Matching Items (158)
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
The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.
ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Local municipalities in the Phoenix Metropolitan Area have voiced an interest in purchasing alternate source water with lower DBP precursors. Along the primary source is a hydroelectric dam in which water will be diverted from. This project is an assessment of optimizing the potential blends of source water to a water treatment plant in an effort to enable them to more readily meet DBP regulations. To perform this analysis existing water treatment models were used in conjunction with historic water quality sampling data to predict chemical usage necessary to meet DBP regulations. A retrospective analysis was performed for the summer months of 2007 regarding potential for the WTP to reduce cost through optimizing the source water by an average of 30% over the four-month period, accumulating to overall treatment savings of $154 per MG ($82 per AF).
ContributorsRice, Jacelyn (Author) / Westerhoff, Paul (Thesis advisor) / Fox, Peter (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created2011
Description
Overcrowding of Emergency Departments (EDs) put the safety of patients at risk. Decision makers implement Ambulance Diversion (AD) as a way to relieve congestion and ensure timely treatment delivery. However, ineffective design of AD policies reduces the accessibility to emergency care and adverse events may arise. The objective of this dissertation is to propose methods to design and analyze effective AD policies that consider performance measures that are related to patient safety. First, a simulation-based methodology is proposed to evaluate the mean performance and variability of single-factor AD policies in a single hospital environment considering the trade-off between average waiting time and percentage of time spent on diversion. Regression equations are proposed to obtain parameters of AD policies that yield desired performance level. The results suggest that policies based on the total number of patients waiting are more consistent and provide a high precision in predicting policy performance. Then, a Markov Decision Process model is proposed to obtain the optimal AD policy assuming that information to start treatment in a neighboring hospital is available. The model is designed to minimize the average tardiness per patient in the long run. Tardiness is defined as the time that patients have to wait beyond a safety time threshold to start receiving treatment. Theoretical and computational analyses show that there exists an optimal policy that is of threshold type, and diversion can be a good alternative to decrease tardiness when ambulance patients cause excessive congestion in the ED. Furthermore, implementation of AD policies in a simulation model that accounts for several relaxations of the assumptions suggests that the model provides consistent policies under multiple scenarios. Finally, a genetic algorithm is combined with simulation to design effective policies for multiple hospitals simultaneously. The model has the objective of minimizing the time that patients spend in non-value added activities, including transportation, waiting and boarding in the ED. Moreover, the AD policies are combined with simple ambulance destination policies to create ambulance flow control mechanisms. Results show that effective ambulance management can significantly reduce the time that patients have to wait to receive appropriate level of care.
ContributorsRamirez Nafarrate, Adrian (Author) / Fowler, John W. (Thesis advisor) / Wu, Teresa (Thesis advisor) / Gel, Esma S. (Committee member) / Limon, Jorge (Committee member) / Arizona State University (Publisher)
Created2011
Analysis of photocatalysis for precursor removal and formation inhibition of disinfection byproducts
Description
Disinfection byproducts are the result of reactions between natural organic matter (NOM) and a disinfectant. The formation and speciation of DBP formation is largely dependent on the disinfectant used and the natural organic matter (NOM) concentration and composition. This study examined the use of photocatalysis with titanium dioxide for the oxidation and removal of DBP precursors (NOM) and the inhibition of DBP formation. Water sources were collected from various points in the treatment process, treated with photocatalysis, and chlorinated to analyze the implications on total trihalomethane (TTHM) and the five haloacetic acids (HAA5) formations. The three sub-objectives for this study included: the comparison of enhanced and standard coagulation to photocatalysis for the removal of DBP precursors; the analysis of photocatalysis and characterization of organic matter using size exclusion chromatography and fluorescence spectroscopy and excitation-emission matrices; and the analysis of photocatalysis before GAC filtration. There were consistencies in the trends for each objective including reduced DBP precursors, measured as dissolved organic carbon DOC concentration and UV absorbance at 254 nm. Both of these parameters decreased with increased photocatalytic treatment and could be due in part to the adsorption to as well as the oxidation of NOM on the TiO2 surface. This resulted in lower THM and HAA concentrations at Medium and High photocatalytic treatment levels. However, at No UV exposure and Low photocatalytic treatment levels where oxidation reactions were inherently incomplete, there was an increase in THM and HAA formation potential, in most cases being significantly greater than those found in the raw water or Control samples. The size exclusion chromatography (SEC) results suggest that photocatalysis preferentially degrades the higher molecular mass fraction of NOM releasing lower molecular mass (LMM) compounds that have not been completely oxidized. The molecular weight distributions could explain the THM and HAA formation potentials that decreased at the No UV exposure samples but increased at Low photocatalytic treatment levels. The use of photocatalysis before GAC adsorption appears to increase bed life of the contactors; however, higher photocatalytic treatment levels have been shown to completely mineralize NOM and would therefore not require additional GAC adsorption after photocatalysis.
