Dominance of Plastic Emission in the High Arctic Aerosol in Light Spring
Bibliographic record
Abstract
Arctic haze has attracted considerable scientific interest for decades. However, limited studies have focused on the molecular composition of atmospheric particulate matter that contributes to Arctic haze. Our study collected atmospheric particles at Alert in the Canadian high Arctic from mid-February to early May 2000. Over 100 organic species were identified in the solvent-extractable fraction by gas chromatography-mass spectrometry, which were grouped by their functional groups. Plasticizer-derived phthalates were the most abundant, followed by polyacids, sugars, sugar alcohols, biogenic SOA tracers, and fossil fuel combustion tracers. During the dark winter, major contributors to Arctic aerosols include plastic emissions, biomass burning, secondary oxidation products, and fossil fuel combustion products. In the light spring, phthalates (58-76% of the identified organics) dominated, followed by microbial and marine sources and secondary oxidation products. By employing a tracer-based method, we discovered that naphthalene and sesquiterpene oxidation products were the major contributors to SOC, and these contributions were much higher in the winter than in the spring. However, monoterpene and isoprene oxidation products peaked in light spring. Our results confirm that organic aerosols in the Arctic atmosphere are dominated by anthropogenic sources, which consist of both long-range-transported particles and combustion-emitted organics, as well as aged anthropogenic secondary organic aerosols. Despite decreasing anthropogenic pollution being replaced by natural emissions, plastic-derived pollution, represented by phthalates, increased significantly in the high Arctic atmosphere after the polar sunrise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".