Dissertations / Theses on the topic 'Air – Pollution – Remote sensing'

This dissertation presents a geographical analysis of air pollution in the Tucson region. Image processing, geographic information system (GIS), climatological, and statistical tools are used to develop and analyze air pollution-related databases. These databases are then used in conjunction with a limited number of spatial measurements of ozone concentrations to create accurate and theoretically sound ground-level ozone maps. High spatial resolution, gridded, multi-temporal, atmospheric emissions inventories (EIs) of ozone precursor chemical (i.e. volatile organic compounds (VOCs) and nitrogen oxides (NOₓ)) emissions are initially developed. GIS-driven "top-down" and "bottom-up" methods are employed to create anthropogenic VOC and NOx emissions inventories while satellite imagery and field surveys are employed to create biogenic VOC (BVOC) emissions inventories. Accounting for approximately 50% of the anthropogenic emissions, on-road vehicles are the dominant anthropogenic source. The forest and desert lands emit nearly all of the BVOCs within the entire Tucson region while exotic trees such as eucalyptus, pine, and palm emit most of the BVOCs within the City of Tucson. Relationships between VOC and NOₓ emissions, atmospheric conditions, and ambient ozone levels are determined by examining spatio-temporal variations in ozone levels, temporal variations in VOC and NOₓ emissions and atmospheric conditions, atmospheric conditions which are conducive to elevated ozone levels. In addition, the likelihood of ozone transport from Phoenix to Tucson is assessed. The highest ozone levels occur at "rural," downwind monitors, occur in August, and occur during the early afternoon hours. Atmospheric conditions conducive to elevated concentrations differ between the months while inter-city ozone transport is most likely to occur in June. Pooled, cross-sectional, times series, regression models are developed with the aid of cluster analysis and principal components analysis to spatially predict daily maximum 1-hr and 8-hr average ozone concentrations. Gridded, multi-temporal estimates of VOCs and NOₓ emissions are the primary predictor variables in the regression models. The pooled models are reasonably accurate with overall R² values from 0.90 to 0.92, 6 to 7% error, and predicted concentrations that are typically within 0.003 to 0.004 ppm of the observed concentrations. The predicted highest ozone concentrations occur in a monitorless area on the eastern edge of the City of Tucson.

Measuring the composition of the planetary boundary layer is essential for monitoring pollutants and for understanding their impact on environment and health. Nadir satellite remote sensing is particularly appealing to sound this part of the atmosphere, but is however challenging because pollutants concentrations are generally weak and confined in a small part of the atmospheric column. Among the sounders currently in orbit, those operating in the thermal infrared have usually their maximum sensitivity in the mid-troposphere, and are thought to be inadequate to measure the near-surface atmospheric composition. Their sensitivity to this part of the atmosphere is indeed generally limited by low temperature contrast (called thermal contrast) between the ground and the air above it. Shortly before the beginning of this PhD, this has however been challenged with different studies, which have shown the possibility to measure air pollution with thermal infrared sounders in case of high thermal contrast conditions. This was especially demonstrated with the measurement of ammonia global distribution using the Infrared Atmospheric Sounding Interferometer (IASI). This work aims at fully exploring the capabilities of thermal infrared sounders to sound the near-surface atmospheric composition. It mainly focuses on the observations of the IASI instrument, and addresses the following questions: where and when is IASI sensitive to the near-surface atmosphere? How large and how variable is the sensitivity to near-surface pollutants? What are the parameters that drive this variability? The answers to these questions are looked at for two pollutants: sulphur dioxide (SO2) and carbon monoxide (CO), and are obtained through a series of different analyses. SO2 is the first constituent on which this work focuses. The retrieval of its near-surface concentration is first of all attempted in an area surrounding the industrial area of Norilsk. This region, well-known for the extraction of heavy metals and its extremely high levels of pollution, encounters large temperature inversions in winter, which trap SO2 close to the ground. By exploiting these (corresponding to high negative thermal contrast), we show that it is possible to retrieve the surface SO2 concentrations in the region. This is done using a simplified version of the optimal estimation method, based on the use of a total measurement error covariance matrix. Further, we show that the surface SO2 concentration retrieval using the ν3 band is limited, in addition to thermal contrast, by the strong water (H2O) absorption, which renders the lowest atmosphere opaque in this spectral range in case of large humidity. Two conditions are therefore shown to be required to monitor near-surface SO2 in the ν3 band: large thermal contrast and low surface humidity. These findings are confirmed with the retrieval of SO2 at global scale, performed using a newly developed retrieval scheme based on the conversion of radiance indexes into SO2 columns using look-up-tables. It is composed of two successive steps: 1) the determination of the altitude of SO2 and the selection of low plumes (below 4 km), 2) for the selected observations, the conversion of radiance indexes into integrated SO2 0–4 km columns. The distributions and time series so obtained are used to better characterise the variability of IASI sensitivity to surface SO2 in the ν3 band at the global scale, and more particularly, in terms of thermal contrast strength and total column of H2O.The characterisation of IASI sensitivity to CO is realised in a second part of the work. Radiative transfer simulations are conducted first to determine the possibility to detect enhancement in CO near-surface concentrations with IASI. The framework of the optimal estimation is then used to investigate the capability of IASI to decorrelate, as a function of thermal contrast, the CO concentration in the low troposphere from that in the high troposphere. Finally, comparisons of IASI CO observations with co-located aircraft and ground-based measurements are shown to confirm with real data how IASI sensitivity to near-surface CO varies in terms of thermal contrast conditions, and to which extent it allows determining the CO abundance in case of high pollution.
Mesurer la composition de la couche limite atmosphérique depuis les satellites est essentiel pour comprendre l’impact des polluants sur l’environnement global et sur la santé. Parmi les sondeurs actuellement en orbite, ceux opérant dans l’infrarouge thermique sont souvent considérés comme inadéquats pour cet objectif, leur sensibilité à l’atmosphère de surface étant généralement limitée par de faibles contrastes de température (appelés contrastes thermiques, TC) entre le sol et l’air au-dessus. Différentes études récentes ont cependant montré la possibilité de mesurer la pollution de l’air avec ce type de sondeur dans des conditions de TC élevé.Ce travail a pour objectif de redéfinir la capacité des sondeurs opérant dans l’infrarouge thermique à mesurer la composition de l’atmosphère de surface. Il se focalise sur les observations de l’instrument IASI (Interféromètre Atmosphérique de Sondage Infrarouge), et tente de répondre, pour les deux polluants que sont le dioxyde de soufre (SO2) et le monoxyde de carbone (CO), aux questions suivantes :Où et quand IASI est-il sensible à l’atmosphère de surface ?Quels sont les paramètres qui influencent cette sensibilité et dans quelle mesure?Dans une première partie, la thèse se focalise sur l’ajustement des concentrations de SO2 de surface pour la région industrielle de Norilsk, connue pour son niveau de pollution élevé. Nous montrons qu’il y est possible de restituer les concentrations de surface de SO2 en hiver, en exploitant les larges inversions de température (TC négatifs) qui s’y développent. Les restitutions reposent sur une version simplifiée de la méthode de l’estimation optimale, utilisant une matrice complète de l’erreur de mesure. En plus du TC, nous montrons que l’ajustement dans la bande ν3 du SO2 est également limité par la forte absorption de la vapeur d’eau (H2O), qui rend les basses couches de l’atmosphère opaques. La nécessité de combiner des TCs élevés et une faible humidité pour permettre la mesure du SO2 en surface est confirmée par une analyse à l’échelle globale, utilisant une méthode basée sur la mesure d’indices de radiance et leur conversion en colonnes de SO2 à l’aide de tables pré-calculées. Composée de 2 étapes, cette méthode identifie et sélectionne d’abord les panaches situés sous 4 km ;elle convertit ensuite les indices de radiance en colonnes de SO2 intégrées entre 0 et 4 km. Les distributions et séries temporelles obtenues sont utilisées pour caractériser, en termes de valeurs de TC et de colonnes totales d’H2O, la variabilité de la sensibilité de IASI au SO2 de surface dans la bande ν3.Dans la seconde partie du travail, des simulations de transfert radiatif sont entreprises pour déterminer la possibilité de détecter avec IASI des augmentations de la concentration de CO dans l’atmosphère de surface. Le formalisme de l’estimation optimale est aussi utilisé pour analyser l’influence du TC sur la capacité de IASI à décorreler les concentrations du CO dans la basse et la haute troposphère. Finalement, des comparaisons entre les concentrations de CO restituées des mesures IASI sous différentes conditions de TC et de pollution et celles mesurées par avions et par des stations au sol complètent la caractérisation.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

A major opportunity for using satellite observations of tropospheric chemical concentrations is to improve our scientific understanding of atmospheric processes by integrated analysis of satellite, aircraft, and ground-based observations with global and regional scale models. One endpoint of such efforts is to reduce modeling biases and uncertainties. The idea of coupling these observations with a regional scale air quality model was the starting point of this research. The overall objective of this research was to improve the NOₓ emission inventories by integrating observations from different platforms and regional air quality modeling. Specific objectives were: 1) Comparison of satellite NO₂ retrievals with simulated NO₂ by the regional air quality model. Comparison of simulated tropospheric gas concentrations simulated by the regional air quality model, with aircraft and ground-based observations; 3) Assessment of the uncertainties in comparing satellite NO₂ retrievals with NOₓ emissions estimates and model simulations; 4) Identification of biases in emission inventories by data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations with an iterative inverse method using the regional air quality model coupled with sensitivity calculations; 5) Improvement of our understanding of NOₓ emissions, and the interaction between regional and global air pollution by an integrated analysis of satellite NO₂ retrievals with the regional air quality model. Along with these objectives, a lightning NOₓ emission inventory was prepared for two months of summer 2004 to account for a significant upper level NOₓ source. Spatially-resolved weekly NO₂ variations from satellite retrievals were compared with estimated NOₓ emissions for different region types. Data assimilation of satellite NO₂ retrievals, and ground-based NO, NO₂ and O₃ observations were performed to evaluate the NOₓ emission inventory. This research contributes to a better understanding of the use of satellite NO₂ retrievals in air quality modeling, and improvements in the NOₓ emission inventories by correcting some of the inconsistencies that were found in the inventories. Therefore, it may provide groups that develop emissions estimates guidance on areas for improvement. In addition, this research indicates the weaknesses and the strengths of the satellite NO₂ retrievals and offers suggestions to improve the quality of the retrievals for further use in the tropospheric air pollution research.

On-road remote sensing has the ability to operate in real-time, and under real world conditions, making it an ideal candidate for detecting gross polluters on major freeways and thoroughfares. In this study, remote sensing was employed to detect carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxide (NO). On-road remote sensing data taken from measurements performed in six states, (Arizona, California, Colorado, Illinois, Texas, and Utah) were cleaned and analyzed. Data mining and exploration were first undertaken in order to search for relationships among variables such as make, year, engine type, vehicle weight, and location. Descriptive statistics were obtained for the three pollutants of interest. The data were found to have non-normal distributions. Applied transformations were ineffective, and nonparametric tests were applied. Due to the extremely large sample size of the dataset (508,617 records), nonparametric tests resulted in "p" values that demonstrated "significance." The general linear model was selected due to its ability to handle data with non-normal distributions. The general linear model was run on each pollutant with output producing descriptive statistics, profile plots, between-subjects effects, and estimated marginal means. Due to insufficient data within certain cells, results were not obtained for gross vehicle weight and engine type. The "year" variable was not directly analyzed in the GLM because "year" was employed in a weighted least squares transformation. "Year" was found to be a source of heteroscedasticity; and therefore, the basis of a least-squares transformation. Grouped-years were analyzed using medians, and the results were displayed graphically. Based on the GLM results and descriptives, Japanese vehicles typically had the lowest CO, HC, and NO emissions, while American vehicles ranked high for the three. Illinois, ranked lowest for CO, while Texas ranked highest. Illinois and Colorado were lowest for HC emissions, while Utah and California were highest. For NO, Colorado ranked highest with Texas and Arizona, lowest.

