Matching Items (11,955)
Filtering by
- Creators: Barrett, The Honors College
Description
Hydrology and biogeochemistry are coupled in all systems. However, human decision-making regarding hydrology and biogeochemistry are often separate, even though decisions about hydrologic systems may have substantial impacts on biogeochemical patterns and processes. The overarching question of this dissertation was: How does hydrologic engineering interact with the effects of nutrient loading and climate to drive watershed nutrient yields? I conducted research in two study systems with contrasting spatial and temporal scales. Using a combination of data-mining and modeling approaches, I reconstructed nitrogen and phosphorus budgets for the northeastern US over the 20th century, including anthropogenic nutrient inputs and riverine fluxes, for ~200 watersheds at 5 year time intervals. Infrastructure systems, such as sewers, wastewater treatment plants, and reservoirs, strongly affected the spatial and temporal patterns of nutrient fluxes from northeastern watersheds. At a smaller scale, I investigated the effects of urban stormwater drainage infrastructure on water and nutrient delivery from urban watersheds in Phoenix, AZ. Using a combination of field monitoring and statistical modeling, I tested hypotheses about the importance of hydrologic and biogeochemical control of nutrient delivery. My research suggests that hydrology is the major driver of differences in nutrient fluxes from urban watersheds at the event scale, and that consideration of altered hydrologic networks is critical for understanding anthropogenic impacts on biogeochemical cycles. Overall, I found that human activities affect nutrient transport via multiple pathways. Anthropogenic nutrient additions increase the supply of nutrients available for transport, whereas hydrologic infrastructure controls the delivery of nutrients from watersheds. Incorporating the effects of hydrologic infrastructure is critical for understanding anthropogenic effects on biogeochemical fluxes across spatial and temporal scales.
ContributorsHale, Rebecca Leslie (Author) / Grimm, Nancy (Thesis advisor) / Childers, Daniel (Committee member) / Vivoni, Enrique (Committee member) / York, Abigail (Committee member) / Wu, Jianguo (Committee member) / Arizona State University (Publisher)
Created2013
Description
Air pollution is a serious problem in most urban areas around the world, which has a number of negative ecological and human health impacts. As a result, it's vitally important to detect and characterize air pollutants to protect the health of the urban environment and our citizens. An important early step in this process is ensuring that the air pollution monitoring network is properly designed to capture the patterns of pollution and that all social demographics in the urban population are represented. An important aspect in characterizing air pollution patterns is scale in space and time which, along with pattern and process relationships, is a key subject in the field of landscape ecology. Thus, using multiple landscape ecological methods, this dissertation research begins by characterizing and quantifying the multi-scalar patterns of ozone (O3) and particulate matter (PM10) in the Phoenix, Arizona, metropolitan region. Results showed that pollution patterns are scale-dependent, O3 is a regionally-scaled pollutant at longer temporal scales, and PM10 is a locally-scaled pollutant with patterns sensitive to season. Next, this dissertation examines the monitoring network within Maricopa County. Using a novel multiscale indicator-based approach, the adequacy of the network was quantified by integrating inputs from various academic and government stakeholders. Furthermore, deficiencies were spatially defined and recommendations were made on how to strengthen the design of the network. A sustainability ranking system also provided new insight into the strengths and weaknesses of the network. Lastly, the study addresses the question of whether distinct social groups were experiencing inequitable exposure to pollutants - a key issue of distributive environmental injustice. A novel interdisciplinary method using multi-scalar ambient pollution data and hierarchical multiple regression models revealed environmental inequities between air pollutants and race, ethnicity, age, and socioeconomic classes. The results indicate that changing the scale of the analysis can change the equitable relationship between pollution and demographics. The scientific findings of the scale-dependent relationships among air pollution patterns, network design, and population demographics, brought to light through this study, can help policymakers make informed decisions for protecting the human health and the urban environment in the Phoenix metropolitan region and beyond.
