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Record W4322210682 · doi:10.5194/egusphere-egu23-14320

Aerosol source identification in the spring and summertime central Arctic Ocean using high-resolution mass spectrometry during MOSAiC.

2023· preprint· en· W4322210682 on OpenAlexaboutno aff
Benjamin Heutte, Lubna Dada, Hélène Angot, Imad El Haddad, Gang Chen, Kaspar Rudolf Dällenbach, Jakob Pernov, Ivo Beck, Lauriane L. J. Quéléver, Tiia Laurila, Tuija Jokinen, Julia Schmale

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceArcticAerosolAtmospheric sciencesAir mass (solar energy)HazeClimatologyCloud condensation nucleiOceanographyMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

The Arctic region is undergoing considerable changes and is warming at a rate three to four times as fast as the rest of the world. Aerosols, which can originate from natural or anthropogenic sources, both of which can be locally emitted or long-range transported, play a crucial role in the Arctic radiative balance by directly absorbing or scattering incoming solar radiation or indirectly by changing cloud properties and modulating cloud formation mechanisms. Here, we investigate the sources of anthropogenic and natural aerosols in the central Arctic Ocean, using data collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition with a high-resolution time-of-flight aerosol mass spectrometer. Using positive matrix factorization on the organic fraction of aerosols during spring and summertime (March – July), we identified six distinct chemical sources of organic aerosols (OA): a hydrocarbon-like factor, a Haze factor, two factors related to two extreme events of warm and moist air mass intrusions (WAMI) in mid-April, an Arctic oxygenated factor, and a Marine factor. We also describe the geographical origin of these factors, inferred from a potential source contribution function applied on 3-hourly back-trajectories. Together, these results suggest that OA from Eurasian anthropogenic origin (including the two extreme WAMI events in mid-April) dominate the central Arctic OA budget until at least the month of May, where episodic spikes in naturally-sourced marine OA, originating from the Fram Strait marginal ice-zone start to become important through June and July. We also highlight a hitherto unreported highly-oxygenated organic factor, whose temporal variability is closely related to that of particulate ammonium (maximum concentration in May) and whose geographical origin, in the Canadian archipelagoes, could indicate a co-emission mechanism of organic aerosols and ammonia from Arctic seabird colonies.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.221
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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