6,626 research outputs found
Low-cost traffic sensing system based on LoRaWAN for urban areas
The advent of Low Power Wide Area Networks (LPWAN) has enabled the feasibility of wireless sensor networks for environmental traffic sensing across urban areas. In this study, we explore the usage of LoRaWAN end nodes as traffic sensing sensors to offer a practical traffic management solution. The monitored Received Signal Strength Indicator (RSSI) factor is reported and used in the gateways to assess the traffic of the environment. Our technique utilizes LoRaWAN as a long-range communication technology to provide a large-scale system. In this work, we present a method of using LoRaWAN devices to estimate traffic flows. LoRaWAN end devices then transmit their packets to different gateways. Their RSSI will be affected by the number of cars present on the roadway. We used SVM and clustering methods to classify the approximate number of cars present. This paper details our experiences with the design and real implementation of this system across an area that stretches for miles in urban scenarios. We continuously measured and reported RSSI at different gateways for weeks. Results have shown that if a LoRaWAN end node is placed in an optimal position, up to 96% of correct environment traffic level detection can be obtained. Additionally, we share the lessons learned from such a deployment for traffic sensing.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/
Cost-effective non-destructive testing of biomedical components fabricated using additive manufacturing
Biocompatible titanium-alloys can be used to fabricate patient-specific medical components using additive manufacturing (AM). These novel components have the potential to improve clinical outcomes in various medical scenarios. However, AM introduces stability and repeatability concerns, which are potential roadblocks for its widespread use in the medical sector. Micro-CT imaging for non-destructive testing (NDT) is an effective solution for post-manufacturing quality control of these components. Unfortunately, current micro-CT NDT scanners require expensive infrastructure and hardware, which translates into prohibitively expensive routine NDT. Furthermore, the limited dynamic-range of these scanners can cause severe image artifacts that may compromise the diagnostic value of the non-destructive test. Finally, the cone-beam geometry of these scanners makes them susceptible to the adverse effects of scattered radiation, which is another source of artifacts in micro-CT imaging.
In this work, we describe the design, fabrication, and implementation of a dedicated, cost-effective micro-CT scanner for NDT of AM-fabricated biomedical components. Our scanner reduces the limitations of costly image-based NDT by optimizing the scanner\u27s geometry and the image acquisition hardware (i.e., X-ray source and detector). Additionally, we describe two novel techniques to reduce image artifacts caused by photon-starvation and scatter radiation in cone-beam micro-CT imaging.
Our cost-effective scanner was designed to match the image requirements of medium-size titanium-alloy medical components. We optimized the image acquisition hardware by using an 80 kVp low-cost portable X-ray unit and developing a low-cost lens-coupled X-ray detector. Image artifacts caused by photon-starvation were reduced by implementing dual-exposure high-dynamic-range radiography. For scatter mitigation, we describe the design, manufacturing, and testing of a large-area, highly-focused, two-dimensional, anti-scatter grid.
Our results demonstrate that cost-effective NDT using low-cost equipment is feasible for medium-sized, titanium-alloy, AM-fabricated medical components. Our proposed high-dynamic-range strategy improved by 37% the penetration capabilities of an 80 kVp micro-CT imaging system for a total x-ray path length of 19.8 mm. Finally, our novel anti-scatter grid provided a 65% improvement in CT number accuracy and a 48% improvement in low-contrast visualization. Our proposed cost-effective scanner and artifact reduction strategies have the potential to improve patient care by accelerating the widespread use of patient-specific, bio-compatible, AM-manufactured, medical components
Adaptive task selection using threshold-based techniques in dynamic sensor networks
Sensor nodes, like many social insect species, exist in harsh environments in large groups, yet possess very limited amount of resources. Lasting for as long as possible, and fulfilling the network purposes are the ultimate goals of sensor networks. However, these goals are inherently contradictory. Nature can be a great source of inspiration for mankind to find methods to achieve both extended survival, and effective operation. This work aims at applying the threshold-based action selection mechanisms inspired from insect societies to perform action selection within sensor nodes. The effect of this micro-model on the macro-behaviour of the network is studied in terms of durability and task performance quality. Generally, this is an example of using bio-inspiration to achieve adaptivity in sensor networks
Fungos marinhos: diversidade e potencial biotecnológico
Marine fungi are a prolific group of organisms that have been largely neglected
for a long time. Over the last years, attention turned to the marine environment,
with the recognition that marine organisms represent a rich source of natural
products. The marine mycobiome has many species still to be identified and
the ones known still underutilized for biotechnological applications. In Portugal,
despite its very large coastline and privileged relationship with the sea, there
are only few studies on marine fungi. Thus, the main goal of this thesis was to
contribute to disclose the untapped diversity and the biotechnological potential
of the marine mycobiome from the Portuguese coastline and in the estuary of
Ria de Aveiro. To achieve this, three different approaches were used.
