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Record W2587742504 · doi:10.5194/acp-17-6975-2017

Sources and mixing state of summertime background aerosol in the north-western Mediterranean basin

2017· article· en· W2587742504 on OpenAlexaff
Jovanna Arndt, Jean Sciare, Marc Mallet, Greg Roberts, Nicolas Marchand, Karine Sartelet, Karine Sellegri, François Dulac, Robert M. Healy, John Wenger

Bibliographic record

VenueAtmospheric chemistry and physics · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsAlberta Biodiversity Monitoring InstituteMinistry of the Environment, Conservation and Parks
FundersCentre National de la Recherche ScientifiqueEuropean Regional Development FundIrish Research CouncilAgence Nationale de la Recherche
KeywordsAerosolMass spectrometrySulfateParticle (ecology)ChemistryMass concentration (chemistry)Carbon fibersMass spectrumTotal organic carbonEnvironmental chemistryParticulatesSootEnvironmental scienceAtmospheric sciencesCombustionGeologyChromatographyMaterials science

Abstract

fetched live from OpenAlex

Abstract. An aerosol time-of-flight mass spectrometer (ATOFMS) was employed to provide real-time single particle mixing state and thereby source information for aerosols impacting the western Mediterranean basin during the ChArMEx-ADRIMED and SAF-MED campaigns in summer 2013. The ATOFMS measurements were made at a ground-based remote site on the northern tip of Corsica. Twenty-seven distinct ATOFMS particle classes were identified and subsequently grouped into eight general categories: EC-rich (elemental carbon), K-rich, Na-rich, amines, OC-rich (organic carbon), V-rich, Fe-rich and Ca-rich particles. Mass concentrations were reconstructed for the ATOFMS particle classes and found to be in good agreement with other co-located quantitative measurements (PM1, black carbon (BC), organic carbon, sulfate mass and ammonium mass). Total ATOFMS reconstructed mass (PM2. 5) accounted for 70–90 % of measured PM10 mass and was comprised of regionally transported fossil fuel (EC-rich) and biomass burning (K-rich) particles. The accumulation of these transported particles was favoured by repeated and extended periods of air mass stagnation over the western Mediterranean during the sampling campaigns. The single particle mass spectra proved to be valuable source markers, allowing the identification of fossil fuel and biomass burning combustion sources, and was therefore highly complementary to quantitative measurements made by Particle into Liquid Sampler ion chromatography (PILS-IC) and an aerosol chemical speciation monitor (ACSM), which have demonstrated that PM1 and PM10 were comprised predominantly of sulfate, ammonium and OC. Good temporal agreement was observed between ATOFMS EC-rich and K-rich particle mass concentrations and combined mass concentrations of BC, sulfate, ammonium and low volatility oxygenated organic aerosol (LV-OOA). This combined information suggests that combustion of fossil fuels and biomass produced primary EC- and OC-containing particles, which then accumulated ammonium, sulfate and alkylamines during regional transport. Three other sources were also identified: local biomass burning, marine and shipping. Local combustion particles (emitted in Corsica) contributed little to PM2. 5 particle number and mass concentrations but were easily distinguished from regional combustion particles. Marine emissions comprised fresh and aged sea salt: the former was detected mostly during a 5-day event during which it accounted for 50–80 % of sea salt aerosol mass, while the latter was detected throughout the sampling period. Dust was not efficiently detected by the ATOFMS, and support measurements showed that it was mainly in the PM2. 5–10 fraction. Shipping particles, identified using markers for heavy fuel oil combustion, were associated with regional emissions and represented only a small fraction of PM2. 5 particle number and mass concentration at the site.

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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.228
Teacher spread0.207 · 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

Citations64
Published2017
Admission routes1
Has abstractyes

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