Made available in DSpace on 2015-03-13T17:08:24Z (GMT). No. of bitstreams: 1 RenataCLA.pdf: 1406363 bytes, checksum: c23d0993b7ed86a192397c6e7f6d9580 (MD5) Previous issue date: 2004-10-14 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Objective to establish a methodology for the oil spill monitoring on the sea surface, located at the Submerged Exploration Area of the Polo Region of Guamar?, in the State of Rio Grande do Norte, using orbital images of Synthetic Aperture Radar (SAR integrated with meteoceanographycs products. This methodology was applied in the following stages: (1) the creation of a base map of the Exploration Area; (2) the processing of NOAA/AVHRR and ERS-2 images for generation of meteoceanographycs products; (3) the processing of RADARSAT-1 images for monitoring of oil spills; (4) the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products; and (5) the structuring of a data base. The Integration of RADARSAT-1 image of the Potiguar Basin of day 21.05.99 with the base map of the Exploration Area of the Polo Region of Guamar? for the identification of the probable sources of the oil spots, was used successfully in the detention of the probable spot of oil detected next to the exit to the submarine emissary in the Exploration Area of the Polo Region of Guamar?. To support the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products, a methodology was developed for the classification of oil spills identified by RADARSAT-1 images. For this, the following algorithms of classification not supervised were tested: K-means, Fuzzy k-means and Isodata. These algorithms are part of the PCI Geomatics software, which was used for the filtering of RADARSAT-1 images. For validation of the results, the oil spills submitted to the unsupervised classification were compared to the results of the Semivariogram Textural Classifier (STC). The mentioned classifier was developed especially for oil spill classification purposes and requires PCI software for the whole processing of RADARSAT-1 images. After all, the results of the classifications were analyzed through Visual Analysis; Calculation of Proportionality of Largeness and Analysis Statistics. Amongst the three algorithms of classifications tested, it was noted that there were no significant alterations in relation to the spills classified with the STC, in all of the analyses taken into consideration. Therefore, considering all the procedures, it has been shown that the described methodology can be successfully applied using the unsupervised classifiers tested, resulting in a decrease of time in the identification and classification processing of oil spills, if compared with the utilization of the STC classifier / Objetiva o estabelecimento de uma metodologia para monitoramento de derramamento de ?leo no mar, na ?rea de Explora??o Submersa do P?lo de Guamar?, no Estado do Rio Grande do Norte, utilizando imagens orbitais de radares de abertura sint?tica (SAR) integradas aos produtos meteoceanogr?ficos. A aplica??o do modelo metodol?gico foi composto pelas seguintes etapas: (1) a cria??o de um mapa base da ?rea de Explora??o; (2) o processamento de imagens NOAA/AVHRR e ERS-2 para gera??o de produtos meteoceanogr?ficos; (3) o processamento de imagens RADARSAT-1 para monitoramento das manchas de ?leo; (4) a integra??o da imagem RADARSAT-1 com os produtos de imagens NOAA/AVHRR e ERS-2; e (5) a estrutura??o de um banco de dados. A Integra??o da imagem RADARSAT-1 da Bacia Potiguar do dia 21.05.99 com o mapa base da ?rea de Explora??o do P?lo de Guamar? para a identifica??o das prov?veis fontes das manchas de ?leo, foi utilizada com sucesso na detec??o da prov?vel mancha de ?leo detectada pr?xima ? sa?da do emiss?rio submarino na ?rea de Explora??o do P?lo de Guamar?. Para subsidiar a integra??o da imagem RADARSAT-1 com os produtos de imagens NOAA/AVHRR e ERS-2, desenvolveu-se uma metodologia para a classifica??o das manchas de ?leo identificadas em imagens RADARSAT-1. Nesta metodologia, testou-se os seguintes algor?tmos de classifica??o n?o-supervisionada: K-means, Fuzzy k-means e Isodata, que s?o parte integrante do software PCI Geomatics, o qual foi utilizado para a filtragem das imagens RADARSAT-1. Para a avalia??o dos resultados, as manchas de ?leo