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Record W4238251373 · doi:10.5194/acpd-11-14127-2011

Observations of volatile organic compounds during ARCTAS – Part 1: Biomass burning emissions and plume enhancements

2011· preprint· en· W4238251373 on OpenAlexaboutno aff
Rebecca S. Hornbrook, D. R. Blake, Glenn S. Diskin, Henry E. Fuelberg, Simone Meinardi, Tomáš Mikoviny, G. W. Sachse, S. A. Vay, A. J. Weinheimer, Christine Wiedinmyer, Armin Wisthaler, Alan J. Hills, D. D. Riemer, Eric C. Apel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersÖsterreichische ForschungsförderungsgesellschaftCalifornia Institute of TechnologyNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPlumeEnvironmental scienceTroposphereTrace gasBiomass burningMixing ratioAtmospheric sciencesBiomass (ecology)Volatile organic compoundEnvironmental chemistrySampling (signal processing)PanacheArcticMixing (physics)MeteorologyAerosolChemistryGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract. Mixing ratios of a large number of volatile organic compounds (VOCs) were observed by the Trace Organic Gas Analyzer (TOGA) on board the NASA DC-8 as part of the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. Many of these VOCs were observed concurrently by one or both of two other VOC measurement techniques on board the DC-8: proton-transfer-reaction mass spectrometry (PTR-MS) and whole air canister sampling (WAS). A comparison of these measurements to the data from TOGA indicates good agreement for the majority of co-measured VOCs. The ARCTAS study, which included both spring and summer deployments, provided opportunities to sample a large number of biomass burning (BB) plumes with origins in Asia, California and Central Canada, ranging from very recent emissions to plumes aged one week or more. For this analysis, identified BB plumes were grouped by flight, source region and, in some cases, time of day, generating 40 individual plume groups, each consisting of one or more BB plume interceptions. Normalized excess mixing ratios (EMRs) to CO were determined for each of the 40 plume groups for up to 19 different VOCs or VOC groups, many of which show significant variability, even within relatively fresh plumes. This variability demonstrates the importance of assessing BB plumes both regionally and temporally, as emissions can vary from region to region, and even within a fire over time. Comparisons with literature confirm that variability of EMRs to CO over an order of magnitude for many VOCs is consistent with previous observations. However, this variability is often diluted in the literature when individual observations are averaged to generate an overall regional EMR from a particular study. Previous studies give the impression that emission ratios are generally consistent within a given region, and this is not necessarily the case, as our results show. For some VOCs, earlier assumptions may lead to significant under-prediction of emissions in fire emissions inventories. Notably, though variable between plumes, observed EMRs of individual light alkanes are highly correlated within BB emissions. Using the NCAR master mechanism chemical box model initialized with concentrations based on two observed scenarios, i.e., fresh Canadian BB and fresh Californian BB, both plumes are expected to experience primarily decreases in oxygenated VOCs during the first 2.5 days, such that any production in the plumes of these compounds is less than the chemical loss. Comparisons of the modeled EMRs to the observed EMRs from BB plumes estimated to be three days in age or less indicate overall good agreement and, for most compounds, no significant difference between BB plumes in these two regions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

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)0.0010.000

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 head0.039
GPT teacher head0.221
Teacher spread0.182 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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