ContributorsPope, Ronald L (Author) / Wu, Jianguo (Thesis advisor) / Boone, Christopher G. (Committee member) / Brazel, Anthony J. (Committee member) / Forzani, Erica S. (Committee member) / Fraser, Matthew P. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Over the past century in the southwestern United States human actions have altered hydrological processes that shape riparian ecosystems. One change, release of treated wastewater into waterways, has created perennial base flows and increased nutrient availability in ephemeral or intermittent channels. While there are benefits to utilizing treated wastewater for environmental flows, there are numerous unresolved ecohydrological issues regarding the efficacy of effluent to sustain groundwater-dependent riparian ecosystems. This research examined how nutrient-rich effluent, released into waterways with varying depths to groundwater, influences riparian plant community development. Statewide analysis of spatial and temporal patterns of effluent generation and release revealed that hydrogeomorphic setting significantly influences downstream riparian response. Approximately 70% of effluent released is into deep groundwater systems, which produced the lowest riparian development. A greenhouse study assessed how varying concentrations of nitrogen and phosphorus, emulating levels in effluent, influenced plant community response. With increasing nitrogen concentrations, vegetation emerging from riparian seed banks had greater biomass, reduced species richness, and greater abundance of nitrophilic species. The effluent-dominated Santa Cruz River in southern Arizona, with a shallow groundwater upper reach and deep groundwater lower reach, served as a study river while the San Pedro River provided a control. Analysis revealed that woody species richness and composition were similar between the two systems. Hydric pioneers (Populus fremontii, Salix gooddingii) were dominant at perennial sites on both rivers. Nitrophilic species (Conium maculatum, Polygonum lapathifolium) dominated herbaceous plant communities and plant heights were greatest in effluent-dominated reaches. Riparian vegetation declined with increasing downstream distance in the upper Santa Cruz, while patterns in the lower Santa Cruz were confounded by additional downstream agricultural input and a channelized floodplain. There were distinct longitudinal and lateral shifts toward more xeric species with increasing downstream distance and increasing lateral distance from the low-flow channel. Patterns in the upper and lower Santa Cruz reaches indicate that water availability drives riparian vegetation outcomes below treatment facilities. Ultimately, this research informs decision processes and increases adaptive capacity for water resources policy and management through the integration of ecological data in decision frameworks regarding the release of effluent for environmental flows.
ContributorsWhite, Margaret Susan (Author) / Stromberg, Juliet C. (Thesis advisor) / Fisher, Stuart G. (Committee member) / White, Dave (Committee member) / Holway, James (Committee member) / Wu, Jianguo (Committee member) / Arizona State University (Publisher)
Created2011
Description
Despite years of effort, the field of conservation biology still struggles to incorporate theories of animal behavior. I introduce in Chapter I the issues surrounding the disconnect between behavioral ecology and conservation biology, and propose the use of behavioral knowledge in population viability analysis. In Chapter II, I develop a framework that uses three strategies for incorporating behavior into demographic models, outline the costs of each strategy through decision analysis, and build on previous work in behavioral ecology and demography. First, relevant behavioral mechanisms should be included in demographic models used for conservation decision-making. Second, I propose rapid behavioral assessment as a useful tool to approximate demographic rates through regression of demographic phenomena on observations of related behaviors. This technique provides behaviorally estimated parameters that may be applied to population viability analysis for use in management. Finally, behavioral indices can be used as warning signs of population decline. The proposed framework combines each strategy through decision analysis to provide quantitative rules that determine when incorporating aspects of conservation behavior may be beneficial to management. Chapter III applies this technique to estimate birthrate in a colony of California sea lions in the Gulf of California, Mexico. This study includes a cost analysis of the behavioral and traditional parameter estimation techniques. I then provide in Chapter IV practical recommendations for applying this framework to management programs along with general guidelines for the development of rapid behavioral assessment.