Este trabalho tem como foco o entendimento das propriedades ópticas do aerossol na Amazônia utilizando várias técnicas: sensoriamento remoto em solo, sensoriamento remoto por satélites e medidas in situ. Propriedades ópticas medidas continuamente ao longo de mais de 15 anos pela rede AERONET na Amazônia foram analisadas buscando compreender como os eventos anuais de queima de biomassa e as emissões urbanas de Manaus afetam as propriedades das partículas. Medidas de longo prazo do sensor MODIS utilizando o sistema Giovanni da NASA foram úteis para caracterizar o impacto da pluma urbana de Manaus nas propriedades de aerossol e nuvens na Amazônia Central. A partir de medições in situ realizadas nas várias estações amostradoras do experimento GoAmazon2014/5, analisou-se em detalhes propriedades de absorção e espalhamento de aerossol antes e depois do impacto da pluma urbana de Manaus. Foi observada uma alta variabilidade na profundidade óptica do aerossol (AOD) bem como em outras propriedades, tais como absorção, espalhamento e distribuição de tamanho. Valores muito elevados de AOD foram observados em todos os sítios durante a estação seca, em particular na região do arco do desflorestamento. Análises dos expoentes Ångström de espalhamento e absorção separam as diferentes componentes absorvedoras do aerossol, entre eles o carbono elementar (EC), carbono orgânico (OC), poeira mineral e partículas biogênicas. Uma análise da forçante radiativa no topo da atmosfera em conjunto com a Matriz Ångström auxiliou no entendimento do papel da componente orgânica de espalhamento (OC) e da componente de absorção (BC) no aerossol de queimadas e urbano na forçante radiativa. A região menos afetada por queimadas na Amazônia foi caracterizada pela presença de um aerossol altamente espalhador durante a estação seca, com valores de albedo de espalhamento simples (SSA) na faixa de 0,91-0,94. Por outro lado, constatou-se valores médios de 0,85 a 0,89 em regiões fortemente impactadas por queimadas. Através de medidas in situ e medidas obtidas a partir da rede AERONET, foi possível observar significativo impacto da pluma de Manaus vento abaixo da cidade, especialmente na componente de absorção. O efeito da absorção do aerossol urbano na forçante radiativa é significativo, indo de uma forçante no sítio da EMBRAPA, antes da pluma, de -24 W/m² para cerca de -18 W/m² em Manacapuru, com o efeito da pluma de Manaus. A partir da análise de 12 anos de medidas dos sensores MODIS e MISR, observou-se alterações nas propriedades de nuvens e na carga atmosférica de aerossol. Foram analisadas a AOD, a temperatura do topo da nuvem e o raio efeito de gotículas de nuvens. Ficou clara a presença de nuvens mais altas e com raio efetivo menor em regiões com maior carga de aerossol, vento abaixo de Manaus.
This work focuses on understanding the optical properties of aerosol in the Amazon, using various techniques: remote sensing from the ground, remote sensing from satellites and in situ measurements. Aerosol optical properties continuously measured over more than 15 years carried out by the AERONET network in the Amazon were analyzed seeking to understand how the annual biomass burning emissions and urban emissions of Manaus affect particle properties. Long-term measurements of MODIS using the Giovanni NASA system were useful to characterize the impact of the urban plume of Manaus in the properties of aerosol and clouds in the central Amazon. Based on in situ measurements in the various sampling sites of the GoAmazon2014/5 experiment, the absorption and scattering properties of aerosol before and after the impact of Manaus urban plume were analyzed in detail. It was observed a large spatial and temporal variability in the aerosol optical depth (AOD) as well as in various properties such as absorption and scattering coefficients and size distribution. Very high levels of AOD were observed at all sites during the dry season, particularly in the deforestation arc region. Analysis of scattering and absorption Ångström exponents helps to identify different absorber components of the aerosol, including elemental carbon (EC), organic carbon (OC), mineral dust and biogenic particles. An analysis of the radiative forcing at the top of the atmosphere together with the Ångström Matrix helps in understanding the role of the scattering organic component (OC) versus the absorption component (BC) in biomass burning and urban aerosol particles in the radiative forcing. The region least affected by fires in the Amazon was characterized by a highly scattering aerosol during the dry season with Single Scattering Albedo (SSA) values in the range of 0.91-0.94, while we found mean values of 0.85 to 0.89 in heavily affected biomass burning regions. Through in situ measurements and AERONET observations it was possible to measure the significant impact of Manaus plume downwind of the urban area, especially in the absorption component. The effect of absorption of urban aerosol in the radiative forcing is significant, with measurements at the EMBRAPA site, before the impact of the plume, at -24 W/m² going to -18 W/m² in Manacapuru that is effect of the Manaus plume. It was also analyzed the changes in the properties of clouds and atmospheric aerosol loading over the last 12 years of MODIS and MISR measurements. The analysis of AOD, cloud top temperature and the radius of cloud droplets show a clear signal of Manaus plume. It was observed clouds with smaller effective radius in regions with higher aerosol load, downwind of Manaus.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Tese (Doutorado em Tecnologia Nuclear)
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP
FAPESP:09/14758-7

The atmospheric boundary layer (ABL) is the layer of air directly influenced by the Earth’s surface and is the layer of the atmosphere most important to humans as this is the air we live in. Methods for measuring the properties of the ABL include three general approaches: satellite-based, ground- based and airborne. A major research challenge is that many contemporary methods provide a restricted spatial resolution or coverage of variations of ABL properties such as how wind speed varies across a landscape with complex topography. To enhance our capacity to measure the properties of the ABL, this thesis presents a new technique that involves a small unmanned aircraft system (sUAS) equipped with a customized payload for measuring wind speed and particulate matter. The research presented herein outlines two key phases in establishing the proof-of-concept of the payload and its integration on the sUAS: (1) design and testing and (2) field demonstration. The first project focuses on measuring wind speed, which has been measured with fixed wing sUASs in previous research, but not with a helicopter sUAS. The second project focuses on the measurement of particulate matter, which is a major air pollutant typically measured with ground- based sensors. Results from both proof-of-concept projects suggest that ABL research could benefit from the proposed techniques.

Atmospheric formaldehyde (H2CO) is an intermediate product common to the degradation of many volatile organic compounds and therefore it is a central component of the tropospheric chemistry. While the global formaldehyde background is due to methane oxidation, emissions of non-methane volatile organic compounds (NMVOCs) from biogenic, biomass burning and anthropogenic continental sources result in important and localised enhancements of the H2CO concentration. Recent spaceborne nadir sensors provide an opportunity to quantify the abundance of tropospheric formaldehyde at the global scale, and thereby to improve our knowledge of NMVOC emissions. This is essential for a better understanding of the processes that control the production and the evolution of tropospheric ozone, a key actor in air quality and climate change, but also of the hydroxyl radical OH, the main cleansing agent of our troposphere. For this reason, H2CO satellite observations are increasingly used in combination with tropospheric chemistry transport models to constrain NMVOC emission inventories in so-called top-down inversion approaches. Such inverse modelling applications require well characterised satellite data products consistently retrieved over long time periods.

This work reports on global observations of formaldehyde columns retrieved from the successive solar backscatter nadir sensors GOME, SCIAMACHY and GOME-2, respectively launched in 1995, 2002 and 2006. The retrieval procedure is based on the differential optical absorption spectroscopy technique (DOAS). Formaldehyde concentrations integrated along the mean atmospheric optical path are derived from the recorded spectra in the UV region, and further converted to vertical columns by means of calculated air mass factors. These are obtained from radiative transfer simulations, accounting for cloud coverage, surface properties and best-guess H2CO profiles, the latter being derived from the IMAGES chemistry transport model. A key task of the thesis has consisted in the optimisation of the H2CO retrieval settings from multiple sensors, taking into account the instrumental specificities of each sounder. As a result of these efforts, a homogeneous dataset of formaldehyde columns covering the period from 1996 to 2010 has been created. This comes with a comprehensive error budget that treats errors related to the spectral fit of the columns as well as those associated to the air mass factor evaluation. The time series of the GOME, SCIAMACHY and GOME-2 H2CO observations is shown to be consistent and stable over time. In addition, GOME-2 brings a significant reduction of the noise on spatiotemporally averaged observations, leading to a better identification of the emission sources. Our dataset is used to study the regional formaldehyde distribution, as well as its seasonal and interannual variations, principally related to temperature changes and fire events, but also to anthropogenic activities. Moreover, building on the quality of our 15-year time series, we present the first analysis of long-term changes in the H2CO columns. Positive trends, in the range of 1.5 to 4% yr-1, are found in Asia, more particularly in Eastern China and India, and are related to the known increase of anthropogenic NMVOC emissions in these regions. Finally, our dataset has been extensively used in several studies, in particular by the BIRA-IASB modelling team to constrain NMVOC emission fluxes. The results demonstrate the high potential of satellite data as top-down constraint for biogenic and biomass burning NMVOC emission inventories, especially in Tropical ecosystems, in Southeastern Asia, and in Southeastern US.

Le formaldéhyde (H2CO) joue un rôle central dans la chimie de la troposphère en tant que produit intermédiaire commun à la dégradation chimique de la plupart des composés organiques volatils dans l’atmosphère. L’oxydation du méthane est responsable de plus de la moitié de la concentration moyenne globale du formaldéhyde. Sur les continents en revanche, les hydrocarbures non-méthaniques (NMVOCs) émis par la végétation, les feux de biomasse et les activités humaines, augmentent de façon significative et localisée la concentration de H2CO. Les récents senseurs satellitaires à visée nadir offrent la possibilité de quantifier à l’échelle globale l’abondance du formaldéhyde dans la troposphère et de ce fait, d’améliorer notre connaissance des émissions de NMVOCs. Ceci est essentiel à la compréhension des mécanismes contrôlant la production et l’évolution de l’ozone troposphérique, élément clé pour la qualité de l’air et les changements climatiques, mais aussi du composé hydroxyle OH, le principal agent nettoyant de notre troposphère. C’est pourquoi, une méthode de plus en plus répandue pour améliorer les inventaires d’émissions des NMVOCs consiste en l’utilisation d’observations satellitaires de H2CO en combinaison avec un modèle de chimie et de transport troposphérique, dans une approche appelée modélisation inverse. Ce genre d’application demande des produits satellitaires bien caractérisés et dérivés de façon cohérente sur de longues périodes de temps.

Le travail présenté dans ce manuscrit porte sur l’inversion des colonnes de formaldéhyde à partir de spectres de la radiation solaire rétrodiffusée par l’atmosphère terrestre, mesurés par les senseurs GOME, SCIAMACHY et GOME-2, lancés successivement en 1995, 2002 et 2006. La méthode d’inversion est basée sur la spectroscopie d’absorption optique différentielle (DOAS). Les concentrations de formaldéhyde intégrées le long du chemin optique moyen dans l’atmosphère sont dérivées à partir des spectres mesurés, et ensuite transformées en colonnes verticales par le biais de facteurs de conversion appelés facteurs de masse d’air. Ces derniers sont calculés à l’aide d’un modèle de transfert radiatif, en tenant compte de la présence de nuages, des propriétés de la surface terrestre et la distribution verticale supposée du formaldéhyde, fournie par le modèle IMAGES. Un des objectifs principaux de la thèse a été d’optimiser les paramètres d’inversion pour H2CO, et ceci pour les trois senseurs, tout en tenant compte des spécificités de chaque instrument. Ces efforts ont conduit à la création d’un jeu de données homogène, couvrant la période de 1996 à 2010. Les colonnes sont fournies avec un bilan d’erreur complet, incluant les erreurs liées à l’inversion des concentrations dans les spectres, ainsi que celles provenant de l’évaluation des facteurs de masse d’air. La série temporelle des observations de GOME, SCIAMACHY et GOME-2 présente une bonne cohérence et stabilité sur toute la période. Nous montrons aussi que la meilleure couverture terrestre de GOME-2 entraîne une réduction significative du bruit sur les observations moyennées, permettant une meilleure identification des sources d’émission. Notre jeu de données est exploité pour étudier la distribution régionale du formaldéhyde, ainsi que ses variations saisonnières et interannuelles, principalement liées aux variations de température et aux feux de végétation, mais aussi aux activités anthropiques. De plus, en s’appuyant sur la qualité de la série temporelle de 15 ans, nous présentons la première analyse des variations à long terme des concentrations de H2CO. Des tendances positives, de l’ordre de 1.5 à 4% par an, sont observées en Asie, en particulier dans l’est de la Chine et en Inde, liées à l’augmentation des émissions anthropiques d’hydrocarbures dans ces régions. Finalement, nos données ont été largement exploitées par le groupe de modélisation de l’IASB pour faire des études de modélisation inverse des émissions de NMVOCs. Les résultats démontrent le haut potentiel des données satellitaires pour contraindre les inventaires d’émissions dues à la végétation et aux feux de biomasse, particulièrement dans les écosystèmes tropicaux, en Asie du sud-est, et dans le sud-est des Etats-Unis.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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Dissertacao (Mestrado)
IPEN/D
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

O Laboratório de Aplicações Ambientais a Laser, situado no Centro de Lasers e Aplicações no IPEN (Instituto de Pesquisas Energéticas e Nucleares), efetua medidas das concentrações de aerossóis atmosféricos, enviando um feixe de laser à atmosfera e coletando a luz retroespalhada. Tal sistema fornece um grande número de parâmetros físicos que devem ser administrados de forma ágil para a obtenção de análises resultantes. Em conseqüência disso, a implementação de um banco de dados tornou-se imprescindível como instrumento de comunicação e visualização gráfica das medidas coletadas. Um critério de classificação destas valiosas informações foi adotado, estabelecendo níveis de armazenamento definidos a partir de características específicas aos tipos de dados determinados. A compilação e automação destas medidas promoverá a integração entre dados, análise e retorno otimizado de resultados das propriedades da atmosfera, propiciando futuras pesquisas e análise de dados.
The LIDAR system (Light Detection and Ranging) laser remote sensing at the Nuclear and Energy Research Institute Laboratory of Environmental Laser Applications allows on line measurements of variations in the concentrations of atmospheric aerosols by sending a laser beam to the atmosphere and collecting the backscattered light. Such a system supplies a great number of physical parameters that must be managed in an agile form to the attainment of a real time analysis. Database implementation therefore becomes an important toll of communication and graphical visualization of measurements. A criterion for classification of this valuable information was adopted, establishing defined levels of storage from specific characteristics of the determined data types. The compilation and automation of these measurements will promote optimized integration between data, analysis and retrieval of the resulting properties and of the atmosphere, improving future research and data analysis.

