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Record W611420436

Simulating the Air Quality Impacts of Traffic Calming Schemes in a Dense Urban Neighbourhood

2014· article· en· W611420436 on OpenAlexaboutno aff
Golnaz Ghafghazi, Marianne Hatzopoulou

Bibliographic record

VenueTransportation Research Board 93rd Annual MeetingTransportation Research Board · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTraffic calmingEnvironmental scienceAir quality indexWind speedMeteorologyNOxNeighbourhood (mathematics)Atmospheric sciencesNitrogen oxidesPollutantNitrogen dioxideWind directionNitrogen oxideGeographyTransport engineeringEngineeringMathematicsChemistryGeology
DOInot available

Abstract

fetched live from OpenAlex

In this study, the effects of isolated traffic calming measures and area-wide calming schemes on air quality in a dense neighborhood in Montreal, Canada are estimated using a combination of microscopic traffic simulation, emission, and dispersion modeling. The results indicate that traffic calming measures do not have as large an effect on nitrogen dioxide (NO2) concentrations as the effect observed on nitrogen oxide (NOx) emissions. Changes in emissions can result in highly disproportional changes in pollutant levels due to daily meteorological conditions, road geometry and its orientation with respect to wind direction. The authors observe that average NO2 levels increase between 0.1% and 10% with respect to the base-case while changes in NOx emissions vary between 5% and 160%. Also, the effects of wind speed and direction are investigated in this study. The results show that higher wind speeds decrease NO2 concentrations on both sides of the roadway. As the wind becomes more orthogonal to the roadway, the difference in NO2 levels between the leeward and windward sides increases. Among the traffic calming measures, speed bumps produce the highest increases in NO2 levels.

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)0.025
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.078
GPT teacher head0.413
Teacher spread0.335 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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