First, fungi were isolated from algae, drift- and submerged wood, saline water,
and sponges. The 1312 isolates obtained were initially subjected to
microsatellite-primed PCR to analyze the genetic fingerprinting patterns to
separate and discriminate different groups in the collection. From this
collection, 243 different fungal species were identified based on DNA sequence
data. Eighteen fungal species and one new genus were identified, which were
circumscribed based on morphological and phylogenetic analysis. Furthermore,
some taxonomic ambiguities have been resolved.
Secondly, eight fungal strains were selected for biological activities
characterization based on their enzymatic profile and antibacterial activity.
Mycelia aqueous extracts and culture media methanolic extracts, obtained from
liquid fermentation, were characterized regarding their antibacterial, antifungal,
antioxidant, and cytotoxic activities. In addition, the effect of sea salt on fungal
bioactivities was evaluated. In general, the extracts of Aspergillus affinis,
Penicillium lusitanum, Emericellopsis cladophorae and Trichoderma
aestuarinum were able to inhibit the growth of pathogenic bacteria and fungi.
Our results also demonstrated that the activity profiles from the extracts of the
marine fungi studied were different in response to the presence of sea salt.
Finally, A. affinis and E. cladophorae were characterized in more detail with
their metabolomes and genomes sequenced. Both species contain many
unknown biosynthetic gene clusters, genes encoding for osmolytes’
biosynthetic processes, ion transport systems, and are rich in carbohydrate
active enzymes, which can contribute to understand their adaptation to the
marine environment. Furthermore, the compound library built from the crude
extracts for both species confirmed the presence of antifungal, antibacterial,
antitumor, antiviral, and anti-inflammatory metabolites.
The outcome of this work confirmed that the marine mycobiome is highly
diverse and can yield novel marine fungal taxa which must continue to be
explored contributing to unveil their biodiversity, ecological importance, and
natural products research.Os fungos marinhos são um grupo prolífico de organismos que tem sido
amplamente negligenciado. Ao longo dos últimos anos, as atenções voltaramse
para o meio marinho, com o reconhecimento de que os organismos
marinhos constituem uma rica fonte de produtos naturais. O micobioma
marinho tem muitas espécies ainda por desvendar e subutilizadas para
aplicação biotecnológica. Em Portugal, apesar da extensa linha costeira e da
relação privilegiada com o mar, são poucos os estudos sobre fungos marinhos.
Assim, o objetivo desta tese foi contribuir para a divulgação da diversidade e
do potencial biotecnológico contido no micobioma marinho da costa
portuguesa e no estuário da Ria de Aveiro. Para isso, três abordagens foram
utilizadas.
Primeiro, os fungos foram isolados de algas, madeira à deriva e submersa,
água salgada e esponjas. Obtiveram-se 1312 isolados que foram inicialmente
submetidos a MSP-PCR para analisar os padrões de impressão digital
genética para discriminar diferentes grupos na coleção. Desta coleção, foram
identificas 243 espécies diferentes de fungos com base em dados de
sequência de DNA. Foram identificadas dezoito novas espécies e um novo
género, que foram descritos com base em análises morfológicas e
filogenéticas. Além disso, algumas ambiguidades taxonómicas foram
resolvidas.
