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Record W6922227002 · doi:10.11575/prism/35405

Ubiquity of ClNO2 in the urban boundary layer of Calgary, AB, Canada

2015· other· en· W6922227002 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMixing ratioYield (engineering)NitrateAir mass (solar energy)Mixing (physics)OzoneChlorineNitrogen

Abstract

fetched live from OpenAlex

The role of nitryl chloride (ClNO2) as a nocturnal nitrogen oxide reservoir species and chlorine atom precursor is well established for polluted coastal areas, but its role at mid-continental locations is less clear. In this manuscript, intermittent measurements over the course of several seasons of ClNO2 mixing ratios by iodide ion chemical ionization mass spectrometry (CIMS) in Calgary, AB, Canada, are presented. Mixing ratios were highly variable between nights and seasons and depended on the abundances of precursors and meteorological conditions. The lowest ClNO2 mixing ratios (nocturnal maximum of 30 parts-per-trillion by volume, pptv) were observed in the summer, rationalized by losses of the nitrate radical (NO3) that were more efficient than in the other months. Higher ClNO2 mixing ratios (up to 330 pptv) were observed in the winter and spring months, but varied between nights. In the fall, ClNO2 mixing ratios increased from night to night following the application of salt to roads. The ClNO2 yield relative to the amount of NO3 produced from oxidation of NO2 by O3 ranged from 0.1% to 4.5% (10th and 90th percentiles; median 1.0%). The ClNO2 yield relative to consumed N2O5 consumed by heterogeneous reactions was estimated using a time-integrated box model and ranged from 0.5% to 12.1% (10th and 90th percentiles; median 3.4%). The ubiquity of ClNO2 implies that the Cl atom needs to be considered as an oxidant in high-latitude urban environments in winter.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.203
Teacher spread0.189 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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