This study examines seasonal changes in the relationship between a test organism and changes in the hydrographic and chemical properties of Indian Arm, a coastal fjord. There is a close relationship between changes in the hydrographic properties of the water and changes in the metal complexing ability of water in the inlet, as determined with the test organism. The relationship between the organism and the availability of metals changes with time; the complexing ability of natural water increases at the time of the major intrusion of water from the Strait of Georgia into Indian Arm, and then decreases. The addition of a variety of metals under experimental conditions affects the relationship between the organism and the complexing ability of the water. Additional studies examine the effect of material extracted from sediment samples on the toxic effect of copper enrichment. The ability of the extracted material to reduce the toxic effect changes and is related to the seasonal productivity in the surface waters of the inlet. / Science, Faculty of / Zoology, Department of / Graduate

Bibliography: leaves 254-267. / Microfiche. / xiii, 267 leaves, bound ill. 29 cm

by Siu, Wai Lok. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 213-218). / ABSTRACT --- p.i -ii / ACKNOWLEDGEMENTS --- p.iii / TABLE OF CONTENTS --- p.iv -vi / LIST OF FIGURES --- p.vi -viii / LIST OF PLATES --- p.ix / LIST OF TABLES --- p.x -xii / Chapter CHAPTER I --- INTRODUCTION --- p.1 / Chapter 1.1 --- Problem Statement --- p.1 / Chapter 1.2 --- Study Area --- p.3 / Chapter 1.3 --- Research Objectives --- p.4 / Chapter 1.4 --- Rationale --- p.9 / Chapter 1.5 --- Organization of the Thesis --- p.10 / Chapter CHAPTER II --- LITERATURE REVIEW --- p.12 / Chapter 2.1 --- Introduction --- p.12 / Chapter 2.2 --- Optical Properties of Sea Water --- p.12 / Chapter 2.3 --- Water Quality Modeling Algorithms --- p.18 / Chapter 2.3.1 --- Suspended Sediment Models --- p.22 / Chapter 2.3.2 --- Chlorophyll Models --- p.29 / Chapter 2.3.3 --- Sea Surface Temperature Models --- p.30 / Chapter 2.3.4 --- Salinity Models --- p.33 / Chapter 2.3.5 --- Total Phosphorus Models --- p.34 / Chapter 2.4 --- Use of Chromaticity Technique --- p.35 / Chapter 2.5 --- Principal Component Transformation --- p.37 / Chapter 2.6 --- Remote Sensing Water Quality in Hong Kong --- p.37 / Chapter 2.7 --- Summary --- p.38 / Chapter CHAPTER III --- METHODOLOGY --- p.40 / Chapter 3.1 --- Data Set --- p.40 / Chapter 3.1.1 --- Water Sampling and Water Quality Parameters --- p.40 / Chapter 3.1.2 --- Satellite Data --- p.45 / Chapter 3.1.2.1 --- Image Preprocessing --- p.45 / Chapter 3.1.2.1.1 --- Radiometric Correction --- p.45 / Chapter 3.1.2.1.2 --- Atmospheric Correction --- p.49 / Chapter 3.1.2.1.3 --- Geometirc Correction --- p.53 / Chapter 3.1.2.2 --- Data Extraction --- p.55 / Chapter 3.1.2.3 --- Spectral Data Transformation --- p.56 / Chapter 3.2 --- Statistical Water Quality Models --- p.59 / Chapter 3.3 --- Water Quality Mapping --- p.61 / Chapter CHAPTER IV --- EXPERIMENTAL SET-UP --- p.63 / Chapter 4.1 --- Introduction --- p.63 / Chapter 4.2 --- Water Quality Samples --- p.63 / Chapter 4.2.1 --- Sample Data for TM Experiment --- p.63 / Chapter 4.2.2 --- Sample Data for SPOT Experiment --- p.68 / Chapter 4.2.3 --- Correlations Among Parameters --- p.76 / Chapter 4.3 --- Image Preprocessing --- p.81 / Chapter 4.3.1 --- Image Destriping --- p.83 / Chapter 4.3.2 --- Atmospheric Correction --- p.85 / Chapter 4.3.3 --- Geometric Correction --- p.88 / Chapter 4.4 --- Data Extraction --- p.89 / Chapter 4.4.1 --- Descriptive Statistics of the Spectral Data Samples --- p.89 / Chapter 4.4.2 --- Data Transformation --- p.100 / Chapter 4.4.3 --- Correlations Among Spectral Variables --- p.103 / Chapter 4.5 --- Summary --- p.107 / Chapter CHAPTER V --- ANALYSIS OF WATER QUALITY MODELS …… --- p.113 / Chapter 5.1 --- Introduction --- p.113 / Chapter 5.2 --- Criteria for Assessing Water Quality Models --- p.113 / Chapter 5.3 --- Models Derived from TM Data --- p.116 / Chapter 5.3.1 --- Models of Various Water Quality Paramters --- p.116 / Chapter 5.3.2 --- Summary --- p.142 / Chapter 5.4 --- Models Derived from SPOT Data --- p.145 / Chapter 5.4.1 --- Models of Various Water Quality Parameters --- p.145 / Chapter 5.4.2 --- Summary --- p.169 / Chapter 5.5 --- Comparisons Among Models --- p.171 / Chapter 5.5.1 --- Comparisons Among Models Derived from TM and SPOT Data --- p.171 / Chapter 5.5.2 --- Comparisons with Past Models --- p.172 / Chapter 5.6 --- Conclusion --- p.173 / Chapter CHAPTER VI --- WATER QUALITY MAPPING --- p.175 / Chapter 6.1 --- Introduction --- p.175 / Chapter 6.2 --- Classification Schemes for Various Water Quality Paramters --- p.175 / Chapter 6.3 --- Water Quality Maps --- p.180 / Chapter 6.3.1 --- Water Quality Mapping Using TM Data --- p.180 / Chapter 6.3.2 --- Water Quality Mapping Using SPOT Data --- p.190 / Chapter 6.4 --- Difficulties Encountered in Water Quality Mapping --- p.202 / Chapter 6.5 --- Summary --- p.204 / Chapter CHAPTER VII --- CONCLUSION --- p.206 / Chapter 7.1 --- Summary of Findings --- p.206 / Chapter 7.1.1 --- Summary on Water Quality Modeling --- p.206 / Chapter 7.1.2 --- Summary on Water Quality Mapping --- p.208 / Chapter 7.2 --- Limitations of the Study --- p.209 / Chapter 7.3 --- Recommendations for Further Studies --- p.210 / BIBLIOGRAPHY --- p.213