ContributorsDaugherty, Erin (Author) / Abbaszadegan, Morteza (Thesis advisor) / Fox, Peter (Committee member) / Mayer, Brooke (Committee member) / Arizona State University (Publisher)
Created2011
Description
To address sustainability issues in wastewater treatment (WWT), Siemens Water Technologies (SWT) has designed a "hybrid" process that couples common activated sludge (AS) and anaerobic digestion (AD) technologies with the novel concepts of AD sludge recycle and biosorption. At least 85% of the hybrid's AD sludge is recycled to the AS process, providing additional sorbent for influent particulate chemical oxygen demand (PCOD) biosorption in contact tanks. Biosorbed PCOD is transported to the AD, where it is converted to methane. The aim of this study is to provide mass balance and microbial community analysis (MCA) of SWT's two hybrid and one conventional pilot plant trains and mathematical modeling of the hybrid process including a novel model of biosorption. A detailed mass balance was performed on each tank and the overall system. The mass balance data supports the hybrid process is more sustainable: It produces 1.5 to 5.5x more methane and 50 to 83% less sludge than the conventional train. The hybrid's superior performance is driven by 4 to 8 times longer solid retention times (SRTs) as compared to conventional trains. However, the conversion of influent COD to methane was low at 15 to 22%, and neither train exhibited significant nitrification or denitrification. Data were inconclusive as to the role of biosorption in the processes. MCA indicated the presence of Archaea and nitrifiers throughout both systems. However, it is inconclusive as to how active Archaea and nitrifiers are under anoxic, aerobic, and anaerobic conditions. Mathematical modeling confirms the hybrid process produces 4 to 20 times more methane and 20 to 83% less sludge than the conventional train under various operating conditions. Neither process removes more than 25% of the influent nitrogen or converts more that 13% to nitrogen gas due to biomass washout in the contact tank and short SRTs in the stabilization tank. In addition, a mathematical relationship was developed to describe PCOD biosorption through adsorption to biomass and floc entrapment. Ultimately, process performance is more heavily influenced by the higher AD SRTs attained when sludge is recycled through the system and less influenced by the inclusion of biosorption kinetics.
ContributorsYoung, Michelle Nichole (Author) / Rittmann, Bruce E. (Thesis advisor) / Fox, Peter (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Arizona State University (Publisher)
Created2011
Description
Hepatocellular carcinoma (HCC) is a malignant tumor and seventh most common cancer in human. Every year there is a significant rise in the number of patients suffering from HCC. Most clinical research has focused on HCC early detection so that there are high chances of patient's survival. Emerging advancements in functional and structural imaging techniques have provided the ability to detect microscopic changes in tumor micro environment and micro structure. The prime focus of this thesis is to validate the applicability of advanced imaging modality, Magnetic Resonance Elastography (MRE), for HCC diagnosis. The research was carried out on three HCC patient's data and three sets of experiments were conducted. The main focus was on quantitative aspect of MRE in conjunction with Texture Analysis, an advanced imaging processing pipeline and multi-variate analysis machine learning method for accurate HCC diagnosis. We analyzed the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Along with this we studied different machine learning algorithms and developed models using them. Performance metrics such as Prediction Accuracy, Sensitivity and Specificity have been used for evaluation for the final developed model. We were able to identify the significant features in the dataset and also the selected classifier was robust in predicting the response class variable with high accuracy.
ContributorsBansal, Gaurav (Author) / Wu, Teresa (Thesis advisor) / Mitchell, Ross (Thesis advisor) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2013
Description
Many manmade chemicals used in consumer products are ultimately washed down the drain and are collected in municipal sewers. Efficient chemical monitoring at wastewater treatment (WWT) plants thus may provide up-to-date information on chemical usage rates for epidemiological assessments. The objective of the present study was to extrapolate this concept, termed 'sewage epidemiology', to include municipal sewage sludge (MSS) in identifying and prioritizing contaminants of emerging concern (CECs). To test this the following specific aims were defined: i) to screen and identify CECs in nationally representative samples of MSS and to provide nationwide inventories of CECs in U.S. MSS; ii) to investigate the fate and persistence in MSS-amended soils, of sludge-borne hydrophobic CECs; and iii) to develop an analytical tool relying on contaminant levels in MSS as an indicator for identifying and prioritizing hydrophobic CECs. Chemicals that are primarily discharged to the sewage systems (alkylphenol surfactants) and widespread persistent organohalogen pollutants (perfluorochemicals and brominated flame retardants) were analyzed in nationally representative MSS samples. A meta-analysis showed that CECs contribute about 0.04-0.15% to the total dry mass of MSS, a mass equivalent of 2,700-7,900 metric tonnes of chemicals annually. An analysis of archived mesocoms from a sludge weathering study showed that 64 CECs persisted in MSS/soil mixtures over the course of the experiment, with half-lives ranging between 224 and >990 days; these results suggest an inherent persistence of CECs that accumulate in MSS. A comparison of the spectrum of chemicals (n=52) analyzed in nationally representative biological specimens from humans and MSS revealed 70% overlap. This observed co-occurrence of contaminants in both matrices suggests that MSS may serve as an indicator for ongoing human exposures and body burdens of pollutants in humans. In conclusion, I posit that this novel approach in sewage epidemiology may serve to pre-screen and prioritize the several thousands of known or suspected CECs to identify those that are most prone to pose a risk to human health and the environment.
