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Individual and Environmental Determinants of Traffic Emissions and Near-Road Air Quality.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Individual and Environmental Determinants of Traffic Emissions and Near-Road Air Quality./
Author:	Xu, Junshi .
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	239 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
Contained By:	Dissertations Abstracts International82-01B.
Subject:	Transportation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27736832
ISBN:	9798662394377

Individual and Environmental Determinants of Traffic Emissions and Near-Road Air Quality.
Xu, Junshi .

Individual and Environmental Determinants of Traffic Emissions and Near-Road Air Quality. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 239 p.

Source: Dissertations Abstracts International, Volume: 82-01, Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2020.

This item must not be sold to any third party vendors.

On-road motor vehicles are responsible for a considerable proportion of near-road air pollution. While background levels of air pollutants are continuously tracked by regional monitoring networks, assessing near-road air quality remains a challenge in urban areas with complex built environments, traffic composition, and meteorological variation, leading to significant spatiotemporal variability in air pollution. This research addresses current gaps in the literature on local traffic emissions and near-road air quality.This thesis first investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Traffic emissions are estimated using radar data as well as simulated traffic based on various speed aggregation methods. It provides recommendations for project-level analysis and particulate matter (PM) hotspot analysis. We further compare fleet averaged emission factors (EFs) derived from a traffic emission model, the Motor Vehicle Emissions Simulator (MOVES), with EFs using plume-based measurements. This second module stresses the need to collect local traffic information for a better understanding of on-road traffic emissions. Besides, we validate default drive cycles in MOVES against representative drive cycles derived based on real-world GPS data. The validation results are helpful for transportation planners to quantify uncertainties in emission estimation and employ appropriate methods to improve the estimation of on-road emission inventories. The third module develops eco-score models and evaluates the effect of various factors such as driver and trip characteristics on emission intensities. The results shed light on the impact of driving style on emissions and identify the most important factors affecting the amount of emissions generated by every individual driver. The fourth module focuses on the impact of traffic emissions on near-road air quality and presents the results of two different experiments. First, it explores the effect of various factors on near-road ultrafine particle (UFP) concentrations based on short-term fixed monitoring, which stresses the significance of using local traffic characteristics to improve near-road air quality prediction. In addition, it captures the distribution of truck movements in urban environments and investigates the impacts of land-use variables and detailed traffic information on near-road Black Carbon (BC) concentrations.

ISBN: 9798662394377Subjects--Topical Terms:

558117
Transportation.
Subjects--Index Terms:

Air quality modelling

Individual and Environmental Determinants of Traffic Emissions and Near-Road Air Quality.
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500 $a Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
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520 $a On-road motor vehicles are responsible for a considerable proportion of near-road air pollution. While background levels of air pollutants are continuously tracked by regional monitoring networks, assessing near-road air quality remains a challenge in urban areas with complex built environments, traffic composition, and meteorological variation, leading to significant spatiotemporal variability in air pollution. This research addresses current gaps in the literature on local traffic emissions and near-road air quality.This thesis first investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Traffic emissions are estimated using radar data as well as simulated traffic based on various speed aggregation methods. It provides recommendations for project-level analysis and particulate matter (PM) hotspot analysis. We further compare fleet averaged emission factors (EFs) derived from a traffic emission model, the Motor Vehicle Emissions Simulator (MOVES), with EFs using plume-based measurements. This second module stresses the need to collect local traffic information for a better understanding of on-road traffic emissions. Besides, we validate default drive cycles in MOVES against representative drive cycles derived based on real-world GPS data. The validation results are helpful for transportation planners to quantify uncertainties in emission estimation and employ appropriate methods to improve the estimation of on-road emission inventories. The third module develops eco-score models and evaluates the effect of various factors such as driver and trip characteristics on emission intensities. The results shed light on the impact of driving style on emissions and identify the most important factors affecting the amount of emissions generated by every individual driver. The fourth module focuses on the impact of traffic emissions on near-road air quality and presents the results of two different experiments. First, it explores the effect of various factors on near-road ultrafine particle (UFP) concentrations based on short-term fixed monitoring, which stresses the significance of using local traffic characteristics to improve near-road air quality prediction. In addition, it captures the distribution of truck movements in urban environments and investigates the impacts of land-use variables and detailed traffic information on near-road Black Carbon (BC) concentrations.
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