Between 1970 and 2010 almost one-third of drinking water related waterborne disease outbreaks reported to the US Centers for Disease Control and Prevention were associated with systems dependent on untreated groundwater (i.e., most commonly, household wells). This is unsurprising, given that numerous past efforts to monitor household well water quality have indicated a high prevalence of fecal coliforms and/or E. coli at the point of use. Non-point sources of pollution, including septic tank leakages and poorly constructed drain fields, have been identified as the leading risk factors associated with outbreaks in households dependent on groundwater. Ideally, the integration of emerging source tracking (ST) analyses in well monitoring programs could be used to identify whether the presence of fecal indicator bacteria (FIB) is associated with human or non-human sources in order to inform remediation strategies. However, the application of ST to groundwater has been limited, and the interpretation of data is consequently difficult. This research compares the fate and transport of FIB (E. coli and enterococci) with a chemical (optical brighteners, OB) and a molecular (Bacteroides HF183) ST target in order to evaluate their potential use as indicators of water quality issues in private drinking water systems. Eighteen PVC soil columns were constructed in an outdoor soil column facility to represent small-scale septic drainfield models; they received synchronized doses of primary-treated wastewater twice daily and were monitored bi-weekly over a 7-month period. Columns were subject to variable influent loading rates of wastewater effluent, and differing degrees of soil compromisation (i.e. synthetic solution channels). Results show that while column effluent volume and constituent levels were related to dosage, they were not always related to soil compromisation (ANOVA, p < 0.05). E. coli and enterococci concentrations were associated with effluent volume and OB levels (Spearman's rank, p < 0.05). The presence of Bacteroides HF183 was not strongly associated with the other measured ST target levels (Point-biserial correlation, p < 0.05). Findings from this study suggest surface water ST methodologies may have a role in groundwater quality monitoring efforts. Quantifying the relative recovery of ST targets and FIB from controlled groundwater simulations will assist in the development of strategies to identify non-point sources of human wastewater pollution efficiently and effectively to inform remediation. / Master of Science

Pathogen contamination, typically quantified by elevated levels of fecal indicator bacteria (FIB), remains the leading cause of surface water-quality impairments in the United States. Continuous watershed-scale models are typically employed to facilitate Total Maximum Daily Load (TMDL) restoration efforts. Due to limited understanding of microbial fate and transport, predictions of FIB concentrations are associated with considerable uncertainty relative to other water-quality contaminants. By focusing on a data-rich instrumented urban watershed, this study aims to improve understanding of microbial fate and transport processes. Weekly FIB concentrations in both the water column and streambed sediments were monitored for one year, and statistical correlations with hydrometeorological and physicochemical variables were identified. An intensive six storm intra-sampling campaign quantified and contrasted loading trends of both traditional regulatory FIB and emerging Microbial Source Tracking (MST) markers. Together, these intensive monitoring efforts facilitated evaluation of the impacts of bacteria-sediment interactions on the predictions of daily FIB concentrations in Hydrological Simulation Program-Fortran (HSPF) over multiple years. While superior overall model performance was demonstrated as compared to earlier efforts, the inclusion of bacteria-sediment interactions did not improve performance. Large wet-weather microbial loading appears to have dwarfed the effects of FIB release and resuspension from sediment. Although wet-weather loading is generally considered as a primary source of waterbody microbial loads, dry-weather periods are more directly associated with public health concern, which may be a more suitable area for future model-refinement efforts. Site evaluation is critical to determine whether the added model complexity and effort associated with partitioning phases of FIB can be sufficiently offset by gains in predictive capacity. Finally, a stochastic framework to translate simulated daily FIB concentrations into estimates of human illness risks is presented that can be can be readily integrated into existing TMDLs. As even small concentrations of FIB from human sources are associated with great risk, and monitoring efforts indicated moderate/high levels of human-associated MST marker in this watershed, remediation efforts to protect public health would be best directed toward infrastructure improvements. Uncertainty analysis indicates more site-specific knowledge of pathogen presence and densities would best improve the estimation of illness risks. / Ph. D.