Par diffusion et absorption des rayonnements solaire et terrestre, et en modifiant les propriétés des nuages, les aérosols atmosphériques exercent un forçage direct et indirect sur le climat encore difficile à estimer dans le présent et à prédire pour le futur. Ces considérations s’appliquent au bassin méditerranéen, l’une des régions du globe climatiquement sensible, où on trouve de fortes concentrations en aérosols issus de sources diverses, naturelles (aérosols marins, poussières sahariennes) et anthropiques (industrie, transport, feux intentionnels). Dans ce contexte, les observations satellitaires sont incontournables pour décrire la distribution spatiale et l’évolution temporelle à long terme des concentrations et propriétés des aérosols, et en déterminer les impacts. En particulier, l’instrument POLDER-3/PARASOL (2005-2013) est un capteur polarisé, multi-spectral/directionnel qui permet d’accéder à différentes propriétés optiques des aérosols, notamment au-dessus des océans, avec une distinction entre les composantes fines, grossières sphériques/non-sphériques des aérosols. Dans ce cadre, ma thèse a porté sur la validation poussée des produits aérosols mesurées par POLDER (notamment des épaisseurs optiques des fractions grossières sphériques/non-sphériques non validées à ce jour), avec les observations de télédétection et in situ des propriétés optiques et physico-chimiques, obtenues grâce aux campagnes du projet ChArMEx en Méditerranée (étés 2012-2013), afin d’évaluer leurs potentiels à caractériser et quantifier les aérosols dans la région. Cette validation permet de dresser une cartographie régionale des aérosols et analyser leur distribution spatiale et temporelle avec une distinction sur leur taille et leur forme
By scattering and absorbing solar and terrestrial radiations, and by modifying the properties of clouds, atmospheric aerosols have a direct and indirect radiative forcing on the climate that is still difficult to estimate and to predict. These considerations apply to the Mediterranean basin, one of the major climatic hot-spot of the globe, where there are high concentrations of aerosols from various natural (marine aerosols, Saharan dust) and anthropogenic (industry, transport, intentional fires) sources. In this context, satellite observations are essential to describe the spatial distribution and long-term temporal evolution of aerosol concentrations and properties, and to determine their impacts. In particular, POLDER-3 / PARASOL spatial instrument (2005-2013) is a polarized, multi-spectral/directional sensor that allows access to different optical properties of aerosols, especially over oceans, with a distinction between fine and coarse spherical / non-spherical aerosol components. In this framework, my thesis focused on the thorough validation of aerosol products measured by POLDER (notably the aerosol optical depth of the coarse spherical/non-spherical fractions not evaluated at the time of this work), with remote sensing and in situ observations of optical and physic-chemical properties obtained from ChArMEx project in the Mediterranean basin (summers 2012-2013) to assess their potential to characterize and quantify aerosols in this region. Thanks to this validation, it is possible to map and to analyze aerosol spatial and temporal distribution with a distinction on their size and their shape

El crecimiento industrial ha acarreado grandes avances tecnológicos para nuestra sociedad. Lamentablemente, el precio a pagar por estos avances ha sido un aumento significativo de los niveles de contaminación del aire en todo el mundo, afectando tanto a zonas urbanas como a las zonas rurales. Por lo general, la monitorización de la calidad aire se realiza mediante estaciones de monitorización fijas. Sin embargo, este método es demasiado costoso, poco escalable y difícil de implementar en nuestras ciudades, las cuales están cada vez más pobladas. El uso de Mobile CrowdSensing, paradigma en el cual la monitorización la realizan los propios usuarios, permite realizar monitorización ambiental utilizando sensores móviles integrados en vehículos. Los posibles escenarios se pueden dividir en dos: entornos urbanos, donde hay un amplio conjunto de vehículos disponibles, y entornos rurales o industriales, donde el tráfico vehicular es escaso y está limitado a las principales arterias de transporte. Teniendo en cuenta estos dos escenarios, esta tesis propone una arquitectura, llamada EcoSensor, que permite monitorizar la contaminación del aire utilizando pequeños sensores de bajo coste instalados en diferentes tipos de vehículos, tales como bicicletas, automóviles o autobuses del sistema de transporte público, en el caso de entornos urbanos, y en drones o UAS en entornos rurales. La arquitectura propuesta está compuesta por tres componentes: un sensor de bajo coste para capturar datos de contaminación, un smartphone para realizar un preprocesamiento de la información y para transmitir los datos hacia un servidor central, y el servidor central, encargado de almacenar y procesar la información de contaminación ambiental. Para entornos urbanos, analizamos diferentes alternativas con respecto al diseño de una unidad de monitorización de bajo coste basada en plataformas de prototipado comerciales como RaspberryPi o Arduino, junto con sensores también de precio reducido. En la tesis realizamos un análisis, y proponemos un proceso, para llevar a cabo la monitorización ambiental utilizando la arquitectura propuesta. Este proceso abarca cuatro operaciones básicas: captura de datos, conversión de unidades, reducción de la variabilidad temporal, e interpolación espacial. Para entornos rurales, proponemos el uso de drones como unidades de sensorización móviles. Específicamente, equipamos el drone con capacidades de monitorización a través de un microordenador RaspberryPi y sensores de calidad del aire de bajo coste. Finalmente, se propone un algoritmo llamado PdUC para controlar el vuelo del UAV con el objetivo de realizar monitorización ambiental, identificando las áreas más contaminadas, y tratando de ese modo de mejorar la precisión general y la velocidad de monitorización. Además, proponemos una mejora a este algoritmo, denominada PdUC-D, basada en la discretización del área a monitorizar dividiéndola en pequeñas áreas (tiles), donde cada tile se monitoriza una sola vez, evitando así realizar muestreos redundantes. En general, verificamos que la monitorización móvil es una aproximación eficiente y fiable para monitorizar la contaminación del aire en cualquier entorno, ya sea usando vehículos o bicicletas en entornos urbanos, o UAVs en entornos rurales. Con respecto al proceso de monitorización ambiental, validamos nuestra propuesta comparando los valores obtenidos por nuestros sensores móviles de bajo coste con respecto a los valores típicos de referencia ofrecidos por las estaciones de monitorización fijas para el mismo período y ubicación, comprobando que los resultados son semejantes, y están acuerdo a lo esperado. Además, demostramos que PdUC-D, permite guiar autónomamente un UAV en tareas de monitorización del aire, ofreciendo un mejor rendimiento que los modelos de movilidad típicos, reduciendo tanto los errores de predicción como el tiempo para cubrir el área completa,
Industrial growth has brought unforeseen technological advances to our society. Unfortunately, the price to pay for these advances has been an increase of the air pollution levels worldwide, affecting both urban and countryside areas. Typically, air pollution monitoring relies on fixed monitoring stations to carry out the pollution control. However, this method is too expensive, not scalable, and hard to implement in any city. The Mobile Crowdsensing (MCS) approach, a novel paradigm whereby users are in charge of performing monitoring tasks, allows environment monitoring to be made using small sensors embedded in mobile vehicles. The possible scenarios can be divided into two: urban scenarios, where a wide set of vehicles are available, and rural and industrial areas, where vehicular traffic is scarce and limited to the main transportation arteries. Considering these two scenarios, in this thesis we propose an architecture, called EcoSensor, to monitor the air pollution using small sensors installed in vehicles, such as bicycles, private cars, or the public transportation system, applicable to urban scenarios, and the use of an Unmanned Aerial System (UAS) in rural scenarios. Three main components compose our architecture: a low-cost sensor to capture pollution data, a smartphone to preprocess the pollution information and transmit the data towards a central server, and the central server, to store and process pollution information. For urban scenarios, we analyze different alternatives regarding the design of a low-cost sensing unit based on commercial prototyping platforms such as Raspberry Pi or Arduino, and Commercial Off-the-shelf (COTS) air quality sensors. Moreover, we analyze and propose a process to perform pollution monitoring using our architecture. This process encompasses four basic operations: data reading, unit conversion, time variability reduction, and spatial interpolation. For rural scenarios, we propose the use of an Unmanned Aerial Vehicle (UAV) as a mobile sensor. Specifically, we equip the UAV with sensing capabilities through a Raspberry Pi microcomputer and low-cost air quality sensors. Finally, we propose an algorithm, called Pollution-driven UAV Control (PdUC), to control the UAV flight for monitoring tasks by focusing on the most polluted areas, and thereby attempting to improve the overall accuracy while minimizing flight time. We then propose an improvement to this algorithm, called Discretized Pollution-driven UAV Control (PdUC-D), where we discretize the target area by splitting it into small tiles, where each tile is monitored only once, thereby avoiding redundant sampling. Overall, we found that mobile sensing is a good approach for monitoring air pollution in any environment, either by using vehicles or bicycles in urban scenarios, or an UAVs in rural scenarios. We validate our proposal by comparing obtained values by our mobile sensors against typical values reported by monitoring stations at the same time and location, showing that the results are right, matching the expected values with a low error. Moreover, we proved that PdUC-D, our protocol for the autonomous guidance of UAVs performing air monitoring tasks, has better performance than typical mobility models in terms of reducing the prediction errors and reducing the time to cover the whole area.Moreover, we analyze and propose a process to perform pollution monitoring using our architecture. This process encompasses four basic operations: data reading, unit conversion, time variability reduction, and spatial interpolation.
El creixement industrial ha implicat grans avanços tecnològics per a la nostra societat. Lamentablement, el preu que cal pagar per aquests avanços ha sigut un augment significatiu dels nivells de contaminació de l'aire a tot el món, que afecta tant zones urbanes com zones rurals. En general, el monitoratge de la qualitat aire es fa mitjançant estacions de monitoratge fixes. No obstant això, aquest mètode és massa costós, poc escalable i difícil d'implementar a les nostres ciutats, les quals estan cada vegada més poblades. L'ús de Mobile CrowdSensing (MCS), paradigma en el qual el monitoratge el duen a terme els mateixos usuaris, permet realitzar monitorització ambiental tenint sensors mòbils integrats en vehicles. Els possibles escenaris es poden dividir en dos: entorns urbans, on hi ha un ampli conjunt de vehicles disponibles, i entorns rurals o industrials, on el trànsit vehicular és escàs i està limitat a les principals artèries de transport. Tenint en compte aquests dos escenaris, aquesta tesi proposa una arquitectura, anomenada EcoSensor, que permet monitorar la contaminació de l'aire utilitzant petits sensors de baix cost instal·lats en diferents tipus de vehicles, com ara bicicletes, automòbils o autobusos del sistema de transport públic, en el cas d'entorns urbans, i en UAVs (Unmanned Aerial Vehicles) en entorns rurals. L'arquitectura proposada està composta per tres components: un sensor de baix cost per a capturar dades de contaminació, un smartphone per a realitzar un preprocessament de la informació i per a transmetre les dades cap a un servidor central, i el servidor central, encarregat d'emmagatzemar i processar la informació de contaminació ambiental. Per a entorns urbans, analitzem diferents alternatives pel que fa al disseny d'una unitat de monitoratge (sensor mòbil) de baix cost basada en plataformes de prototipatge comercials com Raspberry Pi o Arduino, juntament amb sensors també de preu reduït. En la tesi fem una anàlisi, i proposem un procés, per a dur a terme el monitoratge ambiental utilitzant l'arquitectura proposada. Aquest procés abasta quatre operacions bàsiques: captura de dades, conversió d'unitats, reducció de la variabilitat temporal, i interpolació espacial. Per a entorns rurals, proposem l'ús de drons o Unmanned Aerial Vehicles (UAVs) com a unitats de sensorització mòbils. Específicament, equipem el dron amb capacitats de monitoratge a través d'un microordinador Raspberry Pi i sensors de qualitat de l'aire de baix cost. Finalment, es proposa un algorisme anomenat PdUC (Pollution-driven UAV Control) per a controlar el vol del UAV amb l'objectiu de realitzar monitoratge ambiental, que identifica les àrees més contaminades i que, d'aquesta manera, tracta de millorar la precisió general i la velocitat de monitoratge. A més, proposem una millora a aquest algorisme, denominada PdUC-D, basada en la discretització de l'àrea a monitorar dividint-la en xicotetes àrees (tiles), on cada tile es monitora una sola vegada, fet que evita dur a terme mostrejos redundants. En general, verifiquem que el monitoratge mòbil és una aproximació eficient i fiable per a monitorar la contaminació de l'aire en qualsevol entorn, ja siga usant vehicles o bicicletes en entorns urbans, o UAVs en entorns rurals. Pel que fa al procés de monitoratge ambiental, validem la nostra proposta comparant els valors obtinguts pels nostres sensors mòbils de baix cost pel que fa als valors típics de referència oferits per les estacions de monitoratge fixes per al mateix període i ubicació, i es comprova que els resultats són semblants, i estan d'acord amb el resultat esperat. A més, es demostra que PdUC-D permet guiar autònomament un UAV en tasques de monitoratge de l'aire, oferint un millor rendiment que els models de mobilitat típics, reduint tant els errors de predicció com el temps per a cobrir l'àrea completa, i aconseguint una major precisió dins de les àrees més
Alvear Alvear, ÓP. (2018). Mobile Sensing Architecture for Air Pollution Monitoring [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/107928
TESIS