submetidas ? classifica??o n?o-supervisionada foram comparadas aos resultados do Classificador Textural por Semivariograma (STC), o qual foi desenvolvido especificamente para esta finalidade e requer a utiliza??o do software PCI Geomatics para efetuar parte do processamento das imagens RADARSAT-1. Por fim, os resultados das classifica??es foram analisados atrav?s de An?lise Visual; C?lculo de Proporcionalidade de Grandezas e An?lise Estat?stica. Dentre os tr?s algoritmos de classifica??o testados n?o houve significantes altera??es em rela??o as manchas classificadas pelo STC, em nenhuma das an?lises efetuadas. Os procedimentos adotados demonstraram que a metodologia descrita aqui poder? ser aplicada com sucesso, utilizando os classificadores n?o supervisionados testados, o que acarretaria em diminui??o de tempo no processo de identifica??o e classifica??o de manchas de ?leo, em compara??o ? utiliza??o do classificador STC

Made available in DSpace on 2014-12-17T14:56:21Z (GMT). No. of bitstreams: 1 DaniloLS.pdf: 2499617 bytes, checksum: 328b5ce6d56f5a92a61ad220565411c7 (MD5) Previous issue date: 2006-07-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Oil spill on the sea, accidental or not, generates enormous negative consequences for the affected area. The damages are ambient and economic, mainly with the proximity of these spots of preservation areas and/or coastal zones. The development of automatic techniques for identification of oil spots on the sea surface, captured through Radar images, assist in a complete monitoring of the oceans and seas. However spots of different origins can be visualized in this type of imaging, which is a very difficult task. The system proposed in this work, based on techniques of digital image processing and artificial neural network, has the objective to identify the analyzed spot and to discern between oil and other generating phenomena of spot. Tests in functional blocks that compose the proposed system allow the implementation of different algorithms, as well as its detailed and prompt analysis. The algorithms of digital image processing (speckle filtering and gradient), as well as classifier algorithms (Multilayer Perceptron, Radial Basis Function, Support Vector Machine and Committe Machine) are presented and commented.The final performance of the system, with different kind of classifiers, is presented by ROC curve. The true positive rates are considered agreed with the literature about oil slick detection through SAR images presents / Derramamentos de ?leo sobre o mar, mesmo que acidentais, geram enormes conseq??ncias negativas para a ?rea afetada. Os preju?zos s?o ambientais e econ?micos, principalmente com a proximidade dessas manchas de ?reas de preserva??o e/ou zonas costeiras. O desenvolvimento de t?cnicas autom?ticas para a identifica??o de manchas de ?leo sobre a superf?cie marinha, capturadas atrav?s de imagens de Radar, auxiliam num completo monitoramento dos oceanos e mares. Contudo, manchas de diferentes origens podem ser visualizadas nesse tipo de produ??o de imagem, tornando o monitoramento dif?cil. O sistema proposto neste trabalho, baseado em t?cnicas de processamento digital de imagens e redes neurais artificiais, tem o objetivo de identificar a mancha analisada e discernir entre ?leo e os demais fen?menos geradores de mancha. Testes nos blocos funcionais que comp?em o sistema proposto permitem a implementa??o de diferentes algoritmos, assim como sua an?lise detalhada e pontual. Os algoritmos que tratam do processamento digital de imagem (filtragem do ru?do speckle e gradiente), assim como o de classifica??o (Perceptron de M?ltiplas Camadas, rede de fun??o de Base Radial, M?quina de Vetor de Suporte e M?quina de comit?) s?o apresentados e comentados.O desempenho final do sistema, com diferentes tipos de classificadores, ? apresentado atrav?s da curva ROC. As taxas de acertos s?o consideradas condizentes com o que a literatura de detec??o de manchas de ?leo na superf?cie oce?nica atrav?s de imagens de SAR apresenta

