Volatile organic compounds in Arctic snow: concentrations and implications for atmospheric processes
Bibliographic record
Abstract
The role of volatile organic compounds (VOC) in the snowpack for atmospheric oxidation, gas-particle transfer and aerosol formation remains poorly understood, partly due to a lack of methodology and unavailable data. We deployed solid phase micro-extraction (SPME) gas chromatography with flame ionization detection for measurement of halogenated, aromatic and oxygenated VOC in the snow pack in Alert, NU, Canada, a High Arctic site. Maximum concentrations in snow were 39 ± 6 μg L(-1) (styrene), indicating a potential VOC contribution to atmospheric oxidation and aerosol formation. Concurrently sampled air had concentrations of up to 1.0 ± 0.3 ng L(-1) (trichloroethene). Back trajectory data showed a change of air mass source region during a depletion event of several VOC in snow (e.g., trichloroethene and benzene). Snow profiles showed an enrichment of most compounds close to the surface. During a second study in Barrow, AK, USA VOC were quantified in snow and frost flowers in the Montreal lab. In Barrow work was carried out as part of the extensive OASIS (Ocean-Atmosphere-Sea Ice-Snowpack) field campaign. Maximum VOC concentrations were up to 1.3 ± 0.1 μg L(-1) (acetophenone). Bromoform in frost flowers averaged 0.19 ± 0.04 μg L(-1), indicating the potential to contribute to bromine generation through photolysis.
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".