Em segundo lugar, oito estirpes de fungos foram selecionadas para a
caracterização de bioatividades com base nos seus perfis enzimáticos e
atividades antimicrobianass. Os extratos aquosos dos micélios e os extratos
metanólicos dos meios de cultura obtidos por fermentação líquida foram
caracterizados quanto às atividades antibacteriana, antifúngica, antioxidante e
citotóxica. De um modo geral, os extratos de Aspergillus affinis, Penicillium
lusitanum, Emericellopsis cladophorae e Trichoderma aestuarinum
apresentaram atividade contra bactérias e fungos patogénicos. Os resultados
demonstraram também que os perfis de atividade dos extratos destes fungos
marinhos são diferentes em resposta à presença de sal marinho.
Por fim, A. affinis e E. cladophorae foram estudados mais detalhadamente,
caracterizando-se os seus metabolomas e genomas. Ambas as espécies
contêm grupos de genes biossintéticos desconhecidos, genes que codificam
processos biossintéticos de osmólitos e sistemas de transporte de iões, e são
ricas em enzimas com atividade sobre hidratos de carbono, o que pode
contribuir para entender a sua adaptação ao ambiente marinho. Além disso, a
biblioteca de compostos construída a partir dos extratos brutos para ambas as
espécies confirmou a presença de metabolitos antifúngicos, antibacterianos,
antitumorais, antivirais e anti-inflamatórios.
O resultado deste trabalho confirmou que o micobioma marinho é altamente
diversificado e pode ter novos taxa de fungos marinhos que devem continuar a
ser explorados contribuindo para desvendar a sua biodiversidade, importância
ecológica e pesquisa de produtos naturais.Programa Doutoral em Biologi
Collected Papers (on various scientific topics), Volume XIII
This thirteenth volume of Collected Papers is an eclectic tome of 88 papers in various fields of sciences, such as astronomy, biology, calculus, economics, education and administration, game theory, geometry, graph theory, information fusion, decision making, instantaneous physics, quantum physics, neutrosophic logic and set, non-Euclidean geometry, number theory, paradoxes, philosophy of science, scientific research methods, statistics, and others, structured in 17 chapters (Neutrosophic Theory and Applications; Neutrosophic Algebra; Fuzzy Soft Sets; Neutrosophic Sets; Hypersoft Sets; Neutrosophic Semigroups; Neutrosophic Graphs; Superhypergraphs; Plithogeny; Information Fusion; Statistics; Decision Making; Extenics; Instantaneous Physics; Paradoxism; Mathematica; Miscellanea), comprising 965 pages, published between 2005-2022 in different scientific journals, by the author alone or in collaboration with the following 110 co-authors (alphabetically ordered) from 26 countries: Abduallah Gamal, Sania Afzal, Firoz Ahmad, Muhammad Akram, Sheriful Alam, Ali Hamza, Ali H. M. Al-Obaidi, Madeleine Al-Tahan, Assia Bakali, Atiqe Ur Rahman, Sukanto Bhattacharya, Bilal Hadjadji, Robert N. Boyd, Willem K.M. Brauers, Umit Cali, Youcef Chibani, Victor Christianto, Chunxin Bo, Shyamal Dalapati, Mario Dalcín, Arup Kumar Das, Elham Davneshvar, Bijan Davvaz, Irfan Deli, Muhammet Deveci, Mamouni Dhar, R. Dhavaseelan, Balasubramanian Elavarasan, Sara Farooq, Haipeng Wang, Ugur Halden, Le Hoang Son, Hongnian Yu, Qays Hatem Imran, Mayas Ismail, Saeid Jafari, Jun Ye, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan Karabašević, Abdullah Kargın, Vasilios N. Katsikis, Nour Eldeen M. Khalifa, Madad Khan, M. Khoshnevisan, Tapan Kumar Roy, Pinaki Majumdar, Sreepurna Malakar, Masoud Ghods, Minghao Hu, Mingming Chen, Mohamed Abdel-Basset, Mohamed Talea, Mohammad Hamidi, Mohamed Loey, Mihnea Alexandru Moisescu, Muhammad Ihsan, Muhammad Saeed, Muhammad Shabir, Mumtaz Ali, Muzzamal Sitara, Nassim Abbas, Munazza Naz, Giorgio Nordo, Mani