Development of an integrated transport and emissions model and applications for population exposure and environmental justice
Bibliographic record
Abstract
Road transport has a tremendous impact on local urban regions as well as global planetary health. This impact is especially great given the large quantities of greenhouse gases and local air pollutants released across the world, quantities that continue to increase. For metropolitan regions, reductions in traffic-related air pollution are paramount. Which baseline is used and which strategies should be implemented are both vital questions in this regard. Integrated transport and emissions models are important tools that aid metropolitan planners in answering those questions. A regional traffic assignment model has been connected to a detailed emission processor for the Montreal metropolitan region. The road transport model contains details on all private driving trips across a standard 24-hr workday, including congested link speeds and stochastic path distributions. Meanwhile, the emissions processor incorporates local vehicle registry data and Montreal-specific ambient conditions in the estimation of both running and start emissions. Outputs include hourly link-level and trip-level emissions for greenhouse gases, hydrocarbons, and nitrogen oxides. Three research studies were then explored that were anchored by the integrated transport and emissions model. The first involved testing model sensitivity to variations in input data and randomness. The second study was aimed at understanding the land-use and socioeconomic determinants of traffic-related air pollution generation and exposure. The third study encompassed an equity analysis of social disadvantage, traffic-related air pollution generation and exposure. Major findings include evidence that: start emissions and accurate vehicle registry data have the biggest impact on accurate regional emission inventories; neighbourhoods closer to downtown tend to be low emitters while having high exposures to traffic-related air pollution, while the opposite is true for neighbourhoods in the suburbs and periphery of the region; and marginalized neighbourhoods with high social disadvantage tend to have the highest exposure levels in the region, while at the same time generating some of the lowest quantities of traffic-related air pollution. These findings support the claim that traffic is creating environmental justice issues at the metropolitan level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".