Identifying the Structure and Fate of Wastewater Derived Organic Micropollutants by High-resolution Mass Spectrometry

Human activities represent a significant burden on the global water cycle, with large and increasing demands placed on limited water resources by manufacturing, energy production and domestic water use. In addition to changing the quantity of available water resources, human activities lead to changes in water quality by introducing a large and often poorly-characterized array of chemical pollutants, which may negatively impact biodiversity in aquatic ecosystems, leading to impairment of valuable ecosystem functions and services. Domestic and industrial wastewaters represent a significant source of pollution to the aquatic environment due to inadequate or incomplete removal of chemicals introduced into waters by human activities. Currently, incomplete chemical characterization of treated wastewaters limits comprehensive risk assessment of this ubiquitous impact to water. In particular, a significant fraction of the organic chemical composition of treated industrial and domestic wastewaters remains uncharacterized at the molecular level. Efforts aimed at reducing the impacts of water pollution on aquatic ecosystems critically require knowledge of the composition of wastewaters to develop interventions capable of protecting our precious natural water resources.

The goal of this dissertation was to develop a robust, extensible and high-throughput framework for the comprehensive characterization of organic micropollutants in wastewaters by high-resolution accurate-mass mass spectrometry. High-resolution mass spectrometry provides the most powerful analytical technique available for assessing the occurrence and fate of organic pollutants in the water cycle. However, significant limitations in data processing, analysis and interpretation have limited this technique in achieving comprehensive characterization of organic pollutants occurring in natural and built environments. My work aimed to address these challenges by development of automated workflows for the structural characterization of organic pollutants in wastewater and wastewater impacted environments by high-resolution mass spectrometry, and to apply these methods in combination with novel data handling routines to conduct detailed fate studies of wastewater-derived organic micropollutants in the aquatic environment.

In Chapter 2, chemoinformatic tools were implemented along with novel non-targeted mass spectrometric analytical methods to characterize, map, and explore an environmentally-relevant “chemical space” in municipal wastewater. This was accomplished by characterizing the molecular composition of known wastewater-derived organic pollutants and substances that are prioritized as potential wastewater contaminants, using these databases to evaluate the pollutant-likeness of structures postulated for unknown organic compounds that I detected in wastewater extracts using high-resolution mass spectrometry approaches. Results showed that application of multiple computational mass spectrometric tools to structural elucidation of unknown organic pollutants arising in wastewaters improved the efficiency and veracity of screening approaches based on high-resolution mass spectrometry. Furthermore, structural similarity searching was essential for prioritizing substances sharing structural features with known organic pollutants or industrial and consumer chemicals that could enter the environment through use or disposal.

I then applied this comprehensive methodological and computational non-targeted analysis workflow to micropollutant fate analysis in domestic wastewaters (Chapter 3), surface waters impacted by water reuse activities (Chapter 4) and effluents of wastewater treatment facilities receiving wastewater from oil and gas extraction activities (Chapter 5). In Chapter 3, I showed that application of chemometric tools aided in the prioritization of non-targeted compounds arising at various stages of conventional wastewater treatment by partitioning high dimensional data into rational chemical categories based on knowledge of organic chemical fate processes, resulting in the classification of organic micropollutants based on their occurrence and/or removal during treatment. Similarly, in Chapter 4, high-resolution sampling and broad-spectrum targeted and non-targeted chemical analysis were applied to assess the occurrence and fate of organic micropollutants in a water reuse application, wherein reclaimed wastewater was applied for irrigation of turf grass. Results showed that organic micropollutant composition of surface waters receiving runoff from wastewater irrigated areas appeared to be minimally impacted by wastewater-derived organic micropollutants. Finally, Chapter 5 presents results of the comprehensive organic chemical composition of oil and gas wastewaters treated for surface water discharge. Concurrent analysis of effluent samples by complementary, broad-spectrum analytical techniques, revealed that low-levels of hydrophobic organic contaminants, but elevated concentrations of polymeric surfactants, which may effect the fate and analysis of contaminants of concern in oil and gas wastewaters.

Taken together, my work represents significant progress in the characterization of polar organic chemical pollutants associated with wastewater-impacted environments by high-resolution mass spectrometry. Application of these comprehensive methods to examine micropollutant fate processes in wastewater treatment systems, water reuse environments, and water applications in oil/gas exploration yielded new insights into the factors that influence transport, transformation, and persistence of organic micropollutants in these systems across an unprecedented breadth of chemical space.

Phase behavior and far-from-equilibrium gelation in charged attractive colloids

In this Rapid Communication we demonstrate the applicability of an augmented Gibbs ensemble Monte Carlo approach for the phase behavior determination of model colloidal systems with short-ranged depletion attraction and long-ranged repulsion. This technique allows for a quantitative determination of the phase boundaries and ground states in such systems. We demonstrate that gelation may occur in systems of this type as the result of arrested microphase separation, even when the equilibrium state of the system is characterized by compact microphase structures.