Through the Global Gas Flaring Reduction (GGFR) initiative a substantial amount of effort and international attention has been focused on the reduction of gas flaring since 2002 (Elvidge et al., 2009). Nigeria is rated as the second country in the world for gas flaring, after Russia. In an attempt to reduce and eliminate gas flaring the federal government of Nigeria has implemented a number of gas flaring reduction projects, but poor governmental regulatory policies have been mostly unsuccessful in phasing it out. This study examines the effects of pollution from gas flaring using multiple satellite based sensors (Landsat 5 TM and Landsat 7 ETM+) with a focus on vegetation health in the Niger Delta. Over 131 flaring sites in all 9 states (Abia, Akwa Ibom, Bayelsa, Cross Rivers, Delta, Edo, Imo, Ondo and Rivers) of the Niger Delta region have been identified, out of which 11 sites in Rivers State were examined using a case study approach. Land Surface Temperature data were derived using a novel procedure drawing in visible band information to mask out clouds and identify appropriate emissivity values for different land cover types. In 2503 out of 3001 Landsat subscenes analysed, Land Surface Temperature was elevated by at least 1 ℃ within 450 m of the flare. The results from fieldwork, carried out at the Eleme Refinery II Petroleum Company and Onne Flow Station, are compared to the Landsat 5 TM and Landsat 7 ETM+ data. Results indicate that Landsat data can detect gas flares and their associated pollution on vegetation health with acceptable accuracy for both Land Surface Temperature (range: 0.120 to 1.907 K) and Normalized Differential Vegetation Index (sd ± 0.004). Available environmental factors such as size of facility, height of stack, and time were considered. Finally, the assessment of the impact of pollution on a time series analysis (1984 to 2013) of vegetation health shows a decrease in NDVI annually within 120 m from the flare and that the spatio-temporal variability of NDVI for each site is influenced by local factors. This research demonstrated that only 5 % of the variability in δLST and only 12 % of the variability in δNDVI, with distance from the flare stack, could be accounted for by the available variables considered in this study. This suggests that other missing factors (the gas flaring volume and vegetation speciation) play a significant role in the variability in δLST and δNDVI respectively.

The ability to map industrial discharges through remote sensing provides a powerful tool in environmental monitoring. Radionuclide effluents have been discharged, under authorization, into the Irish Sea from BNFL (British Nuclear Fuels Plc.) sites at Sellafield and Springfields since 1952. The quantitative mapping of this anthropogenic radioactivity in estuarine intertidal zones is crucial for absolute interpretations of radionuclide transport. The spatial resolutions of traditional approaches e.g. point sampling and airborne gamma surveys are insufficient to support geomorphic interpretations of the fate of radionuclides in estuaries. The research presented in this thesis develops the use of airborne remote sensing to derive high-resolution synoptic data on the distribution of anthropogenic radionuclides in the intertidal areas of the Ribble Estuary, Lancashire, UK. From multidate surface sediment samples a significant relationship was identified between the Sellafieldderived 137Cs & 241Am and clay content (r2=0.93 & 0.84 respectively). Detailed in situ, and laboratory, reflectance (0.4-2.5mn) experiments demonstrated that significant relationships exist between Airborne Thematic Mapper (ATM) simulated reflectance and intertidal sediment grain-size. The spectral influence of moisture on the reflectance characteristics of the intertidal area is also evident. This had substantial implications for the timing of airborne image acquisition. Low-tide Daedalus ATM imagery (Natural Environmental Research Council) was collected of the Ribble Estuary on May 30th 1997. Preprocessing and linear unmixing of the imagery allowed accurate sub-pixel determinations of sediment clay content distributions (r2=0.8 1). Subsequently, the established relationships between 137Cs & 241Am and sediment grain-size enabled the radionuclide activity distributions across the entire intertidal area (92km2) to be mapped at a geomorphic scale (1.75m). The accuracy of these maps was assessed by comparison with in situ samples and the results of previous radiological studies within the estuary. Finally, detailed conclusions are made regarding radionuclide sinks and sources, and surface activity redistribution within the Ribble Estuary environment.

Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2007.
Weber, Rodney, Committee Member ; Schauer, James, Committee Member ; Mulholland, James, Committee Member ; Bergin, Mike, Committee Chair ; Russell, Armistead, Committee Member.

Monitoring nonpoint source pollution in a large area is often impractical. However, estimating nonpoint pollution through use of empirical models such as the Universal Soil Loss Equation (USLE) provides a basis for identifying problem areas, and setting management priorities. The purpose of this study was to determine the feasibility of using Landsat imagery and existing geographic data to estimate the effects of land use changes on water quality in four North Texas watersheds over a twelve year period.

Urban air pollution is of major concern in Spain and also throughout Europe and globally with numerous effects on human health and ecosystems. Since air quality (AQ) is predominantly a problem for human health and the environment, the lowest levels of the atmosphere are the most important to investigate, mainly the planetary boundary-layer (PBL). Atmospheric variables (i.e. temperature, humidity, winds) in the PBL are critically important as inputs for accurate simulations in AQ models. From a modeling standpoint PBL height can be extremely difficult to compute accurately due to the fact that boundary layer processes occur at smaller geographical scales than mesoscale meteorological models can resolve. To that end, atmospheric models make use of parameterizations to represent the boundary layer structure in the lower atmosphere. This Ph.D. thesis evaluates the sensitivity of high-resolution AQ simulations from the CALIOPE AQ forecast system (www.bsc.es/caliope) in the distribution of gaseous photochemical pollutants using different PBL schemes in the Weather Research and Forecasting (WRF) model. The project is separated into three main activities. First, was an evaluation of available methods for estimating PBL height from lidar measurements based on data from the Barcelona multiwavelength Raman lidar, a member site of the European Aerosol Research Lidar Network (EARLINET). Lidar-estimated PBL heights were compared with those obtained from radiosoundings. It was found that a time-adaptive extended Kalman filter (EKF) technique provided lidar estimates closest (coefficient of determination = 0.96) to values estimated with radiosoundings. The 13-yr average PBL height was 1.28 ± 0.4 km with the EKF method, which is similar to previous studies. In the second activity, eight PBL schemes from the WRF model were evaluated as compared to observations over Barcelona and Athens. Instrumentation included two lidars, and numerous radiosondes and surface meteorological stations. Data from Athens was collected during the 39-day HygrA-CD field campaign, which took place in summer 2014. In both studies it was generally found that non-local PBL schemes perform better. For example, the non-local asymmetric convective model version 2 (ACM2) scheme showed coefficient of determination values of 0.33 and 0.15, for the Barcelona and Athens studies, respectively. WRF model simulations with the ACM2 scheme only slightly under-estimated PBL heights in Athens during Etesians events, with a mean bias around 0.11 km. In the final activity, four WRF PBL schemes were sensitivity-tested with model simulations from the CALIOPE AQ forecast system, as compared with surface observations from ground AQ stations and lidar data from the Barcelona micropulse lidar (MPL), a new station in the NASA Micropulse Lidar Network (MPLNET). It was found that WRF model-simulated PBL height and surface meteorology can largely impact the simulations of air pollution variables (ozone, nitrogen dioxide, particulate matter less than 10 microns). CMAQ model simulations coupled to WRF with the ACM2 and Bougeault¿Lacarrère (BouLac) PBL schemes performed the best for surface ozone concentrations at rural background stations, with correlation values of 0.82 and 0.79 compared to surface AQ observations, respectively. In addition, spatial analysis of the CMAQ model simulations showed the lowest biases using the ACM2 and BouLac schemes. The outcome from this project is a deeper understanding of the sensitivity of AQ simulations to model PBL schemes, which may result in more accurate operational AQ forecasts.
La contaminación atmosférica urbana es de gran preocupación en España, Europa y en el mundo por sus efectos sobre la salud humana y los ecosistemas. La calidad del aire (AQ) es un problema para la salud humana y el medio ambiente, los niveles más bajos de la atmósfera son importantes para investigar, sobre todo la capa límite planetaria (PBL; acrónimo del inglés). Variables atmosféricas (como la temperatura, humedad y vientos) en la PBL son de importancia crítica para simulaciones precisas en los modelos de calidad del aire (AQM). Modelizar la altura de la PBL es extremadamente difícil de calcular con precisión debido a que los procesos de capa límite se producen a escalas más pequeñas que en los modelos meteorológicos de mesoescala. Los modelos atmosféricos hacen uso de parametrizaciones para representar la estructura de la PBL. Esta tesis doctoral evalúa la sensibilidad de las simulaciones de contaminantes gaseosos en AQ en alta resolución en el sistema CALIOPE AQFS (www.bsc.es/caliope), utilizando diferentes esquemas de PBL del modelo de predicción numérica: WRF. El proyecto se divide en tres actividades principales. En primer lugar, se evaluaron los métodos disponibles para estimar la altura de la PBL a partir de mediciones lidar (multiwavelength Raman) usando 13 años de la base de datos de Barcelona, miembro de la Red Europea de Investigación Aerosol Lidar (EARLINET). Las alturas de la PBL estimadas se compararon con las obtenidas a partir radiosondeos. Se encontró que un filtro de Kalman extendido (EKF) proporciona mejores estimaciones (coeficiente de determinación = 0,96) con respecto a los valores estimados con radiosondeos. La altura media de la PBL fue de 1,28 ± 0,4 km con el método EKF, en concordancia a resultados de estudios anteriores. En la segunda actividad, ocho esquemas de PBL del modelo WRF se evaluaron en comparación con las observaciones de Barcelona y Atenas. La instrumentación incluyó dos lidares, observaciones de radiosondas y estaciones meteorológicas de superficie. Los datos de Atenas se recogieron durante la campaña HygrA-CD de 39 días en el verano de 2014. Se encontró que en ambos estudios los es quemas PBL no locales funcionan mejor. El esquema no local “asymmetric convective model version 2” (ACM2) mostró coeficientes de determinación de 0,33 y 0,15, para Barcelona y Atenas respectivamente. Las simulaciones del modelo WRF con el esquema ACM2 subestimaron ligeramente las alturas de la PBL en Atenas durante los eventos "Etesians", con un sesgo de 0,11 km. Finalmente, cuatro esquemas de PBL del modelo WRF fueron probados en el sistema CALIOPE AQFS y comparados con observaciones de las estaciones de AQ y datos del micropulse lidar (MPL) de Barcelona, una nueva estación de NASA Micropulse Lidar Network (MPLNET). Se encontró que la altura de la PBL puede afectar en gran medida las simulaciones de las variables de contaminación del aire (O3, NO2 y PM10). Las simulaciones con el modelo CMAQ acopladas a WRF con los esquemas ACM2 y Bougeault-Lacarrère (Boulac) de la PBL calculan mejor las concentraciones de O3 en las estaciones rurales de fondo, con una r de 0,82 y 0,79 comparados con las observaciones de estaciones de AQ respectivamente. Además, el análisis espacial de las simulaciones mostró las menores desviaciones utilizando los esquemas ACM2 y Boulac. El resultado de este proyecto es una comprensión más profunda de la sensibilidad de las simulaciones de la calidad del aire a los esquemas de cálculo de la PBL, que pueden dar lugar a pronósticos operacionales de AQ más precisos.

