Despite their important ecosystem benefits for terrestrial and marine flora and fauna and the human livelihood mangrove forests suffer a high loss rate mainly due to human activity. Aside from these impacts, natural forest disturbances exist more commonly in mangroves compared to other forests as a direct consequence of their exposed coastal location. Within this thesis I investigate the influence of natural disturbance regimes on the mangrove forest dynamics focusing in particular on the ecological role of disturbances, disturbance patterns, forest structure, succession behavior and long-term vulnerability evaluation. The study areas were set in the Indian River Lagoon in Florida (USA) and in Can Gio an UNESCO Biosphere Reserve (Vietnam). In addition, theoretical simulation studies were carried out to complement the field studies. Thereby, in our study at the Indian River Lagoon site I investigated the ecosystem response to hurricane events of an artificially impounded mangrove forest. In Can Gio, the suitability of lightning strike – caused gaps for setting a homogenous plantation into more natural-like state according to species composition and forest structure was analyzed. Finally, a theoretical simulation study was carried out to compare lightning strike and hurricane events regarding their homogenization and heterogenization effects on the spatio-temporal forest structure. The findings of the field study in the Indian River Lagoon indicate that hurricane events had a severe impact on forest areas in higher successional stages by creating open patches, whereas areas in lower successional stages remained largely undisturbed. Furthermore, the impoundment determines the species selection of the post-hurricane succession by favoring flooding-tolerant species. However, regeneration was found to be impaired by the artificially high inundation regime at some disturbed patches. The lightning-strike disturbances enhance the species composition in the monospecific plantation in Can Gio by providing a sufficient light regime for entering seeds to establish. In addition, lightning-strike gaps increased the plantation structure complexity. Regenerating lightning-strike gaps remained as “green islands” within windthrow sites in the plantation due to their low stature and provided seeds for surrounding disturbed areas thereby accelerating their recolonization. The results of the simulation analysis of a theoretical landscape showed that in the simulated highly complex natural mature forests all disturbance regimes entail homogenization on the spatial structure compared to an undisturbed scenario. The hurricane scenario showed an increased temporal variation of the forest dynamics whereas lightning-strike gaps were not able to contribute to additional heterogeneity in the simulated area, despite of having the same tree mortality probability during disturbances. The interaction of the large-scale impoundment in the Indian River Lagoon and medium-sized hurricane events is characterized by partially impeded post-hurricane regeneration. In contrast, small-scaled lightning strikes influenced the regeneration of medium-sized windthrow sites positively within the homogenous plantation. We therefore suggest management activities aimed at creating small clearances within the plantation in Can Gio to simulate additional small-scale disturbances in order to facilitate heterogenization of the plantation structure. Natural disturbances are found to be able to enhance the species diversity and the interactions of ecological processes. In particular, where sustainable management strategies focused on maintaining ecosystem services especially in restored sites or plantations act as a supportive part. Natural disturbances are an integral component of mangrove forests and fulfill specific ecological functions. However, our findings indicate that these disturbances, on top of altered environmental conditions associated with climate change and direct human impacts, might jeopardize the natural development in unnatural forest structures as on plantations or restored sites. This thesis gives an extensive overview about the effect of various disturbances in different mangrove forest systems, including semi-natural forests and strongly modified plantations, on species composition and forest structure. Field studies and simulation analyses contribute in equal parts to the results of the thesis.