Parimala, Ion Pătrașcu, Gabrijela Popović, K. Porselvi, Surapati Pramanik, D. Preethi, Qiang Guo, Riad K. Al-Hamido, Zahra Rostami, Said Broumi, Saima Anis, Muzafer Saračević, Ganeshsree Selvachandran, Selvaraj Ganesan, Shammya Shananda Saha, Marayanagaraj Shanmugapriya, Songtao Shao, Sori Tjandrah Simbolon, Florentin Smarandache, Predrag S. Stanimirović, Dragiša Stanujkić, Raman Sundareswaran, Mehmet Șahin, Ovidiu-Ilie Șandru, Abdulkadir Șengür, Mohamed Talea, Ferhat Taș, Selçuk Topal, Alptekin Ulutaș, Ramalingam Udhayakumar, Yunita Umniyati, J. Vimala, Luige Vlădăreanu, Ştefan Vlăduţescu, Yaman Akbulut, Yanhui Guo, Yong Deng, You He, Young Bae Jun, Wangtao Yuan, Rong Xia, Xiaohong Zhang, Edmundas Kazimieras Zavadskas, Zayen Azzouz Omar, Xiaohong Zhang, Zhirou Ma.
Smart-antenna techniques for energy-efficient wireless sensor networks used in bridge structural health monitoring
Abstract: It is well known that wireless sensor networks differ from other computing platforms in that 1- they typically require a minimal amount of computing power at the nodes; 2- it is often desirable for sensor nodes to have drastically low power consumption. The main benefit of the this work is a substantial network life before batteries need to be replaced or, alternatively, the capacity to function off of modest environmental energy sources (energy harvesting). In the context of Structural Health Monitoring (SHM), battery replacement is particularly problematic since nodes can be in difficult to access locations. Furthermore, any intervention on a bridge may disrupt normal bridge operation, e.g. traffic may need to be halted. In this regard, switchbeam smart antennas in combination with wireless sensor networks (WSNs) have shown great potential in reducing implementation and maintenance costs of SHM systems. The main goal of implementing switch-beam smart antennas in our application is to reduce power consumption, by focusing the radiated energy only where it is needed. SHM systems capture the dynamic vibration information of a bridge structure in real-time in order to assess the health of the structure and to predict failures. Current SHM systems are based on piezoelectric patch sensors. In addition, the collection of data from the plurality of sensors distributed over the span of the bridge is typically performed through an expensive and bulky set of shielded wires which routes the information to a data sink at one end of the structure. The installation, maintenance and operational costs of such systems are extremely high due to high power consumption and the need for periodic maintenance. Wireless sensor networks represent an attractive alternative, in terms of cost, ease of maintenance, and power consumption. However, network lifetime in terms of node battery life must be very long (ideally 5–10 years) given the cost and hassle of manual intervention. In this context, the focus of this project is to reduce the global power consumption of the SHM system by implementing switched-beam smart antennas jointly with an optimized MAC layer. In the first part of the thesis, a sensor network platform for bridge SHM incorporating switched-beam antennas is modelled and simulated. where the main consideration is the joint optimization of beamforming parameters, MAC layer, and energy consumption. The simulation model, built within the Omnet++ network simulation framework, incorporates the energy consumption profiles of actual selected components (microcontroller, radio interface chip). The energy consumption and packet delivery ratio (PDR) of the network with switched-beam antennas is compared with an equivalent network based on omnidirectional antennas. In the second part of the thesis, this system model is leveraged to examine two distinct but interrelated aspects: Gallium Arsenide (GaAs) based solar energy harvesting and switched-beam antenna strategies. The main consideration here is the joint optimization of solar energy harvesting and switchedbeam directional antennas, where an equivalent network based on omnidirectional antennas acts as a baseline reference for comparison purposes.Il est bien connu que les réseaux de capteurs sans fils diffèrent des autres plateformes informatiques