La croissance de la population humaine et l'intensification des activités associées exercent une pression considérable sur les écosystèmes marins côtiers. Afin d’évaluer l’impact des activités humaines sur ces écosystèmes, des efforts considérables ont été consacrés au développement de biomarqueurs. En dépit d'un potentiel pour une large gamme d'applications, les biomarqueurs les plus couramment utilisés ciblent des effets situés à des niveaux organisationnels faibles (molécules, cellules ou tissus) et très peu sont susceptibles d'indiquer des impacts sur des niveaux organisationnels supérieurs (organisme, population, écosystème). Or, ces derniers sont d'une pertinence considérable pour les activités humaines, l'économie et le bien-être. Ils sont en effet le résultat intégratif de l'histoire environnementale d'un organisme et, en même temps, ils reflètent leur vulnérabilité et leur résilience aux changements de leurs conditions de vie. Cette opinion a été corroborée par la révision récente du concept de santé animale qui dorénavant, intègre les effets latents des conditions de vie antérieures, les conséquences des expositions et des expériences passées, ainsi que les conséquences du cumul de ces effets. L’utilisation conjointe des marqueurs de hauts niveaux organisationnels et du concept de santé pourrait donc aider à fournir des informations écologiquement pertinentes grâce à l’utilisation de biomarqueurs. Dans ce contexte, les objectifs de ma thèse étaient 1) de développer une méthodologie pour évaluer la santé des poissons, 2) d'étudier les mécanismes sous-jacents des performances mesurées et 3) de vérifier l’applicabilité de cette approche à de nombreux contextes en s’appuyant sur deux études de cas. Au cours de ces recherches, nous avons démontré que la tolérance à l'hypoxie, la sensibilité à la température et les performances de nage sont des biomarqueurs fiables de la santé des poissons. Leurs réponses sont en effet stables dans le temps, prédictives de la survie des poissons dans leur milieu naturel et sensibles à une exposition à un polluant. De plus, l'application de cette méthodologie à des études de cas a démontré que notre approche est généralisable à différents contextes et qu'elle fournit des informations opérationnelles facilement transférables aux secteurs socio-économiques et au grand public. / Growth of human population and associated intensification of human activities is putting considerable pressure on coastal marine ecosystems. To assess anthropogenic impact on these ecosystems, substantial efforts have been devoted to the development of biomarkers. Despite a potential for a broad application range, most commonly used biomarkers target effects at sub-organismal organizational level (molecules, cells or tissues) and very few are liable to indicate impacts at organism or higher organizational levels. On the other hand, although too far removed from causal events to constitute early warning signals of environmental stress, high-level organizational biomarkers are of considerable relevance to human activities, economy and well-being. They are indeed the integrative result of the environmental history of an organism and, at the same time, reflect their vulnerability and resilience to changes in their living conditions. This view is corroborated by the recent revision of the concept of animal health, which nowadays incorporates the latent effects of past living conditions, the consequences of past exposures and experiences and the cumulative consequences of these effects. The joint use of high organizational levels markers and the concept of health could help overcome the failure of current biomarkers to provide ecologically relevant information. In this context, the objectives of my thesis were 1) to develop a methodology to assess fish health; 2) to investigate the underlying mechanisms and confounding factors of the performances measured and 3) to verify the applicability of our approach through two case studies.Throughout these researches, we demonstrated that hypoxia tolerance, temperature susceptibility and swimming performances are promising biomarkers of fish health. Their responses are stable over time, predictive of fish survival in the field and sensitive to an exposure to dispersant-treated oil. Also, application of this methodology to case studies demonstrated that our approach is generalizable to different contexts and that it provides operational information easily transferable to socio-economic sectors and general public.

