Matching Items (98)
Description
Infrastructure systems are facing non-stationary challenges that stem from climate change and the increasingly complex interactions between the social, ecological, and technological systems (SETSs). It is crucial for transportation infrastructures—which enable residents to access opportunities and foster prosperity, quality of life, and social connections—to be resilient under these non-stationary challenges. Vulnerability assessment (VA) examines the potential consequences a system is likely to experience due to exposure to perturbation or stressors and lack of the capacity to adapt. Post-fire debris flow and heat represent particularly challenging problems for infrastructure and users in the arid U.S. West. Post-fire debris flow, which is manifested with heat and drought, produces powerful runoff threatening physical transportation infrastructures. And heat waves have devastating health effects on transportation infrastructure users, including increased mortality rates. VA anticipates the potential consequences of these perturbations and enables infrastructure stakeholders to improve the system's resilience. The current transportation climate VA—which only considers a single direct climate stressor on the infrastructure—falls short of addressing the wildfire and heat challenges. This work proposes advanced transportation climate VA methods to address the complex and multiple climate stressors and the vulnerability of infrastructure users. Two specific regions were chosen to carry out the progressive transportation climate VA: 1) the California transportation networks’ vulnerability to post-fire debris flows, and 2) the transportation infrastructure user’s vulnerability to heat exposure in Phoenix.
ContributorsLi, Rui (Author) / Chester, Mikhail V. (Thesis advisor) / Middel, Ariane (Committee member) / Hondula, David M. (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2022
Description
This work presents a thorough analysis of reconstruction of global wave fields (governed by the inhomogeneous wave equation and the Maxwell vector wave equation) from sensor time series data of the wave field. Three major problems are considered. First, an analysis of circumstances under which wave fields can be fully reconstructed from a network of fixed-location sensors is presented. It is proven that, in many cases, wave fields can be fully reconstructed from a single sensor, but that such reconstructions can be sensitive to small perturbations in sensor placement. Generally, multiple sensors are necessary. The next problem considered is how to obtain a global approximation of an electromagnetic wave field in the presence of an amplifying noisy current density from sensor time series data. This type of noise, described in terms of a cylindrical Wiener process, creates a nonequilibrium system, derived from Maxwell’s equations, where variance increases with time. In this noisy system, longer observation times do not generally provide more accurate estimates of the field coefficients. The mean squared error of the estimates can be decomposed into a sum of the squared bias and the variance. As the observation time $\tau$ increases, the bias decreases as $\mathcal{O}(1/\tau)$ but the variance increases as $\mathcal{O}(\tau)$. The contrasting time scales imply the existence of an ``optimal'' observing time (the bias-variance tradeoff). An iterative algorithm is developed to construct global approximations of the electric field using the optimal observing times. Lastly, the effect of sensor acceleration is considered. When the sensor location is fixed, measurements of wave fields composed of plane waves are almost periodic and so can be written in terms of a standard Fourier basis. When the sensor is accelerating, the resulting time series is no longer almost periodic. This phenomenon is related to the Doppler effect, where a time transformation must be performed to obtain the frequency and amplitude information from the time series data. To obtain frequency and amplitude information from accelerating sensor time series data in a general inhomogeneous medium, a randomized algorithm is presented. The algorithm is analyzed and example wave fields are reconstructed.
ContributorsBarclay, Bryce Matthew (Author) / Mahalov, Alex (Thesis advisor) / Kostelich, Eric J (Thesis advisor) / Moustaoui, Mohamed (Committee member) / Motsch, Sebastien (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2023
Description
Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.