ContributorsVenkatesan, Arjunkrishna (Author) / Halden, Rolf U. (Thesis advisor) / Westerhoff, Paul (Committee member) / Fox, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contaminants of emerging concern (CECs) present in wastewater effluent can threat its safe discharge or reuse. Additional barriers of protection can be provided using advanced or natural treatment processes. This dissertation evaluated ozonation and constructed wetlands to remove CECs from wastewater effluent. Organic CECs can be removed by hydroxyl radical formed during ozonation, however estimating the ozone demand of wastewater effluent is complicated due to the presence of reduced inorganic species. A method was developed to estimate ozone consumption only by dissolved organic compounds and predict trace organic oxidation across multiple wastewater sources. Organic and engineered nanomaterial (ENM) CEC removal in constructed wetlands was investigated using batch experiments and continuous-flow microcosms containing decaying wetland plants. CEC removal varied depending on their physico-chemical properties, hydraulic residence time (HRT) and relative quantities of plant materials in the microcosms. At comparable HRTs, ENM removal improved with higher quantity of plant materials due to enhanced sorption which was verified in batch-scale studies with plant materials. A fate-predictive model was developed to evaluate the role of design loading rates on organic CEC removal. Areal removal rates increased with hydraulic loading rates (HLRs) and carbon loading rates (CLRs) unless photolysis was the dominant removal mechanism (e.g. atrazine). To optimize CEC removal, wetlands with different CLRs can be used in combination without lowering the net HLR. Organic CEC removal in denitrifying conditions of constructed wetlands was investigated and selected CECs (e.g. estradiol) were found to biotransform while denitrification occurred. Although level of denitrification was affected by HRT, similar impact on estradiol was not observed due to a dominant effect from plant biomass quantity. Overall, both modeling and experimental findings suggest considering CLR as an equally important factor with HRT or HLR to design constructed wetlands for CEC removal. This dissertation provided directions to select design parameters for ozonation (ozone dose) and constructed wetlands (design loading rates) to meet organic CEC removal goals. Future research is needed to understand fate of ENMs during ozonation and quantify the contributions from different transformation mechanisms occurring in the wetlands to incorporate in a model and evaluate the effect of wetland design.
ContributorsSharif, Fariya (Author) / Westerhoff, Paul (Thesis advisor) / Halden, Rolf (Committee member) / Fox, Peter (Committee member) / Herckes, Pierre (Committee member) / Arizona State University (Publisher)
Created2013
Description
A Pairwise Comparison Matrix (PCM) is used to compute for relative priorities of criteria or alternatives and are integral components of widely applied decision making tools: the Analytic Hierarchy Process (AHP) and its generalized form, the Analytic Network Process (ANP). However, a PCM suffers from several issues limiting its application to large-scale decision problems, specifically: (1) to the curse of dimensionality, that is, a large number of pairwise comparisons need to be elicited from a decision maker (DM), (2) inconsistent and (3) imprecise preferences maybe obtained due to the limited cognitive power of DMs. This dissertation proposes a PCM Framework for Large-Scale Decisions to address these limitations in three phases as follows. The first phase proposes a binary integer program (BIP) to intelligently decompose a PCM into several mutually exclusive subsets using interdependence scores. As a result, the number of pairwise comparisons is reduced and the consistency of the PCM is improved. Since the subsets are disjoint, the most independent pivot element is identified to connect all subsets. This is done to derive the global weights of the elements from the original PCM. The proposed BIP is applied to both AHP and ANP methodologies. However, it is noted that the optimal number of subsets is provided subjectively by the DM and hence is subject to biases and judgement errors. The second phase proposes a trade-off PCM decomposition methodology to decompose a PCM into a number of optimally identified subsets. A BIP is proposed to balance the: (1) time savings by reducing pairwise comparisons, the level of PCM inconsistency, and (2) the accuracy of the weights. The proposed methodology is applied to the AHP to demonstrate its advantages and is compared to established methodologies. In the third phase, a beta distribution is proposed to generalize a wide variety of imprecise pairwise comparison distributions via a method of moments methodology. A Non-Linear Programming model is then developed that calculates PCM element weights which maximizes the preferences of the DM as well as minimizes the inconsistency simultaneously. Comparison experiments are conducted using datasets collected from literature to validate the proposed methodology.
ContributorsJalao, Eugene Rex Lazaro (Author) / Shunk, Dan L. (Thesis advisor) / Wu, Teresa (Thesis advisor) / Askin, Ronald G. (Committee member) / Goul, Kenneth M (Committee member) / Arizona State University (Publisher)
Created2013