Les matériaux carbonés présentent de nombreux avantages pour les domaines des nanotechnologies et de l'environnement.La mixité de chiralité des nanotubes de carbone limite leur application dans les appareils électroniques et le nano-confinement. Dans une première partie, ce travail de thèse s'est concentré sur la séparation en chiralité de nanotubes de carbone de type CoMoCAT, afin d'élaborer de nouveaux nano-conteneurs.Après sélection en chiralité, nous avons évalué les propriétés sous hautes pressions des fagots de nanotubes sélectionnés, et leur interaction avec l'eau. Les résultats ont montré que les fagots supportent des pressions jusqu'à 17 GPa, avant de subir un effondrement radial réversible, permettant de les utiliser en tant que nano-enclumes.L'élaboration d'une éponge de carbone (super)hydrophobe pour le traitement des eaux après pollution aux hydrocarbures a été décrite dans une seconde partie. La pyrolyse de mousses polymères a permis de conserver la très grande porosité de la mousse (> 99%), tout en lui conférant des propriétés proches de la superhydrophobie et de grandes capacités d'absorption de pétrole et solvants organiques (85-200 g/g). L'élasticité du matériau permet sa régénération par simple compression mécanique : récupération du polluant et réutilisation de l'absorbant. De plus, cette caractéristique reste valable même après une centaine de cycles de compression-décompression, en conservant 81% de sa capacité d'absorption dans le cas du pétrole brut / Carbon materials present many advantages for the nanotechnology and environment fields. The chirality mixity of carbon nanotubes limits their application in electronic devices and the nano-confinement. In the first part, this thesis has focused on the chirality separation of CoMoCAT carbon nanotubes, in order to elaborate new nano-containers.After the chirality selection, the properties of selected nanotubes bundles under high pressure were evaluated, as well as their interaction with water. The results show that the bundles support pressures until 17 GPa, before to undergo a reversible collapse, allowing their application as nano-anvils.The (super)hydrophobic carbon sponge elaboration for the clean-up of water polluted by oils was described in the second part. The polymeric foams pyrolysis allows to keep the very high foam porosity (> 99%), to give properties next to the superhydrophobicity and large absorption capacities in oils and organic solvents (85-200 g/g). The material elasticity allows its regeneration by simple mechanical compression : recovery of pollutant and re-use of the absorbant. Moreover, this feature remains valid after a hundred compression-decompression cycles, with 81% of the initial crude oil absorption capacity

The role of economic theory in the preservation of nature in Kazakhstan is very current, because mining industry in this country is quite large and it is a considerable burden for the environment. This burden is obvious mostly in extraction and processing of crude oil. The main aim of this thesis is the analysis of state environmental policy with the focus on prevention of oil spills in the Caspian Sea in accordance with "State concept of ecological safety in Kazakhstan in 2004-2015" and also methods of oil spills removal as tools for environment protection including factual techniques and firm based costs linked to the removal of oil spills. Thesis will also include the comparison of effectiveness of existing models for oil spills removal used by companies in Kazakhstan. The first part of work is methodologically based on analysis of secondary sources: scientific articles and literature, ecology bulletins, official data of Ministry of environment in Kazakhstan, firm based data and "State concept of ecological safety".

The Deepwater Horizon Oil Spill (DWH) released about 4.4 million barrels of crude oil into the Gulf of Mexico (GOM), making it one of the largest oil spills in U.S. history. Additionally, the depth of the spill (i.e., 1500 meters) created a unique research opportunity because most oil spills occur at the surface and affect coastal rather than deepwater habitats. Polycyclic aromatic hydrocarbons (PAHs) are the most toxic components of oil, and are often the focus of oil exposure studies. PAHs are quickly metabolized by vertebrates; therefore, indicators of biological responses to PAH exposure (PAH “biomarkers”) such as the levels of PAH detoxification enzymes and the resulting metabolites are commonly used to examine oil exposure. This study measured multiple PAH biomarkers including hepatic activity of the PAH detoxification enzymes cytochrome P4501a1 (CYP1A) and glutathione-S-transferase (GST), as well as biliary PAH metabolites in deep sea sharks and bony fishes from areas affected by the Deepwater Horizon Oil Spill. Samples were collected from 2011-2013 from seven species of sharks, with special focus on the four most abundant deep sea species: Centrophorus niakang, Centrophorus cf granulosus, Squalus cubensis and Squalus cf mitsikurii. Overall enzyme activity was low in these sharks, yet it was higher in oiled sites compared to reference locations. Additionally some species showed declining CYP1A activity since the time of the oil spill, suggesting exposure to CYP-inducing compounds during the beginning of the survey period. Last, PAHs of a petrogenic nature were more abundant in oiled sites compared to reference locations. Overall, this project provides the much need biomarker data for sharks as well as insight on exposure and metabolism of PAHs in deep sea sharks after the DWH.