A zona costeira de Pernambuco compreende uma faixa de 187 km de extensão e abrange 21 municípios. Apresenta o maior aglomerado populacional do Estado, onde está concentrada aproximadamente 44 % da população. Pesca intensiva e poluição ambiental, aliadas a uma mudança de clima global conduzirão a um colapso de todos os pescados em 2048. Esses impactos ambientais alteram a concentração ou distribuição de nitrogênio, fósforo e silício (biolimitantes da produção primária) e interferem em seus ciclos biogeoquímicos. A aplicação de modelos numéricos a zonas costeiras pode indicar uma previsão melhor dos fluxos de nutrientes associados com suas transferências e conseqüência geoquímica. O objetivo deste trabalho foi estimar concentrações dos principais nutrientes dissolvidos: nitrogênio, fósforo e silício através de modelagem matemática. Na metodologia foram utilizados dados do Programa REVIZEE, coletados pelo Departamento de Oceanografia (UFPE) no verão pernambucano de 1997. As estimativas das concentrações dos nutrientes nitrogênio, fósforo e silício da costa marinha no estado de Pernambuco geram informações que dão suporte à atividades futuras de pesca artesanal. O apoio científico serve de suporte para o aumento da demanda pesqueira que é de produção econômica para a região, considerando ser a mesma uma zona costeira / The coastal zone of Pernambuco comprises a range of 187 km and covers 21 cities. It presents the greatest agglomeration of the state, which concentrates approximately 44% of the population. Overfishing and pollution environment, coupled with global climate change will lead to a collapse of all fish in 2048. These environmental impacts change the concentration or the distribution of nitrogen, phosphorus and silicon (limiting production primary). It also interferes in their biogeochemical cycles. The application of numerical models in coastal areas may indicate a better estimate of nutrient fluxes associated with their transfer and geochemistry consequence. The objective of this study was to estimate concentrations of the major nutrients:nitrogen, phosphorus and silicon through mathematical modeling. In the methodology it was used the data Program REVIZEE, collected by the Department of Oceanography (UFPE) in the summer of 1997. The estimates of concentrations of nitrogen, phosphorus and silicon from the sea coast in the state of Pernambuco generated information to support the future activities of fishing. The scientific support for the increased demand of fishing is related to the economic production of the coastal area region

