern Ocean. A clear latitudinal gradient was observed for airborne PAHs from the western Pacific to the Southern Ocean. Back trajectories (BTs) analysis showed that air masses predominantly originated from populated land had significantly higher concentrations of PAHs than those from the oceans or Antarctic continents/islands. The air mass origins and temperature have significant influences on the gas–particle partitioning of PAHs. Source analysis by positive matrix factorization (PMF) showed that the highest contribution to PAHs was from coal combustion emissions (52%), followed by engine combustion emissions (27%) and wood combustion emissions (21%). A higher contribution of PAHs from wood combustion was found in the eastern coastal region of Australia. In contrast, engine combustion emissions primarily influenced the sites in Southeast Asia.
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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 | 0.000 | 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) | 0.000 | 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".