Environmental factors can have a significant impact on an individual's health and well-being, and a primary characteristic of environments is air quality. Air sensing equipment is available to the public, but it is often expensive,stationary, or unusable for persons without technical expertise. The goal of this project is to develop an inexpensive and portable sensor module for public use. The system is capable of measuring temperature in Celsius and Fahrenheit, heat index, relative humidity, and carbon dioxide concentration. The sensor module, referred to as the "sniffer," consists of a printed circuit board that interconnects a carbon dioxide sensor, a temperature/humidity sensor, an Arduino microcontroller, and a Bluetooth module. The sniffer is small enough to be worn as a pendant or a belt attachment, and it is rugged enough to consistently collect and transmit data to a user's smartphone throughout their workday. The accompanying smartphone app uses Bluetooth and GPS hardware to collect data and affix samples with a time stamp and GPS coordinates. The accumulated sensor data is saved to a file on the user's phone, which is then examined on a standard computer.

No description available.

Este estudo avalia os efeitos adversos na saúde causados pelas variações na composição do material particulado em curtos períodos de tempo. Para tanto, foram determinados Black Carbon (BC), massa e composição elementar do PM2.5 coletado em dois diferentes períodos, Dia e Noite, na cidade de São Paulo. Além das análises químicas, gravimetria, análise de reflectância e espectrometria por fluorescência de Raios-X, foram realizados testes toxicológicos de contagem de micronúcleos utilizando ressuspensão do PM2,5 em raízes de Allium cepa (cebola). O PM2.5 foi amostrado na saída do Concentrador de Partículas Finas Ambientais de Harvard (CPFAH) e as amostras foram coletadas diariamente por quinze dias, entre Maio e Junho (Experimento 1) e entre Outubro e Novembro (Experimento 2) de 2009, durante o Dia (8:00h - 11:00h e 14:00h - 17:00h) e a Noite (20:00h - 23:00h). A variabilidade na composição do PM2.5 foi analizada utilizando o método de componentes principais, por meio da qual foram identificados três fatores significantes: Fator 1, onde encontramos BC e os elementos Ca, Fe, K, Si, Zn e Ti que podem ser característicos da emissão do tráfego veicular em geral; Fator 2 onde encontramos Ni e Cr que podem ser interpretados como resultado das emissões industriais; e o Fator 3 é formado pelos elementos V e S que podem ser característicos das emissões das queimas de diesel e óleo combustível. A análise univariada da contagem dos três fatores mostrou uma diferença significativa somente para o Fator 3 (p=0,022) entre os dois períodos, fazendo-se mais presente no período noturno. A frequência do número de micronúcleos das células de raízes de cebola foi maior para período noturno. Os resultados indicam que existem diferenças na composição e toxicidade do PM2,5 coletado em diferentes períodos na cidade de São Paulo. Os dados sugerem que a toxicidade do PM2,5 pode ser influenciada pelas mudanças nos padrões de tráfego e processos fotoquímicos da atmosfera. / The objective of this study was to evaluate the adverse health effects of shortterm variations in particle composition. For this purpose, black carbon, particle mass and elemental composition of PM2.5 collected along the two different periods in São Paulo city were determined. In addition to the chemical measurements, toxicological evaluation was performed using the Allium cepa micronuclei assay test and particle suspensions of PM2.5 as test substrate. PM2.5 was sampled at the output of the Harvard Ambient Fine Particle Concentrator (HAFPC). The samples were collected daily from May 19th to June 4th (period 1) of 2009, during the morning (from 8 AM to 11 AM), afternoon (from 2 PM to 5 PM), and evening (from 8 PM to 11 PM), and from November 11th (period 2) of 2009, during the morning (from 8 AM to 11 AM), and evening (from 8 PM to 11 PM). Samples collected during the morning and afternoon were called Day and those collected during the evening, EVENING. Concentrations of PM2.5 mass (gravimetric method), black carbon (reflectance analysis) and trace elementals (X-ray fluorescence spectrometry) were determined. Variability in PM2.5 composition measured was analyzed using Principal Components Analysis in which 3 significant factors were identified. Results of black carbon (BC) and of elements Ca, Fe, K, Si, Zn and Ti were grouped in Factor 1, that can be characteristic of general traffic emission; In Factor 2, Ni and Cr were included and it can be interpreted as a result of industry emissions; and Factor 3 is formed by V and S what characterizes diesel/oil combustion emissions. Univariate Analysis of factor scores indicated a significant difference between the periods scores of factor 3 (p=0.022). The frequency of number of micronuclei obtained was also higher in samples collected at the EVENING than those of DAY. In conclusion, our results indicate that there are differences in the composition and toxicity of PM2.5 collected in different periods of the day. These findings suggest that particle toxicity may be influenced by changes in traffic patterns and photochemical processes.