In this study Geographic Information System (GIS) technology was integrated with remote sensing techniques in order to determine the potential for nonpoint source pollution in the Lake Palestine and Cedar Creek Reservoir watersheds of North Central Texas. The Universal Soil Loss Equation was used to determine soil erosion potential from the watersheds, and export coefficients were used to estimate nutrient loadings into the reservoirs.

Master of Arts
Department of Geography
J. M. Shawn Hutchinson
Military installations are important assets for the proper training of armed forces. To ensure the continued viability of training lands, management practices need to be implemented to sustain the necessary environmental conditions for safe and effective training. For this study two analyses were done, a contemporary burn history and a time series analysis. The study area is Smoky Hill Air National Guard Range (ANGR), an Impact Area (within the range) and a non-military Comparison Site. Landsat 5 TM / 7 ETM+ imagery was used to create an 11 year composite burn history image. NDVI values were derived from MODIS imagery for the time series analysis using the statistical package BFAST. Results from both studies were combined to make conclusions about training impacts at Smoky Hill ANGR and determine if BFAST is a viable environmental management tool. Based on this study the training within Smoky Hill ANGR does not seem to be having a negative effect on the overall vegetation condition. It was also discovered that BFAST was able to accurately detect known vegetation disturbances. BFAST is a viable environmental management tool if the limitations are understood.

For decades, traditional remote sensing retrieval methods that rely solely on the spectral intensity of the water-leaving light have provided indicators of aquatic ecosystem health. With the increasing demand for new water quality indicators and improved accuracy of existing ones, the limits of traditional remote sensing approaches are becoming apparent. Use of the additional information intrinsic to the polarization state of light is therefore receiving more attention. One of the major challenges inherent in any above-surface determination of the water-leaving radiance, scalar or vector, is the removal of extraneous light which has not interacted with the water body and is therefore not useful for remote sensing of the water itself. Due in-part to the lack of a proven alternative, existing polarimeter installations have thus far assumed that such light was reflected by a flat sea surface, which can lead to large inaccuracies in the water-leaving polarization signal. This dissertation rigorously determines the full Mueller matrices for both surface-reflected skylight and upwardly transmitted light by a wind-driven ocean surface. A Monte Carlo code models the surface in 3D and performs polarized ray-tracing, while a vector radiative transfer (VRT) simulation generates polarized light distributions from which the initial Stokes vector for each ray is inferred. Matrices are computed for the observable range of surface wind speeds, viewing and solar geometries, and atmospheric aerosol loads. Radiometer field-of-view effects are also assessed. Validation of the results is achieved using comprehensive VRT simulations of the atmosphere-ocean system based on several oceanographic research cruises and specially designed polarimeters developed by the City College of New York: one submerged beneath the surface and one mounted on a research vessel. When available, additional comparisons are made at 9 km altitude with the NASA Research Scanning Polarimeter (RSP). Excellent agreement is achieved between all instrumentation, demonstrating the accuracy of the modeling approach and validating the computed Mueller matrices. Further, the results are used to demonstrate the feasibility for polarimetric retrieval of the total attenuation coefficient for Case II waters, a feat which is not possible using scalar remote sensing methods.

According to World Health Organization, around 7 million people die every year due to diseases caused by air pollution. With the improvements in Internet of Things in the recent years, environmental sensing systems has started to gain importance. By using technologies like Cloud Computing, RFID, Wireless Sensor Networks, and open Application Programming Interfaces, it has become easier to collect data for visualization on different platforms. However, collected data need to be represented in an efficient way for better understanding and analysis, which requires design of data visualization tools. The GreenIoT initiative aims to provide open data with its infrastructure for sustainable city development in Uppsala. An environmental web application is presented within this thesis project, which visualizes the gathered environmental data to help municipality organizations to implement new policies for sustainable urban planning, and citizens to gain more knowledge to take sustainable decisions in their daily life. The application has been developed making use of the 4Dialog API, which is developed to provide data from a dedicated cloud storage for visualization purposes. According to the evaluation presented in this thesis, further development is needed to improve the performance to provide faster and more reliable service as well as the accessibility to promote openness and social inclusion.
Enligt World Health Organization dör 7 miljoner människor varje år på grund av sjukdomar orsakade av luftföroreningar. Med förbättringar inom Internet of Things under senare år, har betydelsen av system för miljösensorer. Genom att använda tekniker som molntjänster, RFID, trådlösa sensornätverk och öppna programmeringsgränssnitt, har det blivit enklare att samla in data för visualisering på olika plattformar. Men insamlad data behöver bli representerad på ett effektivt sätt för bättre förståelse och analys, vilket kräver utformande av verktyg för visualisering av data. Initiativet GreenIoT strävar mot att erbjuda öppen data med sin infrastruktur för hållbar stadsutveckling i Uppsala. I detta arbete presenteras en webb-tillämpning, som visualiserar den insamlade miljödatan för att hjälpa kommunen att implementera nya policies för hållbar stadsutveckling, och stimulera medborgare till att skaffa mer kunskap för att göra miljövänliga val i sin vardag. Tillämpningen har utvecklats med hjälp av 4Dialog API, som tillhandahåller data från lagring i molnet för visualiseringssyfte. Enligt den utvärdering som presenteras i denna rapport konstateras att vidare utveckling behövs för att förbättra dels prestanda för att erbjuda en snabbare och mer tillförlitlig service, och dels åtkomstmöjligheter för att främja öppenhet och social inkludering.

Ozone in surface air is the primary cause of polluted air in the United States. The current ozone observing network is insufficient either to assess air quality or to fully inform our understanding of the factors controlling tropospheric ozone. This thesis investigates the benefit of an instrument in geostationary orbit for observing near surface ozone using Observing System Simulation Experiments (OSSEs).
Earth and Planetary Sciences

This study used a geographic information system (GIS) and an empirical pollutant load model to estimate average annual pollutant loads contained in storm water charges from the City of Carrollton, Texas. The Geographic Resource Analyses Support System (GRASS) was used to create a number of spatial data layers as input variables to a model that estimates loads in Kilograms/Hectare/Year, and Kilogram/Basin/Year. Average annual pollutant loads for twelve pollutants were derived from average annual rainfall/runoff volumes, coupled with Local Dallas/Fort Worth Metroplex storm water Event Mean Concentrations.

An automated procedure was developed to identify and extract confined poultry facilities from color 35-mm slide imagery collected by the United States Department of Agriculture/Farm Service Agency (USDA/FSA). The imagery is used by the USDA/FSA to monitor compliance with various farm support programs and to determine crop production acreage within a given county. The imagery is generally available for all counties within the state on an annual basis. The imagery, however, is not flown to rigid specifications as flight height, direction, and overlap can vary significantly. The USDA/FSA attempts to collect imagery with reasonably clear skies, as visual interpretations could be drastically impacted by cloudiness. The goal of this study was to develop procedures to effectively utilize this imagery base to identify and extract poultry facilities using automated techniques based on image processing and GIS. The procedure involved pre-screening the slides to determine coverage, geopositioning to USGS quadrangle base, color scanning to convert slide image to a digital format and archiving each data file with a naming convention that would allow rapid retrieval in later analysis. Image processing techniques were developed for identifying poultry facilities based on spectral characteristics. GIS tools were used to select poultry facilities from an array of features with similar spectral characteristics. A training data set was selected from which the spectral characteristics of poultry facilities were analyzed and compared with background conditions. Poultry facilities were found to have distinguishable characteristics. Descriptive statistics were used to define the range of spectral characteristics encompassing poultry facilities. Thresholding analyses were then utilized to eliminate all image features with spectral characteristics outside of this range. Additional analyses were made to remove noise in the spectral image due to the sun angle, line of sight of camera, variation in roof reflectance due to rust and/or aging, shading by trees, etc. A primary objective in these analyses was to enhance the spectral characteristics for the poultry facility while, at the same time, retaining physical characteristics, i.e. the spectral characteristic is represented by a single blue color with a high brightness value. The techniques developed to achieve a single blue color involved the use of Principal Component Analysis (PCA) on the red color band followed by RGB to Hue and RGB to Saturation analyses on the red and green color bands, respectively, from the resulting image. The features remaining from this series of analyses were converted into polygons (shape file) using ArcView GIS, which was then used to calculate the area and perimeter of each polygon. The parameters utilized to describe the shape of a poultry house included width, length, compactness, length-width ratio, and polygon centroid analysis. Poultry facilities were found to have an average width of approximately 12.6m with a low standard deviation indicating that the widths of all houses were very similar. The length of poultry facilities ranged from 63m to 261m with and average length of 149m. The compactness parameter, which also is related to length and width, ranged from 30 to 130 with a mean value of approximately 57. The shape parameters were used by ArcView GIS to identify polygons that represent poultry facilities. The order of selection was found to be compactness followed by length-width ratio and polygon centroid analysis. A data set that included thirty 35-mm slide images randomly selected from the Rockingham County data set, which contained over 2000 slides, was used to evaluate the automated procedure. The slides contained 182 poultry houses previously identified through manual procedures. Seven facilities were missed and 175 were correctly identified. Ninety-seven percent (97%) of existing poultry facilities were correctly identified which compares favorably with the 97 % accuracy resulted by manual procedures. . The manual procedure described by Mostaghimi, et. al.(1999) only gave the center coordinates for each poultry facility. The automated procedure not only gives the center coordinate for each poultry building but also gives estimates for geometric parameters area, length and width along with an estimate of the capacity of building (i.e. number of birds), and waste load generated by birds including nutrient and bacteria content. The nutrient and bacteria load generated by each poultry facility is important information for conducting TMDL studies currently being developed for impaired Virginia streams. The information is expected to be very helpful to consultants and state agencies conducting the studies. Agricultural support agencies such as USDA/NRCS and USDA/FSA, Extension Service, consultants, etc. will find the information very helpful in the development of implementation plans designed to meet TMDL target water quality goals. The data also should be useful to Water Authorities for selection of appropriate treatment of water supplies and to county and local government jurisdictions for developing policies to minimize the degradation of water supplies.
Ph. D.

A poluição atmosférica é hoje uma questão que afeta as megacidades por todo o mundo. As partículas de aerossol participam do balanço radiativo, da formação de nuvens, da química atmosférica, e são prejudiciais à saúde da população exposta. A extensão e o rápido crescimento das megacidades têm levado à necessidade do desenvolvimento de ferramentas para o monitoramento da poluição do ar, urbana e regional, por sensoriamento remoto via satélites. Foram analisadas as propriedades ópticas dos aerossóis da região metropolitana de São Paulo com medidas obtidas por fotômetros da rede mundial AERONET (Aerosol Robotic Network) operada pela NASA. Foi desenvolvida uma nova metodologia para a determinação da espessura óptica das partículas de aerossol com alta resolução espacial de 1x1 km, sobre a região metropolitana de São Paulo, por satélite. Cinco modelos ópticos de aerossol, representativos da região, foram definidos como função do albedo simples. No comprimento de onda de 550 nm, os modelos possuem valores de albedo simples que variam de 0,83 a 0,93. Foram utilizadas medidas de radiâncias obtidas com o sensor MODIS (Moderate-Resolution Imaging Spectroradiometer), a bordo dos satélites Terra e Aqua da NASA. Extensos testes de sensibilidade foram realizados, analisando o papel do albedo simples, parâmetro de assimetria, refletância de superfície, vapor de água e outras propriedades na espessura óptica derivada. O algoritmo desenvolvido utiliza a propriedade de refletância crítica, que pode ser obtida pelo próprio sensor, para determinar o modelo de aerossol a ser empregado. Este procedimento permitiu a identificação mais precisa do modelo de aerossol, de forma dinâmica e interativa, reduzindo a incerteza na determinação da espessura óptica em alta resolução com o sensor MODIS. Os resultados de validação mostraram uma melhora significativa na comparação entre os produtos de espessura óptica obtidos com o sensor MODIS, quando comparado com as medidas de referência obtidas com radiômetros em superfície. Foram obtidos com esta metodologia mapas com a distribuição espacial de aerossóis com resolução de 1x1 km. Os estudos de casos também apresentaram a potencialidade do método em identificar o modelo de aerossol mais adequado, seja em eventos de poluição local, seja de transporte de poluentes de longa distância. Os modelos e procedimentos desenvolvidos podem ser aplicados a outras regiões urbanas, após as devidas validações. Como ferramenta complementar ao monitoramento ambiental de estações de superfície o produto apresentado pode-se tornar operacional e ser utilizado em rotina por órgãos de controle ambiental em megacidades, como, por exemplo, pela CETESB em São Paulo
Urban air pollution is a public concern in all megacities around the world. Aerosol particles are active participants in the atmospheric energy budget, cloud properties, atmospheric chemistry and have adverse effects on human health. The spatial extension and the high growth rate of the megacities show the need of the use of remote sensing technologies on urban air pollution monitoring. Optical properties of São Paulo aerosol particles were analyzed using global sun photometer measurements from the AERONET (Aerosol Robotic Network) operated by NASA. A new methodology was developed to retrieve aerosol optical thickness in 1x1 km resolution over São Paulo metropolitan area from satelites measurements. Five aerosol optical models representative of the region were defined as a function of the single scattering albedo. The single scattering albedo in 550 nm varied from 0,83 to 0,93 in the models. Radiances were used from MODIS (Moderate-Resolution Imaging Spectroradiometer) sensor on Terra and Aqua NASA platforms. Sensitivities studies have shown the importance of the single scattering albedo, assymmetry parameter, surface reflectance, water vapor and other properties in the aerosol optical thickness retrieval from space. The developed algorithm uses the critical reflectance aerosol property, that is obtainable from the sensor measurements, to identify the aerosol model to be used. This procedure allow a more precise and dynamic definition of the aerosol model, reducing the uncertainties in the aerosol optical thickness retrieved from the MODIS sensor. Validation results have shown a significant improvement in a comparison between aerosol optical thickness obtained from MODIS and from surface radiometers measurements. Aerosol optical thickness images with 1x1 km resolution were obtained with this methodology and shows that the increase in the resolution of the aerosol optical thickness provides a more effective monitoring of the aerosol distribution in São Paulo. The case studies have shown the potentiality of this methodology to identify an adequate aerosol model, for both local aerosol pollution and in the long distance transport of pollutants. The models and procedures developed in this work can be applied in other urban regions with the appropriate validation. The presented product can be operational and used as routine measurement by environmental agencies in megacities, as an example, for CETESB in São Paulo, as a complementary tool to the regular ground based particulate matter monitoring.