Le développement de nouvelles technologies pour le recyclage des eaux usées est une priorité pour les régions arides et semi-arides tels que les pays du bassin méditerranéen. L'objectif de ce travail a été, dans un premier temps, de développer un système de traitement tertiaire des eaux usées en testant l'efficacité de diodes électroluminescentes émettant des UV-A et/ou des UV-C (DEL-UV) sur l'inactivation de souches bactériennes indicatrices de pollution fécale (Escherichia coli et Enterococcus faecalis) puis sur des effluents urbains, en étudiant la diminution des pollutions microbiennes et chimiques. Cette première étape a mis en évidence l'efficacité accrue du couplage UV-A et UV-C, permettant d'obtenir une eau de qualité répondant aux exigences législatives relatives à la réutilisation des eaux usées pour l'irrigation. La deuxième partie de ce travail était l'étude de l'impact de l'arrosage avec des eaux usées traitées par DEL-UV sur les paramètres du sol et sur le devenir de certain polluant aromatiques dans les sols (anthracène et carbamazépine). L'arrosage avec des eaux usées traitées par DEL-UV ne modifie pas la composition chimique de la matière organique de sol, l'activité catabolique globale des microorganismes, et le nombre et la diversité de bactéries indicatrices de pollution fécale par rapport au sol arrosé avec de l'eau d'irrigation. Certaines activités enzymatiques liées à la dégradation de la matière organique augmentent dans les sols arrosés avec des eaux usées traitées par DEL-UV, montrant que la matière organique apportée par l'effluent est activement dégradée par les microorganismes. / The development of new technologies for wastewater reuse is a priority for arid and semi-arid areas such as Mediterranean countries. The objective of this work was, firstly, to develop a system for tertiary treatment of wastewaters testing LEDs emitting UV-A and / or UV-C (UV LEDs) on fecal indicator inactivation (Escherichia coli and Enterococcus faecalis) and then on urban effluents, by studying the decrease in both microbial and chemical pollution. This first step has highlighted a higher efficiency of this system when UV UV-A and UV-C are coupled. Wasterwaters with quality meeting the statutory requirements for the reuse of wastewater for irrigation were obtained and used in the second part of this work was to study the impact of watering with UV-LED- treated wastewaters on soil parameters and on the fate of pollutants (anthracene and carbamazepine) in soils. Watering with UV-LED treated wastewater does not change the chemical composition of soil organic matter, the global catabolic activity of soil microorganisms, and the number and diversity of fecal indicators compared to control (irrigation water). Certain enzymatic activities related to the degradation of organic matter increased in soil watered with UV-LED treated wastewater, showing that the organic matter added by the effluent is actively degraded by microorganisms. In addition, aromatic pollutants are oxidized two to three times faster in soils watered with UV-LED treated wastewater.

Master of Science / Geology / Saugata Datta / The province (upazila) of Matlab in SE Bangladesh is highly affected with elevated concentrations of dissolved As content and widespread fecal contamination in untreated drinking waters. The study area is sedimentologically composed of thick floodplain deposits of Holocene age overlying Plio-Pleistocene grey fine to coarse sands with considerable clays (consisting of Dupi Tila formation). The goal of the current study is to understand the possible impact of co-occurrence of dissolved organic carbon (along with As release) and fecal indicator bacteria (e.g., E.coli) in aquifers from shallow to deep groundwater quality in this area. Nineteen groundwater (spanning a depth range of 14 to 240 m) samples and nine surface water samples (eight ponds and one canal in proximity to the piezometer nests) were collected from four different piezometric nests within north and south Matlab Upazila in Bangladesh during the monsoonal season (Jun-Jul 2014). The analyses of dissolved organic carbon (DOC) and its fluorescence properties indicate that the chemical character of DOC from shallow to intermediate groundwaters (<150 m) and surface water is dominated by more aromatic and humic materials than deeper groundwaters. Both groundwaters and surface waters may receive humic substances leached from soil and/or from the cellular constituents and exudates of indigenous aquatic organisms. Dissolved organic carbons in groundwater and surface waters are composed of predominantly UVA and UVC-humic like along with tryptophan like components. Only 15% of total C is modern carbon at shallowest depths (<30 m) in groundwaters. The recharge source of groundwaters is from local precipitation, with or without some evaporation before infiltration as depicted by the δ2H and δ¹⁸O variations and the water is infiltrating through mostly terrestrially derived weathered sediments into the aquifers. The type of water in the study area is Ca-Na-HCO₃⁻ type. More toxic and soluble As (III) is present in shallow groundwaters (<30 m). High concentrations of As (V) and As[subscript (t)] are observed high in shallow and intermediate depth wells (<150 m). The most probable number based on the Colilert test and qPCR result for E.coli suggest that unprotected surface waters are harbingers for high microbial population compared to hand pumped wells. However, the very low observed concentrations of cultured E. coli (<1-10 MPN/100 mL) and E. coli DNA (<40 Copies/100 mL) in the wells indicates that the abundance of E.coli cells decrease rapidly with residence time in oligotrophic aquifers. Thus, it may be suggested that more humic DOC in shallow and intermediate groundwaters may be involved in complexation or other biogeochemical reactions that may mobilize As in groundwater. The non-indigenous bacteria can be the primary producers of DOC in the aquifers which can be utilizing surface derived DOC.

