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published_or_final_version / Geography / Master / Master of Philosophy

Lee, Ching Yuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 275-288). / Abstracts in English and Chinese. / List of Figures --- p.vii / List of Tables --- p.xv / List of Abbreviations and Symbols Used --- p.xix / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Roadside Air Pollution Problem --- p.1 / Chapter 1.1.1 --- Nitrogen Dioxide --- p.9 / Chapter 1.1.2 --- Benzene --- p.12 / Chapter 1.1.3 --- Heat and Noise --- p.13 / Chapter 1.2 --- Treatment Methods for Removal of Ambient Air Pollutants --- p.15 / Chapter 1.2.1 --- Physical and Chemical Methods --- p.15 / Chapter 1.2.2 --- Bioremediation --- p.17 / Chapter 1.2.3 --- Passive System and Active System --- p.18 / Chapter 1.3 --- Research Strategy --- p.18 / Chapter 1.3.1 --- Plant as a Bioremediating Agent --- p.18 / Chapter 1.3.2 --- Spent Mushroom Compost (SMC) as a Bioremediating Agent --- p.20 / Chapter 1.3.3 --- An Integrated System for Air Bioremediation --- p.24 / Chapter 1.3.4 --- Aim and Objectives of the Project --- p.24 / Chapter 1.4 --- Significance of the Project --- p.25 / Chapter 2. --- Materials and Methods --- p.26 / Chapter 2.1 --- Source of Materials --- p.28 / Chapter 2.1.1 --- Ingredients of Plant Growth Substrate --- p.28 / Chapter 2.1.2 --- Plants --- p.30 / Chapter 2.2 --- Formulation of the Plant Substrate --- p.31 / Chapter 2.2.1 --- Water Holding Capacity --- p.31 / Chapter 2.2.2 --- Water Retention --- p.32 / Chapter 2.2.3 --- Seed Germination Toxicity and Tissue Elongation --- p.33 / Chapter 2.2.4 --- Bulk Density and Porosity --- p.34 / Chapter 2.2.5 --- Substrate Shrinkage --- p.35 / Chapter 2.3 --- Characterization of the Materials --- p.36 / Chapter 2.3.1 --- pH --- p.36 / Chapter 2.3.2 --- Electrical Conductivity --- p.36 / Chapter 2.3.3 --- % Organic Matter --- p.37 / Chapter 2.3.4 --- "Nutrient Contents (Nitrogen, Phosphorus, Potassium, Magnesium, Calcium, Sodium, Iron)" --- p.37 / Chapter 2.3.5 --- Total Organic Carbon --- p.40 / Chapter 2.3.6 --- Detection for Heavy Metal Contaminants --- p.40 / Chapter 2.3.7 --- Detection for Organic Contaminants --- p.41 / Chapter 2.3.8 --- Extraction Efficiency of Heavy Metal Content and Organic Contaminants --- p.43 / Chapter 2.3.9 --- Outdoor Growing Trial of the Bioremediation System using Various Plant Species --- p.45 / Chapter 2.4 --- Characterization of the Plant --- p.47 / Chapter 2.4.1 --- Leaf Area Estimation --- p.47 / Chapter 2.4.2 --- Density of Plantlet --- p.48 / Chapter 2.4.3 --- Growth Rate of Plantlet in Water --- p.49 / Chapter 2.5 --- Temperature Stabilization Test --- p.50 / Chapter 2.6 --- NO2 Removal Test --- p.52 / Chapter 2.6.1 --- Preparation of Plantlets --- p.52 / Chapter 2.6.2 --- Generation and Sampling of NO2 --- p.52 / Chapter 2.6.3 --- Effect of N02 Concentration on RE --- p.55 / Chapter 2.6.4 --- Effect of Various Combinations in the Bioremediation System --- p.56 / Chapter 2.6.5 --- "Comparison to Photocatalytic Paint, Physical Sorbents and Other Planting Media" --- p.57 / Chapter 2.6.6 --- Effect of Temperature --- p.60 / Chapter 2.6.7 --- Effect of Retention Time --- p.61 / Chapter 2.6.8 --- Effect of Exposed Time --- p.61 / Chapter 2.6.9 --- Composition Analysis --- p.62 / Chapter 2.6.10 --- Post Tests after N02 Removal Test --- p.63 / Chapter 2.6.11 --- Chlorophyll and Carotenoid Contents --- p.63 / Chapter 2.6.12 --- Phenolic Content --- p.64 / Chapter 2.6.13 --- Total Microbial Count --- p.65 / Chapter 2.6.14 --- Activities of Antioxidative Enzymes --- p.66 / Chapter 2.6.15 --- Nitrite Oxidizing Enzyme --- p.68 / Chapter 2.7 --- Benzene Removal Test --- p.69 / Chapter 2.7.1 --- Preparation of Plantlets --- p.69 / Chapter 2.7.2 --- Generation and Sampling of Benzene --- p.69 / Chapter 2.7.3 --- Effect of Benzene Concentration on RE --- p.74 / Chapter 2.7.4 --- Effect of Various Combinations in the