Abstract in English / Mathematical modeling of the spread of infectious diseases in a population has always been recognized as a powerful tool that can help decision-makers understand how a disease evolves over time. With the evolution of science and humanity, it has become evident that Mathematical models are too simplistic and have some limitations in modeling environmental phenomena, such as the spread of epidemics in a population, when they are applied without combining them with other sciences. In understanding the dynamics of epidemics in a population, the weakness of these models is their difficulty in grasping the complexity inherent in the spread of diseases in real life because, life is supported by human interactions and behaviors that are understood through networks of social and spatial interactions. Modeling the spread of epidemics which takes this reality into account requires the implementation of new tools to refine the results already obtained by mathematical models. The aim of this thesis is to explore and attempt to extend new developments in mathematical modeling of the spread of infectious diseases by proposing new tools based on mathematical models from differential equations and agent-based models from intelligent agents derived from artificial intelligence. To achieve this objective, the study starts from a comparative study of two ways of modeling and simulation of the spread of infectious diseases in the population, namely mathematical modeling and agent-based modeling with a concrete case study of the spread of tuberculosis based on data from the Democratic Republic of the Congo (DRC). Then comes a coupling study of these two approaches in a single model and its implementation in a multi-scale environment. The results show that the coupled model is more realistic compared to mathematical models generally implemented in the literature. Four case studies are presented in this thesis. Mathematical modeling based on differential equations is used in the first and second cases. The third case is based on intelligent agents model while the last one is based on the coupling of mathematical models and agent-based models. Application of implemented models to the spread of tuberculosis reveals that detection of people with latent tuberculosis and their treatment are among the actions to be taken into account in addition to those currently carried out by the Congolese health system. The models assert that the current TB situation in DRC remains endemic and that the necessary measures need to be taken to reduce the burden of TB, especially to control it, through the tuberculosis elimination strategy and its elimination in the future in accordance with the Sustainable Development Goals. Our hybrid model benefiting from the advantages of EBM and ABM confirms that taking the individual into account as a fully-fledged entity and managing their behavior gives the microscopic aspect of the model set up and brings it closer as much as possible to reality. Mathematical management of the spread of the disease in cities gives a macroscopic aspect to the model. Numerical simulations of this last model on a multi-scale virtual environment affirm that the mobility of individuals from city to city has a significant impact on the spread of tuberculosis in the population. Controlling the rate of population mobility from one city to another is one of the most important measures for large-scale disease control. This model therefore draws its richness from this dynamic at two different scales (two time scales modeling approaches: at the microscopic/individual level (ABM) and macroscopic/city level (ODE)), which gives the emergence of the model at the global level. As a result, it seems that the coupling of mathematical models to agent-based models should be applied when the dynamics of the complex system under consideration is at different scales. Based on our research results, it seems that the choice of an approach must depend on how the modeler would like to achieve the expected results. Mathematical models remain essential due to their analytical and synthetic aspect, but their coupling with intelligent agent-based models makes it possible to refine known results and thus reflect the reality of real life, because the resulting model integrate interactions of individuals and their heterogeneous behaviors that are necessary for understanding the spread of infectious diseases in the population that only mathematical models based on differential equations can not capture. / Mathematical Sciences / Ph D. (Applied Mathematics)

The increasing demand for food and fiber, the need for climate change mitigation and adaptation as well as for environmental protection impose severe challenges on land systems worldwide. Solutions to support the transformation towards a sustainable development of land systems are needed. One response to the multiple challenges is the introduction of policy options aimed at steering land use activities towards a bundle of societal goals. However, it is difficult to empirically foresee the effectiveness and unintended consequences of policy options prior to their deployment. A second response is environmental education because human consumption behavior, among other factors, strongly influences natural ecosystems. However, it is a non-trivial task to develop effective communication strategies for complex topics such as sustainable land management. In both cases, modeling can help to overcome the different obstacles along the way. In this thesis, dynamic process-based social-ecological models at the individual scale are developed and analyzed to study effectiveness and unintended side effects of policy options, which promote agricultural management strategies and were intentionally designed to cope with multiple societal challenges. Two case studies of political intervention are investigated: the promotion of perennial woody crops in European agricultural landscapes for a sustainable bioeconomy and governmental supplementary feeding programs to cope with climate risks in pastoral systems in drylands. These two case studies are complemented by the development of a serious online game on sustainable land management in general that bridges the gap between land use modeling and environmental education. Simulation results of this thesis provide insights into (i) the performance of the politically promoted agricultural management strategies in meeting various intended goals such as poverty alleviation or the maintenance of biodiversity and ecosystem services, (ii) the emergence of unintended (environmental and social) side effects such as land use conflicts, land degradation or cost explosion and (iii) the mitigation of such side effects by appropriately adjusting the design of the policy options. These insights are enabled by representing temporal as well as spatial variability in the developed models. Furthermore, different mechanistic approaches of transferability analyses based on stylized landscapes are developed and applied. They enable to check whether and in what respect policy impacts actually differ substantially between regional contexts, to identify what regional factors steer the impact and to derive indicators for grouping regions of similar policy impacts. Finally, based on a conducted survey-based evaluation and experiences from various applications, the value of the developed serious game for environmental education is revealed and discussed.Altogether, this thesis contributes to model-based decision support for steering transformation towards the sustainable development of land systems in an appropriate way. This is done by developing appropriate social-ecological modeling approaches, by performing specific policy impact analyses in two transformative agricultural systems using these models and by providing a model-based communication tool for environmental education.