étant donné 1- qu’ils requièrent typiquement une puissance de calcul minimale aux
noeuds du réseau ; 2- qu’il est souvent désirable que les noeuds capteurs aient une consommation
d’énergie dramatiquement faible. La principale retombée de ce travail réside en la durée
de vie allongée du réseau avant que les piles ne doivent être remplacées ou, alternativement,
la capacité de fonctionner indéfiniment à partir de modestes sources d’énergie ambiente (glânage
d’énergie). Dans le contexte du contrôle de la santé structurale (CSS), le remplacement de
piles est particulièrement problématique puisque les noeuds peuvent se trouver en des endroits
difficiles d’accès. De plus, toute intervention sur un pont implique une perturbation de l’opération
normale de la structure, par exemple un arrêt du traffic. Dans ce contexte, les antennes
intelligentes à commutation de faisceau en combinaison avec les réseaux de capteurs sans fils
ont démontré un grand potentiel pour réduire les coûts de réalisation et d’entretien de systèmes
de CSS. L’objectif principal de l’intégration d’antennes à commutation de faisceau dans notre
application réside dans la réduction de la consommation énergétique, réalisée en concentrant
l’énergie radiée uniquement là où elle est nécessaire. Les systèmes de CSS capturent l’information
dynamique de vibration d’une structure de pont en temps réel de manière à évaluer la santé
de la structure et prédire les failles. Les systèmes courants de CSS sont basés sur des senseurs
piézoélectriques planaires. De plus, la collecte de données à partir de la pluralité de senseurs
distribués sur l’étendue du pont est typiquement effectuée par le biais d’un ensemble coûteux
et encombrant de câbles blindés qui véhiculent l’information jusqu’à un point de collecte à une
extremité de la structure. L’installation, l’entretien, et les coûts opérationnels de tels systèmes
sont extrêmement élevés étant donné la consommation de puissance élevée et le besoin d’entretien
régulier. Les réseaux de capteurs sans fils représentent une alternative attrayante, en termes
de coût, facilité d’entretien et consommation énergétique. Toutefois, la vie de réseau en termes
de la durée de vie des piles doit être très longue (idéalement de 5 à 10 ans) étant donné le coût
et les problèmes liés à l’intervention manuelle. Dans ce contexte, ce projet se concentre sur la
réduction de la consommation de puissance globale d’un système de CSS en y intégrant des
antennes intelligentes à commutation de faisceau conjointement avec une couche d’accès au
médium (couche MAC) optimisée. Dans la première partie de la thèse, une plateforme de réseau
de capteurs sans fils pour le CSS d’un pont incorporant des antennes à commutation de faisceaux
est modélisé et simulé, avec pour considération principale l’optimisation des paramètres
de sélection de faisceau, de la couche MAC et de la consommation d’énergie. Le modèle de
simulation, construit dans le logiciel de simulation de réseaux Omnet++, incorpore les profils
de consommation d’énergie de composants réels sélectionnés (microcontrôleur, puce d’interface
radio). La consommation d’énergie et le taux de livraison de paquets du réseau avec antennes
à commutation de faisceau est comparé avec un réseau équivalent basé sur des antennes omnidirectionnelles.
