Matching Items (45)
Description
Researchers across a variety of fields are often interested in determining if data are of a random nature or if they exhibit patterning which may be the result of some alternative and potentially more interesting process. This dissertation explores a family of statistical methods, i.e. space-time interaction tests, designed to detect structure within three-dimensional event data. These tests, widely employed in the fields of spatial epidemiology, criminology, ecology and beyond, are used to identify synergistic interaction across the spatial and temporal dimensions of a series of events. Exploration is needed to better understand these methods and determine how their results may be affected by data quality problems commonly encountered in their implementation; specifically, how inaccuracy and/or uncertainty in the input data analyzed by the methods may impact subsequent results. Additionally, known shortcomings of the methods must be ameliorated. The contributions of this dissertation are twofold: it develops a more complete understanding of how input data quality problems impact the results of a number of global and local tests of space-time interaction and it formulates an improved version of one global test which accounts for the previously identified problem of population shift bias. A series of simulation experiments reveal the global tests of space-time interaction explored here to be dramatically affected by the aforementioned deficiencies in the quality of the input data. It is shown that in some cases, a conservative degree of these common data problems can completely obscure evidence of space-time interaction and in others create it where it does not exist. Conversely, a local metric of space-time interaction examined here demonstrates a surprising robustness in the face of these same deficiencies. This local metric is revealed to be only minimally affected by the inaccuracies and incompleteness introduced in these experiments. Finally, enhancements to one of the global tests are presented which solve the problem of population shift bias associated with the test and better contextualize and visualize its results, thereby enhancing its utility for practitioners.
ContributorsMalizia, Nicholas (Author) / Anselin, Luc (Thesis advisor) / Murray, Alan (Committee member) / Rey, Sergio (Committee member) / Arizona State University (Publisher)
Created2013
Description
Vehicle type choice is a significant determinant of fuel consumption and energy sustainability; larger, heavier vehicles consume more fuel, and expel twice as many pollutants, than their smaller, lighter counterparts. Over the course of the past few decades, vehicle type choice has seen a vast shift, due to many households making more trips in larger vehicles with lower fuel economy. During the 1990s, SUVs were the fastest growing segment of the automotive industry, comprising 7% of the total light vehicle market in 1990, and 25% in 2005. More recently, due to rising oil prices, greater awareness to environmental sensitivity, the desire to reduce dependence on foreign oil, and the availability of new vehicle technologies, many households are considering the use of newer vehicles with better fuel economy, such as hybrids and electric vehicles, over the use of the SUV or low fuel economy vehicles they may already own. The goal of this research is to examine how vehicle miles traveled, fuel consumption and emissions may be reduced through shifts in vehicle type choice behavior. Using the 2009 National Household Travel Survey data it is possible to develop a model to estimate household travel demand and total fuel consumption. If given a vehicle choice shift scenario, using the model it would be possible to calculate the potential fuel consumption savings that would result from such a shift. In this way, it is possible to estimate fuel consumption reductions that would take place under a wide variety of scenarios.
ContributorsChristian, Keith (Author) / Pendyala, Ram M. (Thesis advisor) / Chester, Mikhail (Committee member) / Kaloush, Kamil (Committee member) / Ahn, Soyoung (Committee member) / Arizona State University (Publisher)
Created2013
Description
The consumption of feedstocks from agriculture and forestry by current biofuel production has raised concerns about food security and land availability. In the meantime, intensive human activities have created a large amount of marginal lands that require management. This study investigated the viability of aligning land management with biofuel production on marginal lands. Biofuel crop production on two types of marginal lands, namely urban vacant lots and abandoned mine lands (AMLs), were assessed. The investigation of biofuel production on urban marginal land was carried out in Pittsburgh between 2008 and 2011, using the sunflower gardens developed by a Pittsburgh non-profit as an example. Results showed that the crops from urban marginal lands were safe for biofuel. The crop yield was 20% of that on agricultural land while the low input agriculture was used in crop cultivation. The energy balance analysis demonstrated that the sunflower gardens could produce a net energy return even at the current low yield. Biofuel production on AML was assessed from experiments conducted in a greenhouse for sunflower, soybean, corn, canola and camelina. The research successfully created an industrial symbiosis by using bauxite as soil amendment to enable plant growth on very acidic mine refuse. Phytoremediation and soil amendments were found to be able to effectively reduce contamination in the AML and its runoff. Results from this research supported that biofuel production on marginal lands could be a unique and feasible option for cultivating biofuel feedstocks.