Ambient and recreational surface waters worldwide experience fecal pollution due to a variety of anthropogenic sources. Fecal waste has been proven, for over a century, to harbor pathogenic microorganisms which subsequently cause a variety of disease and illness in human hosts. The benefits of utilizing fecal indicator bacteria (FIB) as a simple, inexpensive means to detect fitful human pathogens within a variety of water matrices are vast. However, no universal agreement exists in regard to which indicator is best suited for detection of fecal contamination and pathogens in environmental waters, and no single standard for bacterial indicators has been federally mandated. This study sought to explore the potential benefits of a multiple-indicator approach to water quality analysis of fresh and brackish surface waters. The distribution and fluctuation of two frequently used, EPA approved groups of FIB – fecal coliform and Enterococcus – were explored, and relationships between the two FIB groups were examined in fresh and brackish surface waters of Central and South Florida. Samples were collected over a period of 12 consecutive months, spanning April 2015 through March 2016, and analyzed using membrane filtration procedures outlined in Standard Methods 9222D and EPA method 1600. Raw and log transformed colony forming unit (CFU) data, per 100 mL, was analyzed annually and seasonally through linear regression, Spearman correlation, and exploratory data analysis techniques performed in R-Studio. The results of this study showed a moderate to strong relationship between fecal coliform and Enterococcus under both fresh and brackish conditions. The presence of a positive, linear relationship between fecal coliform and Enterococcus in both fresh and brackish water was apparent in both seasonal and annual regression analysis; upward and downward fluctuation(s) in one variable was shown to predict similar fluctuation(s) in the other year-round. However, while fecal coliform and Enterococcus showed moderate to strong correlations, causation was not implied. Low R2 values showed that the FIB groups were not dependent upon one another in any case, either annually or seasonally. The results of this study challenge previously accepted views of fecal coliform and Enterococcus effectiveness as ideal fresh and brackish water FIB, their suitability as sole indicators of fecal pollution, and their ideal usage as indicators for waters of varying salinities; results support those previously seen in studies such as Hanes and Fragala 1967, which emphasize the need for a multiple indicator approach to water quality analysis of ambient and recreational waters experiencing brackish conditions.

Agricultural runoff can carry FIB that can pollute surface waters through the soil matrix. This study was designed to inspect the impact of temperature and matrix on the survival of FIB. The FIB were routinely enumerated over an 18-day period from fecal samples and freshwater microcosms maintained at 4oC, 22oC, and 35oC. It was found that the FIB studied underwent a primary growth of up to 1-log10 to 3-log10, highlighting the weakness of conventional FIB as indicators of pathogen contamination. The concentrations of FIB in the water phase were found to be significantly greater than those observed in the fecal phase in all FIB and their associated survivals were found to be significantly different too. Similarly, temperature was also found to be a significant factor for the survival of FIB. While the differences in the survival were significant, there was a slight variation in the patterns regarding the differences.