ContributorsSwarup, Shray (Author) / Eikenberry, Steffen (Thesis director) / Mahalov, Alex (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
There is an estimated five trillion pieces of plastic in the global ocean, with 4.8 to 12.7 million metric tons entering the ocean annually. Much of the plastic in the ocean is in the form of microplastics, or plastic particles <5mm in size. Microplastics enter the marine environment as primary or secondary microplastics; primary microplastics are pre-manufactured micro-sized particles, such as microbeads used in cosmetics, while secondary microplastics form from the degradation of larger plastic objects, such water bottles. Once in the ocean, plastics are readily colonized by a consortium of prokaryotic and eukaryotic organisms, which form dense biofilms on the plastic; this biofilm is termed the “plastisphere”. Despite growing concerns about the ecological impact of microplastics and their respective plastispheres on the marine environment, there is little consensus about the factors that shape the plastisphere on environmentally relevant secondary microplastics. The goal of my dissertation is to comprehensively analyze the role of plastic polymer type, incubation time, and geographic location on shaping plastisphere communities attached to secondary microplastics. I investigated the plastisphere of six chemically distinct plastic polymer types obtained from common household consumer products that were incubated in the coastal Caribbean (Bocas del Toro, Panama) and coastal Pacific (San Diego, CA) oceans. Genotyping using 16S and 18S rRNA gene amplification and next-generation Illumina sequencing was employed to identify bacterial and eukaryotic communities on the polymer surfaces. Statistical analyses show that there were no polymer-specific assemblages for prokaryotes or eukaryotes, but rather a microbial core community that was shared among plastic types. I also found that rare hydrocarbon degrading bacteria may be specific to certain chemical properties of the microplastics. Statistical comparisons of the communities across both sites showed that prokaryotic plastispheres were shaped primarily by incubation time and geographic location. Finally, I assessed the impact of biofilms on microplastic degradation and deposition and conclude that biofilms enhance microplastic sinking of negatively buoyant particles and reduce microplastic degradation. The results of my dissertation increases understanding of the factors that shape the plastisphere and how these communities ultimately determine the fate of microplastics in the marine environment.
ContributorsDudek, Kassandra Lynn (Author) / Neuer, Susanne (Thesis advisor) / Polidoro, Beth (Committee member) / Garcia-Pichel, Ferran (Committee member) / Cao, Huansheng (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the development and successful landing of the NASA Perseverance rover, there has been growing interest in identifying how evidence of ancient life may be preserved and recognized in the geologic record. Environments that enable fossilization of biological remains are termed, “taphonomic windows”, wherein signatures of past life may be detected. In this dissertation, I have sought to identify taphonomic windows in planetary-analog environments with an eye towards the exploration of Mars. In the first chapter, I describe how evidence of past microbial life may be preserved within serpentinizing systems. Owing to energetic rock-water reactions, these systems are known to host lithotrophic and organotrophic microbial communities. By investigating drill cores from the Samail Ophiolite in Oman, I report morphological and associated chemical biosignatures preserved in these systems as a result of subsurface carbonation. As serpentinites are known to occur on Mars and potentially other planetary bodies, these deposits potentially represent high-priority targets in the exploration for past microbial life. Next, I investigated samples from Atacama Desert, Chile, to understand how evidence of life may be preserved in ancient sediments formed originally in evaporative playa lakes. Here, I describe organic geochemical and morphological evidence of life preserved within sulfate-dominated evaporite rocks from the Jurassic-Cretaceous Tonel Formation and Oligocene San Pedro Formation. Because evaporative lakes are considered to have been potentially widespread on Mars, these deposits may represent additional key targets to search for evidence of past life. In the final chapter, I describe the fossilization potential of surficial carbonates by investigating Crystal Geyser, an active cold spring environment. Here, carbonate minerals precipitate rapidly in the presence of photosynthetic microbial mat communities. I describe how potential biosignatures are initially captured by mineralization, including cell-like structures and microdigitate stromatolites. However, these morphological signatures quickly degrade owing to diagenetic dissolution and recrystallization reactions, as well as textural coarsening that homogenizes the carbonate fabric. Overall, my dissertation underscores the complexity of microbial fossilization and highlights chemically-precipitating environments that may serve as high-priority targets for astrobiological exploration.