ContributorsZhao, Xi (Author) / Landis, Amy (Thesis advisor) / Fox, Peter (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2013
Description
Life Cycle Assessment (LCA) quantifies environmental impacts of products in raw material extraction, processing, manufacturing, distribution, use and final disposal. The findings of an LCA can be used to improve industry practices, to aid in product development, and guide public policy. Unfortunately, existing approaches to LCA are unreliable in the cases of emerging technologies, where data is unavailable and rapid technological advances outstrip environmental knowledge. Previous studies have demonstrated several shortcomings to existing practices, including the masking of environmental impacts, the difficulty of selecting appropriate weight sets for multi-stakeholder problems, and difficulties in exploration of variability and uncertainty. In particular, there is an acute need for decision-driven interpretation methods that can guide decision makers towards making balanced, environmentally sound decisions in instances of high uncertainty. We propose the first major methodological innovation in LCA since early establishment of LCA as the analytical perspective of choice in problems of environmental management. We propose to couple stochastic multi-criteria decision analytic tools with existing approaches to inventory building and characterization to create a robust approach to comparative technology assessment in the context of high uncertainty, rapid technological change, and evolving stakeholder values. Namely, this study introduces a novel method known as Stochastic Multi-attribute Analysis for Life Cycle Impact Assessment (SMAA-LCIA) that uses internal normalization by means of outranking and exploration of feasible weight spaces.
ContributorsPrado, Valentina (Author) / Seager, Thomas P (Thesis advisor) / Landis, Amy E. (Committee member) / Chester, Mikhail (Committee member) / White, Philip (Committee member) / Arizona State University (Publisher)
Created2013
Description
The partitioning of available solar energy into different fluxes at the Earth's surface is important in determining different physical processes, such as turbulent transport, subsurface hydrology, land-atmospheric interactions, etc. Direct measurements of these turbulent fluxes were carried out using eddy-covariance (EC) towers. However, the distribution of EC towers is sparse due to relatively high cost and practical difficulties in logistics and deployment. As a result, data is temporally and spatially limited and is inadequate to be used for researches at large scales, such as regional and global climate modeling. Besides field measurements, an alternative way is to estimate turbulent fluxes based on the intrinsic relations between surface energy budget components, largely through thermodynamic equilibrium. These relations, referred as relative efficiency, have been included in several models to estimate the magnitude of turbulent fluxes in surface energy budgets such as latent heat and sensible heat. In this study, three theoretical models based on the lumped heat transfer model, the linear stability analysis and the maximum entropy principle respectively, were investigated. Model predictions of relative efficiencies were compared with turbulent flux data over different land covers, viz. lake, grassland and suburban surfaces. Similar results were observed over lake and suburban surface but significant deviation is found over vegetation surface. The relative efficiency of outgoing longwave radiation is found to be orders of magnitude deviated from theoretic predictions. Meanwhile, results show that energy partitioning process is influenced by the surface water availability to a great extent. The study provides insight into what property is determining energy partitioning process over different land covers and gives suggestion for future models.
ContributorsYang, Jiachuan (Author) / Wang, Zhihua (Thesis advisor) / Huang, Huei-Ping (Committee member) / Vivoni, Enrique (Committee member) / Mays, Larry (Committee member) / Arizona State University (Publisher)
Created2012
Description
A methodology is developed that integrates institutional analysis with Life Cycle Assessment (LCA) to identify and overcome barriers to sustainability transitions and to bridge the gap between environmental practitioners and decisionmakers. LCA results are rarely joined with analyses of the social systems that control or influence decisionmaking and policies. As a result, LCA conclusions generally lack information about who or what controls different parts of the system, where and when the processes' environmental decisionmaking happens, and what aspects of the system (i.e. a policy or regulatory requirement) would have to change to enable lower environmental impact futures. The value of the combined institutional analysis and LCA (the IA-LCA) is demonstrated using a case study of passenger transportation in the Phoenix, Arizona metropolitan area. A retrospective LCA is developed to estimate how roadway investment has enabled personal vehicle travel and its associated energy, environmental, and economic effects. Using regional travel forecasts, a prospective life cycle inventory is developed. Alternative trajectories are modeled to reveal future "savings" from reduced roadway construction and vehicle travel. An institutional analysis matches the LCA results with the specific institutions, players, and policies that should be targeted to enable transitions to these alternative futures. The results show that energy, economic, and environmental benefits from changes in passenger transportation systems are possible, but vary significantly depending on the timing of the interventions. Transition strategies aimed at the most optimistic benefits should include 1) significant land-use planning initiatives at the local and regional level to incentivize transit-oriented development infill and urban densification, 2) changes to state or federal gasoline taxes, 3) enacting a price on carbon, and 4) nearly doubling vehicle fuel efficiency together with greater market penetration of alternative fuel vehicles. This aggressive trajectory could decrease the 2050 energy consumption to 1995 levels, greenhouse gas emissions to 1995, particulate emissions to 2006, and smog-forming emissions to 1972. The potential benefits and costs are both private and public, and the results vary when transition strategies are applied in different spatial and temporal patterns.