Climatic variations, together with large-scale environmental forces and human development affect the quality of coastal recreational waters, creating potential risks to human health. These environmental forces, including increased temperature and precipitation, often promote specific vector-borne diseases in the Caribbean and Gulf of Mexico. Human activities affect water quality through discharges from urban areas, including nutrient and other pollutants derived from wastewater systems. Both water quality of recreational beaches and vector-borne diseases can be better managed by understanding their relationship with local environmental forces. I evaluated how changes in vector-borne diseases and poor recreational water quality were related to specific environmental factors through the application of satellite-derived observations, field observations, and public health records. Variability in dengue fever incidence rates in coastal towns of the Yucatan Peninsula (Mexico) was evaluated with respect to environmental factors in Chapter Two. Correlations between fecal indicator bacteria concentrations (i.e., culturable enterococci) at Escambron Beach (San Juan, Puerto Rico, USA) and regional environmental factors are discussed in Chapter Three. Predictions of dengue fever occurrences in the Yucatan Peninsula were tested using a nonlinear approach (i.e., Artificial Neural Networks) and are presented in Chapter Four. The Artificial Neural Network (ANN) model was also used to predict culturable enterococci concentration exceeding safe recreational water quality standards in Escambron Beach and results are presented in Chapter Five. Environmental factors assessed to understand their influence on dengue fever occurrences and culturable enterococci concentrations included precipitation, mean sea level (MSL), air temperatures (e.g., maximum, minimum, and average), humidity, and satellite-derived sea surface temperature (SST), dew point, direct normal irradiance (DNI), and turbidity. These factors were combined with demographic data (e.g., population size) and compared with dengue fever incidence rates and culturable enterococci concentration using linear and nonlinear statistical approaches. Dengue incidence rates in Yucatan (Mexico) generally increased in July/August and decreased during November/December. A linear regression model showed that previous dengue incidence rates explained 89% of dengue fever variability (p < 0.05). Dengue incidence two weeks prior (previous incidence) influences future outbreaks by allowing the virus to continue propagating. Yet dengue incidence was best explained by precipitation, minimum air temperature, humidity, and SST (p < 0.05). Dengue incidence variability was best explained by SST and minimum air temperature in our study region (r = 0.50 and 0.48, respectively). Increases in SST preceded increased dengue incidence rate by eight weeks. Dengue incidence time series were positively correlated to SST and minimum air temperature anomalies. This is related to the virus and mosquito behavior. Including oceanographic variables among environmental factors in the model improved modelling skill of dengue fever in Mexico. Chapter Three shows that precipitation, MSL, DNI, SST, and turbidity explained some of the enterococci variation in Escambron Beach surface waters (AIC = 26.76; r = 0.20). Variation in these parameters preceded increased culturable enterococci concentrations, with lags spanning from 24 h up to 11 days. The highest influence on culturable enterococci was precipitation between 480 mm–900 mm. Rainy events often result in overflows of sewage systems and other non-point sources near Escambron Beach in Puerto Rico. A significant decrease in culturable enterococci concentrations was observed during increased irradiance (r = -0.24). This may be due to bacterial inactivation. Increased culturable enterococci concentrations were significantly associated with higher turbidity daily anomalies (r = 0.25), in part because bacteria were protected from light inactivation. Increased culturable enterococci concentrations were related to warmer SST anomalies (r = 0.12); this is likely due to increased bacterial activity and reproduction. Higher culturable enterococci concentrations were also significantly correlated to medium to high values of dew point daily anomalies (r = 0.19). A significant decrease in culturable enterococci during higher daily MSL anomalies (r = -0.19) is possibly due to dilution of bacteria in beach waters, whereas during lower MSL anomalies the back-washing promotes increased bacteria concentrations through mixing from sediments. These environmental variables improve our understanding of the ecology of these bacteria over time. The predictive capability increases by including more than one environmental variable. Chapter Four explains a predictive model of dengue fever occurrences in San Juan, Puerto Rico (1994–2012), and Yucatan (2007–2012). The model was modified to predict dengue fever outbreak occurrences for two population segments: population at risk of infection (i.e., < 24 years old) and vulnerable population (i.e., < 5 years old and > 65 years old). There were a total of four predictive models, two sets for each location using the specified population segments. Model predictions showed previous dengue cases, minimum air temperature, date, and population size as the factors with the most influence to predict dengue fever outbreak occurrences in