ContributorsZaloumis, Jonathan (Author) / Farmer, Jack D (Thesis advisor) / Garcia-Pichel, Ferran (Committee member) / Trembath-Reichert, Elizabeth (Committee member) / Ruff, Steven W (Committee member) / Shock, Everett L (Committee member) / Arizona State University (Publisher)
Created2021
Description
Machine learning (ML) and deep learning (DL) has become an intrinsic part of multiple fields. The ability to solve complex problems makes machine learning a panacea. In the last few years, there has been an explosion of data generation, which has greatly improvised machine learning models. But this comes with a cost of high computation, which invariably increases power usage and cost of the hardware. In this thesis we explore applications of ML techniques, applied to two completely different fields - arts, media and theater and urban climate research using low-cost and low-powered edge devices. The multi-modal chatbot uses different machine learning techniques: natural language processing (NLP) and computer vision (CV) to understand inputs of the user and accordingly perform in the play and interact with the audience. This system is also equipped with other interactive hardware setups like movable LED systems, together they provide an experiential theatrical play tailored to each user. I will discuss how I used edge devices to achieve this AI system which has created a new genre in theatrical play. I will then discuss MaRTiny, which is an AI-based bio-meteorological system that calculates mean radiant temperature (MRT), which is an important parameter for urban climate research. It is also equipped with a vision system that performs different machine learning tasks like pedestrian and shade detection. The entire system costs around $200 which can potentially replace the existing setup worth $20,000. I will further discuss how I overcame the inaccuracies in MRT value caused by the system, using machine learning methods. These projects although belonging to two very different fields, are implemented using edge devices and use similar ML techniques. In this thesis I will detail out different techniques that are shared between these two projects and how they can be used in several other applications using edge devices.
ContributorsKulkarni, Karthik Kashinath (Author) / Jayasuriya, Suren (Thesis advisor) / Middel, Ariane (Thesis advisor) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Cities globally are experiencing substantial warming due to ongoing urbanization and climate change. However, existing efforts to mitigate urban heat focus mainly on new technologies, exacerbate social injustices, and ignore the need for a sustainability lens that considers environmental, social, and economic perspectives. Heat in urban areas is amplified and urgently needs to be considered as a critical sustainability issue that crosses disciplinary and sectoral (traditional) boundaries. The missing urgency is concerning because urban overheating is a multi-faceted threat to the well-being and performance of individuals as well as the energy efficiency and economy of cities. Urban heat consequences require transformation in ways of thinking by involving the best available knowledge engaging scientists, policymakers, and communities. To do so, effective heat mitigation planning requires a considerable amount of diverse knowledge sources, yet urban planners face multiple barriers to effective heat mitigation, including a lack of usable, policy-relevant science and governance structures. To address these issues, transdisciplinary approaches, such as co-production via partnerships and the creation of usable, policy-relevant science, are necessary to allow for sustainable and equitable heat mitigation that allow cities to work toward multiple Sustainable Development Goals (SDGs) using a systems approach. This dissertation presents three studies that contribute to a sustainability lens on urban heat, improve the holistic and multi-perspective understanding of heat mitigation strategies, provide contextual guidance for reflective pavement as a heat mitigation strategy, and evaluate a multilateral, sustainability-oriented, co-production partnership to foster heat resilience equitably in cities. Results show that science and city practice communicate differently about heat mitigation strategies while both avoid to communicate disservices and trade-offs. Additionally, performance evaluation of heat mitigation strategies for decision-making needs to consider multiple heat metrics, people, and background climate. Lastly, the partnership between science, city practice, and community needs to be evaluated to be accountable and provide a pathway of growth for all partners. The outcomes of this dissertation advance research and awareness of urban heat for science, practice, and community, and provide guidance to improve holistic and sustainable decision-making in cities and partnerships to address SDGs around urban heat.