Long-term hydrologic and nonpoint source (NPS) pollutant response is quantified for four headwater basins of the Occoquan River in northern Virginia using 24 years of observed rainfall, basin discharge, water chemistry, and derived land use/land cover (LULC) data. Long-term summaries document that several hydrologic and NPS pollutant delivery characteristics in the urbanizing Cub Run basin are significantly different from adjacent non-urban basins. Higher annual NPS fluxes of total suspended solids (TSS), phosphorus (P), and nitrogen (N) in Cub Run are identified with periods of increased soil disturbance from urban land development and significantly increased storm volumes resulting from higher mean impervious cover. Long-term summaries of nutrient flux are consistent with literature documenting increased ratios of particulate to soluble nutrients with increased discharge. Storm fluxes of NPS particulate P, soluble P, particulate N, and soluble N make up 92, 67, 89, and 50 percent, respectively, of total NPS nutrient fluxes from all headwater basins, with between 88 and 98 percent of mean annual TSS fluxes delivered by storm flow. Higher sediment and nutrient fluxes observed in Cub Run basin during the summer and fall growing season after 1983 demonstrate the impact of replacing vegetated cover with urban impervious surface (IS). Annual regression models indicate that mean IS above 10 percent and precipitation are significantly associated with total basin discharge (r2=0.65). The positive association of annual storm soluble phosphorus flux with cumulative mean IS suggests the need for continued evaluation of urban NPS soluble phosphorus strategies. Urban soil disturbance is indicated by measuring the annual change in mean IS (delta IS). Regression models show that urban soil disturbance is a significant source of TSS flux in all seasons. Long-term total soluble phosphorus and nitrogen fluxes are significantly and positively associated with precipitation, delta IS, and agricultural land use (r2=0.50 and 0.58, respectively). The significant impact of urbanization on hydrologic and NPS pollutant flux, especially during the growing season, is a major finding of this study.
Ph. D.

Due to economic, accessibility and security constraints, the majority of developing countries within the low-latitudes lack the capacity to establish networks of ground-based air pollution monitoring stations. As a result, there is a lack of systematic and consistent measurements and monitoring of the concentrations of air pollutants in most of these countries. In addition, there is the lack of verifiable inventories of sources of atmospheric emissions. In order to mitigate these constraints, this research presents an investigation into the relevance of currently available satellite sensors to estimate concentrations of air pollutants (carbon monoxide, nitrogen dioxide and tropospheric ozone) and carbon dioxide over the Niger Delta, a developing region in southern part of Nigeria. It further presents a methodological framework designed to interpolate column concentrations from satellite sensors over the entire study area using ordinary kriging interpolation techniques in ArcGIS Geostatistical Analyst. The study also carries out an assessment of the reliability and resolution of the interpolated surfaces based on a subjective categorisation of the number of column measurements available from satellites sensors and the mean of the Euclidean distances between the column measurements. The results indicate varying degrees of reliability and resolution depending on the climatic seasons in the Niger Delta. The results further show that measurements from satellite sensors are reliable means of measuring and monitoring total and tropospheric column concentrations of air pollutants over the Niger Delta. However, ground-based measurements are required to infer ground-level concentration of pollutants from satellite sensor measurements. Seasonal variations observed in the concentrations of air pollutants indicate higher concentrations during the dry season than during the wet season. In addition to the satellite sensor-based assessment of concentrations of air pollutants and CO2, this research designed and constructed a GIS-based Niger Delta Emission Inventory (NDEI) infrastructure for criteria air pollutants (carbon monoxide, particulate matter, nitrogen oxides and sulphur dioxide), methane, non-methane volatile organic compounds and carbon dioxide. The NDEI has point-source, line-source and area-source components. The construction of the inventory infrastructure effectively relied on the interoperability of GIS and spreadsheet. Due to the lack of access to data, the infrastructure is populated with data generated based on a series of assumptions. This produced estimates with varying degree of uncertainties. Despite these uncertainties, the methods applied in generating data are transferrable to other developing regions where there is limited access to data required for estimating emissions. In addition, the process successfully validates the functionality of the infrastructure to produce accurate emission estimates as and when data that are more accurate are available. Although the estimates of emissions generated from the inventory have limited accuracy, the spatial distributions of the emissions have varying degree of accuracy. The most reliable estimates and spatial distribution of estimates are generated from the area-source (residential) component of the inventory due to the available data and the assumptions applied. The spatial distribution of emissions generated from the inventory are accurate to the extent of the available input spatial data. The thesis concludes by recommending further research work to explore opportunities to infer ground level concentrations of pollutants from satellite sensor measurements. In addition, the thesis recommends the implementation of a site survey to collect site-specific information in order to refine the input data into the NDEI to produce accurate estimates of emissions. The recommendations made from this study aim towards enhancing the development of relatively inexpensive means of measuring and assessing air quality for developing regions within the low latitudes.

The purpose of this research is to develop best practices for deploying air quality sensors in a remote location such as Mongolia. I discussed the architecture and design constraints when collecting remote air quality sensors data, the challenges that emerge while implementing a sensor-based network in a remote location such as Mongolia. The tradeoffs of using different architectures are described. I observed the usage of electrical heaters in modified gers in remote locations and conclude how effective they are in reducing PM2.5 levels by analyzing air quality data and go through the process of cleaning up the data and removing humidity from low-cost sensors used to deploy in a remote location such as Mongolia so that the PM2.5 reading is more accurate. In order to help many humanitarian efforts dealing with better air quality in developing countries, an air quality sensor was designed to keep low cost as much as possible. The cost is about $200 to build, which is cheaper than other low-cost sensors, yet provides more functionality (e.g., CO2 sensing) and used cellular connectivity to upload data in real-time. This sensor has implications beyond Mongolia. The sensor can be used anywhere WiFi connectivity is not available, such as parks, bus stops, and along roadways, breaking the constraints that other low-cost sensors have. Removing the need for WiFi is a necessary step in allowing ubiquitous air quality sensing. The contributions in this thesis are: First, I presented the challenges one should consider while deploying air quality sensors in developing countries. Second, since Mongolia offers a unique environment and constraints, I shared experiences in deploying sensors in a remote location like Mongolia. This experience goes beyond air quality sensors and can inform anyone who is deploying sensors in remote areas. Third the analysis of the PM2.5 on the gers gives us better insights as to whether modifying gers with insulation and using electrical heaters as opposed to burning coal to heat up the gers makes a difference in regard to better air quality in the gers.

Les aérosols troposphériques influencent la composition chimique de l’atmosphère, le bilan radiatif terrestre et le climat. Après formation, les aérosols subissent des processus de vieillissement altérant leurs propriétés microphysiques et chimiques. L’étude de l’impact environnemental des aérosols à différentes échelles spatio-temporelles doit donc tenir compte des transformations physico-chimiques. Les objectifs poursuivis sont : (i) d’étudier par microscopie analytique électronique à balayage et en transmission la composition élémentaire et l'état de mélange des particules atmosphériques prélevées lors d'épisodes de charge élevée en aérosol ; (ii) d’analyser les effets des propriétés microphysiques sur les caractéristiques optiques mesurées et restituées par télédétection ; (iii) de proposer un paramétrage ainsi qu’une représentation de la composition et de la structure des particules dans les algorithmes de télédétection. Ces travaux portent sur des observations dans le Nord de la France et en Afrique de l’Ouest (Sénégal) dans le cadre du Labex CaPPA et des campagnes de terrain SHADOW. Sont inclues des analyses complémentaires de la composition chimique et de la structure de particules individuelles, de mesures in situ et par télédétection de particules urbano-industrielles, désertiques et de biomasse prélevées en surface et à différentes altitudes. Une série de simulations numériques ont été utilisées dans le but d'analyser la sensibilité des observations par télédétection à l’état de mélange des aérosols. Enfin, l'intégration d'un paramétrage de la structure en "core-shell" des particules dans les algorithmes de restitution est présentée en perspective
Tropospheric aerosols play an important role in atmospheric chemistry, Earth’s radiative budget and climate. After their generation, aerosol can suffer ageing processes and altering their physicochemical properties. An accurate accounting for these processes requires observations of the aerosol properties on different temporal and spatial scales. The current thesis work is dedicated to: (i) study of physicochemical properties and mixing state of individual particles by means of analytical scanning and transmission electron microscopy for aerosols collected during episodes of elevated aerosol loading; (ii) analysis of the effect of microphysical properties on optical characteristics as measured and retrieved by remote sensing; and (iii) investigation of possible parameterization of aerosol composition and structure in remote sensing algorithms. The work presents observations conducted in northern France and western Africa (Senegal) as part of Labex CaPPA project and SHADOW field campaigns. It includes simultaneous analyses of collected individual particles composition and structure, remote sensing and in situ observations of urban/industrial, Saharan dust and biomass burning particles near the surface and on different altitudes. A series of numerical simulation devoted to an analysis of sensitivity of remote sensing observations to aerosol mixing state is conducted. Insights on possible parameterization of aerosol core-shell structure in retrieval algorithms are finally presented

The aim of this study is to determine the distribution of Chla in Eymir Lake using remotely sensed data and in-situ data. The study was carried out in three phases
the first phase was taking ground real data from the lake for a 6-month period, secondly the remotely sensed satellite image was taken and analyzed, thirdly a correlation was obtained between the ground data and satellite image, and lastly mapping of the Chla in the lake was made. During the study also the change of the lake during the 6-month period was monitored. The results showed a great variation in the concentration of Chla in the period measured from spring till early fall, from very low almost undetectable concentrations to noticeably very high values especially during summer. The secchi disc depth values ranged from about 3 meters in early spring, to as low as 15 centimeters in late summer
this made it very much related to Chla values. Chla concentrations had a high relationship with the following parameters: DO, TSS, Depth and secchi disc. As for the remotely sensed data also an acceptable level of correlation was obtained between them and Chla data both from laboratory results and in-situ probe.