Mexico. Previous dengue cases, maximum air temperature, date, and population size were the most influential factors for San Juan, Puerto Rico. The models showed an accuracy around 50% and a predictive capability of 70%. These environmental and demographic variables are important primary predictors for dengue fever outbreaks in Puerto Rico and Mexico. Chapter Five shows the application of the ANNs model to predict culturable enterococci exceedance based on the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. The model identified DNI, turbidity, 48 h cumulative precipitation, MSL, and SST as the most influential factors to predict enterococci concentration exceedance, based on the U.S. EPA RWQC at Escambron Beach from 2005–2014. The model showed an accuracy of 76%, with a predictive capability greater than 60%, which is higher than linear models. Results showed the applicability of remote sensing data and ANNs to predict recreational water quality and help improve early warning system and public health. This work helps to better understand complex relationships between climatic variations and public health issues in tropical coastal areas and provides information that can be used by public health practitioners.

The bacterium Escherichia coli is internationally recognized for being the only exact environmental indicator for fecal contamination. In the decades of 2000 and 2010, research found this bacterium does not live only in intestines habitat, losing its exactitude as a fecal indicator. Some important questions were not answered, for instance, soil types and under what physic, chemical and geological conditions, E. coli grows in the environment; how its concentrations in river bank interferes in the water column concentration; why the concentration is so high in soil and sediments in relation to water column; what is its validity as a fecal indicator. This research aimed to answer these questions, more specifically, (1) to verify the occurrence and growth of this bacterium in river bank soil in tropical rivers in Brazil; (2) to evaluate if it is a natural soil specie; (3) to establish some of its ecological relations; (4) to identify techniques to potentialize its use as a fecal indicator. Mathematical models were utilized for E. coli dispersion simulation in river considering the sediments ressuspension and the bank erosion. The E. coli concentration was measured with membrane filtration method using the culture medium Endo at 37° C. The results show a natural E. coli occurrence in soil with concentrations such as 104 CFU/g dry soil, identified as an E. coli source. This work also concluded that the population persistence and growth depend on the clay properties to maintain the temperature and humidity and to adsorb the bacterium, decreasing its lateral flux in the water column. The E. coli adhesion is an evolution adaptation to fix it into its habitat. River bank E. coli goes to water column through erosion and ressuspension, being associated to suspended sediment concentration. The relation between soil and water concentration was 26.762 times more in soil at low velocities of the river, and 266 times in high velocities, showing a direct relation with bank erosion rate and sediment ressuspension. / A bactéria Escherichia coli é reconhecida internacionalmente como o único indicador ambiental exato para contaminação fecal. Nas décadas de 2000 e 2010, pesquisas constataram que essa bactéria não tem habitat exclusivamente intestinos, perdendo sua exatidão. Algumas questões importantes não foram respondidas, como quais tipos de solo e sob quais condições físicas, químicas e geológicas E. coli cresce no meio ambiente; como sua concentração nas margens de rios interfere na sua concentração na coluna d água; porque a concentração é alta no solo das margens e sedimento dos rios em relação a coluna d água; qual sua validade como indicador de contaminação fecal. Essa pesquisa objetivou responder essas questões, mais especificamente, (1) verificar a ocorrência e crescimento desta bactéria no solo das margens em rios tropicais no Brasil; (2) avaliar se faz parte da microbiota natural do solo; (3) estabelecer algumas de suas relações ecológicas; (4) identificar técnicas de potencializar seu uso como indicador de contaminação fecal. Foram utilizados modelos matemáticos para simulação da dispersão de E. coli na água considerando a ressuspensão de sedimentos e erosão das margens. A concentração de E. coli foi medida através do método de filtração em membrana e cultivo em meio de cultura Endo a 37° C. Os resultados mostram ocorrência natural de E. coli no solo com concentrações de 104 UFC/ g de solo seco, que foi identificada como a fonte de entrada de E. coli na coluna d água. Concluiu-se também a sobrevivência e crescimento da população dependente das propriedades da argila de manter a temperatura e umidade relativamente constantes, e de fixar a bactéria no solo diminuindo seu arraste para a coluna d água. A adesão de E. coli ambiental ao solo é uma adaptação evolutiva de fixação no habitat, tem forte adsorção à partícula de sedimento. E. coli das margens somente entra na coluna d água através da erosão e ressuspensão, estando associada à concentração de sedimento suspenso. A relação da concentração no solo e na água foi de 26.762 vezes maior no solo em baixas velocidades do rio, e 266 maior no solo em altas velocidades, apresentando relação direta com a taxa de erosão das margens e de ressuspensão de sedimentos.