La presente investigación se realizó para evaluar el desempeño estructural y funcional del sistema de evacuación, de un edificio educativo superior durante un sismo severo, en atención a lo cual, se analizó mediante el método basado en Agent Based Modelling y Análisis Tiempo-Historia no Lineal. No obstante, el análisis no se limita solo a edificaciones esenciales; se puede aplicar para edificios importantes y comunes que concentran gran cantidad de personas, ya que el lineamiento que se plantea en esta tesis busca una alternativa más que se debe tener en cuenta en los proyectos de la construcción civil. La información estructural y geotécnica del edificio en estudio, se recopila y se introduce en una base de datos para su análisis. Se realiza un procedimiento similar para la información relacionada de los ocupantes. Usando estas informaciones: a) Se determinará la fragilidad estructural y el colapso localizado, b) se establecerá la interacción de la persona con el colapso focalizado. Para el primer aspecto, se utilizará el historial de tiempo no lineal; para el segundo, el modelado basado en agentes se empleará para recrear la reacción de las personas que enfrentan el micro colapso. Los resultados importantes de estas evaluaciones son: 1) Se ha localizado vigas, columnas y muros de corte colapsadas que afectan la evacuación; 2) Se ha ubicado de cuellos de botella, donde presentan gran concentración de personas durante la evacuación; y 3) se ha cuantificado a las personas afectadas, en términos de individuos atrapadas en el edificio que no pudieron evacuar. / The present investigation was carried out to evaluate the structural and functional performance of the evacuation system of a high-rise higher education building during a severe earthquake, which was analyzed using the Agent Based Modeling and Non-Linear Time-History Analysis method. However, the analysis is not limited only to essential buildings; it can be applied to important and common buildings that concentrate a large number of people, such as: Health establishments, airports, shopping centers, stadiums, condominiums, etc., since the guideline proposed in this thesis seeks one more alternative to be taken into account in civil construction projects. The structural and geotechnical information of the building under study is compiled and entered into a database for analysis. A similar procedure is performed for the related occupant information. Using this information: a) the structural fragility and localized collapse will be determined, b) the interaction of the person with the focused collapse will be established. For the first aspect, nonlinear time history will be used; for the second, agent-based modeling will be used to recreate the reaction of people facing the micro collapse. The important results of these evaluations are: 1) collapsed beams, columns and shear walls have been located, which make evacuation routes inoperable; 2) bottleneck areas have been located, where they present high concentration of people during evacuation; and 3) affected people have been quantified, in terms of individuals trapped in the building who could not evacuate. / Tesis

Disertační práce se zabývá modelováním znečištění ovzduší, jeho transportních a disperzních procesů ve spodní části atmosféry a zejména numerickými metodami, které slouží k řešení těchto modelů. Modelování znečištění ovzduší je velmi důležité pro předpověď kontaminace a pomáhá porozumět samotnému procesu a eliminaci následků. Hlavním tématem práce jsou metody pro řešení modelů popsaných parciálními diferenciálními rovnicemi, přesněji advekčně-difúzní rovnicí. Polovina práce je zaměřena na známou metodu přímek a je zde ukázáno, že tato metoda je vhodná k řešení určitých konkrétních problémů. Dále bylo navrženo a otestováno řešení paralelizace metody přímek, jež ukazuje, že metoda má velký potenciál pro akceleraci na současných grafických kartách a tím pádem i zvětšení přesnosti výpočtu. Druhá polovina práce se zabývá poměrně mladou metodou ELLAM a její aplikací pro řešení atmosférických advekčně-difúzních rovnic. Byla otestována konkrétní forma metody ELLAM společně s navrženými adaptacemi. Z výsledků je zřejmé, že v mnoha případech ELLAM překonává současné používané metody.

