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Record W6959521243 · doi:10.1016/j.trd.2025.104904

Comparative assessment of black carbon levels in public transit systems across three cities

2025· article· en· W6959521243 on OpenAlexafffundabout

Bibliographic record

VenueTransportation Research Part D Transport and Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsMcGill University Health CentreMcGill University
FundersEnvironment and Climate Change CanadaNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsPublic transportTransit (satellite)TrainMode (computer interface)Urban transitCarbon blackRapid transitCarbon fibers

Abstract

fetched live from OpenAlex

This research investigates users’ exposure to black carbon (BC) in subways, buses, and BRTs. The study was conducted in Mexico City and Puebla (Mexico) and Montreal (Canada) to identify city-specific variations across modes and factors associated with levels of BC. Over 1500 min were collected, and regression model analyses were used to identify the impacts of mode and other factors. Among all measured transit modes, subway systems exhibited the highest BC concentrations (median: 10.83 μ g / m 3 ; MAD: ±6.97 μ g / m 3 ). In contrast, BRT systems showed the lowest exposure levels (3.7228 ± 2.27 μ g / m 3 ), with buses demonstrating comparable concentrations (4.00 ± 2.22 μ g / m 3 ). Notably, high concentrations in subways are primarily observed in segments where the subway operates in underground tunnels. Both surface transit modes measured slightly above ambient outdoor levels (2.82 ±1.66 μ g / m 3 ), suggesting minimal additional exposure during above-ground commute. • 42% of the observations in Montreal values above the accepted limit of 3.5 μ g m − 3 . • In Mexico, 60% of observations in BRTs and 84% in subways surpass the threshold. • In Puebla, 78% of BRT’s observations and 59% of buses surpass the 3.5 μ g m − 3 limit. • Excessive BC levels in subway trains are linked with tunnels along the route. • Engine technology plays a fundamental role in buses and BRTs.

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)0.002
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 score0.018
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.214
GPT teacher head0.426
Teacher spread0.212 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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