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

Este estudo avalia os efeitos adversos na saúde causados pelas variações na composição do material particulado em curtos períodos de tempo. Para tanto, foram determinados Black Carbon (BC), massa e composição elementar do PM2.5 coletado em dois diferentes períodos, Dia e Noite, na cidade de São Paulo. Além das análises químicas, gravimetria, análise de reflectância e espectrometria por fluorescência de Raios-X, foram realizados testes toxicológicos de contagem de micronúcleos utilizando ressuspensão do PM2,5 em raízes de Allium cepa (cebola). O PM2.5 foi amostrado na saída do Concentrador de Partículas Finas Ambientais de Harvard (CPFAH) e as amostras foram coletadas diariamente por quinze dias, entre Maio e Junho (Experimento 1) e entre Outubro e Novembro (Experimento 2) de 2009, durante o Dia (8:00h - 11:00h e 14:00h - 17:00h) e a Noite (20:00h - 23:00h). A variabilidade na composição do PM2.5 foi analizada utilizando o método de componentes principais, por meio da qual foram identificados três fatores significantes: Fator 1, onde encontramos BC e os elementos Ca, Fe, K, Si, Zn e Ti que podem ser característicos da emissão do tráfego veicular em geral; Fator 2 onde encontramos Ni e Cr que podem ser interpretados como resultado das emissões industriais; e o Fator 3 é formado pelos elementos V e S que podem ser característicos das emissões das queimas de diesel e óleo combustível. A análise univariada da contagem dos três fatores mostrou uma diferença significativa somente para o Fator 3 (p=0,022) entre os dois períodos, fazendo-se mais presente no período noturno. A frequência do número de micronúcleos das células de raízes de cebola foi maior para período noturno. Os resultados indicam que existem diferenças na composição e toxicidade do PM2,5 coletado em diferentes períodos na cidade de São Paulo. Os dados sugerem que a toxicidade do PM2,5 pode ser influenciada pelas mudanças nos padrões de tráfego e processos fotoquímicos da atmosfera. / The objective of this study was to evaluate the adverse health effects of shortterm variations in particle composition. For this purpose, black carbon, particle mass and elemental composition of PM2.5 collected along the two different periods in São Paulo city were determined. In addition to the chemical measurements, toxicological evaluation was performed using the Allium cepa micronuclei assay test and particle suspensions of PM2.5 as test substrate. PM2.5 was sampled at the output of the Harvard Ambient Fine Particle Concentrator (HAFPC). The samples were collected daily from May 19th to June 4th (period 1) of 2009, during the morning (from 8 AM to 11 AM), afternoon (from 2 PM to 5 PM), and evening (from 8 PM to 11 PM), and from November 11th (period 2) of 2009, during the morning (from 8 AM to 11 AM), and evening (from 8 PM to 11 PM). Samples collected during the morning and afternoon were called Day and those collected during the evening, EVENING. Concentrations of PM2.5 mass (gravimetric method), black carbon (reflectance analysis) and trace elementals (X-ray fluorescence spectrometry) were determined. Variability in PM2.5 composition measured was analyzed using Principal Components Analysis in which 3 significant factors were identified. Results of black carbon (BC) and of elements Ca, Fe, K, Si, Zn and Ti were grouped in Factor 1, that can be characteristic of general traffic emission; In Factor 2, Ni and Cr were included and it can be interpreted as a result of industry emissions; and Factor 3 is formed by V and S what characterizes diesel/oil combustion emissions. Univariate Analysis of factor scores indicated a significant difference between the periods scores of factor 3 (p=0.022). The frequency of number of micronuclei obtained was also higher in samples collected at the EVENING than those of DAY. In conclusion, our results indicate that there are differences in the composition and toxicity of PM2.5 collected in different periods of the day. These findings suggest that particle toxicity may be influenced by changes in traffic patterns and photochemical processes.

Mountaintop removal mining (MTR) is a notorious stressor of stream ecosystems in the Central Appalachians. Valley fills (VF) lead to reduced occupancy, abundance, and species richness of stream salamanders. Multiple factors may be responsible for these reductions, but specifically habitat fragmentation and degradation may reduce colonization rates and increase local extinction rates. From 2013-2015, repeated counts of salamanders were conducted in stream reaches impacted by MTR/VF and compared to counts in reference reaches to answer the question: do stream salamander population dynamics differ between stream reaches impacted by MTR/VF and reference stream reaches? I also investigated dynamics of stream habitat using measures relevant to stream salamander persistence. Accordingly, I examined number of cover objects, percent detritus, hydroperiod, and specific conductance. From the salamander capture data, colonization and survival probabilities were lower in MTR/VF reaches than reference reaches. MTR/VF reaches also had fewer cover objects, higher percent detritus, constant stream flow, and elevated specific conductance. Although specific conductance was increased in MTR/VF reaches, it was not strongly correlated with colonization and survival. I suggest reduced rates of colonization and survival in MTR/VF stream reaches are driven by inhibited dispersal and reduced individual survival due to degraded terrestrial and aquatic environments.

Thesis (MScEng (Electrical and Electronic Engineering))--Stellenbosch University, 2008. / A WSN (wireless sensor network) consists of a collection of small, low power electronic devices that can sense their environment and communicate with each other in order to send data to a base station for logging and monitoring. Research done on WSNs has increased rapidly over the past few years, as the necessary RF hardware has become cheaper and smaller. The wealth of information and hardware available in this field has made it possible to design and deploy networks for a multitude of monitoring purposes, on almost any terrain, without an existing telecommunication infrastructure. This thesis presents research into some major aspects of WSNs and the implementation of a test system with wireless sensor motes, that can be used for environmental monitoring, conservation purposes, impact studies, early warning systems for floods, fires etc. The system also has a wide range of possible uses in agriculture, as more data and better control over crops can increase yield. The power constraint of sensor nodes is one of the biggest concerns, as batteries can be depleted quickly and render a system useless. For this reason, work was focused on reducing power consumption of the hardware by means of various methods. Power use was also simulated very successfully, giving a accurate way of predicting node lifetime with a variety of battery types. The system was implemented on the Tmote Sky hardware platform using the open source sensor network operating system, TinyOS.

No description available.