ContributorsSchneider, Florian Arwed (Author) / Middel, Ariane (Thesis advisor) / Vanos, Jennifer K (Committee member) / Withycombe Keeler, Lauren (Committee member) / Arizona State University (Publisher)
Created2023
Description
Extreme heat and its human impacts are significant public health challenges that disproportionately affect certain populations. Often, people with the least resources to cope with the heat also live in the hottest regions of cities. Previous heat vulnerability research has predominantly been conducted at a coarse geographic scale, yet translating relationships measured at aggregated scales to the individual level can result in ecological fallacy. Prior work has also primarily studied the most severe health outcomes: hospitalization/emergency care and mortality. It is likely that magnitudes more people are experiencing negative health impacts from heat that do not necessarily result in medical care. Such less severe impacts are under-researched in the literature.This dissertation addresses these knowledge gaps by identifying how social characteristics and physical measurements of heat at the individual and household level act independently and in concert to influence human heat-related outcomes, especially less severe outcomes. In the first paper, meta-analysis was used to quantify the summary effects of vulnerability indicators on incidence of heat-related illness. More proximal vulnerability indicators (e.g., residential air conditioning use, indoor heat exposure, etc.) tended to have the strongest impact on odds of experiencing heat-related illness than more distal indicators. In the next paper, indoor air temperature observations were related to the social characteristics of the residents. The strongest predictor of indoor air temperature was the residents’ ideal thermally comfortable temperature, despite affordability. In the final paper, fine scale biometeorological observations of the outdoor thermal environment near residents’ homes were linked to their experience with heat-related illness. The outdoor thermal environment appeared to have a stronger, more consistent impact on heat-related illness among households in a lower income neighborhood compared to a higher income one. These findings affirm the value of employing residential heat mitigation solutions at the individual and household scale, indoors and outdoors. Across all chapters, the indoor thermal environment, and the ability to modify it, had a clear impact on residents’ comfort and health. Solutions that target the most proximal causal factors of heat-related illness will likely have the greatest impact on reducing the burden of heat on human health and well-being.
ContributorsWright, Mary K (Author) / Hondula, David M (Thesis advisor) / Larson, Kelli L (Committee member) / Middel, Ariane (Committee member) / Arizona State University (Publisher)
Created2023
Description
Adaptive capacity to climate change is the ability of a system to mitigate or take advantage of climate change effects. Research on adaptive capacity to climate change suffers fragmentation. This is partly because there is no clear consensus around precise definitions of adaptive capacity. The aim of this thesis is to place definitions of adaptive capacity into a formal framework. I formalize adaptive capacity as a computational model written in the Idris 2 programming language. The model uses types to constrain how the elements of the model fit together. To achieve this, I analyze nine existing definitions of adaptive capacity. The focus of the analysis was on important factors that affect definitions and shared elements of the definitions. The model is able to describe an adaptive capacity study and guide a user toward concepts lacking clarity in the study. This shows that the model is useful as a tool to think about adaptive capacity. In the future, one could refine the model by forming an ontology for adaptive capacity. One could also review the literature more systematically. Finally, one might consider turning to qualitative research methods for reviewing the literature.
ContributorsManuel, Jason (Author) / Bazzi, Rida (Thesis director) / Pavlic, Theodore (Committee member) / Middel, Ariane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
With the increase in the severity of drought conditions in the Southwest region of the U.S. paired with rising temperatures, it is becoming increasingly important to look at the systems used to keep people cool in hot-arid cities like Tempe, Arizona. Outdoor misting systems are often deployed by businesses. These systems rely on the evaporative cooling effect of water. This study examines the relationship between misting droplet size, water usage, and thermal comfort using low-pressure misting systems, tested within hot and dry conditions representative of the arid U.S. southwest. A model misting system using three nozzle orifice sizes was set up in a controlled heat chamber environment (starting baseline conditions of 40°C air temperature and 15 % relative humidity). Droplet size was measured using water-reactive paper, while water use was determined based on weight-change measurements. These measurements were paired with temperature and humidity measurements observed in several locations around the chamber to allow for a spatial analysis. Thermal comfort is determined based on psychrometric changes (temperature and absolute humidity) within the room. On average, air temperatures decreased between 2 to 4°C depending on nozzle size and sensor location. The 0.4 mm nozzle had a decent spread across the heat chamber and balanced water usage and effectiveness well. Limitations within the study showed ventilation is important for an effective system, corroborating other studies findings and suggesting that adding air circulation could improve evaporation and comfort and thus effectiveness. Finally, visual cues, such as wetted surfaces, can signal businesses to change nozzle sizes and/or make additional modifications to the system area.
ContributorsJohnson, Trevor (Author) / Vanos, Jennifer (Thesis director) / Middel, Ariane (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2023-05