La filamentation laser femtoseconde dans l'air est un phénomène qui implique une riche famille d'effets optiques non linéaires. Effet laser de filaments a émergé comme un phénomène nouveau en 2011. Il a été activement étudiée au cours des dernières cinq années, non seulement en raison de ses applications potentielles dans les techniques de télédétection mais aussi la physique pour découvrir. Cette thèse est consacrée principalment à l'étude de deux types d'effets laser à partir du plasma filamentaire généré par 800 nm impulsions laser femtosecondes dans l'air ou l'azote pur. Le premier est l'émission spontanée amplifiée à 337 nm longueur d'ondes de molécules d'azote neutre qui est bidirectionnel, activée uniquement par des impulsions polarisées circulairement. Le mécanisme d'inversion de population est attribuée à électrons-molécules collisions inélastiques entre les électrons énergiques et les molécules d'azote neutres sur l'état du sol. La caractérisation complète de 337 nm impulsion laser vers l'avant et vers l'arrière est réalisée. En particulier, les mesures de profil temporelles sont comparées à des simulations numériques basées sur l'équation de Maxwell-Bloch à une dimension, qui se révèle être en bon accord. Un autre type d'effet laser est lié à ions d'azotes excités, émettant à 391 nm et 428 nm longueurs d'onde. Ce type d'effet laser est observée avec laser pompe polarisée linéairement. Il est caractérisé systématiquement dans des domaines spatiaux, temporels et spectrales. Les résultats du profil temporel prouve que l'émission laser ionique est fondamentalement superradiance. Un nouveau mécanisme, à savoir le processus d'excitation recollision d'électrons, est proposé pour la réalisation de la distribution de la population dans le niveau supérieur de la transition. Il est soutenu par deux mesures expérimentales consistant en la dépendance de 391 nm émission laser à l'ellipticité et la dépendance à longueur d'onde de laser pompe. Des simulations numériques donnent un bon accord avec l'observation expérimentale
Femtosecond laser filamentation in air is a phenomenon that involves a rich family of nonlinear optic effects. Lasing effect from filaments has emerged as a new phenomenon in 2011. It has been actively studied in recent 5 years not only because of its potential applications in remote sensing techniques but also the fruitful physics involved. This thesis is devoted to the study of two types of lasing effect from filament plasma generated by 800 nm femtosecond laser pulses in air or pure nitrogen. The first is the bidirectional amplified spontaneous emission at 337 nm wavelength of neutral nitrogen molecules, only enabled by circularly-polarized pulses. The population inversion mechanism is attributed to inelastic electron-molecule collisions between energetic electrons and neutral nitrogen molecules on the ground state. Full characterization of both forward and backward 337 nm lasing pulse is conducted. Particularly the temporal profile measurements is compared to numerical simulations based on one-dimensional Maxwell-Bloch equation, which turns out to be in good agreement. Another type of lasing effect is related to excited nitrogen ion, emitting at 391 nm and 428 nm wavelengths. This type of lasing effect can only be observed with linearly-polarized pump laser. It is systematically characterized in spatial, temporal and spectral domains. The temporal profile results proves that ionic lasing emission is fundamentally superradiance. A new mechanism, namely the electron recollision excitation process, is proposed for the achievement of population distribution in the upper level of transition. It is supported by two experimental measurements consisting of pump ellipticity dependence and pump wavelength dependence of 391 nm lasing intensity. Numerical simulations give good agreement with the experimental observation

Oceanic microwave remote sensing provides the data necessary for the estimation of significant geophysical parameters such as the near-surface vector wind. To obtain accurate estimates, a precise understanding of the measurements is critical. This work clarifies and quantifies specific uncertainties in the scattered power measured by an active radar instrument. While there are many sources of uncertainty in remote sensing measurements, this work concentrates on three significant, yet largely unstudied effects. With a theoretical derivation of the backscatter from an ocean-like surface, results from this dissertation demonstrate that the backscatter decays with surface roughness with two distinct modes of behavior, affected by the size of the footprint. A technique is developed and scatterometer data analyzed to quantify the variability of spaceborne backscatter measurements for given wind conditions; the impact on wind retrieval is described in terms of bias and the Cramer-Rao lower bound. The probability density function of modified periodogram averages (a spectral estimation technique) is derived in generality and for the specific case of power estimates made by the NASA scatterometer. The impact on wind retrieval is quantified.

Satellite observations provide information on land surface processes over a large spatial extent with a frequency dependent on the satellite revisit time. These observations are not subject to the spatial limitations of the traditional point measurements and are usually collected in a global scale. With a reasonable spatial resolution and temporal frequency, the Moderate Resolution Imaging Spectroradiometer (MODIS) is one of these satellite sensors which enables the study of land-atmospheric interactions and estimation of climate variables for over a decade from remotely sensed data. This research investigated the potential of remotely sensed land surface temperature (LST) data from MODIS for air temperature (Ta) and soil moisture (SM) estimation in New Zealand and how the satellite derived parameters relate to the numerical model simulations and the in-situ ground measurements. Additionally, passive microwave SM product from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) was applied in this research. As the first step, the MODIS LST product was validated using ground measurements at two test-sites as reference. Quality of the MODIS LST product was compared with the numerical simulations from the Weather Research and Forecasting (WRF) model. Results from the first validation site, which was located in the alpine areas of the South Island, showed that the MODIS LST has less agreement with the in-situ measurements than the WRF model simulations. It turned out that the MODIS LST is subject to sources of error, such as the effects of topography and variability in atmospheric effects over alpine areas and needs a careful pre-processing for cloud effects and outliers. On the other hand, results from the second validation site, which was located on the flat lands of the Canterbury Plains, showed significantly higher agreement with the ground truth data. Therefore, ground measurements at this site were used as the main reference data for the accuracy assessment of Ta and SM estimates. Using the MODIS LST product, Ta was estimated over a period of 10 years at several sites across New Zealand. The main question in this part of the thesis was whether to use LST series from a single MODIS pixel or the series of a spatially averaged value from multiple pixels for Ta estimation. It was found that the LST series from a single pixel can be used to model Ta with an accuracy of about ±1 ºC. The modelled Ta in this way showed r ≈ 0.80 correlation with the in-situ measurements. The Ta estimation accuracy improved to about ±0.5 ºC and the correlation to r ≈ 0.85 when LST series from spatially averaged values over a window of 9x9 to 25x25 pixels were applied. It was discussed that these improvements are due to noise reduction in the spatially averaged LST series. By comparison of LST diurnal trends from MODIS with Ta diurnal trends from hourly measurements in a weather station, it was shown that the MODIS LST has a better agreement with Ta measurements at certain times of the day with changes over day and night. After estimation of Ta, the MODIS LST was applied to derive the near-surface SM using two Apparent Thermal Inertia (ATI) functions. The objective was to find out if more daily LST observations can provide a better SM derivation. It was also aimed to identify the potential of a land-atmospheric coupled model for filling the gaps in derived SM, which were due to cloud cover. The in-situ SM measurements and rainfall data from six stations were used for validation of SM derived from the two ATI functions and simulated by the WRF model. It was shown that the ATI function based on four LST observations has a better ability to derive SM temporal profiles and is better able to detect rainfall effects. Finally, the MODIS LST was applied for spatial and temporal adjustment of the near-surface SM product from AMSR-E passive microwave observations over the South Island of New Zealand. It was shown that the adjustment technique improves AMSR-E seasonal trends and leads to a better matching with rainfall events. Additionally, a clear seasonal variability was observed in the adjusted AMSR-E SM in the spatial domain. Findings of this thesis showed that the satellite observed LST has the potential for the estimation of the land surface variables, such as the near-surface Ta and SM. This potential is greatly important on remote and alpine areas where regular measurements from weather stations are not often available. According to the results from the first validation site, however, the MODIS LST needs a careful pre-processing on those areas. The concluding chapter included a discussion of the limitations of remotely sensed data due to cloud cover, dense vegetation and rugged topography. It was concluded that the satellite observed LST has the potential for SM and Ta estimations in New Zealand. It was also found that a land-atmospheric model (such as the WRF coupled with the Noah and surface model) can be applied for filling the gaps due to cloud cover in remotely sensed variables.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
This dissertation aimed to fulfill a general analysis of the intensity of the light pollution triggered by urban growth occurred in the Cerrado biome and in the Metropolitan Region of Goiânia in the period from 1992 to 2010. To this end, the present work drew on remote sensing data, specifically of the images of the sensor system DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) and of Geoprocessing techniques for spatialization and measurement of light pollution. As part of the results, it is recognized that the light spots are concentrated mainly in metropolis and major urban centers, in consequence of the largest population concentration. In the metropolitan region of Goiânia, the capital has the highest indexes of consumption of electrical energy and the largest population concentration of the state; consequently, holds the largest spot of the light pollution in the region.
A presente dissertação de mestrado teve como objetivo geral realizar uma análise da intensidade da poluição luminosa desencadeada pelo crescimento urbano ocorrido no bioma Cerrado e na Região Metropolitana de Goiânia no período de 1992 a 2010. Para tanto, o presente trabalho valeu-se de dados de sensoriamento remoto, especificamente das imagens do sistema sensor DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) e de técnicas de geoprocessamento para espacialização e mensuração da poluição luminosa. Como parte dos resultados, reconhece-se que as manchas de luz concentram-se, sobretudo, nas metrópoles e nos principais centros urbanos, em consequência da maior concentração populacional. Na Região Metropolitana de Goiânia, a capital apresenta os maiores índices de consumo de energia elétrica e a maior concentração populacional do Estado; consequentemente, possui a maior mancha de poluição luminosa da Região.