ContributorsWildermuth, Robert (Author) / Gerber, Leah R. (Thesis advisor) / Collins, James (Committee member) / Smith, Andrew (Committee member) / Arizona State University (Publisher)
Created2012
Description
Parental care provides many benefits to offspring. One widely realized benefit is enhanced regulation of offspring's thermal environment. The developmental thermal environment during development can be optimized behaviorally through nest site selection and brooding, and it can be further enhanced by physiological heat production. In fact, enhancement of the developmental thermal environment has been proposed as the initial driving force for the evolution of endothermy in bird and mammals. I used pythons (Squamata: Pythonidae) to expand existing knowledge of behavioral and physiological parental tactics used to regulate offspring thermal environment. I first demonstrated that brooding behavior in the Children's python (Antaresia childreni) is largely driven by internal mechanisms, similar to solitary birds, suggesting that the early evolution of the parent-offspring association was probably hormonally driven. Two species of python are known to be facultatively thermogenic (i.e., are endothermic during reproduction). I expand current knowledge of thermogenesis in Burmese pythons (Python molurus) by demonstrating that females use their own body temperature to modulate thermogenesis. Although pythons are commonly cited as thermogenic, the actual extent of thermogenesis within the family Pythonidae is unknown. Thus, I assessed the thermogenic capability of five previously unstudied species of python to aid in understanding phylogenetic, morphological, and distributional influences on thermogenesis in pythons. Results suggest that facultative thermogenesis is likely rare among pythons. To understand why it is rare, I used an artificial model to demonstrate that energetic costs to the female likely outweigh thermal benefits to the clutch in species that do not inhabit cooler latitudes or lack large energy reserves. In combination with other studies, these results show that facultative thermogenesis during brooding in pythons likely requires particular ecological and physiological factors for its evolution.
ContributorsBrashears, Jake (Author) / DeNardo, Dale (Thesis advisor) / Harrison, Jon (Committee member) / Deviche, Pierre (Committee member) / McGraw, Kevin (Committee member) / Smith, Andrew (Committee member) / Arizona State University (Publisher)
Created2012
Description
The relationship between biodiversity and ecosystem functioning (BEF) is a central issue in ecology, and a number of recent field experimental studies have greatly improved our understanding of this relationship. Spatial heterogeneity is a ubiquitous characterization of ecosystem processes, and has played a significant role in shaping BEF relationships. The first step towards understanding the effects of spatial heterogeneity on the BEF relationships is to quantify spatial heterogeneity characteristics of key variables of biodiversity and ecosystem functioning, and identify the spatial relationships among these variables. The goal of our research was to address the following research questions based on data collected in 2005 (corresponding to the year when the initial site background information was conducted) and in 2008 (corresponding to the year when removal treatments were conducted) from the Inner Mongolia Grassland Removal Experiment (IMGRE) located in northern China: 1) What are the spatial patterns of soil nutrients, plant biodiversity, and aboveground biomass in a natural grassland community of Inner Mongolia, China? How are they related spatially? and 2) How do removal treatments affect the spatial patterns of soil nutrients, plant biodiversity, and aboveground biomass? Is there any change for their spatial correlations after removal treatments? Our results showed that variables of biodiversity and ecosystem functioning in the natural grassland community would present different spatial patterns, and they would be spatially correlated to each other closely. Removal treatments had a significant effect on spatial structures and spatial correlations of variables, compared to those prior to the removal treatments. The differences in spatial pattern of plant and soil variables and their correlations before and after the biodiversity manipulation may not imply that the results from BEF experiments like IMGRE are invalid. However, they do suggest that the possible effects of spatial heterogeneity on the BEF relationships should be critically evaluated in future studies.