[ES] La definición que entrega el diccionario de la Real Academia Española de la Lengua sobre Epidemiología indica que ésta es una ciencia y como tal, tiene aquellos elementos propios de un conjunto sistematizado de conocimientos entre los que se destaca la metodología, aquella que analiza los procedimientos usados en el objeto de estudio. Desde el surgimiento del ser humano se ha podido evidenciar, a lo largo de la historia, cómo se ha hecho uso del agua en el abastecimiento tanto a nivel de sustento y salud, como en el nivel industrial. Igualmente, se puede evidenciar la evolución de la epidemiología para analizar las enfermedades que principalmente se transmiten por aguas contaminadas y cómo la metodología ha hecho avances tan significativos que permiten predecir el comportamiento de los patógenos y mitigar las consecuencias de un posible contagio. El desarrollo de la epidemiología se ha volcado principalmente en el área de la medicina en la cual ha mostrado una enorme evolución al afrontar grandes retos como la propagación de las enfermedades infecciosas y el replanteamiento continuo de los modelos de análisis. No obstante, esta ciencia se puede adaptar a cualquier área del conocimiento humano como la gestión de los recursos hídricos y más concretamente en la gestión de las redes de abastecimiento de agua urbana. La puesta en marcha de una red de abastecimiento de agua en una ciudad, cuyas dimensiones y construcción generalmente son monumentales, implica un diseño y una operabilidad que surge de la aplicación de modelos matemáticos y/o estadísticos, los cuales permiten analizar las distintas condiciones de funcionamiento antes de iniciar obras. Ese comportamiento puede caracterizarse a partir de métodos de resolución basados en los procedimientos epidemiológicos y que han sido contrastados ampliamente en forma empírica y funcional. En toda red de suministro existen dos componentes independientes e interdependientes, como lo son la gestión de la demanda y la gestión de fallos. En ambos hay incertidumbres que, generalmente, provienen de variables externas, aleatorias, que dificultan su cuantificación y por lo mismo, su predicción. Para la gestión de la demanda, resulta importante la aplicación de modelos de estimación de la demanda precisos, pues con ellos se pueden determinar las capacidades y cargas que soporte la red. A la par, para la gestión de fallos en las redes, resultan importantes modelos de estimación precisos que ayuden a mitigar el impacto de contingencias generadas por fallos en cascada y la propagación de éstos hasta un posible colapso. En los procesos de gestión de demanda se vienen utilizando principalmente los modelos de series temporales, llegando a la aplicación de modelos que impliquen el algoritmo SAX. En los procesos de gestión de fallos se han aplicado métodos como el análisis de supervivencia y más recientemente, las redes neuronales, llegando a los sistemas multiagente con los modelos SIR, SIRS y SEIR. El desarrollo de los modelos SAX se pueden apreciar en un caso de estudio de la ciudad de Franca en Brasil, en la que se combinan patrones de similitud entre sectores con patrones de las MINDIST que respaldan los métodos predictivos, mejorando su precisión y facilitando la detección de lecturas anormales en los medidores de flujo e incluso la presencia de usos o fugas inesperados. El Modelo Basado en Agentes (MBA) se puede desarrollar mediante, por ejemplo, la herramienta NetLogo, y su aplicación en una red de suministro resulta muy efectiva para determinar el comportamiento de los posibles fallos en cascada; ejemplo de ello se aprecia en el caso de estudio de la ciudad de Coro en Venezuela en la que se pueden establecer momentos para cada comportamiento: susceptibilidad, infección (fallo) y