Дисертації з теми "Geospatial maps"

Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2009.
Title from electronic title page (viewed Oct. 5, 2009). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum for the Environment and Ecology." Discipline: Anthropology; Department/School: Anthropology.

Creating physical simulations between virtual and real objects in Augmented Reality (AR) is essential for the user experience. Otherwise the user might lose sense of depth, distance and size. One of these simulations is occlusion, meaning that virtual content should be partially or fully occluded if real world objects is in the line-of-sight between the user and the content. The challenge for simulating occlusion is to construct the geometric model of the current AR environment. Earlier studies within the field have all tried to create realistic pixel-perfect occlusion and most of them have either required depth-sensing hardware or a static predefined environment. This study proposes and evaluates an alternative model-based approach to the problem. It uses geospatial data to construct the geometric model of all the buildings in the current environment, making virtual content occluded by all real buildings in the current environment. This approach made the developed function compatible with non depth-sensing devices and in a dynamic outdoor urban environment. To evaluate the solution it was implemented in a sensor-based AR application visualizing a future building in Stockholm. The effect of the developed function was that the future virtual building was occluded as expected. However, it was not pixel-perfect, meaning that the simulated occlusion was not realistic, but results from the conducted user study said that it fulfilled its goal. A majority of the participants thought that their AR experience got better with the solution activated and that their depth perception improved. However, any definite conclusions could not be drawn due to issues with the sensor-based tracking. The result from this study is interesting for the mobile AR field since the great majority of smartphones are not equipped with depth sensors. Using geospatial data for simulating occlusions, or other physical interactions between virtual and real objects, could then be an efficient enough solution until depth-sensing AR devices are more widely used.
För att uppnå en god användarupplevelse i Augmented Reality (AR) så är det viktigt att simulera fysiska interaktioner mellan de virtuella och reella objekten. Om man inte gör det kan användare uppfatta saker som djup, avstånd och storlek felaktigt. En av dessa simulationer är ocklusion som innebär att det virtuella innehållet ska vara delvis eller helt ockluderat om ett reellt objekt finns i siktlinjen mellan användaren och innehållet. För att simulera detta är utmaningen att rekonstruera den geometriska modellen av den nuvarande miljön.Tidigare studier inom fältet har försökt att uppnå en perfekt simulation av ocklusion, men majoriteten av dem har då krävt antingen djupavkännande hårdvara eller en statisk fördefinierad miljö. Denna studie föreslår och utvärderar en alternativ modellbaserad lösning på problemet. Lösningen använder geospatial data för att rekonstruera den geometriska modellen av alla byggnader i den nuvarande omgivningen, vilket resulterar i att det virtuella innehållet blir ockluderat av alla reella byggnader i den nuvarande miljön. Den utvecklade funktionen blev i och med det kompatibel på icke djupavkännande enheter och fungerande i en dynamisk urban miljö. För attutvärdera denna funktion så var den implementerad i en sensorbaserad AR applikation som visualiserade en framtida byggnad i Stockholm. Resultatet visade att den utvecklade funktionen ockluderade den virtuella byggnaden som förväntat. Dock gjorde den ej det helt realistiskt, men resultatet från den utförda användarstudien visade att den uppnådde sitt mål. Majoriteten av deltagarna ansåg att deras AR upplevelse blev bättre med den utvecklade funktionen aktiverad och ett deras uppfattning av djup förbättrades. Dock kan inga definitiva slutsatser dras eftersom AR applikationen hade problem med den sensorbaserade spårningen. Resultaten är intressant för det mobila AR fältet eftersom majoriteten av alla smartphones ej har stöd för djupavkänning. Att använda geospatial data för att simulera ocklusion, eller någon annan fysisk interaktion mellan virtuella och reella objekt, kan då vara en tillräckligt effektiv lösning tills djupavkännande AR enheter används mer.