Urban stormwater runoff is both an environmental threat and a valuable water resource. This dissertation explores the use of two stormwater management strategies, namely green stormwater infrastructure and stormwater real-time control (RTC), for capturing and treating urban stormwater runoff. Chapter 2 focuses on clean bed filtration theory and its application to fecal indicator bacteria removal in experimental laboratory-scale biofilters. This analysis is a significant step forward in our understanding of how physicochemical theories can be melded with hydrology, engineering design, and ecology to improve the water quality benefits of green infrastructure. Chapter 3 focuses on the novel application of unsteady transit time distribution (TTD) theory to solute transport in a field-scale biofilter. TTD theory closely reproduces experimental bromide breakthrough concentrations, provided that lateral exchange with the surrounding soil is accounted for. TTD theory also provides insight into how changing distributions of water age in biofilter storage and outflow affect key stormwater management endpoints, such as biofilter pollutant treatment credit. Chapter 4 focuses on stormwater RTC and its potential for improving runoff capture and water supply in areas with Mediterranean climates. We find that the addition of RTC increases the percent of runoff captured, but does not increase the percent of water demand satisfied. Our results suggest that stormwater RTC systems need to be implemented in conjunction with context-specific solutions (such as spreading basins for groundwater recharge) to reliably augment urban water supply in areas with uneven precipitation. Through a combination of modeling and experimental studies at a range of scales, this dissertation lays the foundation for future integration of TTD theory with RTC to improve regional stormwater management. / Doctor of Philosophy / Urban stormwater runoff contains a variety of pollutants. Conventional storm drain systems are designed to move stormwater as quickly as possible away from cities, delivering polluted runoff to local streams, rivers, and the coastal ocean – and discarding a valuable freshwater resource. By contrast, green stormwater infrastructure captures and retains stormwater as close as possible to where the rain falls. Green stormwater infrastructure can also help remove pollutants from stormwater through physical, chemical, and biological treatment processes. This dissertation describes two modeling approaches for understanding and predicting pollutant removal processes in green stormwater infrastructure (Chapters 2 and 3). Chapter 4 explores the implementation of smart stormwater systems, which use automated controllers and sensors to adaptively address stormwater management challenges. Through a combination of modeling and experimental studies at a range of scales, this dissertation lays the foundation for future improvements to regional stormwater management.

Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a “One Health” approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. / Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a "One Health" approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. / Doctor of Philosophy / Antibiotic resistance is ability of bacteria to withstand an antibiotic to which they were once sensitive. Antibiotic resistance is a global threat that can pose a serious threat to public health. In order to curb the spread of antibiotic resistance, it is imperative that efforts commensurate with the "One Health" approach. Since ecosystem networks can act as channels for the spread and spread of antibiotic resistance, there is growing recognition that a robust global environmental monitoring framework is required to promote a true one-health approach. The ideal goal would be to develop approaches that can inform the global spread of antibiotic resistance, help prioritize monitoring objectives and present robust data analysis frameworks for resistance profiling, and ultimately help develop strategies to contain the spread of antibiotic resistance. The objective of the work described in this thesis was to evaluate and develop different data analysis paradigms and their applications in the study and characterization of antibiotic resistance in different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. The Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. The results of Chapters 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes.