From Bike Lanes to Highway Trucks, Assessing the Impact of Transportation Policies on Air Pollution Exposure at Local and Regional Scales
Bibliographic record
Abstract
In a context of increasing concern for population exposure to ambient air pollution, it is crucial to develop tools to assist urban policies tackling air quality. In this thesis, techniques to investigate air quality at local and regional scales were developed and applied to analyze the impact of transportation policies on population exposure and health. The first part of this thesis consists in a large data collection campaign involving short-term stationary and mobile measurements of air pollutant concentrations in Toronto. Land-use regression (LUR) models based on the two data collection protocols are developed and compared with data from a panel study. Recommendations are provided for the design of short-term monitoring campaigns. In the second part of this thesis, we set-up a chemical transport model (CTM) and a plume-in-grid (PinG) model for the Greater Toronto and Hamilton Area (GTHA) to simulate the levels of various air contaminants at a scale of 1 km2. These models are based on a detailed traffic emission inventory and enable a refined analysis of population exposure to traffic-related air pollution. The CTM is combined with a health impact assessment tool to investigate the benefits of greening freight movements on urban air quality and health. In the third module, we use our LUR and air quality models to analyze the impact of transportation policies on population exposure and health. The fine spatial resolution of the LUR exposure surfaces is optimal to assess a major urban planning strategy of the City of Toronto: the installation of new bike facilities. We use our CTM to investigate the health and climate benefits of renewing the fleets of private household vehicles, transit buses and commercial vehicles. While we recommend the use of detailed air pollutant maps such as those obtained from LUR models to assist local policy making, spatially refined CTMs supplemented by comprehensive emission inventories are outstanding tools to assess and compare the benefits of transportation policies at a regional scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".