Consumer Behavior and Firm Competition

Understanding consumer behavior is critical for firms' decision making. How consumers make decisions about what they want and buy directly affect the profits of firms. Therefore, it is important to consider consumer behaviors and incorporate them into the model when studying the optimal strategy of firms and competition between firms. In this dissertation, I study rich and interesting consumer behaviors and their impact on firms' strategy in two essays. The first essay considers consumers' shopping cost which leads to their preference for one-stop shopping. I examine how store visit costs and consumer knowledge about a product affect the strategic store choice of consumers and, in turn, the pricing, customer service and advertising decisions of competing retailers. My analysis offers insights on how specialty stores can compete with big-box retailers. In the second essay, I focus on a well-established psychology phenomenon, cognitive dissonance. I incorporate the idea of cognitive dissonance into a model of spatial competition and examine its implications for selling strategy. I provide new insight on the profitability of advance selling and spot selling as well as the pricing of bundle and its components. Collectively, two essays in this dissertation introduce novel ways to model consumer behaviors and help to understand the impact of consumer behaviors on firm profitability and strategy.

Understanding Patients’ Needs and Healthcare Seeking Behavior in Rural Southern India: The Comparison of Providers through Patients’ Perceptions and Cost Issues

This study explores patients’ needs in rural Thanjavur, southern India through understanding how people with diabetes choose providers and perceive care-seeking experience. To measure perception, the study surveyed people regarding six common barriers to care-seeking behavior, selected from both literature and local expert interview. Ninety-one percent of the sampled population goes to public or private allopathic providers out of the six presented providers. The low socioeconomic group and people with more complications or comorbidities are more likely to go to private allopathic providers. What is more, there is no difference between public and private allopathic providers in patients’ perception of care except for perceived cost. Positive perceptions in both providers are very common except for perceptions in blood-sugar management, distance to facilities, and cost of care. Sixty-six percent of patients perceived their blood-sugar control to fluctuate or have no change versus improved control. Twenty-seven percent of patients perceived the distance to facilities as unreasonable, and sixty-two percent of patients perceived the cost as high for them. The results suggest that cost may affect low socioeconomic people’s choice of care significantly. However, for people in middle and higher socioeconomic groups, cost does not appear to be a major factor. For qualitative text analyses, physician’s behavior and reputation emerge as themes, which require further studies.

Impact of Genetic Testing and Family Health History Based Risk Counseling on Behavior Change and Cognitive Precursors for Type 2 Diabetes.

Family health history (FHH) in the context of risk assessment has been shown to positively impact risk perception and behavior change. The added value of genetic risk testing is less certain. The aim of this study was to determine the impact of Type 2 Diabetes (T2D) FHH and genetic risk counseling on behavior and its cognitive precursors. Subjects were non-diabetic patients randomized to counseling that included FHH +/- T2D genetic testing. Measurements included weight, BMI, fasting glucose at baseline and 12 months and behavioral and cognitive precursor (T2D risk perception and control over disease development) surveys at baseline, 3, and 12 months. 391 subjects enrolled of which 312 completed the study. Behavioral and clinical outcomes did not differ across FHH or genetic risk but cognitive precursors did. Higher FHH risk was associated with a stronger perceived T2D risk (pKendall < 0.001) and with a perception of "serious" risk (pKendall < 0.001). Genetic risk did not influence risk perception, but was correlated with an increase in perception of "serious" risk for moderate (pKendall = 0.04) and average FHH risk subjects (pKendall = 0.01), though not for the high FHH risk group. Perceived control over T2D risk was high and not affected by FHH or genetic risk. FHH appears to have a strong impact on cognitive precursors of behavior change, suggesting it could be leveraged to enhance risk counseling, particularly when lifestyle change is desirable. Genetic risk was able to alter perceptions about the seriousness of T2D risk in those with moderate and average FHH risk, suggesting that FHH could be used to selectively identify individuals who may benefit from genetic risk testing.

Neural Mechanisms of Young Adult Sexual Decision-Making and Risk Behavior

Sexual risk behavior among young adults is a serious public health concern; 50% will contract a sexually transmitted infection (STI) before the age of 25. The current study collected self-report personality and sexual history data, as well as neuroimaging, experimental behavioral (e.g., real-time hypothetical sexual decision making data), and self-report sexual arousal data from 120 heterosexual young adults ages 18-26. In addition, longitudinal changes in self-reported sexual behavior were collected from a subset (n = 70) of the participants. The primary aims of the study were (1) to predict differences in self-report sexual behavior and hypothetical sexual decision-making (in response to sexually explicit audio-visual cues) as a function of ventral striatum (VS) and amygdala activity, (2) test whether the association between sexual behavior/decision-making and brain function is moderated by gender, self-reported sexual arousal, and/or trait-level personality factors (i.e., self-control, impulsivity, and sensation seeking) and (3) to examine how the main effects of neural function and interaction effects predict sexual risk behavior over time. Our hypotheses were mostly supported across the sexual behavior and decision-making outcome variables, such that neural risk phenotypes (heightened reward-related ventral striatum activity coupled with decreased threat-related amygdala activity) were associated with greater lifetime sexual partners at baseline measured and over time (longitudinal analyses). Impulsivity moderated the relationship between neural function and self-reported number of sexual partners at baseline and follow up measures, as well as experimental condom use decision-making. Sexual arousal and sensation seeking moderated the relationship between neural function and baseline and follow up self-reports of number of sexual partners. Finally, unique gender differences were observed in the relationship between threat and reward-related neural reactivity and self-reported sexual risk behavior. The results of this study provide initial evidence for the potential role for neurobiological approaches to understanding sexual decision-making and risk behavior. With continued research, establishing biomarkers for sexual risk behavior could help inform the development of novel and more effective individually tailored sexual health prevention and intervention efforts.