Nowadays with the rapid development of cities, understanding the human mobility patterns of subscribers is crucial for urban planning and for network infrastructure deployment. Today mobile phones are electronic devices used for analyzing the mobility patterns of the subscribers in the network, because humans in their daily activities they carry mobile phones for communication purpose. For effective utilization of network infrastructure (NI) there is a need to study on mobility patterns of subscribers.   The aim of the thesis is to simulate the geospatial Telenor mobility data (i.e. three different subscriber categorized segments) and provide a visual support in google maps using google maps API, which helps in decision making to the telecommunication operators for effective utilization of network infrastructure (NI).    In this thesis there are two major objectives. Firstly, categorize the given geospatial telenor mobility data using subscriber mobility algorithm. Secondly, providing a visual support for the obtained categorized geospatial telenor mobility data in google maps using a geovisualization simulation tool.    The algorithm used to categorize the given geospatial telenor mobility data is subscriber mobility algorithm. Where this subscriber mobility algorithm categorizes the subscribers into three different segments (i.e. infrastructure stressing, medium, friendly). For validation and confirmation purpose of subscriber mobility algorithm a tetris optimization model is used. To give visual support for each categorized segments a simulation tool is developed and it displays the visualization results in google maps using Google Maps API.   The result of this thesis are presented to the above formulated objectives. By using subscriber mobility algorithm and tetris optimization model to a geospatial data set of 33,045 subscribers only 1400 subscribers are found as infrastructure stressing subscribers. To look informative, a small region (i.e. boras region) is taken to visualize the subscribers from each of the categorized segments (i.e. infrastructure stressing, medium, friendly).    The conclusion of the thesis is that the functionality thus developed contributes to knowledge discovery from geospatial data and provides visual support for decision making to telecommunication operators. Nowadays with the rapid development of cities, understanding the human mobility patterns of subscribers is crucial for urban planning and for network infrastructure deployment. Today mobile phones are electronic devices used for analyzing the mobility patterns of the subscribers in the network, because humans in their daily activities they carry mobile phones for communication purpose. For effective utilization of network infrastructure (NI) there is a need to study on mobility patterns of subscribers.   The aim of the thesis is to simulate the geospatial Telenor mobility data (i.e. three different subscriber categorized segments) and provide a visual support in google maps using google maps API, which helps in decision making to the telecommunication operators for effective utilization of network infrastructure (NI).    In this thesis there are two major objectives. Firstly, categorize the given geospatial telenor mobility data using subscriber mobility algorithm. Secondly, providing a visual support for the obtained categorized geospatial telenor mobility data in google maps using a geovisualization simulation tool.    The algorithm used to categorize the given geospatial telenor mobility data is subscriber mobility algorithm. Where this subscriber mobility algorithm categorizes the subscribers into three different segments (i.e. infrastructure stressing, medium, friendly). For validation and confirmation purpose of subscriber mobility algorithm a tetris optimization model is used. To give visual support for each categorized segments a simulation tool is developed and it displays the visualization results in google maps using Google Maps API.   The result of this thesis are presented to the above formulated objectives. By using subscriber mobility algorithm and tetris optimization model to a geospatial data set of 33,045 subscribers only 1400 subscribers are found as infrastructure stressing subscribers. To look informative, a small region (i.e. boras region) is taken to visualize the subscribers from each of the categorized segments (i.e. infrastructure stressing, medium, friendly).    The conclusion of the thesis is that the functionality thus developed contributes to knowledge discovery from geospatial data and provides visual support for decision making to telecommunication operators.

6 pp.
Remotely sensed data are valuable for monitoring, assessing, and managing impacts to arid and semi-arid lands caused by drought or other changes in the natural environment. With this in mind, we collaborated with scientists and technologists to redevelop DroughtView, a web-based decision-support tool that combines satellite-derived measures of surface greenness with additional geospatial data so that users can visualize and evaluate vegetation dynamics across space and over time. To date, users of DroughtView have been local drought impact groups, ranchers, federal and state land management staff, environmental scientists, and plant geographers. Potential new applications may include helping to track wildland fire danger. Here, we present the functionality of DroughtView, including new capabilities to report drought impacts and share map information, as well as the data behind it.

This thesis contributes to improvements in effectively communicating population related cancer distributions and the associated burden of cancer on Australian communities. This thesis presents a new algorithm for creating an alternative map displays of tessellating hexagons. Alternative map displays can emphasise statistics in countries that contain densely populated cities. It is accompanied by a software implementation that automates the choice of one hexagon to represent each geographic unit, ensuring the statistic for each is equitably presented. The case study comparing a traditional choropleth map to the alternative hexagon tile map contributes to a growing field of visual inference studies.

