Urban traffic emissions cost estimation based on an integrated modeling approach
Bibliographic record
Abstract
According to Canadian Environmental Sustainability Indicators (CESI), in 2015, the transport sector was the 4th leading source of PM2.5 emissions in Canada. Vehicular emissions contribute significantly to air quality problems and public health issues in urban areas. Nevertheless, previous studies have shown that transport users do not perceive their travel-related emissions as out of pocket costs. In addition, travelers prefer emissions’ information in monetary values rather than in their own units (tons or grams of emissions). In this study, I estimate the health-related costs of transport emissions for Montreal residents. In addition, I examine the use of an air dispersion model (AERMOD) in estimating travel-related air pollution concentrations for four intersections in Bucaramanga, Columbia.In the second chapter, I estimate and quantify emissions generated on the Montreal road network. First, I transform the emissions rates estimated from the MOVES software into air pollution concentrations. Then, I convert the concentrations into health outcomes. Finally, I valuate these health outcomes in monetary terms. My results show that among three key emission types, NOx has the highest emission cost (up to $0.38/km), followed by PM2.5 ($0.31/km) and CO ($0.0074/km), during peak hours. In addition, the downtown and Plateau areas have the highest total emissions costs per km. In the second part of the thesis, I apply an air dispersion model (AERMOD) to simulate the air pollutant movements at four intersections in Bucaramanga, Colombia. My results show that the higher traffic volume, the higher the emission rates for both PM2.5 and Black Carbon, except for when heavy trucks’ percentage is high. The La Provenza intersection generates the highest PM2.5 rate (90g/h during peak hours and 16g/h during off-peak hours) and Black Carbon (15g/h during peak hours and 3g/h during off-peak hours). In addition, the air pollution concentrations are highest among the most congested links, in all studied intersections. Moreover, the PM2.5 and Black Carbon concentrations drop off substantially when moving away from the intersections’ centers, and then gradually decrease after 50 meters. In addition, compared to the real measurements (by the equipment installed in the intersections), the proposed set of models (MOVES+AERMOD) captures most of the general trends in PM2.5 and Black Carbon. However, the predicted concentrations are less than the observed measurements. This could be due to the fact that some factors are neglected, and those can affect the results, factors including emissions generated by people’s other daily activities (e.g., cooking), the relatively old vehicle fleet in Colombia (different from MOVES’s fleet), etc. I conducted a set of sensitivity analyses to understand the performance of the AERMOD dispersion model in estimating PM2.5 concentrations, by altering the input data. My results show that AERMOD is highly sensitive to wind conditions. The temperature was observed to have a slightly negative correlation with PM2.5 concentrations. My results could be used to raise public awareness regarding the health impacts of traffic-induced air pollution, and eventually could change travel behavior of urban travelers. Keywords: Urban traffic; health-related emissions cost; Montreal transport users; MOVES; Emission rates; Bucaramanga, Colombia intersections; Air pollution dispersion modeling, and air pollution concentration
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".