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dc.contributor.authorDabbas, Wafa M
dc.date.accessioned2010-02-14
dc.date.available2010-02-14
dc.date.issued2010-02-14
dc.identifier.urihttp://hdl.handle.net/2123/5866
dc.descriptionDoctor of Philosophy(PhD)en_AU
dc.description.abstractAbstract This thesis employs a statistical regression method to estimate models for testing the hypothesis of the thesis of vehicle emissions interdependencies. The thesis at the beginnings, reviews critically the formation of emissions in gasoline-fuelled engines, and also reviews existing and emerging models of automotive emissions. The thesis then, presents the relationships between the urban transport system and vehicle emissions. Particularly, it summarises different types of emissions and the contributory factors of the urban transport system to such emissions. Subsequently, the thesis presents the theory of vehicle emissions interdependencies and the empirical framework for testing the hypothesis of the thesis. The scope of testing the hypothesis of the thesis is only limited to gasoline-fuelled conventional vehicles in the urban transport environment. We use already available laboratory-based testing dataset of 542 passenger vehicles, to investigate the hypothesis of the thesis of vehicle emissions interdependencies. HC, CO, and NOX emissions were collected under six test drive-cycles, for each vehicle before and after vehicles were tuned. Prior to using any application, we transform the raw dataset into actionable information. We use three steps, namely conversion, cleaning, and screening, to process the data. We use classification and regression trees (CART) to narrow down the input number of variables in the models formulated for investigating the hypothesis of the thesis. We then, utilise initial results of the analysis to fix any remaining problems in the data. We employ three stage least squares (3SLS) regression to test the hypothesis of the thesis, and to estimate the maximum likelihood of vehicle variables and other emissions to influence HC, CO, and NOX emissions simultaneously. We estimate twelve models, each of which consists of a system of three simulations equations that accounts for the endogenous relations between HC, CO and NOX emissions when estimating vehicle emissions simultaneously under each test drive-cycle. The major contribution of the thesis is to investigate the inter-correlations between vehicle emissions within a well controlled data set, and to test the hypothesis of vehicle emissions interdependencies. We find that HC, CO, and NOX are endogenously or jointly dependent in a system of simultaneous-equations. The results of the analysis demonstrate that there is strong evidence against the null hypothesis (H0) in favour of the alternative hypothesis (H1) that HC, CO, and NOX are statistically significantly interdependent. We find, for the thesis sample, that NOX and CO are negatively related, whereas HC and CO emissions are positively related, and HC and NOX are positively related. The results of the thesis yield new insights. They bridge a very important gap in the current knowledge on vehicle emissions. They advance not only our current knowledge that HC, CO, and NOX should be predicted jointly since they are produced jointly, but also acknowledge the appropriateness of using 3SLS regression for estimating vehicle emissions simultaneously. The thesis measures the responses of emissions to changes with respect to changes in the other emissions. We investigate emission responses to a one percent increase in an emission with respect to the other emissions. We find the relationship between CO and NOX is of special interest. After vehicles were tuned, we find those vehicles that exhibit a one percent increase in NOX exhibit simultaneously a 0.35 percent average decrease in CO. Similarly, we find that vehicles which exhibit a one percent increase in CO exhibit simultaneously a 0.22 percent average decrease in NOX. We find that the responses of emission to changes with respect to other emissions vary with various test drive-cycles. Nonetheless, a band of upper and lower limits contains these variations. After vehicle tuning, a one percent increase in HC is associated with an increase in NOX between 0.5 percent and 0.8 percent, and an increase in CO between 0.5 percent and one percent Also, for post-tuning vehicles, a one percent increase in CO is associated with an increase in HC between 0.4 percent and 0.9 percent, and a decrease in NOX between 0.07 percent and 0.32 percent. Moreover, a one percent increase in NOX is associated with increase in HC between 0.8 percent and 1.3 percent, and a decrease in CO between 0.02 percent and 0.7 percent. These measures of the responses are very important derivatives of the hypothesis investigated in the thesis. They estimate the impacts of traffic management schemes and vehicle operations that target reducing one emission, on the other non-targeted emissions. However, we must be cautious in extending the results of the thesis to the modern vehicles fleet. The modern fleet differs significantly in technology from the dataset that we use in this thesis. The dataset consists of measurements of HC, CO, and NOX emissions for 542 gasoline-fuelled passenger vehicles, under six test drive-cycles, before and after the vehicles were tuned. Nevertheless, the dataset has a number of limitations such as limited model year range, limited representations of modal operations, and limitations of the measurements of emissions based only on averages of test drive-cycles, in addition to the exclusion of high-emitter emission measurements from the dataset. The dataset has a limited model year range, i.e., between 1980 and 1991. We highlight the age of the dataset, and acknowledge that the present vehicle fleet varies technologically from the vehicles in the dataset used in this thesis. Furthermore, the dataset has a limited number of makes - Holden, Ford, Toyota, Nissan, and Mitsubishi. There are also a limited number of modal operations. The model operations presented in the dataset are cold start, warming-up, and hot stabilised driving conditions. However, enrichment episodes are not adequately presented in the test-drive cycles of the dataset. Moreover, the dataset does not take into account driving behaviour influences, and all measurements are cycle-based averages. The emission measurements of laboratory-based testings are aggregated over a test drive cycle, and the test drive-cycle represents an average trip over an average speed. The exclusion of the measurements of high emitting vehicles from the dataset introduces further limitations. Remote sensing studies show that 20 percent of the on-road vehicle fleet is responsible for 80 percent of HC and CO emissions. The findings of the thesis assist in the identification of the best strategies to mitigate the most adverse effects of air-pollution, such as the most severe pollution that have the most undesirable pollution effects. Also, they provide decision-makers with valuable information on how changes in the operation of the transport system influence the urban air-quality. Moreover, the thesis provides information on how vehicle emissions affect the chemistry of the atmosphere and degrade the urban air-quality.en_AU
dc.publisherUniversity of Sydney
dc.publisherSydney Business School
dc.publisherInstitute of Transport and Logistics Studies
dc.rightsThe author retains copyright of this thesis.
dc.rights.urihttp://www.library.usyd.edu.au/copyright.html
dc.subjectVehicle Emissions Modelsen_AU
dc.subjectUrban Air-qualityen_AU
dc.subjectTransport Pollutionen_AU
dc.subjectVehicles Air-pollutionen_AU
dc.subjectCARTen_AU
dc.subject3SLSen_AU
dc.subjectGasoline-Fuelled Engine Combustionen_AU
dc.subjectHC ,CO , and NOX emissionsen_AU
dc.titleModelling vehicle emissions from an urban air-quality perspective:testing vehicle emissions interdependenciesen_AU
dc.typePhD Doctorateen_AU
dc.date.valid2010-01-01en_AU


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