recuperación; proporcionando así un modelo predictivo mejorado para este tipo de situaciones. / [CAT] La definició que lliura el diccionari de la Reial Acadèmia Espanyola de la Llengua sobre l'Epidemiologia indica que aquesta és una ciència i com a tal té aquells elements propis d'un conjunt sistematitzat de coneixements entre els quals es destaca la metodologia, aquella que analitza els procediments usats en l'objecte d'estudi. Des del sorgiment de l'ésser humà s'ha pogut evidenciar al llarg de la història, com s'ha fet ús de l'aigua en l'abastament tant pel que fa al suport i la salut, com al nivell industrial. Igualment, es pot evidenciar l'evolució de l'epidemiologia per analitzar les malalties que principalment es transmeten per aigües contaminades i com la metodologia ha fet avanços tan significatius que permeten predir el comportament dels patògens i mitigar les conseqüències d'un possible contagi. El desenvolupament de l'epidemiologia s'ha abocat principalment a l'àrea de la medicina on ha mostrat una enorme evolució en afrontar grans reptes com la propagació de les malalties infeccioses i el replantejament continu dels models d'anàlisi. Això no obstant, aquesta ciència es pot adaptar a qualsevol àrea del coneixement humà com la gestió dels recursos hídrics i més concretament en la gestió de les xarxes d'abastament d'aigua urbana. La posada en marxa d'una xarxa d'abastament d'aigua en una ciutat, les dimensions i la construcció de la qual generalment són monumentals, implica un disseny i una operativitat que sorgeix de l'aplicació de models matemàtics i/o estadístics, els quals permeten analitzar les diferents condicions de funcionament abans d'iniciar obres. Aquest comportament es pot caracteritzar a partir de mètodes de resolució basats en els procediments epidemiològics i que han estat contrastats àmpliament en forma empírica i funcional. En tota xarxa de subministrament hi ha dos components independents i interdependents, com ho són la gestió de la demanda i la gestió de fallades. En tots dos hi ha incerteses que generalment provenen de variables externes, aleatòries, que dificulten la quantificació i, per tant, la seva predicció. Per a la gestió de la demanda, és important l'aplicació de models d'estimació de la demanda precisos, ja que s'hi poden determinar les capacitats i les càrregues que suporti la xarxa. Alhora, per a la gestió d'errors a les xarxes, són importants els models d'estimació precisos que ajudin a mitigar l'impacte de contingències generades per errors en cascada i la propagació d'aquests fins a un possible col·lapse. En els processos de gestió de demanda, s'utilitzen principalment els models de sèries temporals, arribant a l'aplicació de models que impliquin l'algorisme SAX. En els processos de gestió de fallades s'han aplicat mètodes com l'anàlisi de supervivència i, més recentment, les xarxes neuronals; arribant als sistemes multiagent amb els models SIR, SIRS i SEIR. El desenvolupament dels models SAX es poden apreciar en un cas d'estudi de la ciutat de Franca al Brasil, en què es combinen patrons de similitud entre sectors amb patrons de MINDIST que donen suport als mètodes predictius millorant-ne la precisió i facilitant la detecció de lectures anormals als mesuradors de flux i fins i tot la presència d'usos o fugues inesperades. El Model Basat en Agents (MBA) es pot desenvolupar mitjançant l'eina NetLogo i la seva aplicació en una xarxa de subministrament resulta molt efectiva per determinar el comportament de les possibles fallades en cascada. Exemple d'això s'aprecia en el cas d'estudi de la ciutat de Coro a Veneçuela en què es poden establir moments per a cada comportament: susceptibilitat, infecció (fallida) i recuperació, proporcionant així un model predictiu millorat per