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Record W2161439076 · doi:10.5194/acp-10-10489-2010

Characterising aerosol transport into the Canadian High Arctic using aerosol mass spectrometry and Lagrangian modelling

2010· article· en· W2161439076 on OpenAlexafffundabout
Thomas Kühn, R. Damoah, Asan Bacak, J. J. Sloan

Bibliographic record

VenueAtmospheric chemistry and physics · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Waterloo
FundersCanadian Foundation for Climate and Atmospheric Sciences
KeywordsAerosolArcticEnvironmental scienceTroposphereAtmospheric sciencesAir mass (solar energy)PearlClimatologyMeteorologyOceanographyGeographyGeology

Abstract

fetched live from OpenAlex

Abstract. We report the analysis of measurements made using an aerosol mass spectrometer (AMS; Aerodyne Research Inc.) that was installed in the Polar Environment Atmospheric Research Laboratory (PEARL) in summer 2006. PEARL is located in the Canadian high Arctic at 610 m above sea level on Ellesmere Island (80° N 86° W). PEARL is unique for its remote location in the Arctic and because most of the time it is situated within the free troposphere. It is, therefore, well suited as a receptor site to study the long-range tropospheric transport of pollutants into the Arctic. Some information about the successful year-round operation of an AMS at a high Arctic site such as PEARL will be reported here, together with design considerations for reliable sampling under harsh low-temperature conditions. Computational fluid dynamics calculations were made to ensure that sample integrity was maintained while sampling air at temperatures that average −40 °C in the winter and can be as low as −55 °C. Selected AMS measurements of aerosol mass concentration, size and chemical composition recorded during the months of August, September and October 2006 will be reported. The air temperature was raised to about 20 °C during sampling, but the short residence time in the inlet system (~25 s) ensured that less than 10% of semivolatiles such as ammonium nitrate were lost. During this period, sulfate was, at most times, the predominant aerosol component with on average 0.115 μg m−3 (detection limit 0.003 μg m−3). The second most abundant component was undifferentiated organic aerosol, with on average 0.11 μg m−3 (detection limit 0.04 μg m−3). The nitrate component, which averaged 0.007 μg m−3, was above its detection limit (0.002 μg m−3), whereas the ammonium ion had an apparent average concentration of 0.02 μg m−3, which was approximately equal to its detection limit. A few episodes, having increased mass concentrations and lasting from several hours to several days, are apparent in the data. These were investigated further using a statistical analysis to determine their common characteristics. High correlations among some of the components arriving during the short-term episodes provide evidence for common sources. Lagrangian methods were also used to identify the source regions for some of the episodes. In all cases, these coincided with the arrival of air that had contacted the surface at latitudes below about 60° N. Most of these lower-latitude footprints were on land, but sulfate emissions from shipping in the Atlantic were also detected. The Lagrangian results demonstrate that there is direct transport of polluted air into the high Arctic (up to 80° N) from latitudes down to 40° N on a time scale of 2–3 weeks. The polluted air originates in a wide variety of industrial, resource extraction and petroleum-related activity as well as from large population centres.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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 designSimulation or modeling
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

Citations17
Published2010
Admission routes3
Has abstractyes

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