Nuestro país se encuentra ubicado en una zona de alta actividad sísmica, por ello es importante que los ingenieros civiles tengan una adecuada capacidad para realizar análisis de vulnerabilidad sísmica y comprendan la importancia de la prevención. En la presente tesis analizamos los diferentes escenarios de daño sísmico para edificios de concreto armado organizados por tipología de número de pisos. Para el desarrollo de la presente investigación utilizamos dos Software, el primer software de simulación sísmica, en el cual modelamos la muestra de edificaciones de concreto armado que representan el distrito de Santiago de Surco, para luego analizarlo con un sismo de tiempo historia que simula varias magnitudes del mismo sismo amplificado con aceleraciones del suelo que varían de 0.1g a 1.0g, con el objetivo de obtener cada vez mayores desplazamientos, y de esa manera mayores niveles de daño mediante la metodología Hazus M-H. Luego estos valores expresarlos en cuadros de curvas de fragilidad sísmica con cuatro tipos de niveles de daño. El Segundo Software que utilizamos es el Sistema de Información Geográfica (SIG), con este programa sistematizamos los datos obtenido de la curva de fragilidad para mostrar mapas de niveles de daño para distintos escenarios sísmicos que muestren el comportamiento de los edificios al ocurrir un evento sísmico. Our country is located in a zone of high seismic activity, it is important that civil engineers have adequate capacity for analysis of seismic vulnerability and understand the importance of prevention. In this thesis we analyze the different scenarios of seismic damage to reinforced concrete buildings organized by type of number of floors. For the development of this research we use two software, the first seismic simulation software, which model the sample of reinforced concrete buildings representing Santiago de Surco, and then scan it with an earthquake simulating various weather history amplified earthquake magnitudes thereof with ground accelerations ranging from 1.0g 0.1ga, in order to obtain increasing displacement, and thus higher levels of damage by HAZUS MH methodology. Then these values express them in boxes seismic fragility curves with four types of damage levels. The second software we use is the Geographic Information System (GIS), with this program systematize data obtained fragility curve to display maps for different levels of damage scenarios showing the seismic performance of buildings to a seismic event occur.

L'augmentation des données dans divers domaines présente un besoin essentiel de techniques avancées pour fusionner et interpréter ces informations. Avec une emphase particulière sur la compilation de données géospatiales, cette intégration est cruciale pour débloquer de nouvelles perspectives à partir des données géographiques, améliorant notre capacité à cartographier et analyser les tendances qui s'étendent à travers différents lieux et environnements avec plus d'authenticité et de fiabilité. Les techniques existantes ont progressé dans l'adresse de la fusion des données ; cependant, des défis persistent dans la fusion et l'harmonisation des données de différentes sources, échelles et modalités. Cette recherche présente une enquête complète sur les défis et les solutions dans l'alignement et la fusion des cartes vectorielles, se concentrant sur le développement de méthodes qui améliorent la précision et l'utilisabilité des données géospatiales. Nous avons exploré et développé trois méthodologies distinctes pour l'alignement des cartes vectorielles polygonales : ProximityAlign, qui excelle en précision dans les agencements urbains; l'Alignement Basé sur l'Apprentissage Profond du Flux Optique, remarquable pour son efficacité ; et l'Alignement Basé sur la Géométrie Épipolaire, efficace dans les contextes riches en données. De plus, notre étude s'est penchée sur l'alignement des cartes de géometries linéaires, soulignant l'importance d'un alignement précis et du transfert d'attributs des éléments, pointant vers le développement de bases de données géospatiales plus riches et plus informatives en adaptant l'approche ProximityAlign pour des géometries linéaires telles que les traces de failles et les réseaux routiers. L'aspect fusion de notre recherche a introduit un pipeline sophistiqué pour fusionner des géométries polygonales en se basant sur le partitionnement d'espace, l'optimisation non convexe de la structure de données de graphes et les opérations géométriques pour produire une carte fusionnée fiable qui harmonise les cartes vectorielles en entrée, en maintenant leur intégrité géométrique et topologique. En pratique, le cadre développé a le potentiel d'améliorer la qualité et l'utilisabilité des données géospatiales intégrées, bénéficiant à diverses applications telles que la planification urbaine, la surveillance environnementale et la gestion des catastrophes. Cette étude avance non seulement la compréhension théorique dans le domaine mais fournit également une base solide pour des applications pratiques dans la gestion et l'interprétation de grands ensembles de données géospatiales
The surge in data across diverse fields presents an essential need for advanced techniques to merge and interpret this information. With a special emphasis on compiling geospatial data, this integration is crucial for unlocking new insights from geographic data, enhancing our ability to map and analyze trends that span across different locations and environments with more authenticity and reliability. Existing techniques have made progress in addressing data fusion; however, challenges persist in fusing and harmonizing data from different sources, scales, and modalities.This research presents a comprehensive investigation into the challenges and solutions in vector map alignment and fusion, focusing on developing methods that enhance the precision and usability of geospatial data. We explored and developed three distinct methodologies for polygonal vector map alignment: ProximityAlign, which excels in precision within urban layouts but faces computational challenges; the Optical Flow Deep Learning-Based Alignment, noted for its efficiency and adaptability; and the Epipolar Geometry-Based Alignment, effective in data-rich contexts but sensitive to data quality. Additionally, our study delved into linear feature map alignment, emphasizing the importance of precise alignment and feature attribute transfer, pointing towards the development of richer, more informative geospatial databases by adapting the ProximityAlign approach for linear features like fault traces and road networks. The fusion aspect of our research introduced a sophisticated pipeline to merge polygonal geometries relying on space partitioning, non-convex optimization of graph data structure, and geometrical operations to produce a reliable fused map that harmonizes input vector maps, maintaining their geometric and topological integrity.In practice, the developed framework has the potential to improve the quality and usability of integrated geospatial data, benefiting various applications such as urban planning, environmental monitoring, and disaster management. This study not only advances theoretical understanding in the field but also provides a solid foundation for practical applications in managing and interpreting large-scale geospatial datasets

El contexto actual es el de una crisis en la Unión Europea, especialmente en los países del sur. Para acabar con la crisis, la Unión Europea elaboró la estrategia Horizon2020 que se centra en la innovación para abordar los desafíos socio-económicos. La innovación tiene un impacto en la sociedad y por tanto también sobre el territorio en el que habitan las personas que forman dicha sociedad. El empleo de la perspectiva geoespacial permite llevar a cabo un tipo de análisis que es raramente utilizado por los expertos en Educación Superior que suelen centrarse en metodologías puramente estadísticas. El objetivo principal de esta tesis es el desarrollo de un modelo que utiliza los Sistemas de Información Geográfica para evaluar la innovación desde el punto de vista de las universidades y el territorio. Además, a través de la aplicación de ese modelo para el caso específico de la innovación producida por las universidades españolas que forman parte de la Conferencia de Rectores de las Universidades Españolas en el año 2015, un segundo objetivo es identificar patrones y tendencias que puedan revelar nueva información. Los resultados muestran dichos patrones así como la posibilidad de mejorar los métodos actuales de evaluación de la innovación. El modelo presentado en esta tesis provee una solución para entender mejor las redes de innovación y su efecto en el territorio. En el caso específico estudiado, la evaluación provee una solución que es fácil de entender para las personas responsables de la toma de decisiones en cada una de las provincias españolas. Estas personas pueden identificar qué tipo de instituciones promueven la innovación y dónde se encuentran, qué conexiones tienen y cómo mejorar los resultados de innovación a través de la identificación de socios significativos. Además, la financiación de investigación e innovación podría dirigirse mejor hacia aquellas instituciones con mayor potencial de innovación.
The current context is one of crisis in the European area, especially in the southern countries. To put an end to the crisis, the European Union elaborated the Horizon 2020 strategy focusing on innovation to tackle the socio-economic challenges. Innovation has an impact on society and, as a result on the territory inhabited by the people forming such society. Employing a geospatial perspective allows performing a type of analysis that is seldom approached by higher education experts, who tend to focus on purely statistical methodologies. The main goal of this dissertation is the development of a model that uses GIS for innovation assessment from the perspective of universities and the territory. Then, through application of the model for the case of the assessment of innovation produced by the Spanish universities listed in CRUE in the year 2015, the secondary goal is to identify patterns that may unearth new significant information. The results show spatial patterns and the potential of improving the current methods of assessing innovation. The model presented in this thesis does provide an innovative solution to better understand innovation networks and their effect on the territory. In the specific case studied, the assessment provides an easy-to-understand solution for decision makers in the different provinces. The decision makers can then identify what type of institutions are driving innovation and where they are located, what connections they have and how to improve their innovation results through the identification of significant partners. Furthermore, the funding for research and innovation could be better directed towards those institutions with the higher potential.
El context actual és el d'una crisi a la Unió Europea, especialment als països del sud. Per acabar amb la crisi, la Unió Europea va elaborar l'estratègia Horizon2020 que es centra en la innovació per afrontar els desafiaments socio-econòmics. La innovació té un impacte en la societat i per tant també sobre el territori en el que habiten les persones que formen eixa societat. L'ús de la perspectiva geoespacial permet realitzar un tipus d'anàlisi que és rarament utilitzat pels experts en Educació Superior que solen centrar-se en metodologies purament estadístiques. L'objectiu principal d'aquesta tesi és el desenvolupament d'un model que utilitza els Sistemes d'Informació Geogràfica per avaluar la innovació des del punt de vista de les universitats i el territori. A més, a través de l'aplicació d'aquest model per al cas específic de la innovació produïda per les universitats espanyoles que formen part de la Conferència de Rectors de les Universitats Espanyoles durant l'any 2015, un segon objectiu és identificar patrons i tendències que puguen mostrar nova informació. Els resultats mostren aquests patrons així com la possibilitat de millorar els mètodes actuals de l'avaluació de la innovació. El model presentat en aquesta tesi proveeix una solució per comprendre millor les xarxes d'innovació i el seu efecte sobre el territori. Al cas específic estudiat, l'avaluació ofereix una solució que és fàcil de comprendre per a les persones responsables de la presa de decisions en cadascuna de les províncies espanyoles. Aquestes persones poden identificar quins tipus d'institucions promouen la innovació i on es troben, quines connexions tenen i com millorar els resultats d'innovació a través de la identificació de socis significatius. A més, el finançament d'investigació podria dirigir-se millor cap aquelles institucions amb major potencial d'innovació.
Fombuena Valero, A. (2018). Geospatial Social Network lnnovation Assessment of the Spanish Higher Education [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/107943
TESIS