Dans la deuxième partie de la thèse, le modèle système proposé est mis à
contribution pour examiner deux aspects distrincts mais interreliés : le glânage d’énergie à partir
de cellules solaire à base d’arséniure de Gallium (GaAs) et les stratégies liées aux antennes
à commutation de faisceau. La considération principale ici est l’optimisation conjointe du glânage d’énergie et des antennes à commutation de faisceau, en ayant pour base de comparaison
un réseau équivalent à base d’antennes omnidirectionnelles
Representation learning for uncertainty-aware clinical decision support
Over the last decade, there has been an increasing trend towards digitalization in healthcare, where a growing amount of patient data is collected and stored electronically. These recorded data are known as electronic health records. They are the basis for state-of-the-art research on clinical decision support so that better patient care can be delivered with the help of advanced analytical techniques like machine learning. Among various technical fields in machine learning, representation learning is about learning good representations from raw data to extract useful information for downstream prediction tasks. Deep learning, a crucial class of methods in representation learning, has achieved great success in many fields such as computer vision and natural language processing. These technical breakthroughs would presumably further advance the research and development of data analytics in healthcare. This thesis addresses clinically relevant research questions by developing algorithms based on state-of-the-art representation learning techniques. When a patient visits the hospital, a physician will suggest a treatment in a deterministic manner. Meanwhile, uncertainty comes into play when the past statistics of treatment decisions from various physicians are analyzed, as they would possibly suggest different treatments, depending on their training and experiences. The uncertainty in clinical decision-making processes is the focus of this thesis. The models developed for supporting these processes will therefore have a probabilistic nature. More specifically, the predictions are predictive distributions in regression tasks and probability distributions over, e.g., different treatment decisions, in classification tasks. The first part of the thesis is concerned with prescriptive analytics to provide treatment recommendations. Apart from patient information and treatment decisions, the outcome after the respective treatment is included in learning treatment suggestions. The problem setting is known as learning individualized treatment rules and is formulated as a contextual bandit problem. A general framework for learning individualized treatment rules using data from observational studies is presented based on state-of-the-art representation learning techniques. From various offline evaluation methods, it is shown that the treatment policy in our proposed framework can demonstrate better performance than both physicians and competitive baselines. Subsequently, the uncertainty-aware regression models in diagnostic and predictive analytics are studied. Uncertainty-aware deep kernel learning models are proposed, which allow the estimation of the predictive uncertainty by a pipeline of neural networks and a sparse Gaussian process. By considering the input data structure, respective models are developed for diagnostic medical image data and sequential electronic health records. Various pre-training methods from representation learning are adapted to investigate their impacts on the proposed models. Through extensive experiments, it is shown that the proposed models delivered better performance than common architectures in most cases. More importantly, uncertainty-awareness of the proposed models is illustrated by systematically expressing higher confidence in more accurate predictions and less confidence in less accurate ones. The last part of the thesis is about missing data imputation in descriptive analytics, which provides essential evidence for subsequent decision-making processes. Rather than traditional mean and median imputation, a more advanced solution based on generative adversarial networks is proposed. The presented method takes the categorical nature of patient features into consideration, which enables the stabilization of the adversarial training. It is shown that the proposed method can better improve the predictive accuracy compared to traditional imputation baselines
Estratégias de apoio para melhorar a qualidade do ar em áreas portuárias
Despite their key contribution to economic development, harbours pose
environmental threat, affecting air quality, local climate, and human health, due to
the release of several pollutants. Poor local air quality episodes are particularly
concerning when harbours are located near densely populated urban areas,
threatening inhabitants’ health.
This Thesis was focused on the assessment of the impact of harbour emissions on
the air quality over harbours and their surrounding urban areas, with a final goal of
producing guidelines to support decision-making in the harbour sector and air
quality management, using Port of Leixões as a case-study. After reviewing the
state-of-the-art in this research field, a high-resolution emission inventory was
developed, based on the two most used methodologies within the scientific
community. Data about ship and cargo handling equipment were compiled, allowing
the quantification of emissions and identification of their main sources. The
comparison of the two methodologies indicates that a new harmonized methodology
is recommended, besides the need of continuous update of emission factors and
activity data.