La contaminación ambiental es uno de los principales problemas que afecta a nuestro planeta. El crecimiento industrial y los aglomerados urbanos, entre otros, están contribuyendo a que dicho problema se diversifique y se cronifique. La presencia de contaminantes ambientales en niveles elevados afecta la salud humana, siendo la calidad del aire y los niveles de ruido ejemplos de factores que pueden causar efectos negativos en las personas tanto psicológicamente como fisiológicamente. Sin embargo, la ubiquidad de los microcomputadores, y el aumento de los sensores incorporados en nuestros smartphones, han hecho posible la aparición de nuevas estrategias para medir dicha contaminación. Así, el Mobile Crowdsensing se ha convertido en un nuevo paradigma mediante el cual los teléfonos inteligentes emergen como tecnología habilitadora, y cuya adopción generalizada proporciona un enorme potencial para su crecimiento, permitiendo operar a gran escala, y con unos costes asumibles para la sociedad. A través del crowdsensing, los teléfonos inteligentes pueden convertirse en unidades de detección flexibles y multiuso que, a través de los sensores integrados en dichos dispositivos, o combinados con nuevos sensores, permiten monitorizar regiones de interés con una buena granularidad tanto espacial como temporal. En esta tesis nos centramos en el diseño de soluciones de crowdsensing usando smartphones donde abordamos problemas de contaminación ambiental, específicamente del ruido y de la contaminación del aire. Con este objetivo, se estudian, en primer lugar, las propuestas de crowdsensing que han surgido en los últimos años. Los resultados de nuestro estudio demuestran que todavía hay mucha heterogeneidad en términos de tecnologías utilizadas y métodos de implementación, aunque los diseños modulares en el cliente y en el servidor parecen ser dominantes. Con respecto a la contaminación del aire, proponemos una arquitectura que permita medir la contaminación del aire, concretamente del ozono, dentro de entornos urbanos. Nuestra propuesta utiliza smartphones como centro de la arquitectura, siendo estos dispositivos los encargados de leer los datos de un sensor móvil externo, y de luego enviar dichos datos a un servidor central para su procesamiento y tratamiento. Los resultados obtenidos demuestran que la orientación del sensor y el período de muestreo, dentro de ciertos límites, tienen muy poca influencia en los datos capturados. Con respecto a la contaminación acústica, proponemos una arquitectura para medir los niveles de ruido en entornos urbanos basada en crowdsensing, y cuya característica principal es que no requiere intervención del usuario. En esta tesis detallamos aspectos tales como la calibración de los smartphones, la calidad de las medidas obtenidas, el instante de muestreo, el diseño del servidor, y la interacción cliente-servidor. Además, hemos validado nuestra solución en escenarios reales para demostrar el potencial de la solución alcanzada. Los resultados experimentales muestran que, con nuestra propuesta, es posible medir niveles de ruido en diferentes zonas urbanas o rurales con un grado de precisión comparable al de los dispositivos profesionales, todo ello sin requerir intervención del usuario, y con un consumo reducido en cuanto a recursos del sistema. En general, las diferentes contribuciones de esta tesis doctoral ofrecen un punto de partida para nuevos desarrollos, ofreciendo estrategias de calibración y algoritmos eficientes de cara a realizar medidas representativas. Además, una importante ventaja de nuestra propuesta es que puede ser implementada de forma directa tanto en instituciones públicas como no gubernamentales en poco tiempo, ya que utiliza tecnología accesible y soluciones basadas en código abierto.
La contaminació ambiental és un dels principals problemes que afecten el nostre planeta. El creixement industrial i els aglomerats urbans, entre altres, estan contribuint al fet que aquest problema es diversifique i es cronifique. La presència de contaminants ambientals en nivells elevats afecta la salut humana, sent la qualitat de l'aire i els nivells de soroll exemples de factors que poden causar efectes negatius en les persones, tant psicològicament com fisiològicament. No obstant això, la ubiqüitat de les microcomputadores i l'augment dels sensors incorporats als nostres telèfons intel·ligents han fet possible l'aparició de noves estratègies per a mesurar aquesta contaminació. Així, el mobile crowdsensing s'ha convertit en un nou paradigma mitjançant el qual els telèfons intel·ligents emergeixen com a tecnologia habilitadora, i l'adopció generalitzada d'aquest proporciona un enorme potencial per al seu creixement, ja que permet operar a gran escala i amb uns costos assumibles per a la societat. A través del crowdsensing, els telèfons intel·ligents poden convertir-se en unitats de detecció flexibles i multiús que, a través dels sensors integrats en els esmentats dispositius, o combinats amb nous sensors, permeten monitoritzar regions d'interès amb una bona granularitat, tant espacial com temporal. En aquesta tesi ens centrem en el disseny de solucions de crowdsensing usant telèfons intel·ligents, on abordem problemes de contaminació ambiental, específicament del soroll i de la contaminació de l'aire. Amb aquest objectiu, s'estudien, en primer lloc, les propostes de crowdsensing que han sorgit en els últims anys. Els resultats del nostre estudi demostren que encara hi ha molta heterogeneïtat en termes de tecnologies utilitzades i mètodes d'implementació, encara que els dissenys modulars en el client i en el servidor semblen ser dominants. Pel que fa a la contaminació de l'aire, proposem una arquitectura que permeta mesurar la contaminació d'aquest, concretament de l'ozó, dins d'entorns urbans. La nostra proposta utilitza telèfons intel·ligents com a centre de l'arquitectura, sent aquests dispositius els encarregats de llegir les dades d'un sensor mòbil extern, i d'enviar després aquestes dades a un servidor central per al seu processament i tractament. Els resultats obtinguts demostren que l'orientació del sensor i el període de mostratge, dins de certs límits, tenen molt poca influència en les dades capturades. Pel que fa a la contaminació acústica, proposem una arquitectura per a mesurar els nivells de soroll en entorns urbans basada en crowdsensing, i la característica principal de la qual és que no requereix intervenció de la persona usuària. En aquesta tesi detallem aspectes com ara el calibratge dels telèfons intel·ligents, la qualitat de les mesures obtingudes, l'instant de mostratge, el disseny del servidor i la interacció client-servidor. A més, hem validat la nostra solució en escenaris reals per a demostrar el potencial de la solució assolida. Els resultats experimentals mostren que, amb la nostra proposta, és possible mesurar nivells de soroll en diferents zones urbanes o rurals amb un grau de precisió comparable al dels dispositius professionals, tot això sense requerir intervenció de l'usuari o usuària, i amb un consum reduït quant a recursos del sistema. En general, les diferents contribucions d'aquesta tesi doctoral ofereixen un punt de partida per a nous desenvolupaments, i ofereixen estratègies de calibratge i algorismes eficients amb vista a realitzar mesures representatives. A més, un important avantatge de la nostra proposta és que pot ser implementada de forma directa tant en institucions públiques com no governamentals en poc de temps, ja que utilitza tecnologia accessible i solucions basades en el codi obert.
Environmental pollution is one of the main problems that affect our planet. Industrial growth and urban agglomerations, among others, are contributing to the diversification and chronification of this problem. The presence of environmental pollutants at high levels affect human health, with air quality and noise levels being examples of factors that can cause negative effects on people both psychologically and physiologically. Traditionally, environmental pollution is measured through monitoring centers, which are usually fixed and have a high cost. However, the ubiquity of microcomputers and the increase in the number of sensors embedded in our smartphones, have paved the way for the appearance of new strategies to measure such pollution. Thus, Mobile Crowdsensing has become a new paradigm through which smartphones emerge as an enabling technology, and whose widespread adoption provides enormous potential for growth, allowing large-scale operations, and with costs acceptable to our society. Through crowdsensing, smartphones can become flexible and multipurpose detection units that, through the sensors integrated into these devices, or combined with new sensors, allow monitoring regions of interest with good spatial and temporal granularity. In this thesis, we focus on the design of crowdsensing solutions using smartphones. We deal with environmental pollution problems, specifically noise and air pollution. With this objective, the crowdsensing proposals that have emerged in recent years are studied in the first place. The results of our study show that there is still a lot of heterogeneity in terms of technologies used and implementation methods, although modular designs at both client and server seem to be dominant. Concerning air pollution, we propose an architecture that allows measuring air pollution, specifically ozone, in urban environments. Our proposal uses smartphones as the center of the architecture, being these devices responsible for reading the data obtained by an external mobile sensor, and then sending such data to a central server for processing and analysis. In this proposal, several problems have been analyzed with regard to the orientation of the external sensor and the sampling time, and the proposed solution has been validated in real scenarios. The results obtained show that the orientation of the sensor and the sampling period, within certain limits, have very little influence on the captured data. Also, by comparing the heat maps generated by our solution with the data from the existing monitoring stations in the city of Valencia, we demonstrate that our approach is capable of providing greater data granularity. Concerning noise pollution, we propose an architecture to measure noise levels in urban environments based on crowdsensing, and whose main characteristic is that it does not require user intervention. In this thesis, we detail aspects such as the calibration of smartphones, the quality of the measurements obtained, the sampling instant, the server design, and the client-server interaction. Besides, we have validated our solution in real scenarios to demonstrate the potential of the proposed solution. Experimental results show that, with our proposal, it is possible to measure noise levels in different urban or rural areas with a degree of precision comparable to that of professional devices, all without requiring the intervention of the user, and with reduced consumption of system resources. In general, the different contributions of this doctoral thesis provide a starting point for new developments, offering efficient calibration strategies and algorithms to make representative measurements. Besides, a significant advantage of our proposal is that it can be implemented straightforwardly by both public and non-governmental institutions in a short time, as it relies on accessible technology and open source software
Zamora Mero, WJ. (2018). Crowdsensing solutions for urban pollution monitoring using smartphones [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/115483
TESIS

This thesis, Earth satellites and detection of air andground based activities by Ulf Ekblad of the Physics departmentat the Royal Institute of Technology (KTH), addresses theproblem of detecting military activities in imagery. Examplesof various techniques are presented. In particular, problemsassociated with "novelties" and "changes" in an image arediscussed and various algorithms presented. The imagery usedincludes satellite imagery, aircraft imagery, and photos offlying aircraft.

The timely delivery of satellite imagery is limited by thelaws of celestial mechanics. This and other information aspectsof imagery are treated. It is e.g. shown that dozens ofsatellites may be needed if daily observations of a specificsite on Earth are to be conducted from low Earth orbit.

New findings from bioinformatics and studies of small mammalvisual systems are used. The Intersecting Cortical Model (ICM),which is a reduced variant of the Pulse-Coupled Neural Network(PCNN), is used on various problems among which are changedetection. Still much more could be learnt from biologicalsystems with respect to pre- and post-processing as well asintermediate processing stages.

Simulated satellite imagery is used for determining theresolution limit for detection of tanks. The necessary pixelsize is shown to be around 6 m under the conditions of thissimulation.

Difference techniques are also tested on Landsat satelliteimagery with the purpose of detecting underground nuclearexplosions. In particular, it is shown that this can easily bedone with 30 m resolution images, at least in the case studied.Satellite imagery from SPOT is used for detecting undergroundnuclear explosions prior to the detonations, i.e. under certainconditions 10 m resolution images can be used to detectpreparations of underground nuclear explosions. This type ofinformation is important for ensuring the compliance of nucleartest ban treaties. Furthermore, the necessity for havingcomplementary information in order to be able to interpretimages is also shown.

Keywords: Remote sensing, reconnaissance, sensor,information acquisition, satellite imagery, image processing,image analysis, change detection, pixel difference, neuronnetwork, cortex model, PCNN, ICM, entanglement, Earthobservation, nuclear explosion, SPOT, Landsat, verification,orbit.

Chlorophyll-a is a parameter which can be used to understand the trophic state of water bodies. Therefore, monitoring of this parameter is required. Yet, distribution of chlorophyll-a in water bodies is not homogeneous and exhibits both spatial and temporal variations. Therefore, frequent sampling and high sample sizes are needed for the determination of chlorophyll-a quantities. This would in return increase the sampling costs and labor requirement, especially if the topography makes the location hard to reach. Remote sensing is a technology that can aid in handling of these difficulties and obtain a continuous distribution of chlorophyll-a concentrations in a water body. In this method, reflectance from water bodies in different wavelengths is used to quantify the chlorophyll-a concentrations. In previous studies in literature, empirical regression models that use the reflectance values in different bands in different combinations have been derived. Yet, prediction performances of these models decline especially in shallow lakes. In this study, the spatial distribution of chlorophyll-a in shallow Lake Eymir is determined using both regression models and artificial neural network models that use hybrid inputs. Unlike the models generated before, field measured parameters which can influence the reflectance values in remotely sensed images have been used in addition to the reflectance values. The parameters that are considered other than reflectance values are photosynthetically active radiation (PAR), secchi depth (SD), water column depth, turbidity, dissolved oxygen concentration (DO), pH, total suspended solids (TSS), total dissolved organic matter (TDOM), water and air temperatures, wind data and humidity. Reflectance values are obtained from QuickBird and World View 2 satellite images. Effect of using hybrid input in mapping the reflectance values to chlorophyll-a concentrations are studied. In the context of this study, three different high-resolution satellite images are analyzed for the spatial distribution of chlorophyll-a concentration in Lake Eymir. Field and laboratory studies are conducted for the measurement of parameters other than the reflectance values. Principle component analysis is applied on the collected data to decrease the number of model input parameters. Then, linear and non-linear regression and artificial neural network (ANN) models are derived to model the chlorophyll-a concentrations in Lake Eymir. Results indicate that ANN model shows better predictability compared to regression models. The predictability of ANN model increases with increasing variation in the dataset. Finally, it is seen that in determination of chlorophyll-a concentrations using remotely sensed data, models with hybrid inputs are superior compared to ones that use only remotely sensed reflectance values.

Les instruments, satellites ou systèmes aéroportés, actuellement utilisés pour la détection et la caractérisation d'hydrocarbure sur la mer sont basés sur des moyens optiques ou radars. Ces moyens présentent une performance dégradée due à une fréquence encore trop importante de fausses alarmes ou à un temps de traitement des données trop conséquent. Les méthodes de détection, d'identification et de quantification des fuites d'hydrocarbures offshores peuvent donc être améliorées en associant robustesse et réactivité. Cette amélioration suppose une compréhension approfondie des phénomènes océanographiques et électromagnétiques à l'œuvre dans cette scène particulière. La thèse s'appuie sur des données regroupant des images optiques et SAR aéroportées ou satellites ainsi que des mesures réalisées en laboratoire. Ce jeu de données permet de vérifier la cohérence des résultats obtenus par modélisation. L'objectif de la thèse est de distinguer une surface de mer polluée d'une surface de mer propre à l'aide de la signature électromagnétique de la surface totale puis de détailler le type et la quantité d'hydrocarbure présent. La thèse se divise en deux domaines, à savoir modélisation océanographique et modélisation électromagnétique. La modélisation océanographique intègre la simulation de la surface rugueuse imitant une surface de mer propre, et polluée. Cette surface de mer doit être générée sur une superficie importante et doit conserver une résolution restituant les petites vagues avec un temps de génération minimal. La partie électromagnétique est centrée sur les modèles asymptotiques de diffusion des ondes électromagnétiques par une interface rugueuse. Ces modèles sont adaptés au contexte de la thèse, complexité de la scène et rapidité du traitement, mais nécessitent plusieurs hypothèses pour être appliqués
Satellites or airborne systems currently used for the detection and characterization of oil slicks on sea surface are based on optical or radar means. These means have a lack of performance due to a too high frequency of false alarms or to an excessively long data processing time. The methods for detecting, identifying and quantifying offshore pollutant can therefore be improved by combining robustness and reactivity. This improvement implies an in-depth understanding of the oceanographic and electromagnetic phenomena at work in this particular scene. The thesis is based on data gathering aerial and satellite images and SAR as well as measurements carried out in laboratory. This dataset makes it possible to check the consistency of the results obtained by modeling. The objective of the thesis is to distinguish a polluted sea surface from a clean sea surface using the electromagnetic signature of the total surface and then to detail the type and quantity of pollutant. The thesis is divided into two domains, namely oceanographic modeling and electromagnetic modeling. Oceanographic modeling integrates the simulation of the rough surface imitating a clean or polluted sea surface. This sea surface must be generated over a large area with a thin resolution. The electromagnetic part is centered on the asymptotic models for the electromagnetic waves diffraction by a rough interface. These models are adapted to the context of the thesis, the complexity of the scene and the speed of processing, but require several hypotheses to be applied

Thesis (M.A.)--Georgia State University, 2006.
Title from title screen. Dona J. Stewart, committee chair; Elaine J. Hallisey, Jeremy Crampton, committee members. Electronic text (135 p. : maps (some col.)) : digital, PDF file. Description based on contents viewed June 18, 2006. Includes bibliographical references (p. 130-134).