Bioremediation System --- p.75 / Chapter 2.7.5 --- Effect of Temperature --- p.76 / Chapter 2.7.6 --- Effect of Exposed Time --- p.77 / Chapter 2.7.7 --- Effect of Retention Time --- p.78 / Chapter 2.7.8 --- Composition Analysis --- p.78 / Chapter 2.7.9 --- "Comparison to Physical Sorbents, Photocatalytic Paint and Other Planting Media" --- p.79 / Chapter 2.7.10 --- Trials in Order to Increase RE of Benzene --- p.80 / Chapter 2.7.11 --- Residual Benzene in Substrate --- p.83 / Chapter 2.7.12 --- Post Tests after Benzene Removal Test --- p.84 / Chapter 2.7.13 --- Catechol Oxidase Activity --- p.85 / Chapter 2.8 --- Removal Tests for Other Air Pollutants --- p.86 / Chapter 2.9 --- Field Study --- p.88 / Chapter 2.10 --- Statistical Analysis --- p.98 / Chapter 3. --- Results --- p.99 / Chapter 3.1 --- Formulation of Plant Substrate --- p.99 / Chapter 3.1.1 --- Dose of SMC in Substrate Formula --- p.99 / Chapter 3.1.2 --- Dose of SAP in Substrate Formula --- p.105 / Chapter 3.1.3 --- Dose of Rice Hull in Substrate Formula --- p.111 / Chapter 3.2 --- Characterization of the Optimized Wedelia- growing Substrate --- p.118 / Chapter 3.2.1 --- Physical and Chemical Analysis --- p.118 / Chapter 3.2.2 --- Nutrient and Metal Contents --- p.120 / Chapter 3.2.3 --- Detection of Heavy Metal Contaminants --- p.124 / Chapter 3.2.4 --- Detection for Organic Contaminants --- p.126 / Chapter 3.3 --- Outdoor Growing Trial of Various Plants --- p.138 / Chapter 3.4 --- Plant Characterization --- p.143 / Chapter 3.4.1 --- Growth Rate of Plantlets in Water --- p.143 / Chapter 3.5 --- Temperature Stabilization Test --- p.146 / Chapter 3.6 --- NO2 Removal Test --- p.149 / Chapter 3.6.1 --- Effect of NO2 Concentration on RE --- p.149 / Chapter 3.6.2 --- Effect of Various Combinations in the Bioremediation System --- p.156 / Chapter 3.6.3 --- "Comparison to Photocatalytic Paint, Physical Sorbents and Other Planting Media" --- p.160 / Chapter 3.6.4 --- Effect of Temperature --- p.164 / Chapter 3.6.5 --- Effect of Retention Time --- p.166 / Chapter 3.6.6 --- Effect of Exposed Time --- p.168 / Chapter 3.6.7 --- Post Test Results After Various Exposed Times --- p.170 / Chapter 3.6.8 --- Microbial Count After Various Exposed Times --- p.176 / Chapter 3.6.9 --- Contribution of the Components of the Bioremediation System to Remove NO2 --- p.178 / Chapter 3.7 --- Benzene Removal Test --- p.183 / Chapter 3.7.1 --- Effect of Benzene Concentration on RE --- p.183 / Chapter 3.7.2 --- Effect of Various Combinations in the Bioremediation System --- p.186 / Chapter 3.7.3 --- Effect of Temperature --- p.190 / Chapter 3.7.4 --- Effect of Retention Time --- p.192 / Chapter 3.7.5 --- Effect of Exposed Time --- p.194 / Chapter 3.7.6 --- Contribution of Components of the Bioremediation System to Remove Benzene --- p.198 / Chapter 3.7.7 --- Optimization of the Benzene Removal of the Bioremediation System --- p.200 / Chapter 3.7.8 --- "Comparison to Photocatalytic Paint Coatings, Physical Sorbents and Other Planting Media" --- p.204 / Chapter 3.8 --- Removal Test for Other Air Pollutants --- p.208 / Chapter 3.9 --- Field Study I --- p.210 / Chapter 3.9.1 --- Environmental Parameters --- p.210 / Chapter 3.9.2 --- Noise --- p.212 / Chapter 3.9.3 --- Removal versus Distance --- p.213 / Chapter 3.9.4 --- Barrier Effect by Canvas --- p.216 / Chapter 3.9.5 --- NO2 Concentration --- p.216 / Chapter 3.9.6 --- VOC Concentration --- p.218 / Chapter 3.10 --- Field Study II --- p.220 / Chapter 3.10.1 --- Environmental Parameters --- p.220 / Chapter 3.10.2 --- Noise --- p.222 / Chapter 3.10.3 --- NO2 Concentration --- p.224 / Chapter 3.10.4 --- VOC Concentration --- p.225 / Chapter 4. --- Discussion --- p.228 / Chapter 4.1 --- Formulation of a Plant-growing Substrate --- p.228 / Chapter 4.2 --- Temperature Stabilization --- p.231 / Chapter 4.3 --- Dynamic Flow Through System in Pollutant Removal Experiment --- p.233 / Chapter 4.4 --- N02 Removal Test --- p.237 / Chapter 4.4.1 --- Limiting Factors of NO2 Removal --- p.237 / Chapter 4.4.2 --- Adsorption Isotherm --- p.239 / Chapter 4.4.3 --- Contribution of NO2 Removal by Various Components --- p.241 / Chapter 4.4.4 --- Comparison of NO2 Removal with Other Systems --- p.242 / Chapter 4.4.5 --- Comparison of NO2 Removal with Other Studies --- p.246 / Chapter 4.4.6 --- Toxicity of NO2 towards the Bioremediation System --- p.247 / Chapter 4.5 --- Interpretation of Results in Benzene Removal Test --- p.251 / Chapter 4.5.1 --- Limiting Factors of Benzene Removal --- p.251 / Chapter 4.5.2 --- Adsorption Isotherm --- p.253 / Chapter 4.5.3 --- Contribution of Benzene Removal by Various Components --- p.254 / Chapter 4.5.4 --- Comparison of Benzene Removal with Other Systems --- p.255 / Chapter 4.5.5 --- Trials in Order to Increase RE of Benzene --- p.256 / Chapter 4.5.6. --- Comparison of Benzene Removal with Other Studies --- p.258 / Chapter 4.6 --- Removal of Other Air Pollutants --- p.261 / Chapter 4.7 --- Field Studies with the Vertical Panels of the Bioremediation System --- p.264 / Chapter 4.7.1 --- Barrier Effect by Canvas --- p.264 / Chapter 4.7.2 --- Temperature Buffering --- p.265 / Chapter 4.7.3 --- Sound Attenuation --- p.266 / Chapter 4.7.4 --- NO2 and VOC Removal --- p.268 / Chapter 5. --- Conclusion --- p.272 / Chapter 6. --- Further Investigation --- p.274 / Chapter 7. --- References --- p.275

Fan, Xiaopeng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 101-108). / Abstracts in English and Chinese. / LIST OF FIGURES --- p.viii / LIST OF TABLES --- p.x / LIST OF ABBREVIATIONS --- p.xi / Chapter Chapter One - --- INTRODUCTION --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Hong Kong as a Case Study --- p.4 / Chapter 1.3 --- Research Objectives --- p.6 / Chapter 1.4 --- Significance of the Research --- p.7 / Chapter Chapter Two - --- LITERATURE REVIEW --- p.9 / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.1.1 --- Origin of environmental justice --- p.9 / Chapter 2.1.2 --- Concept --- p.10 / Chapter 2.2 --- Review of environmental inequality studies --- p.12 / Chapter 2.2.1 --- The siting of hazardous waste treatment storage or disposal facilities --- p.12 / Chapter 2.2.2 --- Release of toxics from industries and facilities --- p.13 / Chapter 2.2.3 --- Population exposure to noise and air pollution --- p.15 / Chapter 2.2.3.1 --- Noise --- p.15 / Chapter 2.2.3.2 --- Air pollution --- p.16 / Chapter 2.2.4 --- Dissimilarity of the findings --- p.20 / Chapter 2.3 --- Research methodology --- p.21 / Chapter 2.3.1 --- Environmental indicators and parameters --- p.21 / Chapter 2.3.2 --- Pollution exposure assessment method --- p.22 / Chapter 2.3.3 --- Choice of socioeconomic indicators --- p.24 / Chapter 2.3.3.1 --- Demographic and socioeconomic indicators --- p.24 / Chapter 2.3.3.2 --- Source of socioeconomic data --- p.26 / Chapter 2.3.4 --- Study unit --- p.26 / Chapter 2.3.5 --- Analytical methods --- p.29 / Chapter 2.4 --- Factors contributing to inequality --- p.29 / Chapter 2.5 --- Summary --- p.31 / Chapter Chapter Three - --- RESEARCH METHODOLOGY --- p.33 / Chapter 3.1 --- Research Framework --- p.33 / Chapter 3.2 --- Study Unit and Sampling Strategy --- p.34 / Chapter 3.2.1 --- Study unit used in other studies --- p.35 / Chapter 3.2.2 --- Study unit --- p.35 / Chapter 3.2.3 --- Sampling Method --- p.37 / Chapter 3.3 --- Air pollution exposure assessment --- p.39 / Chapter 3.3.1 --- Assessment method --- p.40 / Chapter 3.3.2 --- Calculation of emission inventory --- p.42 / Chapter 3.3.2.1 --- Emission factors estimated by EMFAC-HK model --- p.42 / Chapter 3.3.2.2 --- Vehicular emission inventory --- p.44 / Chapter 3.3.3 --- Simulation by air pollution dispersion model --- p.44 / Chapter 3.3.3.1 --- IMMISnet Model --- p.44 / Chapter 3.3.3.2 --- Data requirement of MMISn e t Model --- p.45 / Chapter 3.3.3.3 --- Output ofIMMISnet Model --- p.49 / Chapter 3.4 --- Population socioeconomic