ContributorsYuan, Fei (Author) / Wu, Jianguo (Thesis advisor) / Smith, Andrew T. (Committee member) / Rowe, Helen I (Committee member) / Arizona State University (Publisher)
Created2011
Description
Geology and its tangential studies, collectively known and referred to in this thesis as geosciences, have been paramount to the transformation and advancement of society, fundamentally changing the way we view, interact and live with the surrounding natural and built environment. It is important to recognize the value and importance of this interdisciplinary scientific field while reconciling its ties to imperial and colonizing extractive systems which have led to harmful and invasive endeavors. This intersection among geosciences, (environmental) justice studies, and decolonization is intended to promote inclusive pedagogical models through just and equitable methodologies and frameworks as to prevent further injustices and promote recognition and healing of old wounds. By utilizing decolonial frameworks and highlighting the voices of peoples from colonized and exploited landscapes, this annotated syllabus tackles the issues previously described while proposing solutions involving place-based education and the recentering of land within geoscience pedagogical models. (abstract)
ContributorsReed, Cameron E (Author) / Richter, Jennifer (Thesis director) / Semken, Steven (Committee member) / School of Earth and Space Exploration (Contributor, Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The ASU COVID-19 testing lab process was developed to operate as the primary testing site for all ASU staff, students, and specified external individuals. Tests are collected at various collection sites, including a walk-in site at the SDFC and various drive-up sites on campus; analysis is conducted on ASU campus and results are distributed virtually to all patients via the Health Services patient portal. The following is a literature review on past implementations of various process improvement techniques and how they can be applied to the ABCTL testing process to achieve laboratory goals. (abstract)
ContributorsKrell, Abby Elizabeth (Co-author) / Bruner, Ashley (Co-author) / Ramesh, Frankincense (Co-author) / Lewis, Gabriel (Co-author) / Barwey, Ishna (Co-author) / Myers, Jack (Co-author) / Hymer, William (Co-author) / Reagan, Sage (Co-author) / Compton, Carolyn (Thesis director) / McCarville, Daniel R. (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
A global warming of two degrees Celsius is predicted to drive almost half the world's lizard populations to extinction. Currently, the Phoenix metropolitan region in Arizona, USA, is an average of 3 oC warmer than the surrounding desert. Using a bare lot as a control, I placed copper lizard models with data loggers in several vegetation and irrigation treatments that represent the dominant backyard landscaping styles in Phoenix (grassy mesic with mist irrigation, drip irrigated xeric, unirrigated native, and a hybrid style known as oasis). Lizard activity time in summer is currently restricted to a few hours in un-irrigated native desert landscaping, while heavily irrigated grass and shade trees allow for continual activity during even the hottest days. Maintaining the existing diversity of landscaping styles (as part of an ongoing mitigation strategy targeted at humans) will be beneficial for lizards.
Fourteen native lizard species inhabit the desert surrounding Phoenix, AZ, USA, but only two species persist within heavily developed areas. This pattern is best explained by a combination of socioeconomic status, land cover, and location. Lizard diversity is highest in affluent areas and lizard abundance is greatest near large patches of open desert. The percentage of building cover has a strong negative impact on both diversity and abundance. Despite Phoenix's intense urban heat island effect, which strongly constrains the potential activity and microhabitat use of lizards in summer, thermal patterns have not yet impacted their distribution and relative abundance at larger scales.
Fourteen native lizard species inhabit the desert surrounding Phoenix, AZ, USA, but only two species persist within heavily developed areas. This pattern is best explained by a combination of socioeconomic status, land cover, and location. Lizard diversity is highest in affluent areas and lizard abundance is greatest near large patches of open desert. The percentage of building cover has a strong negative impact on both diversity and abundance. Despite Phoenix's intense urban heat island effect, which strongly constrains the potential activity and microhabitat use of lizards in summer, thermal patterns have not yet impacted their distribution and relative abundance at larger scales.
ContributorsAckley, Jeffrey (Author) / Wu, Jianguo (Thesis advisor) / Sullivan, Brian (Thesis advisor) / Myint, Soe (Committee member) / DeNardo, Dale (Committee member) / Angilletta Jr., Michael (Committee member) / Arizona State University (Publisher)
Created2015