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Record W4293831626 · doi:10.1039/d1em00393c

Emerging investigator series: ozone uptake by urban road dust and first evidence for chlorine activation during ozone uptake by agro-based anti-icer: implications for wintertime air quality in high-latitude urban environments

2022· article· en· W4293831626 on OpenAlexafffund
Maya Abou‐Ghanem, Danial Nodeh-Farahani, Devon T. McGrath, Trevor C. VandenBoer, Sarah A. Styler

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsMemorial University of NewfoundlandYork UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationUniversity of Alberta
KeywordsOzoneEnvironmental scienceAir quality indexChlorineAtmospheric sciencesLatitudeClimatologyMeteorologyEnvironmental chemistryGeographyChemistryGeology

Abstract

fetched live from OpenAlex

road salt) and commercial anti-icer solution. Although ozone uptake by pure calcium chloride was negligible, ozone uptake by anti-icer was significant, which implies that the reactivity of anti-icer is conferred by its organic content. Importantly, ozone uptake by anti-icer-and, to a lesser extent, road dust doped with anti-icer-leads to the release of inorganic chlorine gas, which we collected using inline reductive trapping and quantified using ion chromatography. To explain these results, we propose a novel pathway for chlorine activation: here, ozone oxidation of the anti-icer organic fraction (in this case, molasses) yields reactive OH radicals that can oxidize chloride. In summary, this study demonstrates the ability of road dust and anti-icer to influence atmospheric oxidant mixing ratios in cold-climate urban areas, and highlights previously unidentified air quality impacts of winter road maintenance decisions.

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.249
Teacher spread0.229 · 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 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

Citations2
Published2022
Admission routes2
Has abstractyes

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