Quality Perspective: Testing Vehicle Emissions Interdependencies

Modelling Vehicle Emissions from an Urban Air- Quality Perspective: Testing Vehicle Emissions Interdependencies Wafa M. Dabbas A Thesis Submitted in Fulfilment of the Requirements for the Degree of Doctor of Philosophy Institute of Transport and Logistics Studies Faculty of Economics and Business The University of Sydney February 2010 Statement of Original Authorship ―I hereby declare that this submission is my own work and, to the best of my knowledge and belief, it contains no material previously published or written by another person, except where due reference is made in the thesis. Nor it contains material that to a substantial extent has been accepted for the award of any other degree or diploma at USYD or any other educational institution, except where due acknowledgment is made in the thesis‖ Wafa M. Dabbas 10 February 2010 I Acknowledgements I would like to express my gratitude to both Professor David A. Hensher and Professor Peter R. Stopher, for being encouraging and supportive throughout the development of my thesis. Professor Hensher has had a great influence on nourishing my critical thinking to comprehend transport research studies. Hensher was always willing to discuss my theoretical trajectories, and to suggest several useful conceptual paradigms. It is an absolute worthy experience to work with him, and to learn various methods for investigating transport studies. Professor Stopher has directly supervised me. He has responded diligently to the drafts of my thesis, and has given academic rigorous critiques to the initial drafts. Professor Stopher is a very demanding supervisor, but working with him is valuable. I am grateful to the study team of the Australian National In-service Emissions (NISE), and Messrs Parsons Australia Ltd. for providing the data, without which my thesis would not have been possible. I wish also, to thank Salford Systems in San Diego California, for providing the specialist software used in the development of this thesis, and Maurice Joseph and Mark Holland at Salford Systems in Sydney, for providing a recent version of the software. I would like to extend my appreciation to my sponsor, International Road Federation (IRF) who had granted me a scholarship to purse my programme, and also to Institute of Transport and Logistics Studies (ITLS) at the University of Sydney who offered a complement scholarship. Also, I would like to express my appreciation to Ministry of Planning and International Cooperation in Jordan, and to thank all, whom had given me assistance throughout working on my thesis, particularly my colleagues in the Ph.D. programme, the staff, and all the academics at ITLS. Support and compassion are indispensable during a Ph.D. journey. My special heartfelt thanks go to my mother, sisters, and brothers, without their support and whole-heartedly feelings; I could not have completed successfully my journey. They have been a constant source of inspiration and encouragement. They have II been also, supportive in their prayers ―Du’aa’‖, and very much thoughtful via their phone calls and text messages. They were at all times, as being next to me, albeit thousands of miles those separate our physical locations. Also, I would never ever forget the continuous support and financial assistance offered by my beloved brothers when I was in a financial hardship. Finally, I must acknowledge with highly regard the memory of my father ―Rahimahu Allah‖. III Abstract 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 IV 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. V 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.

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