Proposer l’adoption de la procédure de phytoremédiation des sites de déversement d’hydrocarbures au Cameroun nécessite des arguments scientifiques vérifiés. L’objectif principal de cette thèse est de (i) faire une étude floristique des sites de déversement anarchique de produits pétroliers au Cameroun, (ii) tester les potentialités de phytoremédiation de quelques espèces végétales sur des sols contaminés au fioul domestique, (iii) évaluer l’effet de différentes concentrations d’hydrocarbures sur la morpho-anatomie et la physiologie des plantes, (iv) modéliser le transfert sol-plante des HAP. Les prospections floristiques des sites de déversement d’hydrocarbures menées dans 4 villes du Cameroun ont permis d’identifier 106 espèces appartenant à 76 genres et 30 familles ; dont seules 15 espèces sont qualifiées de polluo-tolérantes. Les expérimentations ont permis de montrer que toutes les 3 espèces (C. dactylon, E. indica et A. sessilis) sont impliquées dans les processus de rhizodégradation des HCT et HAP ; E. indica and C. dactylon ayant favorisé une meilleure phytoextraction de quelques HAP comparé à A. sessilis. La modélisation mécaniste du transfert sol-plante des HAP a confirmé ces observations. La tolérance aux HAP est plus accrue chez E. indica et C. dactylon par rapport à A. sessilis. La culture mixte de E. indica et C. dactylon est très impactée par les HAP. En culture seule, le ralentissement des processus physiologiques de C. dactylon dans les sols pollués n’a pas d’impacts majeurs sur ses capacités phytoremédiatrices. Ceci permet donc de recommander C. dactylon pour la gestion durable des sols pollués par les hydrocarbures dans le monde entier en général, et au Cameroun en particulier ; pour des pollutions de sols n’excédant pas 33500 ppm. / To propose the implementation of phytoremediation process to clean up oil spill sites in Cameroon requires verified scientific arguments. The main objective of this thesis is to (i) carry out a floristic study of anarchic oil spill sites in Cameroon, (ii) test the phytoremediation potential of some plant species in soils contaminated with fuel oil, (iii) evaluate the effect of different hydrocarbon concentrations on morpho-anatomy and plant physiology, (iv) modelling the soil-plant transfer of PAHs. Floristic surveys of oil spill sites carried out in 4 Cameroonian cities have identified 106 species belonging to 76 genera and 30 families; amoung which 15 species are qualified as polluo-tolerant. Experiments have shown that the (C. dactylon, E. indica and A. sessilis) are involved into rhizodegradation of TPHs and PAHs; E. indica and C. dactylon having promoted a better phytoextraction of some PAHs compared to A. sessilis. Mechanistic modelling of the soil-plant transfer of PAHs confirmed these observations. Tolerance to PAHs is higher for both E. indica and C. dactylon than A. sessilis. The mixed cropping of E. indica and C. dactylon is highly impacted by PAHs. Cropping alone, the slowing down of C. dactylon's physiological processes in polluted soils does not have a significant impact on its phytoremediation potentialities. This makes it possible to recommend C. dactylon in the sustainable management of hydrocarbon-polluted soils worldwide in general, and particularly in Cameroon; for soil pollution not exceeding 33500 ppm.