ContributorsKimball, Mindy (Author) / Chester, Mikhail (Thesis advisor) / Allenby, Braden (Committee member) / Golub, Aaron (Committee member) / Arizona State University (Publisher)
Created2014
Description
Quantum resilience is a pragmatic theory that allows systems engineers to formally characterize the resilience of systems. As a generalized theory, it not only clarifies resilience in the literature, but also can be applied to all disciplines and domains of discourse. Operationalizing resilience in this manner permits decision-makers to compare and contrast system deployment options for suitability in a variety of environments and allows for consistent treatment of resilience across domains. Systems engineers, whether planning future infrastructures or managing ecosystems, are increasingly asked to deliver resilient systems. Quantum resilience provides a way forward that allows specific resilience requirements to be specified, validated, and verified.
Quantum resilience makes two very important claims. First, resilience cannot be characterized without recognizing both the system and the valued function it provides. Second, resilience is not about disturbances, insults, threats, or perturbations. To avoid crippling infinities, characterization of resilience must be accomplishable without disturbances in mind. In light of this, quantum resilience defines resilience as the extent to which a system delivers its valued functions, and characterizes resilience as a function of system productivity and complexity. System productivity vis-à-vis specified “valued functions” involves (1) the quanta of the valued function delivered, and (2) the number of systems (within the greater system) which deliver it. System complexity is defined structurally and relationally and is a function of a variety of items including (1) system-of-systems hierarchical decomposition, (2) interfaces and connections between systems, and (3) inter-system dependencies.
Among the important features of quantum resilience is that it can be implemented in any system engineering tool that provides sufficient design and specification rigor (i.e., one that supports standards like the Lifecycle and Systems Modeling languages and frameworks like the DoD Architecture Framework). Further, this can be accomplished with minimal software development and has been demonstrated in three model-based system engineering tools, two of which are commercially available, well-respected, and widely used. This pragmatic approach assures transparency and consistency in characterization of resilience in any discipline.
Quantum resilience makes two very important claims. First, resilience cannot be characterized without recognizing both the system and the valued function it provides. Second, resilience is not about disturbances, insults, threats, or perturbations. To avoid crippling infinities, characterization of resilience must be accomplishable without disturbances in mind. In light of this, quantum resilience defines resilience as the extent to which a system delivers its valued functions, and characterizes resilience as a function of system productivity and complexity. System productivity vis-à-vis specified “valued functions” involves (1) the quanta of the valued function delivered, and (2) the number of systems (within the greater system) which deliver it. System complexity is defined structurally and relationally and is a function of a variety of items including (1) system-of-systems hierarchical decomposition, (2) interfaces and connections between systems, and (3) inter-system dependencies.
Among the important features of quantum resilience is that it can be implemented in any system engineering tool that provides sufficient design and specification rigor (i.e., one that supports standards like the Lifecycle and Systems Modeling languages and frameworks like the DoD Architecture Framework). Further, this can be accomplished with minimal software development and has been demonstrated in three model-based system engineering tools, two of which are commercially available, well-respected, and widely used. This pragmatic approach assures transparency and consistency in characterization of resilience in any discipline.
ContributorsRoberts, Thomas Wade (Author) / Allenby, Braden (Thesis advisor) / Chester, Mikhail (Committee member) / Anderies, John M (Committee member) / Arizona State University (Publisher)
Created2015
Description
Effective collection and dissemination of project information, including best practices, help increase the likelihood of project performance and are vital to organizations in the architecture-engineering-construction (AEC) industry. Best practices can help improve project performance, yet these practices are not universally implemented and used in the industry, due to the following: 1) not all practices are applicable to every project or organization, 2) knowledge lost in organizational turnover which leads to inconsistent collection and implementation of best practices and 3) the lack of standardized processes for best practice management in an organization.
This research, sponsored by National Academy of Construction, the Construction Industry Institute and Arizona State University, used structured interviews, a Delphi study and focus groups to explore: 1) potential benefit and industry interest in an open repository of best practices and 2) important elements of a framework/model that guides the creation, management and sustainment of an open repository of best practices.
This dissertation presents findings specifically exploring the term "Practices for Excellence", its definition, elements that hinder implementation, the potential value of an open online repository for such practices and a model to develop an open repository.
