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Record W2153872943 · doi:10.5194/acp-16-3485-2016

Production of peroxy nitrates in boreal biomass burning plumes over Canada during the BORTAS campaign

2016· article· en· W2153872943 on OpenAlexaboutno aff
Marcella Busilacchio, Piero Di Carlo, Eleonora Aruffo, Fabio Biancofiore, Cesare Dari Salisburgo, Franco Giammaria, Stéphane Bauguitte, James Lee, Sarah Möller, James R. Hopkins, Shalini Punjabi, Stephen Andrews, Alastair C. Lewis, Mark Parrington, Paul I. Palmer, E. J. Hyer, Glenn M. Wolfe

Bibliographic record

VenueAtmospheric chemistry and physics · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UKLeverhulme Trust
KeywordsAtmospheric sciencesEnvironmental scienceOzoneTaigaNOxTroposphereBorealCarbon monoxideEnvironmental chemistryBiomass burningPlumeChemistryMeteorologyCombustionAerosolForestryGeology

Abstract

fetched live from OpenAlex

The observations collected during the BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS) campaign in summer 2011 over Canada are analysed to study the impact of forest fire emissions on the formation of ozone (O 3 ) and total peroxy nitrates ∑PNs, ∑ROONO 2 ). The suite of measurements on board the BAe-146 aircraft, deployed in this campaign, allows us to calculate the production of O 3 and of ∑PNs, a long-lived NO x reservoir whose concentration is supposed to be impacted by biomass burning emissions. In fire plumes, profiles of carbon monoxide (CO), which is a well-established tracer of pyrogenic emission, show concentration enhancements that are in strong correspondence with a significant increase of concentrations of ∑PNs, whereas minimal increase of the concentrations of O 3 and NO 2 is observed. The ∑PN and O 3 productions have been calculated using the rate constants of the first- and second-order reactions of volatile organic compound (VOC) oxidation. The ∑PN and O 3 productions have also been quantified by 0-D model simulation based on the Master Chemical Mechanism. Both methods show that in fire plumes the average production of ∑PNs and O 3 are greater than in the background plumes, but the increase of ∑PN production is more pronounced than the O 3 production. The average ∑PN production in fire plumes is from 7 to 12 times greater than in the background, whereas the average O 3 production in fire plumes is from 2 to 5 times greater than in the background. These results suggest that, at least for boreal forest fires and for the measurements recorded during the BORTAS campaign, fire emissions impact both the oxidized NO y and O 3, but (1 ∑PN production is amplified significantly more than O 3 production and (2) in the forest fire plumes the ratio between the O 3 production and the ∑PN production is lower than the ratio evaluated in the background air masses, thus confirming that the role played by the ∑PNs produced during biomass burning is significant in the O 3 budget. The implication of these observations is that fire emissions in some cases, for example boreal forest fires and in the conditions reported here, may influence more long-lived precursors of O 3 than short-lived pollutants, which in turn can be transported and eventually diluted in a wide area.

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 score0.097
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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