Physical and chemical properties of pollution aerosol particles transported from North America to Greenland as measured during the POLARCAT summer campaign
Bibliographic record
Abstract
Abstract. Within the framework of the POLARCAT-France campaign, aerosol physical, chemical and optical properties over Greenland were measured onboard the French ATR-42 research aircraft. The origins of CO excess peaks detected in the aircraft measurements then have been identified through FLEXPART simulations. The study presented here focuses particularly on the characterization of air masses transported from the North American continent to Greenland. Air masses that picked up emissions from Canadian boreal forest fires as well as from the cities on the American east coast were identified and selected for a detailed study. Measurements of CO concentrations, aerosol chemical composition, aerosol number size distributions, aerosol volume volatile fractions and aerosol light absorption (mainly from black carbon) are used in order to study the relationship between CO enhancement (ΔCO), aerosol particle concentrations and number size distributions. Aerosol number size distributions (normalised with their respective ΔCO) are in good agreement with previous studies. Nonetheless, wet scavenging may have occurred along the pathway between the emission sources and Greenland leading to a less pronounced accumulation mode in the POLARCAT data. Chemical analyses from mass spectrometry show that submicrometer aerosol particles are mainly composed of sulphate and organics. The observed bimodal (Aitken and accumulation) aerosol number size distributions show a significant enhancement in Aitken mode particles. Furthermore, results from the thermodenuder analysis demonstrate the external mixture of boreal fire (BF) air masses from North America (NA). This is particularly observed in the accumulation mode, containing a volume fraction of up to 25–30% of refractory material at the applied temperature of 280 °C. NA anthropogenic air masses with only 6% refractory material in the accumulation mode can be clearly distinguished from BF air masses. Overall, during the campaign rather small amounts of black carbon from the North American continent were transported towards Greenland during the summer POLARCAT observation period, which also is a valuable finding with respect to potential climate impacts of black carbon in the Arctic.
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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 | 0.000 |
| 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) | 0.001 | 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".