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.

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.

Les rejets urbains par temps de pluie dégradent l’état écologique des écosystèmes aquatiques et peuvent induire une exposition des populations humaines aux contaminants chimiques et microbiens (bactéries, virus, parasites). L’objectif de ce travail de thèse était d’évaluer les effectifs et de prédire le devenir de bactéries pathogènes introduites dans les milieux aquatiques par une source majeure comme les eaux usées rejetées par des dispositifs tels que les déversoirs d’orage (DO) et les lagunes d’épuration (WWTL). La répartition d’un agent pathogène fortement liée aux milieux hydriques, Pseudomonas aeruginosa, a été comparée avec celles observées pour des indicateurs de contaminations fécales (E. coli et les entérocoques intestinaux), mais également avec celle de l’espèce pathogène Aeromonas caviae. La dangerosité des formes retrouvées dans ces milieux a été évaluée par approches moléculaires (PFGE et MLST). Les résultats obtenus montrent un fort apport en P. aeruginosa via les eaux usées, avec un effet significatif sur les effectifs observés en fonction de l’intensité des pluies et des périodes de temps sec, et les fluctuations du régime hydrologique et des paramètres physico-chimiques. Une grande diversité infra-spécifique des P. aeruginosa, et la capacité de certains génotypes à s’installer durablement dans ces milieux (macrophytes et périphyton) ont été observées. Certaines souches ont par ailleurs montré une parenté avec des lignées d’infections communautaires, ou encore des clones épidémiques majeurs (PA14 et C). Des études en microcosme ont été effectuées pour valider les interactions observées avec certains macrophytes, et identifier des propriétés d’adhérence bactérienne (dont les lectines) impliquées dans ces interactions. Ces travaux ont impliqué une analyse de la distribution des gènes lecA et lecB, codant des lectines chez P. aeruginosa, et une étude de leurs ligands. Le gène lecA a été localisé dans une zone de forte plasticité génomique. Ces travaux ont permis la description d’une nouvelle structure de l’adhésine LecB / Urban wet-weather discharges degrade the ecological status of aquatic ecosystems and may expose human populations to chemical and microbial contaminants (bacteria, viruses, parasites). The aim of this thesis was to evaluate the numbers and predict the fate of pathogenic bacteria introduced into aquatic ecosystems by a major source like wastewater from devices such as combined sewer overflows (CSO) and wastewater treatment lagoons (WWTL). The distribution of a human pathogen closely linked to hydric environments, Pseudomonas aeruginosa, was compared with those observed for fecal indicators (E. coli and intestinal enterococci), but also with that of Aeromonas caviae pathogenic species. Dangerousness of strains found in these environments was evaluated by molecular approaches (PFGE and MLST). Obtained results showed a high contribution of wastewater in P. aeruginosa release, with a significant effect of rainfall intensity and preceding dry periods, in addition to changes in hydrological regime and physico-chemical parameters on recorded data. A large infra-specific diversity was observed within P. aeruginosa and the ability of some genotypes to colonize permanently aquatic surfaces (macrophytes and periphyton) were observed. Some strains showed a kinship with lineages of community infections or major epidemic clones (PA14 and C). Microcosm studies were performed to validate observed interactions with macrophytes, and to identify bacterialadhesion properties (including lectins) involved in these interactions. These investigations involved analysis of the distribution of lectin encoding loci lecA and lecB within P. aeruginosa, and a study of their ligands. lecA was located in a highly unstable genomic region. This work allowed the description of a new structure of the adhesin LecB

En santé publique, de nouveaux programmes de surveillance et de gestion sont à proposer notamment pour les masses d'eaux fortement affectées par l'urbanisation et les rejets urbains par temps de pluie. Les niveaux, les réservoirs et les sources de contamination microbiologique des milieux aquatiques doivent être évalués et identifiés, et leur incidence sur la dissémination des bactéries pathogènes et les risques d'exposition des populations humaines comprises. Parmi les agents pathogènes retrouvés dans les milieux hydriques, Pseudomonas aeruginosa représentent une préoccupation sanitaire majeure. A partir de deux sites expérimentaux, les objectifs de ce travail de thèse furent de définir la contribution des rejets d'eaux usées sur la prévalence de P. aeruginosa dans les cours d'eau récepteurs et d'étudier l'écologie des formes introduites dont ces habitats incluant leur dynamique spatiotemporelle. Les sédiments, les biofilms de surface (périphyton) et les végétaux aquatiques submergés, permettraient leur survie voire la multiplication de certains génotypes de Pseudomonas aeruginosa. La répartition des indicateurs de contaminations fécales et de la bactérie pathogène Aeromonas caviae ont également été étudiée. Il a été mis en évidence que les forces hydrauliques, le morpho-dynamisme de la rivière et les variations saisonnières étaient des facteurs structurants de la répartition des contaminants microbiens analysés. La dangerosité des souches isolées a été évaluée et montré que toutes les souches avaient un potentiel de virulence élevée. Ceci s'est confirmé par la détection de clones épidémiques majeurs au sein de la collection dont des souches apparentées aux clones PA14 ou C. Les capacités métaboliques de ces souches ont été étudiées dont leur antibio-résistance / In Public Health, new monitoring and management programs are needed, especially for the aquatic environments strongly affected by urbanization and urban wet weather discharges. The levels, reservoirs and sources of microbiological contamination of the aquatic environment should be assessed and identified, and their impact on the spread of pathogenic bacteria and the risk of exposure of human populations understood. Among the pathogens found in water environments, Pseudomonas aeruginosa is of major health concern. From two experimental sites, the objectives of this work were to define the contribution of wastewater discharges on the prevalence of P. aeruginosa in receiving watercourses and study the ecology of the introduced forms, including their spatiotemporal dynamic and preferential habitats. Sediments, surface biofilms (periphyton) and the submerged aquatic vegetations appeared to favour the survival or growth of some genotypes of Pseudomonas aeruginosa. The distribution of fecal indicator bacteria and of Aeromonas caviae were also studied. It was highlighted that hydraulic forces, the morpho-dynamics of the river and the seasonal vaiations were determinant factors in the distribution of the analyzed microbial contaminants. The health hazard of the clones found in these systems was estimated through indirect molecular approaches. It was shown that all Pseudomonas aeruginosa strains had a high virulence potential and that some were related to the PA14 and C clones which are spread worldwide and pathogenic

The savannas of southern Africa are a highly variable and globally-important biome supporting rapidly-expanding human populations, along with one of the greatest concentrations of wildlife on the continent. Savannas occupy a fifth of the earth's land surface, yet despite their ecological and economic significance, understanding of the complex couplings and feedbacks that drive spatiotemporal patterns of change are lacking. In Chapter 1 of my dissertation, I discuss some of the different theoretical frameworks used to understand complex and dynamic changes in savanna structure and composition. In Chapter 2, I evaluate spatial drivers of water quality declines in the Chobe River using spatiotemporal and geostatistical modeling of time series data collected along a transect spanning a mosaic of protected, urban, and developing urban land use. Chapter 3 explores the complex couplings and feedbacks that drive spatiotemporal patterns of land cover (LC) change across the Chobe District, with a particular focus on climate, fire, herbivory, and anthropogenic disturbance. In Chapter 4, I evaluated the utility of Distance sampling methods to: 1) derive seasonal fecal loading estimates in national park and unprotected land; 2) provide a simple, standardized method to estimate riparian fecal loading for use in distributed hydrological water quality models; 3) answer questions about complex drivers and patterns of water quality variability in a semi-arid southern African river system. Together, these findings have important implications to land use planning and water conservation in southern Africa's dryland savanna ecosystems. / Ph. D.