Urban traffic emissions cost estimation based on an integrated modeling approach
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».