published_or_final_version / abstract / China Development Studies / Master / Master of Arts

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

Ma Fung-yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 137-141). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Table of Contents --- p.v / List of Tables --- p.ix / List of Figures --- p.x / Chapter CHAPTER 1 --- INTRODUCTION / Chapter 1.1 --- Introduction and background --- p.1 / Chapter 1.2 --- Objectives --- p.4 / Chapter 1.3 --- Significance --- p.5 / Chapter 1.4 --- Organization of the thesis --- p.5 / Chapter CHAPTER 2 --- LITERATURE REVIEW / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Hyperspectral remote sensing --- p.7 / Chapter 2.2.1 --- Current imaging spectrometers available --- p.8 / Chapter 2.2.2 --- Applications of hyperspectral remote sensing --- p.9 / Chapter 2.2.2.1 --- Biochemistry of vegetation --- p.10 / Chapter 2.2.2.2 --- Spatial and temporal patterns of vegetation --- p.12 / Chapter 2.3 --- Tree species recognition --- p.12 / Chapter 2.3.1 --- Factors affecting spectral reflectance of vegetation --- p.14 / Chapter 2.3.1.1 --- Optical properties of leaf --- p.14 / Chapter 2.3.1.2 --- Canopy structure --- p.15 / Chapter 2.3.1.3 --- Canopy cover --- p.16 / Chapter 2.3.1.4 --- Illumination and viewing geometry --- p.16 / Chapter 2.3.1.5 --- Spatial and temporal dynamics of plants --- p.17 / Chapter 2.3.2 --- Classification algorithms for hyperspectral analysis --- p.17 / Chapter 2.3.2.1 --- Use of derivative spectra for tree species recognition --- p.17 / Chapter 2.3.2.2 --- Linear discriminant analysis --- p.18 / Chapter 2.3.2.3 --- Artificial neural network --- p.19 / Chapter 2.3.3 --- Tree species recognition using hyperspectral data --- p.21 / Chapter 2.4 --- Data compression and feature extraction --- p.22 / Chapter 2.4.1 --- Analytical techniques of data compression --- p.23 / Chapter 2.4.2 --- Analytical techniques of feature extraction --- p.25 / Chapter 2.4.2.1 --- Feature selection by correlation with biochemical and biophysical data --- p.25 / Chapter 2.4.2.2 --- Spatial autocorrelation-based feature selection --- p.27 / Chapter 2.4.2.3 --- Spectral autocorrelation-based feature selection --- p.29 / Chapter 2.4.2.3.1 --- Optimization with distance metrics --- p.29 / Chapter 2.4.2.3.2 --- Stepwise linear discriminant analysis --- p.30 / Chapter 2.5 --- Summary --- p.31 / Chapter CHAPTER 3 --- METHODOLOGY / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.2 --- Study site --- p.33 / Chapter 3.3 --- Instrumentation --- p.34 / Chapter 3.4 --- Data collection --- p.35 / Chapter 3.4.1 --- Laboratory measurement --- p.36 / Chapter 3.4.2 --- In situ measurement --- p.39 / Chapter 3.5 --- Methods of data analysis --- p.40 / Chapter 3.5.1 --- Preprocessing of data --- p.40 / Chapter 3.5.2 --- Compilation of hyperspectral database --- p.42 / Chapter 3.5.3 --- Tree species recognition --- p.42 / Chapter 3.5.3.1 --- Linear discriminant analysis --- p.44 / Chapter 3.5.3.2 --- Artificial neural network --- p.44 / Chapter 3.5.3.3 --- Accuracy assessment --- p.45 / Chapter 3.5.3.4 --- Comparison of different data processing strategies and classifiers --- p.45 / Chapter 3.5.3.5 --- Comparison of data among different seasons --- p.46 / Chapter 3.5.3.6 --- Comparison of laboratory and in situ data --- p.46 / Chapter 3.5.4 --- Data compression --- p.47 / Chapter 3.5.5 --- Band selection --- p.47 / Chapter 3.6 --- Summary --- p.48 / Chapter CHAPTER 4 --- RESULTS AND DISCUSSIONS OF TREE SPECIES RECOGNITION / Chapter 4.1 --- Introduction --- p.50 / Chapter 4.2 --- Characteristics of hyperspectral data --- p.50 / Chapter 4.3 --- Tree species recognition --- p.79 / Chapter 4.3.1 --- Comparison of different classifiers --- p.82 / Chapter 4.3.1.1 --- Efficiency of the classifiers --- p.83 / Chapter 4.3.1.2 --- Discussions --- p.83 / Chapter 4.3.2 --- Comparison of different data processing strategies --- p.84 / Chapter 4.3.3 --- Comparison of data among different seasons --- p.86 / Chapter 4.3.4 --- Comparison of laboratory and in situ data --- p.88 / Chapter 4.4 --- Summary --- p.92 / Chapter CHAPTER 5 --- RESULTS AND DISCUSSIONS OF DATA COMPRESSION AND BAND SELECTION / Chapter 5.1 --- Introduction --- p.93 / Chapter 5.2 --- Data compression --- p.93 / Chapter 5.2.1 --- PCA using in situ spectral data --- p.93 / Chapter 5.2.1.1 --- Characteristics of PC loadings --- p.95 / Chapter 5.2.1.2 --- Scatter plots of PC scores --- p.96 / Chapter 5.2.2 --- PCA using laboratory spectral data --- p.99 / Chapter 5.2.2.1 --- Characteristics of PC loadings --- p.102 / Chapter 5.2.2.2 --- Scatter plots of PC scores --- p.103 / Chapter 5.2.2.3 --- Results of tree species recognition using PC scores --- p.107 / Chapter 5.2.3 --- Implications --- p.107 / Chapter 5.3 --- Band selection --- p.108 / Chapter 5.3.1 --- Preliminary band selection using stepwise discriminant analysis --- p.108 / Chapter 5.3.1.1 --- Selection of spectral bands --- p.109 / Chapter 5.3.1.2 --- Classification results of the selected bands --- p.109 / Chapter 5.3.1.3 --- Seasonal comparison using stepwise linear discriminant analysis --- p.114 / Chapter 5.3.1.4 --- Implications --- p.116 / Chapter 5.3.2 --- Band selection using hierarchical clustering technique --- p.116 / Chapter 5.3.2.1 --- Hierarchical clustering procedure --- p.116 / Chapter 5.3.2.2 --- Selection of spectral band sets --- p.119 / Chapter 5.3.2.3 --- Classification results of the selected band sets --- p.124 / Chapter 5.4 --- Summary --- p.127 / Chapter CHAPTER 6 --- SUMMARY AND CONCLUSION / Chapter 6.1 --- Introduction --- p.129 / Chapter 6.2 --- Summary --- p.129 / Chapter 6.2.1 --- Tree species recognition --- p.129 / Chapter 6.2.2 --- Data compression --- p.130 / Chapter 6.2.3 --- Band selection --- p.131 / Chapter 6.3 --- Limitations of this study --- p.132 / Chapter 6.4 --- Recommendations for further studies --- p.133 / Chapter 6.5 --- Conclusion --- p.136 / BIBLIOGRAPHY --- p.137 / APPENDICES / Appendix 1 Reflectance of the 25 tree species in four seasons with three levels of leaf density --- p.142-166 / "Appendix 2 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by linear discriminant analysis for each season" --- p.167-178 / "Appendix 3 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra with 138 bands classified by neural networks for each season" --- p.179-190 / Appendix 4 Confusion matrices of tree species recognition using 21 tree species with original spectra classified by linear discriminant analysis for seasonal comparison --- p.191-193 / Appendix 5 Confusion matrices of tree species recognition using the first eight PC scores classified by linear discriminant analysis for each season --- p.194-197 / "Appendix 6 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 2) for each season" --- p.198-209 / "Appendix 7 Confusion matrices of tree species recognition using original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis (Case 3) for each season" --- p.210-220 / "Appendix 8 Confusion matrices of tree species recognition using 21 tree species with original spectra, first derivatives spectra and second derivatives spectra classified by stepwise linear discriminant analysis for seasonal comparison" --- p.221-229 / Appendix 9 Confusion matrices of tree species recognition using the spectral bands selected by hierarchical clustering procedures and classified by linear discriminant analysis for each season --- p.230-257

