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Record W2096882575 · doi:10.5194/acp-16-383-2016

Source attribution of aerosol size distributions and model evaluation using Whistler Mountain measurements and GEOS-Chem-TOMAS simulations

2016· article· en· W2096882575 on OpenAlexaffabout
S. D. D'Andrea, Jessica Ng, John K. Kodros, Samuel A. Atwood, Michael Wheeler, A. M. Macdonald, W. R. Leaitch, Jeffrey R. Pierce

Bibliographic record

VenueAtmospheric chemistry and physics · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsAerosolTroposphereBoundary layerAtmospheric sciencesRelative humidityMeteorologyEnvironmental sciencePlanetary boundary layerChemistryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Remote and free-tropospheric aerosols represent a large fraction of the climatic influence of aerosols; however, aerosol in these regions is less characterized than those polluted boundary layers. We evaluate aerosol size distributions predicted by the GEOS-Chem-TOMAS global chemical transport model with online aerosol microphysics using measurements from the peak of Whistler Mountain, British Columbia, Canada (2182 m a.s.l., hereafter referred to as Whistler Peak). We evaluate the model for predictions of aerosol number, size, and composition during periods of free-tropospheric (FT) and boundary-layer (BL) influence at "coarse" 4° × 5° and "nested" 0.5° × 0.667° resolutions by developing simple FT/BL filtering techniques. We find that using temperature as a proxy for upslope flow (BL influence) improved the model–measurement comparisons. The best threshold temperature was around 2 °C for the coarse simulations and around 6 °C for the nested simulations, with temperatures warmer than the threshold indicating boundary-layer air. Additionally, the site was increasingly likely to be in cloud when the measured relative humidity (RH) was above 90 %, so we do not compare the modeled and measured size distributions during these periods. With the inclusion of these temperature and RH filtering techniques, the model–measurement comparisons improved significantly. The slope of the regression for N80 (the total number of particles with particle diameter, D p , > 80 nm) in the nested simulations increased from 0.09 to 0.65, R 2 increased from 0.04 to 0.46, and log-mean bias improved from 0.95 to 0.07. We also perform simulations at the nested resolution without Asian anthropogenic emissions and without biomass-burning emissions to quantify the contribution of these sources to aerosols at Whistler Peak (through comparison with simulations with these emissions on). The long-range transport of Asian anthropogenic aerosol was found to be significant throughout all particle number concentrations, and increased N80 by more than 50 %, while decreasing the number of smaller particles because of suppression of new-particle formation and enhanced coagulation sink. Similarly, biomass burning influenced Whistler Peak during summer months, with an increase in N80 exceeding 5000 cm −3 . Occasionally, Whistler Peak experienced N80 > 1000 cm −3 without significant influence from Asian anthropogenic or biomass-burning aerosol. Air masses were advected at low elevations through forested valleys during times when temperature and downwelling insolation were high, ideal conditions for formation of large sources of low-volatility biogenic secondary organic aerosol (SOA). This condensable material increased particle growth and hence N80. The low-cost filtering techniques and source apportionment used in this study can be used in other global models to give insight into the sources and processes that shape the aerosol at mountain sites, leading to a better understanding of mountain meteorology and chemistry.

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)0.001
metaresearch head score (Gemma)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.574

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.252
Teacher spread0.216 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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