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica
The rapidly increasing volume of digital geographic data is overwhelming for conventional analysis techniques and methods. Therefore new approaches are needed to transform data into information, and ultimately, into knowledge. Exploratory data analysis is a foundation stone in this process. It is concerned with the formation of a simplified overview of data sets. Clustering and projection are among the examples of useful methods to achieve this task. The Self-Organizing Map (SOM) algorithm performs both, in a non-linear mapping from a high-dimensional data space to a low-dimensional space aiming to preserve the topological relations in the data. The aim of this thesis is to demonstrate the effectiveness of SOM application in visual exploration of physical geography data to support the delineation of Portuguese mainland regions. The main justifications for the application of SOM in this issue are its features of stressing local factors and topological ordering. For experimental assessment, the public domain thematic maps from Instituto do Ambiente are used. Several authors’ maps of Portuguese regions are used for evaluation of empirical results.(...)

碩士
國立中興大學
水土保持學系所
101
Taiwan is an island located at the junction of the Eurasia Plate and the Philippine Sea plate, which causes the geology fragility and multi-faults due to plate collision. In addition, Taiwan encounters several debris and flooding disasters during heavy rain in typhoon seasons annually due to the Western Pacific Ocean monsoon climate. Moreover, 73% of the lands are slope lands and Alpine woodlands, most of the population lived in plains, which occupied 25% of Taiwan and developed into dense urban areas of industry and commerce often. Extreme rainfall patterns and earthquakes occur in recent years make geology conditions into an unstable state. This consequence leads to a higher frequency of various disasters such as flood, drought, land slide and complex disasters. To deal with this situation, the government formulates several kinds of disaster prevention maps and implements necessary response. The purpose of this study is to delineate the disaster prevention map of Taichung city via Geospatial PDF and to compare with the existing one as practicality and feasibility, escape routes planning, feasibility analysis of the disaster prevention refuge and the training of disaster prevention education to discuss the feasibility, advantages and disadvantages of these maps. Recommends to the improvements of disaster prevention map will be put forward then. The disaster prevention map developed in this study with the characteristics of manipulating disaster information instantly, which is helpful to strengthen the disaster prevention ability.