Poon Mei-yan, Pauline. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 113-118). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Table of Contents --- p.v / List of Tables --- p.viii / List of Figures --- p.x / List of Plates --- p.xi / Chapter CHAPTER 1 --- INTRODUCTION / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- The problem: deficiencies of EIA process --- p.2 / Chapter 1.3 --- Necessity of EIA follow-up --- p.4 / Chapter 1.4 --- Objectives and scope of the study --- p.5 / Chapter 1.5 --- Significance of the study --- p.6 / Chapter 1.6 --- Thesis outline --- p.7 / Chapter CHAPTER 2 --- EIA FOLLOW-UP PRACTICES: TRENDS AND FUNCTIONS / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.2 --- EIA follow-up - the conceptual background --- p.9 / Chapter 2.2.1 --- Working definitions of monitoring and auditing --- p.9 / Chapter 2.2.2 --- Types of monitoring --- p.10 / Chapter 2.2.3 --- Types of auditing --- p.10 / Chapter 2.3 --- Trends of EIA follow-up practice --- p.11 / Chapter 2.4 --- Functions of EIA follow-up --- p.13 / Chapter 2.5 --- Role of EIA follow-up in EIA --- p.16 / Chapter 2.6 --- Ingredients of an effective EIA follow-up system --- p.18 / Chapter 2.6.1 --- Independence of the monitoring and audit team --- p.18 / Chapter 2.6.2 --- Proactive project management --- p.18 / Chapter 2.6.3 --- Clearly defined EIA follow-up programme --- p.19 / Chapter 2.6.4 --- Well-designed monitoring scheme --- p.20 / Chapter 2.6.5 --- Good information flow and feedback mechanism --- p.21 / Chapter 2.7 --- EIA follow-up practice in some countries --- p.21 / Chapter 2.8 --- EIA follow-up practice in Hong Kong --- p.22 / Chapter 2.8.1 --- Necessity of EIA follow-up in Hong Kong --- p.22 / Chapter 2.8.2 --- Characteristics of EIA follow-up in Hong Kong --- p.23 / Chapter 2.8.3 --- How EM&A is implemented in Hong Kong? --- p.23 / Chapter 2.9 --- Conclusion --- p.27 / Chapter CHAPTER 3 --- METHODOLOGY / Chapter 3.1 --- Introduction --- p.29 / Chapter 3.2 --- Selection of the study area --- p.30 / Chapter 3.3 --- The Pak Skek Kok reclamation project --- p.31 / Chapter 3.4 --- The EM&A programme --- p.37 / Chapter 3.5 --- Methods of assessing the EM&A programme --- p.44 / Chapter 3.6 --- Use of the monitoring data --- p.47 / Chapter 3.7 --- Limitations of the study --- p.50 / Chapter 3.8 --- Conclusion --- p.51 / Chapter CHAPTER 4 --- ENVIRONMENTAL PERFORMANCE OF THE PROJECT / Chapter 4.1 --- Introduction --- p.52 / Chapter 4.2 --- Impact audit --- p.52 / Chapter 4.2.1 --- Noise impacts --- p.52 / Chapter 4.2.2 --- Dust impacts --- p.58 / Chapter 4.3 --- Compliance audit --- p.68 / Chapter 4.3.1 --- Daytime noise level --- p.68 / Chapter 4.3.2 --- Evening noise level --- p.69 / Chapter 4.3.3 --- 24hr-average TSP level --- p.71 / Chapter 4.3.4 --- 24hr-average RSP level --- p.71 / Chapter 4.3.5 --- 1hr-average TSP level --- p.72 / Chapter 4.4 --- Implementation audit --- p.72 / Chapter 4.4.1 --- Implementation of noise mitigation measures --- p.73 / Chapter 4.4.2 --- Implementation of dust mitigation measures --- p.75 / Chapter 4.4.3 --- Effectiveness of dust mitigation measures --- p.78 / Chapter 4.5 --- Conclusion --- p.83 / Chapter CHAPTER 5 --- EVALUATION OF THE EM&A PROGRAMME / Chapter 5.1 --- Introduction --- p.85 / Chapter 5.2 --- Fulfillment of the