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Record W6996740101

Stable carbon isotope composition of ambient VOC and its use in the determination of photochemical ages of air masses

2016· other· en· W6996740101 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2016
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsThermal desorptionIsotope analysisAtmosphere (unit)Isotope dilutionAdsorptionCarbon fibersMass spectrometryCarbon dioxideIsotopes of carbonCombustion
DOInot available

Abstract

fetched live from OpenAlex

Stable Carbon Isotopic Composition measurements can provide valuable information about the processing of trace gases in the atmosphere. Not only can it be used to distinguish physical processes such as dilution and mixing from photochemical ageing, but it can also be an important tool in identification of sources, in calculating the photochemical age and qualitatively and quantitatively connecting precursors with their atmospheric products. \n\nEven though isotopic composition analysis is a valuable technique, its use is hindered by the low concentrations of compounds in the atmosphere, complexity of the samples and complex measuring instrumentation. The intention of this research project was to develop and validate sampling and instrumental analysis techniques that can be used to perform isotopic composition measurements of volatile organic compounds (VOC) and to apply these methods to analysis of ambient samples. \n\nSince most VOC are present in the atmosphere in sub-ppbv to ppbv levels and more than 1 ng of carbon is required for isotopic analysis, collection of large volumes of air is required. A method based on sampling onto cartridges filled with an adsorbent (Carboxene-569) for VOC collection in the field has been developed. VOC are selectively collected by passing large volumes (up to 100 L) of air through the cartridges. Thermal desorption of VOC from the cartridges is followed by two step cryogenic trapping and separation by gas chromatography. Once separated, all VOC are oxidized in a combustion interface. The isotopic composition of resulting carbon dioxide is then determined on-line by isotope ratio mass spectrometry. Various validation tests were performed in order to test accuracy and precision of both the preconcentration system and sampling-desorption procedure. \n\nThe newly developed sampling and analysis techniques were applied in field studies: Border air quality study (BAQS) (2007) and Environment Canada-York University campaign (EC-YU) (2009-2010). Ambient samples were collected over various time periods and the isotopic composition of individual compounds was analyzed. Determined mixing ratios were in pptv to low ppbv ranges and isotope composition varied from -30%0 to -20%0 for most of the compounds. Analysis of mixing ratios and isotope composition, their distribution and trends indicated that sampling locations can be qualitatively classified as rural (Ridgetown), semi-rural (Harrow and Egbert) and semi-urban (Toronto) areas, with strong local vehicle emission sources. Quantitative analysis of the photochemical ages (PCA) determined using hydrocarbon and isotope hydrocarbon clocks (Egbert and Toronto samples) resulted in similar values, suggesting that both of these methods are valid and are applicable. However, while both PCA methods indicated that local sources have larger impact on the air quality in these two locations, PCA from isotope composition analysis has demonstrated that different VOC in photochemically processed air masses differ in their PCA depending on VOC reactivity.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.178
Teacher spread0.166 · 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
Published2016
Admission routes1
Has abstractyes

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