a aquest tipus de situacions. / [EN] The definition of Epidemiology given by the dictionary of the Real Academia Española de la Lengua states that it is a science and, as such, it exhibits those elements of a systematized set of knowledge, among which the methodology, which analyzes the procedures used in the object of study, stands out. Since the emergence of the human being, it has been possible to demonstrate throughout history, how water has been used in supply at the level of both sustenance and health, as well as at the industrial level. Likewise, the evolution of epidemiology can be evidenced to analyze diseases that are mainly transmitted by contaminated water and how the methodology has made such significant advances that allow predicting the behavior of pathogens and mitigating the consequences of possible contagion. The development of epidemiology has focused mainly on the area of medicine, in which it has shown enormous evolution when facing great challenges such as the spread of infectious diseases and the continuous rethinking of analysis models. However, this science can be adapted to any area of human knowledge, such as the management of water resources and more specifically the management of urban water supply networks. Implementing a water supply network in a city, whose dimensions and construction are generally monumental, implies design and operability aspects that arise from applying mathematical and/or statistical models, which allow the analysis of the different conditions of operation before starting its operation. This behavior can be characterized by resolution methods based on epidemiological procedures, which have been widely contrasted empirically and functionally. In any supply network, there are two independent and interdependent components, such as demand management and fault management. In both, there are uncertainties that generally come from external, random variables, which make it difficult to quantify and therefore to predict. Regarding demand management, the application of accurate demand estimation models is essential, since the capacities and loads supported by the network can be determined by using them. At the same time, for network failure management, accurate estimation models are also key to help mitigate the impact of contingencies generated by cascading failures and their propagation until a possible collapse. In demand management processes, time series models have been mainly used, including the application of models that involve the SAX algorithm. In failure management processes, methods such as survival analysis and, more recently, neural networks have been applied; reaching multi-agent systems with the SIR, SIRS, and SEIR models. The development of SAX models can be seen in a case study from the city of Franca in Brazil, in which patterns of similarity between districts are combined with MINDIST patterns that support predictive methods, improving their accuracy and facilitating the detection of errors, abnormal readings on flow meters and even the presence of unexpected uses or leaks. The Agent-Based Model (MBA) can be developed using, for example, the NetLogo tool, and its application in a supply network is very effective in determining the behavior of possible cascading failures. An example can be seen in the case study of the city de Coro in Venezuela in which moments for each behavior can be established: susceptibility, infection (failure), and recovery. This provides an improved predictive model for this type of situation. / Navarrete López, CF. (2023). Metodologías epidemiológicas de análisis de datos para la operación y gestión de redes de abastecimiento urbano de agua [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/195739

No description available.