stated objectives of the EM&A programme --- p.85 / Chapter 5.3 --- Effectiveness of the EM&A programme --- p.89 / Chapter 5.3.1 --- Independence of the monitoring and audit team --- p.90 / Chapter 5.3.2 --- Proactive project management --- p.90 / Chapter 5.3.3 --- Clearly defined EIA follow-up programme --- p.93 / Chapter 5.3.4 --- Well-designed monitoring scheme --- p.94 / Chapter 5.3.5 --- Good information flow and feedback mechanism --- p.100 / Chapter 5.4 --- Recommendations for improving EM&A in Hong Kong --- p.101 / Chapter 5.5 --- Conclusion --- p.103 / Chapter CHAPTER 6 --- CONCLUSION / Chapter 6.1 --- Summary of findings --- p.104 / Chapter 6.2 --- Discussion of findings --- p.106 / Chapter 6.3 --- Suggestions for further studies --- p.109 / BIBLIOGRAPHY --- p.113

Chan Kwan-ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 91-105). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.v / Contents --- p.vi / List of figures and tables --- p.ix / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- Literature review --- p.1 / Chapter 1.1.1 --- The effect of pollutants on the genetic materials of aquatic organisms --- p.1 / Chapter 1.1.1.1 --- Response of individual to genotoxicants --- p.1 / Chapter 1.1.1.2 --- Effects of genotoxicants on population structure --- p.3 / Chapter 1.1.2 --- Application of genetic markers in monitoring water pollution --- p.3 / Chapter 1.1.2.1 --- DNA adduct --- p.4 / Chapter 1.1.2.2 --- Sister chromatid exchange (SCE) test --- p.5 / Chapter 1.1.2.3 --- Micronucleus --- p.6 / Chapter 1.1.2.4 --- DNA strand breaks --- p.7 / Chapter 1.1.3 --- Single-cell gel electrophoresis (comet) assay --- p.9 / Chapter 1.1.4 --- Test organisms for comet assay --- p.12 / Chapter 1.2 --- Objective of the present study --- p.13 / Chapter Chapter 2 --- Genotoxicity of pollutants on Hyale crassicornis / Chapter 2.1 --- Introduction --- p.22 / Chapter 2.2 --- Materials and methods --- p.24 / Chapter 2.2.1 --- Sampling of amphipods --- p.24 / Chapter 2.2.2 --- Acclimation --- p.24 / Chapter 2.2.3 --- Acute toxicity test --- p.26 / Chapter 2.2.4 --- The effect of test duration on DNA damage --- p.27 / Chapter 2.2.5 --- Effect of toxicants on DNA damage --- p.28 / Chapter 2.2.6 --- Comet assay --- p.29 / Chapter 2.2.7 --- Chemicals --- p.34 / Chapter 2.2.8 --- Data analysis --- p.34 / Chapter 2.3 --- Results --- p.34 / Chapter 2.4 --- Discussion --- p.47 / Chapter Chapter 3 --- Genotoxicity of hydrogen peroxide on different tissue types of Metapenaeus ensis / Chapter 3.1 --- Introduction --- p.54 / Chapter 3.2 --- Materials and Methods --- p.57 / Chapter 3.2.1 --- Collection and acclimation of shrimps --- p.57 / Chapter 3.2.2 --- Incubation --- p.59 / Chapter 3.2.3 --- Comet Assay --- p.60 / Chapter 3.2.4 --- Chemicals --- p.61 / Chapter 3.2.5 --- Data analysis --- p.61 / Chapter 3.3 --- Results --- p.61 / Chapter 3.4 --- Discussion --- p.67 / Chapter Chapter 4 --- Genotoxicity of wastewater on Hyale crassicornis / Chapter 4.1 --- Introduction --- p.71 / Chapter 4.2 --- Materials and Methods --- p.72 / Chapter 4.2.1 --- Collection of wastewater samples --- p.72 / Chapter 4.2.2 --- Metal content analysis --- p.73 / Chapter 4.2.3 --- Genotoxic effect of wastewater samples on Hyale crassicornis --- p.74 / Chapter 4.2.4 --- Chemicals --- p.76 / Chapter 4.2.5 --- Statistical analysis --- p.77 / Chapter 4.3 --- Results --- p.77 / Chapter 4.3.1 --- Metals content in water samples --- p.77 / Chapter 4.3.2 --- DNA damage --- p.79 / Chapter 4.4 --- Discussion --- p.79 / Chapter Chapter 5 --- Conclusions --- p.89 / References --- p.91