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification, and high precipitation in parts of the south west and south east leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency and amount of rainfall over Nigeria. A type of Artificial Neural Network called Self Organizing Map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties for each cluster are discussed. The cluster spatially closest to the Atlantic has high values of precipitation intensity, frequency and duration, whereas the cluster spatially closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days in the northern region of Nigeria.

abstract: A volunteered geographic information system, e.g., OpenStreetMap (OSM), collects data from volunteers to generate geospatial maps. To keep the map consistent, volunteers are expected to perform the tedious task of updating the underlying geospatial data at regular intervals. Such a map curation step takes time and considerable human effort. In this thesis, we propose a framework that improves the process of updating geospatial maps by automatically identifying road changes from user-generated GPS traces. Since GPS traces can be sparse and noisy, the proposed framework validates the map changes with the users before propagating them to a publishable version of the map. The proposed framework achieves up to four times faster map matching performance than the state-of-the-art algorithms with only 0.1-0.3% accuracy loss.
Dissertation/Thesis
Masters Thesis Computer Science 2017

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information Systems
The size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...)

The aim of this work is to look for freely available map servers and web map services on the Web that are useful for surveying activities, including developement of land adjustments. This thesis deals with theoretical problems of the functioning of map servers, web map services and standards for creating maps. The most famous map servers are described in detail and then compared with the less popular mapservers in the next section. The conclusion then relates to the use maps and assess the suitability of map servers for geodetic purposes and desin of land adjustments. This work aims to determine which map servers are most useful for geodetic purposes.

This thesis describes the on-road analysis of criteria air pollutants (CAPs) and volatile organic compounds (VOCs) from a moving vehicle. CAPs and VOCs have numerous direct and indirect effects on the environment and public health and are generated from a variety of point and diffuse sources. The concentration of these pollutants can vary on the scale of metres and seconds due to variable emission rates of sources, meteorology, and the topography of an area. CAPs are conventionally measured on a spatial scale of tens of kilometres and one hour or longer time resolution, which limits the understanding of their impact and leaving many communities lacking information regarding their air quality. VOCs are not measured as frequently as CAPs, owing to the difficulty, challenges, and cost associated with sampling. The Mobile Mass Spectrometry Lab (MMSL) was developed to collect high geospatial (15 – 1,500 m) and temporal (1 – 10 s) resolution measurements of CAPs (O3, NOx, PM2.5), CO2, CH4, and VOCs. CAPs and greenhouse gases were monitored using standard analyzers, while VOCs were measured using a proton-transfer reaction time-of-flight mass spectrometer (PTR-MS). PTR-MS is a real-time, direct, in situ technique that can monitor VOCs in the ambient atmosphere without sample collection. The PTR-MS monitored up to mass-to-charge 330 with a sample integration time of 1 or 10 seconds and had detection limits into the low- to mid-ppt. PTR-MS is a soft ionization technique that is selective to all compounds with a proton affinity less than water, which excludes the atmospheric matrix and includes most VOCs. The measurements provided by the PTR-MS provided a rich dataset for which to develop workflow and processing methods alongside sampling strategies for the collection of high geospatial and temporal VOC data. The first on-road deployment of the MMSL was performed across the Regional District of Nanaimo and the Alberni-Clayoquot Regional District in British Columbia, Canada, from July iv 2018 – April 2019 to monitor the geospatial and temporal variation in the concentration of CAPs and VOCs. VOCs detected in the areas include hydrocarbons like toluene, C2-benzenes, and terpenes, organic acids like acetic acid, oxygenated compounds like acetone and acetaldehyde, and reduced sulfur compounds like methanethiol and dimethyl sulfide. While observed concentrations of VOCs were mostly below detection limits, concentration excursions upwards of 2,200 ppb for C2-benzenes (reported as ethylbenzene) for instance, were observed across the various communities and industries that comprise central Vancouver Island. VOCs like monoterpenes, were observed near the wood industries up to 229 ppb. Combustion related VOCs, like toluene and C2-benzenes, were often observed on major transportation corridors and was found to vary significantly between seasons, with winter measurements often exceeding those made in the summer. Reduced sulfur compounds, common components of nuisance odours, were measured around a few industries like waste management and wood industries. The second on-road deployment of the MMSL focused on the analysis of VOCs in the community around a wastewater treatment plant (WWTP) to identify the source of odours in the area. VOCs were also monitored in the odour control process of the WWTP to identify the VOCs being emitted, how much were emitted, and where potential deficiencies were in the process in a unique study. Median emission rates at the facility for methanethiol, dimethyl sulfide, and dimethyl disulfide were determined to be 100, 19, and 21 kg yr-1, respectively. VOC monitoring in the community encompassed the WWTP and the other major industries in the area, including agricultural land, a composting facility, and a marina. The highest measurements of odorous reduced sulfur compounds were observed around the WWTP, upwards of 36 ppb for methanethiol. Unsupervised multivariate analysis was performed to identify groups of VOCs present and their potential sources. Three groups were identified, one of which was related to reduced sulfur compounds. This group was observed around the WWTP, indicating that the WWTP was the likely source of malodours in the community.
Graduate