The transportation sector is responsible for the majority of airborne particles and global energy consumption in urban areas. Its role in generating air pollution in urban areas is even more critical, as many visitors, commuters and citizens travel there daily for various reasons. Emissions released by transport fleets have an exhaust (tailpipe) and a non-exhaust (brake wears ) origin. Both exhaust and non-exhaust airborne particles can have destructive effects on the human cardiovascular and respiratory systems and even lead to premature deaths. This dissertation aims to estimate the amount of exhaust and brake emissions in a real case study by proposing an innovative methodology. For this purpose, different levels of study have been introduced, including the subsystem level, the system level, the environmental level and the suprasystem level. To address these levels, two approaches were proposed along with a data collection process. First, a comprehensive field survey was conducted in the area of Buonconsiglio Castle and data was collected on traffic and non-traffic during peak hours. Then, in the first approach, a state-of-the-art simulation-based method was presented to estimate the amount of exhaust emissions generated and the rate of fuel consumption in the case study using the VISSIM traffic microsimulation software and Enviver emission modeler at the suprasystem level. In order to calculate the results under different mobility conditions, a total of 18 scenarios were defined based on changes in vehicle speeds and the share of heavy vehicles (HV%) in the modal split. Subsequently, the scenarios were accurately modelled in the simulation software VISSIM and repeated 30 times with a simulation runtime of three hours. The results of the first approach confirmed the simultaneous effects of considering vehicle speed and HV % on fuel consumption and the amount of exhaust emissions generated. Furthermore, the sensitivity of exhaust emissions and fuel consumption to variations in vehicle speed was found to be much higher than HV %. In other words, the production of NOx and VOC emissions can be increased by up to 20 % by increasing the maximum speed of vehicles by 10 km/h. Conversely, increasing the HV percentage at the same speed does not seem to produce a significant change. Furthermore, increasing the speed from 30 km/h to 50 km/h increased CO emissions and fuel consumption by up to 33%. Similarly, a reduction in speed of 20 or 10 km/h with a 100% increase in HV resulted in a 40% and 27% decrease in exhaust emissions per seat, respectively. In the second approach, a novel methodology was proposed to estimate the number of brake particles in the case study. To achieve this goal, a downstream approach was proposed starting from the suprasystem level (microscopic traffic simulation models in VISSIM) and using a developed mathematical vehicle dynamics model at the system level to calculate the braking torques and angular velocities of the front and rear wheels, and proposes an artificial neural network (ANN) as a brake emission model, which has been appropriately trained and validated using emission data collected through more than 1000 experimental tribological tests on a reduced-scale dynamometer at the subsystem level (braking system). Consideration of this multi-level approach, from tribological to traffic-related aspects, is necessary for a realistic estimation of brake emissions. The proposed method was implemented on a targeted vehicle, a dominant SUV family car in the case study, considering real driving conditions. The relevant dynamic quantities of the targeted vehicle (braking torques and angular velocities of the wheels) were calculated based on the vehicle trajectory data such as speed and deceleration obtained from the traffic microsimulation models and converted into braking emissions via the artificial neural network. The total number of brake emissions emitted by the targeted vehicles was predicted for 10 iterations route by route and for the entire traffic network. The results showed that a large number of brake particles (in the order of billions of particles) are released by the targeted vehicles, which significantly affect the air quality in the case study. The results of this dissertation provide important information for policy makers to gain better insight into the rate of exhaust and brake emissions and fuel consumption in metropolitan areas and to understand their acute negative impacts on the health of citizens and commuters.

No description available.

<p dir="ltr">Tuberculosis (TB) remains a global public health crisis, causing over 10 million new infections and 1.3 million deaths in 2022 alone. TB is caused by <i>Mycobacterium tuberculosis </i>(<i>Mtb</i>), which initiates heterogeneous pathology in the lungs, including granulomas and cavities. Granulomas are organized structures of immune cells, traditionally thought to contain bacteria. Cavities are pathological spaces caused by the destruction of extracellular matrix (ECM), which can worsen disease outcomes and cause long-lasting pulmonary impairment.<i> In vitro </i>methods are commonly used to study host-pathogen interactions in <i>Mtb</i> infection, and recent developments have led to models that represent the TB granuloma environment more closely than traditional cell culture. These advances include the development of 3D models and the inclusion of physiological ECM components like collagen. Increasing complexity has been accomplished in a piece-wise manner – minimally necessary components are included to minimize cost while maintaining throughput and tractability. This creates a need for tools to analyze these systems and, more importantly, integrate the independent data created. We developed an agent-based model to characterize multiple <i>in vitro</i> models of TB and apply it to 1) separate the contributions of dimension and structure to bacterial control in granuloma-like spheroids and 2) explore how the interactions of collagen and matrix metalloproteinases (MMP) contribute to clinically relevant outputs such as bacterial load and ECM destruction. The model provides insights into the role of granuloma structure and the conflicting results of MMP inhibition, generating new hypotheses to be tested in tandem with <i>in vitro</i> models.</p>