Fine particulate matter (PM2.5) affects the environment in a variety of ways, including of human health, visibility impairment, acid deposition, and climate change. As of March, 2006, 47 counties are designated as non-attainment areas in terms of PM2.5 in the southeastern United States. State agencies with PM2.5 non-attainment counties must develop plans that demonstrate how they will achieve attainment status. State agencies also have to address emission sources of visibility impairment and develop strategies to improve visibility. It is essential to understand PM2.5 composition and sources in order to develop effective control strategies to reduce PM2.5. In this thesis, actual prescribed burning emissions were characterized for better estimation of their impacts on air quality. Chemical mass balance (CMB) modeling, a receptor-oriented source apportionment technique, was applied to understand regional characteristics of PM2.5 source impacts in the Southeast. Uncertainty issues in the CMB source apportionment results due to both poor spatial representativeness and measurement errors was addressed for better understanding and estimation of the uncertainties. Possible future research is recommended based on the findings in this thesis.

Submitted by Deyse Queiroz (deysequeirozz@hotmail.com) on 2018-06-06T12:40:50Z No. of bitstreams: 1 MARCUS VINÍCIUS DO CARMO LOIOLA - DISSERTAÇÃO PPGSA PROFISSIONAL 2018..pdf: 820964 bytes, checksum: 8762c281ed02351e37733b5465883902 (MD5) / Made available in DSpace on 2018-06-06T12:40:50Z (GMT). No. of bitstreams: 1 MARCUS VINÍCIUS DO CARMO LOIOLA - DISSERTAÇÃO PPGSA PROFISSIONAL 2018..pdf: 820964 bytes, checksum: 8762c281ed02351e37733b5465883902 (MD5) Previous issue date: 2018-03 / A informática está cada vez mais presente na vida escolar, seja via internet, multimídia ou outros. Compreender a espacialidade dos fenômenos estudados, no presente e no passado, e compará-lo por meio de suas sobreposições, é algo que a própria Geografia busca fazer, e compreender e utilizar a linguagem cartográfica, ampliando as possibilidades dos alunos de analisar informações em vários campos do conhecimento, além de contribuir para a estruturação de uma noção espacial. O objetivo desse projeto foi de aplicar as possibilidades de uso de geotecnologias como incentivo para o ensino da geografia na EEEFM Plínio Lemos, do município de Puxinanã – PB, buscando verificar a visão temporal dos educandos a cerca de seu meio ambiente e as modificações ao longo do tempo. Foi aplicado um questionário nas turmas do 3º Ano médio, turno manhã, onde 50% afirmaram saber o que são geotecnologias e que têm acesso a softwares do gênero (Google Earth, Wikimapia e GPS-Sistema de Posicionamento Global), 73,8% disseram não terem sentido dificuldade em responder o questionário, no entanto, o tempo usado para responder o mesmo bem como as vezes em que os aplicadores do questionário foram chamados para tirar dúvidas mostram o contrário. É importante destacar que 85,7% dos alunos afirmaram que nunca tinham ouvido falar esse termo por parte do professor da disciplina. / Information technology is increasingly present in school life, either via the Internet or other multimedia. Understanding the spatiality of the phenomena studied, the present and the past, and compare it with their overlapping, is something that own search geography do, and understand and use the cartographic language, expanding the possibilities of students to analyze information in various fields of knowledge, and contribute to the development of a space notion. The project goal was to apply the geo usage possibilities as an incentive to the teaching of geography in EEEFM Pliny Lemos, the municipality of Puxinanã – PB, seeking to verify the temporal vision of the students about their environment and changes over of time. A questionnaire in class 3rd year average was applied, morning shift, where 50% said they know what they are geotechnology and who have access to the genus software (Google Earth, Wikimapia and Global Positioning System-GPS), 73.8% said they did not have experienced difficulty in answering the questionnaire, however, the time used to answer the same and the times in which the questionnaire applicators were called to answer questions show otherwise. Importantly, 85.7% of students said they had never heard that term by the subject teacher.

Wireless sensor networks (WSN) are event based systems that rely on the collective effort of densely deployed sensor nodes continuously observing a physical phenomenon. The spatio-temporal correlation between the sensor observations and the cross-layer design advantages are significant and unique to the design of WSN. Due to the high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the energy-radiating physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. This unique characteristic of WSN can be exploited through a cross-layer design of communication functionalities to improve energy efficiency of the network. In this thesis, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to capture the spatial and temporal correlations in WSN and to enable the development of efficient communication protocols. Based on this framework, spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol is described, which exploits the spatial correlation in the WSN in order to achieve efficient medium access. Furthermore, the cross-layer module (XLM), which melts common protocol layer functionalities into a cross-layer module for resource-constrained sensor nodes, is developed. The cross-layer analysis of error control in WSN is then presented to enable a comprehensive comparison of error control schemes for WSN. Finally, the cross-layer packet size optimization framework is described.

Dans cette thèse, nous proposons des techniques de gestion de données pour économiser l’énergie dans les réseaux de capteurs périodiques basés sur l’architecture de clustering. Premièrement, nous proposons d’adapter le taux d’échantillonnage du capteur à la dynamique de la condition surveillée en utilisant le modèle de one-way ANOVA et des tests statistiques (Fisher, Tukey et Bartlett), tout en prenant en compte l’énergie résiduelle du capteur. Le deuxième objectif est d’éliminer les données redondantes générées dans chaque cluster. Au niveau du capteur, chaque capteur cherche la similarité entre les données collectées à chaque période et entre des périodes successives, en utilisant des fonctions de similarité. Au niveau du CH, nous utilisons des fonctions de distance pour permettre CH d’éliminer les ensembles de données redondantes générées par les nœuds voisins. Enfin, nous proposons deux stratégies actif/inactif pour ordonnancer les capteurs dans chaque cluster, après avoir cherché la corrélation spatio-temporelle entre les capteurs. La première stratégie est basée sur le problème de couverture des ensembles tandis que la seconde prend avantages du degré de corrélation et les énergies résiduelles de capteurs pour ordonnancer les nœuds dans chaque cluster. Pour évaluer la performance des techniques proposées, des simulations sur des données de capteurs réelles ont été menées. La performance a été analysée selon la consommation d’énergie, la latence et l’exactitude des données, et la couverture, tout en montrant comment nos techniques peuvent améliorer considérablement les performances des réseaux de capteurs. / In this thesis, we propose energy-efficient data management techniques dedicated to periodic sensor networks based on clustering architecture. First, we propose to adapt sensor sampling rate to the changing dynamics of the monitored condition using one-way ANOVA model and statistical tests (Fisher, Tukey and Bartlett), while taking into account the residual energy of sensor. The second objective is to eliminate redundant data generated in each cluster. At the sensor level, each sensor searches the similarity between readings collected at each period and among successive periods, based on the sets similarity functions. At the CH level, we use distance functions to allow CH to eliminate redundant data sets generated by neighboring nodes. Finally, we propose two sleep/active strategies for scheduling sensors in each cluster, after searching the spatio-temporal correlation between sensor nodes. The first strategy uses the set covering problem while the second one takes advantages from the correlation degree and the sensors residual energies for scheduling nodes in the cluster. To evaluate the performance of the proposed techniques, simulations on real sensor data have been conducted. We have analyzed their performances according to energy consumption, data latency and accuracy, and area coverage, and we show how our techniques can significantly improve the performance of sensor networks.