indicators --- p.51 / Chapter 3.5 --- Analytical method --- p.53 / Chapter 3.6 --- Summary --- p.53 / Chapter Chapter Four - --- FINDINGS AND DISCUSSION --- p.55 / Chapter 4.1 --- Pollution Exposure Assessment --- p.55 / Chapter 4.2 --- The differential exposure of different age and SDI groups --- p.60 / Chapter 4.2.1 --- The selection of socioeconomic indicators --- p.60 / Chapter 4.2.2 --- Decile analysis --- p.64 / Chapter 4.2.2.1 --- Differential exposure based on age groups --- p.64 / Chapter 4.2.2.2 --- Differential exposure based on SDI groups --- p.71 / Chapter 4.3 --- Regression Analysis --- p.75 / Chapter 4.3.1 --- Pearson's correlation analysis --- p.75 / Chapter 4.3.2 --- Stepwise regression analysis --- p.81 / Chapter 4.4 --- Discussion --- p.87 / Chapter Chapter Five - --- CONCLUSION --- p.90 / Chapter 5.1 --- Introduction --- p.90 / Chapter 5.2 --- Summary of Findings --- p.90 / Chapter 5.3 --- Limitation of the study --- p.92 / Chapter 5.4 --- Recommendations for further study --- p.93 / APPENDIX --- p.94 / REFERENCES --- p.101

published_or_final_version / abstract / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

by Luk Shiu-fai. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (p. 234-240). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

There has been an increased push away from the traditional combustion-engine powered vehicle due to environmental, health, and political concerns. As a result, alternative methods of transportation such as electric vehicles (EVs) have gaining popularity in the market. However, the EVs are not penetrating the market as quickly as expected, due in part to a combination of range, charge anxiety, and their financial costs. EVs cannot travel far due to limited driving range and require longer charge times than combustion-engine powered vehicles to recharge. Coupled with a lacking infrastructure for charging, the feasibility of an all-electric transportation market is still not possible. We propose a novel system in which we study and characterize the feasibility of increasing the effective driving range of a battery electric vehicle by utilizing inductive charge transfer to create an ad-hoc charging network where vehicles can “share” charge with one another. The application of wireless charge transfer from vehicle-to-vehicle (V2V) is the first of its kind and does not require any changes to current metropolitan infrastructures. Through the use of computer networking and communications algorithms, we analyze real-world commuter and taxi data to determine the potential effectiveness of such a system. We propose a participation and incentive mechanism to encourage participation in this network that enables the system to be functional.To illustrate proof of principle for V2V charging at traffic lights, we simulate a simplified model in which vehicles only exchange charge at traffic lights without coordination with other vehicles. Using a greedy heuristic, vehicles only exchange charge if they happen to meet another vehicle that has charge to share. The heuristic is greedy since decisions are made at each iteration with longer optimality not being considered. We are able to demonstrate an increase in effective driving range of EVs using these simplistic assumptions. In this thesis, we develop and quantify a complete simulation framework, which allows EVs to operate using charge sharing. We analyze data from the United States Department of Transportation, New York City Taxi and Limousine Commission, and Regional New York City data sources to understand the cumulative driving distance distributions for passenger/commuter vehicles and taxicabs in large metropolitan areas such as New York City. We show that the driving distributions can best be represented as heavy-tail distribution functions with most commuter vehicles not requiring additional charge during a typical day’s usage of their vehicle as compared to taxicabs, which regularly travel