Made available in DSpace on 2017-06-01T18:20:29Z (GMT). No. of bitstreams: 1 dissertacao_luiz_queiroz.pdf: 1547317 bytes, checksum: 857e36c6f9b83aa7f85250f7a37ba4ec (MD5) Previous issue date: 2010-06-17 / The coastal zone of Pernambuco comprises a range of 187 km and covers 21 cities. It presents the greatest agglomeration of the state, which concentrates approximately 44% of the population. Overfishing and pollution environment, coupled with global climate change will lead to a collapse of all fish in 2048. These environmental impacts change the concentration or the distribution of nitrogen, phosphorus and silicon (limiting production primary). It also interferes in their biogeochemical cycles. The application of numerical models in coastal areas may indicate a better estimate of nutrient fluxes associated with their transfer and geochemistry consequence. The objective of this study was to estimate concentrations of the major nutrients:nitrogen, phosphorus and silicon through mathematical modeling. In the methodology it was used the data Program REVIZEE, collected by the Department of Oceanography (UFPE) in the summer of 1997. The estimates of concentrations of nitrogen, phosphorus and silicon from the sea coast in the state of Pernambuco generated information to support the future activities of fishing. The scientific support for the increased demand of fishing is related to the economic production of the coastal area region / A zona costeira de Pernambuco compreende uma faixa de 187 km de extensão e abrange 21 municípios. Apresenta o maior aglomerado populacional do Estado, onde está concentrada aproximadamente 44 % da população. Pesca intensiva e poluição ambiental, aliadas a uma mudança de clima global conduzirão a um colapso de todos os pescados em 2048. Esses impactos ambientais alteram a concentração ou distribuição de nitrogênio, fósforo e silício (biolimitantes da produção primária) e interferem em seus ciclos biogeoquímicos. A aplicação de modelos numéricos a zonas costeiras pode indicar uma previsão melhor dos fluxos de nutrientes associados com suas transferências e conseqüência geoquímica. O objetivo deste trabalho foi estimar concentrações dos principais nutrientes dissolvidos: nitrogênio, fósforo e silício através de modelagem matemática. Na metodologia foram utilizados dados do Programa REVIZEE, coletados pelo Departamento de Oceanografia (UFPE) no verão pernambucano de 1997. As estimativas das concentrações dos nutrientes nitrogênio, fósforo e silício da costa marinha no estado de Pernambuco geram informações que dão suporte à atividades futuras de pesca artesanal. O apoio científico serve de suporte para o aumento da demanda pesqueira que é de produção econômica para a região, considerando ser a mesma uma zona costeira

Thesis (Ph. D.)--Joint Program in Marine Geology and Geophysics (Massachusetts Institute of Technology, Dept. of Earth, Atmospheric and Planetary Sciences, and the Woods Hole Oceanographic Institution), 2003. / Includes bibliographical references. / Prevailing wisdom holds that the vertical distribution of molybdenum (Mo) in the open ocean is conservative, despite Mo's important biological role and association with Mn oxides and anoxic sediments. Mo is used in both nitrogenase, the enzyme responsible for N2 fixation, and nitrate reductase, which catalyzes assimilatory and dissimilatory nitrate reduction. Laboratory culture work on two N2 fixing marine cyanobacteria, Trichodesmium and Crocosphaera, and a marine facultative denitrifier, Marinobacter hydrocarbanoclasticus, showed that Mo cell quotas in these organisms were positively correlated with Mo-containing enzyme activity. Mo concentrations in Crocosphaera dropped almost to blank levels when not fixing N2 suggesting daily synthesis and destruction of the entire nitrogenase enzyme and release of Mo. Trichodesmium cultures, however, retained a pool of cellular Mo even when not fixing N2. Colonies of Trichodesmium collected in the field have Mo:C tenfold higher than seen in culture, these Mo:C ratios were reflected in SPM samples from the same region. Fe:C ratios for Trichodesmium were between 12-160 pmol:mol in field and culured samples. The Fe:C ratio of Crocosphaera was established to be 15.8 =/+ 11.3 under N2 fixing conditions. Mo cellular concentrations in cultured organisms were too small to significantly influence dissolved Mo distributions, but may slightly affect Suspended Particulate Matter (SPM) distributions. Mean SPM Mo:C ratios were slightly elevated in regions of N2 fixation and denitrification.. A high precision (=/+ 0.5%) isotope dilution ICP-MS method for measuring Mo was developed to re-evaluate the marine distribution of Mo in the dissolved and particulate phase. / (cont.) Mn oxides were not found to significantly influence either the dissolved or SPM Mo distribution. Dissolved Mo profiles from the Sargasso and Arabian Sea were conservative. However, dissolved Mo profiles from the Eastern Tropical Pacific showed both depletion and enrichment of dissolved Mo possibly associated with interaction of Mo with coastal sediments. Dissolved Mo profiles in several California Borderland Basins showed 1-2 nM Mo depletions below sill depth. A more focused study of water column response to sediment fluxes using the high precision Mo analyses is necessary to determine whether these phenomena are related. / by Caroline Beth Tuit. / Ph.D.