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Modeling the Effects of VOC/NO<sub>x</sub> Emissions on Ozone Synthesis in the Cascadia Airshed of the Pacific Northwest

2001· article· en· W2035517372 on OpenAlexfundno aff
Mike Barna, Brian Lamb, Hal Westberg

Bibliographic record

VenueJournal of the Air & Waste Management Association · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersNational Research Council CanadaWashington State University
KeywordsOzoneNOxEnvironmental scienceAir quality indexGround Level OzoneNitrogen oxideVolatile organic compoundMM5Air pollutionEnvironmental chemistryAtmospheric sciencesMeteorologyChemistryPrecipitation

Abstract

fetched live from OpenAlex

A modeling system consisting of MM5, Calmet, and Calgrid was used to investigate the sensitivity of anthropogenic volatile organic compound (VOC) and oxides of nitrogen (NOx) reductions on ozone formation within the Cascadia airshed of the Pacific Northwest. An ozone episode that occurred on July 11-14, 1996, was evaluated. During this event, high ozone levels were recorded at monitors downwind of Seattle, WA, and Portland, OR, with one monitor exceeding the 1 hr/120 ppb National Ambient Air Quality Standard (at 148 ppb), and six monitors above the proposed 8 hr/80 ppb standard (at 82-130 ppb). For this particular case, significant emissions reductions, between 25 and 75%, would be required to decrease peak ozone concentrations to desired levels. Reductions in VOC emissions alone, or a combination of reduced VOC and NOx emissions, were generally found to be most effective; reducing NOx emissions alone resulted in increased ozone in the Seattle area. When only VOC emissions were curtailed, ozone reductions occurred in the immediate vicinity of densely populated areas, while NOx reductions resulted in more widespread ozone reductions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.180
Teacher spread0.174 · 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.

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

Citations16
Published2001
Admission routes1
Has abstractyes

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