Anthropogenic emissions into the troposphere can impact air quality, leading to poorer health outcomes in the affected areas. Volatile organic compounds (VOCs) are a group of chemical compounds, including some which are toxic, that are precursors in the formation of ground-level ozone and secondary organic aerosols. VOCs have a variety of sources, and the distribution of atmospheric VOCs differs significantly over time and space. Historically, the large number of chemical species present at low concentrations (parts-per-trillion to parts-per-billion by volume) have made VOCs difficult to measure in ambient air. However, with improvements in analytical instrumentation, these measurements are becoming more common place. Direct mass spectrometry (MS), such as membrane introduction mass spectrometry (MIMS) and proton-transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) facilitate real-time, continuous measurements of VOCs in air, with full scan mass spectral data capturing changes in chemical composition with high temporal resolution. Operated on-road, mobilized direct MS has been used for quantitative mapping of VOCs at the neighborhood scale, but identifying VOC sources based on the observed mixture of molecules in the full scan MS dataset has yet to be explored. This dissertation describes the use of chemometric techniques to interrogate full scan MS data, and the progression from discriminating VOC samples of known chemical composition based on full scan MIMS data through to the apportionment of VOC sources measured continuously with a PTR-ToF-MS system operating in a moving vehicle. Lab‐constructed VOC samples of known chemical composition and concentration demonstrated the use of principal component analysis (PCA) to discriminate, and k-nearest neighbours to classify, samples based on normalized full scan MIMS data. Furthermore, multivariate curve resolution-alternating least squares (MCR-ALS) was used to resolve mixtures into molecular component contributions. PCA was also used to discriminate ‘real-world’ VOC mixtures (e.g., woodsmoke VOCs, headspace above aqueous hydrocarbon samples) of unknown chemical composition measured by MIMS. Using vehicle mounted MIMS and PTR-ToF-MS systems, full scan MS data of ambient atmospheric VOCs were collected and PCA was applied to the normalized full scan MS data. A supervised analysis performed PCA on samples collected near known VOC sources, while an unsupervised analysis using PCA followed by cluster analysis was used to identify groups in a continuous, time series PTR-ToF-MS dataset measured between Nanaimo and Crofton, British Columbia (BC). In both the supervised and unsupervised analysis, samples impacted by emissions from different sources (e.g., internal combustion engines, sawmills, composting facilities, pulp mills) were discriminated. With PCA, samples were discriminated based on differences in the observed full scan MS data, however real-world samples are often impacted by multiple VOC sources. MCR-weighted ALS (MCR-WALS) was applied to the continuous, time series PTR-ToF-MS data from three field campaigns on Vancouver Island, BC for source apportionment. Variable selection based on signal-to-noise ratios was used to reduce the mass list while retaining the observed m/z that capture changes in the mixture of VOCs measured, improving model results, and reducing computation time. Both point (e.g., anthropogenic hydrocarbon emissions, pulp mill emissions) and diffuse (e.g., VOCs from forest fire smoke) VOC sources were identified in the data, and were apportioned to determine their contributions to the measured samples. The data analyzed captured fine scale changes in the ambient VOCs present in the air, and geospatial maps of each individual source, and of the source apportionment were used to visualize the distribution of VOC sources across the sampling area. This work represents the first use of MCR-WALS to identify and apportion ambient VOC sources based on continuous PTR-ToF-MS data measured from a moving vehicle. The methods described can be applied to larger scale field campaigns for the source apportionment of VOCs across multiple days to capture diurnal and seasonal variations. Identifying spatial and temporal trends in the sources of VOCs at the regional scale can help to identify pollution ‘hot spots’ and inform evidence-based public policy for improving air quality.
Graduate
2022-08-17