more than 100 miles during a 12-hour shift. We develop and parameterize several variables for input into our simulation framework including driving distance, charge exchange heuristics, models for determining pricing of charge units, traffic density, and geographic location. The inclusion of these parameters helps to build a framework that can be utilized for any metropolitan area to determine the feasibility of EVs. We have performed extensive evaluation of our model using real data. Our current simulations indicate that we can increase the effective distance that an electric vehicle travels by a factor of at least 2.5. This increased driving range makes EVs a more feasible mode of transportation for fleet vehicles such as taxicabs that rely heavily on commuting long cumulative distances. We have identified areas for future improvement to add further parameters to make the model even more sensitive. Finally, we focus on the application of our charge sharing framework in a real-world application for utilizing this methodology for the New York City bus system. In partnership with the New York City MTA, we launched a feasibility study of converting the currently majority hybrid bus fleet into a complete electric bus fleet with charging available at bus stops during scheduled bus stops. Unlike the earlier charge sharing framework, this simulation focuses on discrete distances that are traveled by the bus before having an opportunity to charge at the next bus stop. In this scenario, a large source of variability is the amount of time that the bus is able to stop at a bus stop for charging since this is determined by the amount of time needed to successfully embark and disembark the passengers at the given bus stop. This particular variability impacts how much charge the bus is able to gain during any given stop. We conclude with a list of opportunities for future work in expanding the model with additional parameters and conclusions of our work. Further, we identify areas of further research that outline the potential positive and negative outcomes from a charge sharing system that can be extended to various other applications including micro-mobility applications such as electric scooters and bicycles.

This thesis reports on the results of a mobile source emissions inventory for the University of Central Florida (UCF). For a large urban university, the majority of volatile organic compounds (VOC), oxides of nitrogen (NOx), and carbon dioxide (CO2) emissions come from onroad sources: personal vehicles and campus shuttles carrying students, faculty, staff, and administrators to and from the university, as well as university business trips. In addition to emissions from daily commutes, non-road equipment such as lawnmowers, leaf blowers, small maintenance vehicles, and other such equipment utilized on campus contributes to a significant portion to the total emissions from the university. UCF has recently become the second largest university in the nation (with over 56,000 students enrolled in the fall 2010 semester), and contributes significantly to VOC, NOx, and CO2 emissions in Central Florida area. In this project, students, faculty, staff, and administrators were first surveyed to determine their commuting distances and frequencies. Information was also gathered on vehicle type, and age distribution of the personal vehicles of students, faculty, administration, and staff as well as their bus, carpool, and alternate transportation usage. The EPA approved mobile source emissions model, Motor Vehicle Emissions Simulator (MOVES2010a), was used to calculate the emissions from on-road vehicles, and UCF fleet gasoline consumption records were used to calculate the emissions from non-road equipment and on campus UCF fleet vehicles. The results of the UCF mobile source emissions inventory are reported and compared to a recently completed emissions inventory for the entire three-county area in Central Florida.