The purpose of this study was to examine the impact of social media on crisis communication. To evaluate this impact, a case study method was utilized examining the crisis communication response of the Deepwater Horizon Oil Spill on April 20, 2010. This study focused on the response of the responsible party, British Petroleum, and the general public over three social media: Facebook, Twitter, and blogs. Through extensive analysis of both the company's and public response to the Gulf spill, nine implications were identified regarding social media's influence on crisis communication. These implications highlighted the potential for organizations to build interpersonal relationships with its publics. These relationships were found to be crucial in times of crises. The implications of this study also pointed to interactivity, using a "human voice," trust, and credibility as crucial factors in building these relationships and leading an effective crisis response across social media. This study also noted the new stress for organization's to respond quickly to crises as a result of instant news brought by social media. Implications of this study also highlighted social media's influence on individuals becoming contributing members of a crisis response. While social media has influenced the practice of crisis communication in many ways, this study found that the principles and ethics of the field have remained the same. In conclusion, analysis suggests that BP neglected using social media in its crisis response, a channel which has entered the mainstream of crisis communication. As a result, this study recommends the use of social media before, during, and after a crisis to ensure the welfare of a company and its relationship with its publics.

No description available.

[pt] O estudo dos eventos naturais e dos gerados pela atividade humana no mar tem tido uma grande prioridade para o setor de petróleo, isso devido à possibilidade de ter um evento perigoso para o ambiente marinho ou a área de produção. Nesse contexto, o objetivo deste trabalho é a avaliação de abordagens baseadas em aprendizado profundo para a classificação de eventos no mar usando imagens de radar de abertura sintética na área de óleo e gás. Métodos baseados em aprendizado profundo têm mostrado um ótimo desempenho através do uso de camadas convolucionais, onde as características são extraídas automaticamente através da definição de um kernel e stride. As seguintes arquiteturas são avaliadas neste trabalho: Inception V3, Xception, Inception ResNet V2, MobileNet, VGG16 e Deep Attention sampling. A avaliação é feita em uma metodologia de classificação de eventos no mar usando duas bases de dados de imagens de radar: a primeira contém 10 eventos comumente presentes no oceano ártico, e a segunda descreve um derramamento de óleo presente na costa da Louisiana. Nos experimentos realizados se obteve os melhores resultados com as arquiteturas Deep Attention sampling as quais atingiram valores de f1-score e Recall de até 0.82 por cento e 0.87 por cento respectivamente, para a classe de interesse no conjunto de dados de derramamento de óleo. Para o conjuntode dados de eventos naturais no mar, um alto desempenho foi evidenciado para arquiteturas baseadas no uso de módulos de Inception, tendo pontuações mais altas de F1-score e Recall para a arquitetura Xception. Além disso, foi observado uma melhoria de até 10 por cento e 13 por cento nas métricas f1-score e Recall no uso da atenção, em relação à sua arquitetura base (VGG16), e 4 por cento respeito a outras arquiteturas baseadas em módulos Inception, isto para o conjunto de dados de eventos no mar, demonstrando as vantagens de usar amostragem com atenção. / [en] The study of natural events and those generated by human activity at sea has been a high priority for the Oil and Gas industry, due to the possibility of a dangerous event for the marine environment or the production area. In this context, the objective of this work is the evaluation of approaches based on deep learning for the classification of events in the sea using synthetic aperture radar images in the oil and gas area. Methods based on deep learning have shown an excellent performance through the use of convolutional layers, where the characteristics are extracted automatically through the definition of a kernel and stride. The following architectures are evaluated in this work: Inception V3, Xception, Inception ResNet V2, MobileNet, VGG16, and Deep Attention sampling. The assessment is made using a methodology for classifying events at sea using two radar image databases: the first contains 10 events commonly present in the Arctic Ocean, and the second describes an oil spill present near the Louisiana coast. In the experiments performed, the best results were obtained with the Deep Attention sampling architectures, which reached f1- score and Recall values of up to 0.82 a per cent nd 0.87 per cent respectively, for the class of interest in the oil spill dataset. For the dataset of natural events in the sea, high performance was evidenced for architectures based on the non-use of Inception modules, having higher values of F1-score and Recall for an Xception architecture. Also, an improvement of up to 10 per cent and 13 per cent in the metrics f1- score and recall in the use of attention was observed, concerning its base architecture (VGG16), and 4 per cent with other architectures based on Inception modules, this for the dataset of events at sea, demonstrating the advantages of using Attention Sampling carefully.