This research, sponsored by National Academy of Construction, the Construction Industry Institute and Arizona State University, used structured interviews, a Delphi study and focus groups to explore: 1) potential benefit and industry interest in an open repository of best practices and 2) important elements of a framework/model that guides the creation, management and sustainment of an open repository of best practices.
This dissertation presents findings specifically exploring the term "Practices for Excellence", its definition, elements that hinder implementation, the potential value of an open online repository for such practices and a model to develop an open repository.
ContributorsBosfield, Roberta Patrice (Author) / Gibson, Edd (Thesis advisor) / Chester, Mikhail (Committee member) / Parrish, Kristen (Committee member) / Sullivan, Kenneth (Committee member) / Arizona State University (Publisher)
Created2014
Description
The North American Monsoon System (NAMS) contributes ~55% of the annual rainfall in the Chihuahuan Desert during the summer months. Relatively frequent, intense storms during the NAMS increase soil moisture, reduce surface temperature and lead to runoff in ephemeral channels. Quantifying these processes, however, is difficult due to the sparse nature of coordinated observations. In this study, I present results from a field network of rain gauges (n = 5), soil probes (n = 48), channel flumes (n = 4), and meteorological equipment in a small desert shrubland watershed (~0.05 km2) in the Jornada Experimental. Using this high-resolution network, I characterize the temporal and spatial variability of rainfall, soil conditions and channel runoff within the watershed from June 2010 to September 2011, covering two NAMS periods. In addition, CO2, water and energy measurements at an eddy covariance tower quantify seasonal, monthly and event-scale changes in land-atmosphere states and fluxes. Results from this study indicate a strong seasonality in water and energy fluxes, with a reduction in Bowen ratio (B, the ratio of sensible to latent heat fluxes) from winter (B = 14) to summer (B = 3.3). This reduction is tied to shallow soil moisture availability during the summer (s = 0.040 m3/m3) as compared to the winter (s = 0.004 m3/m3). During the NAMS, I analyzed four consecutive rainfall-runoff events to quantify the soil moisture and channel flow responses and how water availability impacted the land-atmosphere fluxes. Spatial hydrologic variations during events occur over distances as short as ~15 m. The field network also allowed comparisons of several approaches to estimate evapotranspiration (ET). I found a more accurate ET estimate (a reduction of mean absolute error by 38%) when using distributed soil moisture data, as compared to a standard water balance approach based on the tower site. In addition, use of spatially-varied soil moisture data yielded a more reasonable relationship between ET and soil moisture, an important parameterization in many hydrologic models. The analyses illustrates the value of high-resolution sampling for quantifying seasonal fluxes in desert shrublands and their improvements in closing the water balance in small watersheds.
ContributorsTempleton, Ryan (Author) / Vivoni, Enrique R (Thesis advisor) / Mays, Larry (Committee member) / Fox, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Carbon capture and sequestration (CCS) is one of the important mitigation options for climate change. Numerous technologies to capture carbon dioxide (CO2) are in development but currently, capture using amines is the predominant technology. When the flue gas reacts with amines (Monoethanaloamine) the CO2 is absorbed into the solution and forms an intermediate product which then releases CO2 at higher temperature. The high temperature necessary to strip CO2 is provided by steam extracted from the powerplant thus reducing the net output of the powerplant by 25% to 35%. The reduction in electricity output for the same input of coal increases the emissions factor of Nitrogen Oxides, Mercury, Particulate matter, Ammonia, Volatile organic compounds for the same unit of electricity produced. The thesis questions if this tradeoff between CO2 and other emissions is beneficial or not. Three different methodologies, Life Cycle Assessment, Valuation models and cost benefit analysis are used to identify if there is a net benefit to the society on implementation of CCS to a Pulverized coal powerplant. These methodologies include the benefits due to reduction of CO2 and the disbenefits due to the increase of other emissions. The life cycle assessment using ecoindicator'99 methodology shows the CCS is not beneficial under Hierarchical and Egalitarian perspective. The valuation model shows that the inclusion of the other emissions reduces the benefit associated with CCS. For a lower CO2 price the valuation model shows that CCS is detrimental to the environment. The cost benefit analysis shows that a CO2 price of at least $80/tCO2 is required for the cost benefit ratio to be 1. The methodology integrates Montecarlo simulation to characterize the uncertainties associated with the valuation models.
ContributorsSekar, Ashok (Author) / Williams, Eric (Thesis advisor) / Chester, Mikhail (Thesis advisor) / Allenby, Braden (Committee member) / Arizona State University (Publisher)
Created2012