Having the detailed emission inventory, the community-scale webtool C-PORT was
applied for the first time in European harbours to simulate the impact of the maritime
emissions on local air quality. The comparison of modelled and observed values
validated its application for the case study of Port of Leixões. The highest PM10
concentrations were found near the South Container Terminal of Port of Leixões,
while NOx concentrations above 100 µg/m3 were also found near the highway.
Land-based emission sources exhibited the highest contribution (around 80 %) to
the PM10 concentrations in the study area, while 50 % of NOx concentration was
due to docked ships.
Mitigation measures were investigated and assessed to improve air quality in
harbours and their surroundings. In a case-study, pollutant dispersion was
addressed, aiming to control fugitive petcoke emissions and their impact on Port of
Aveiro’s neighbour communities. Optimal structure, size and position of a physical
barrier were defined based on numerical and physical modelling, achieving a
maximum reduction in petcoke dust reaching the nearby residential area of 74 – 88
% for the most frequent/critical wind directions. The studied barrier has been
implemented in the field and monitoring campaigns are currently being carried out
to assess its effectiveness.Apesar do seu papel-chave no desenvolvimento económico, os portos marítimos
constituem uma ameaça ambiental, com impactes na qualidade do ar, clima local,
e saúde humana, devido à emissão de inúmeros poluentes. Episódios de má
qualidade do ar a nível local são particularmente preocupantes no caso de portos
localizados nas imediações de áreas urbanas densamente povoadas, pondo em
risco a saúde dos habitantes locais.
Esta Tese focou-se no impacte das emissões portuárias na qualidade do ar em
portos e suas vizinhanças urbanas. O objetivo final foi a produção de
recomendações de suporte à tomada de decisão no setor portuário e gestão da
qualidade do ar, usando o Porto de Leixões como caso-de-estudo. Após uma
revisão do estado-da-arte neste campo, foi desenvolvido um inventário de
emissões de alta-resolução, aplicando as duas metodologias mais frequentemente
usadas na comunidade científica. Foram compilados dados sobre navios e
equipamentos portuários, permitindo a quantificação das emissões e identificação
das suas fontes maioritárias. Deste procedimento resultaram recomendações
sobre o desenvolvimento de uma nova metodologia harmonizada. Ficou ainda
evidenciada a relevância da atualização dos fatores de emissão e dos dados
disponíveis sobre as diferentes atividades portuárias.
Dispondo deste inventário de emissões, o C-PORT, uma ferramenta web de escala
comunitária, foi aplicado pela primeira vez em portos europeus, para simular o
impacte das emissões marítimas na qualidade do ar local. A comparação dos
valores modelados com medições de campo validou a aplicação desta ferramenta
ao caso-de-estudo do Porto de Leixões. A concentração mais elevada de PM10 foi
registada no Terminal de Contentores Sul, registando-se também elevada (> 100
µg/m3
) concentração de NOx junto à autoestrada vizinha. A maior contribuição
(cerca de 80 %) para a emissão global de PM10 na área de estudo adveio de fontes
de emissão terrestres, enquanto os navios atracados contribuíram com cerca de
50 % das emissões de NOx. Esta Tese inclui a análise de medidas de mitigação
capazes de melhorar a qualidade do ar em portos marítimos e sua vizinhança. O
caso-de-estudo apresentado foca-se na dispersão de poluentes, com o intuito de
controlar a emissão de partículas de petcoke do Porto de Aveiro, e o seu impacte
nas comunidades vizinhas. Com esse objetivo, foi estudada, através de simulação
física e numérica, a composição, dimensão e posicionamento de uma barreira
física. A solução otimizada permitiu reduzir em 74 % – 88 % para as direções de
vento mais frequentes/críticas nesta região, estando atualmente implementada no
terreno.Programa Doutoral em Ciências e Engenharia do Ambient
- …