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Airborne microorganisms, such as bacteria, viruses and fungi, are abundant in the natural and built environments. This research encompasses two studies on virus aerosolization and transport in the built environment and the subsequent implications for human health. In the first study, we quantified the impact of a barrier on the spatial distribution of different-sized particles released by speaking in a poorly ventilated room. The room was outfitted with 108 passive sampling sites. The barrier resulted in an increase in 0.5 µm particles deposited on the source-side of the barrier and an increase in 0.5 µm particles at other locations 4-6 m from the source. The barrier had minor impacts on the distribution of 1, 6, 10 and 20 µm particles. The results from this study indicated that barriers may not serve as adequate protection to others in the room, depending on their locations relative to the barrier and the timescale of exposure. In the second study, we reviewed the applications of next-generation sequencing for viruses in water environments. We also characterized the occurrence of two viruses (crAssphage and SARS-CoV-2) from a local wastewater treatment plant (WWTP) in both water and air samples at two locations within the WWTP (influent and aeration basin). We found that crAssphage, a fecal indicator, was quantifiable in most air and water samples, but was not detected in control samples. SARS-CoV-2 N2 RNA was detected in a fraction of the water and air samples but was present in the control water samples, so results for this virus are confounded by laboratory contamination. We also found that there was no correlation between airborne and waterborne SARS-CoV-2 concentrations at the WWTP. A quantitative microbial risk assessment model was constructed to determine inhalation risks associated with airborne SARS-CoV-2 for WWTP operators. The probability of infection ranged from about 2.4 x 10-4 to 5.6 x 10-8 and was heavily dependent on exposure time, airborne concentration and other parameters. Doctor of Philosophy Airborne microorganisms, such as bacteria, viruses and fungi, are abundant in the natural and built environments. This research encompassed two studies that evaluated the impact of viruses in the built environment on public health. The first study investigated whether a barrier, like Plexiglas, could protect people from another person's exhaled particles in a poorly ventilated room. The barrier resulted in an increase in the smallest particles (0.5 µm) on the same side of the barrier as the source and an increase in these particles at other locations 4-6 m from the source, indicating that individual exposure depends on their location relative to the barrier. The barrier had minor impacts on larger particles (1, 6, 10, and 20 µm). The second study focused on viruses at wastewater treatment plants (WWTPs). As part of this study, we reviewed how one can use knowledge about the DNA and RNA of viruses in water and wastewater. We also measured the amount of two viruses (crAssphage and SARS-CoV-2) in wastewater and in air surrounding the WWTP. We detected crAssphage, a virus that infects bacteria and a marker for human activity, in most wastewater and air samples. We also detected SARS-CoV-2 in some wastewater and air samples, but this virus was also present in some control samples, so laboratory contamination was an issue. Using the concentrations of airborne SARS-CoV-2, we constructed a computational model to estimate the risk of infection for SARS-CoV-2 inhalation for WWTP employees. Our calculations indicated that the risk of infection ranged from 2.4 x 10-4 to 5.6 x 10-8 and heavily depended on parameters such as exposure time and airborne SARS-CoV-2 concentrations.

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.

Wastewater treatment plants (WWTPs) receive a confluence of sewage containing antibiotics, antibiotic resistant bacteria, antibiotic resistance genes (ARGs), and pathogens, thus serving as key point of interest for the surveillance of antibiotic resistance (AR) dissemination. This thesis advances knowledge about the fate of AR indicators throughout treatment and reuse. The field study informs approaches for monitoring AR at a WWTP by characterizing the resistome (i.e., full profile of ARGs) and microbiome across eight sampling events via metagenomic sequencing, complemented by antibiotic data. The WWTP significantly reduced the total load of ARGs and antibiotics, although correlations between ARGs and antibiotics were generally weak. Quantitative polymerase chain reaction was applied to validate the quantitative capacity of metagenomics, whereby we found strong correlations. The influent and effluent to the WWTP were remarkably stable with time, providing further insight into the sampling frequency necessary for adequate surveillance. The laboratory study examined the effects of commonly applied disinfection processes (chlorination, chloramination, and ultraviolet irradiation [UV]) on the inactivation of antibiotic resistant pathogens and corresponding susceptible pathogens in recycled and potable water. Further, we evaluated their regrowth following disinfection by simulating distribution. Acinetobacter baumannii, an environmental opportunistic pathogen, regrew especially well following UV disinfection, although not when a disinfectant residual was present. Enterococcus faecium, a fecal pathogen, did not regrow following any disinfection process. There were no significant differences between water types. The findings of this study emphasize a need to move beyond the framework of assessing treatment efficacy based on the attenuation of fecal pathogens. Master of Science Wastewater treatment plants (WWTPs) have traditionally been designed and further enhanced to minimize environmental contamination caused by solid waste, fecal pathogens, nutrients (e.g., nitrogen), and organic matter. However, treatment has not been optimized to remove the contaminants of emerging concern (CECs) investigated in this thesis: antibiotic resistant bacteria (ARB), antibiotic resistance genes (ARGs), and antibiotics. WWTPs are key point of interest for local and global surveillance of antibiotic resistance as they can receive the aforementioned CECs (via human excretion or improper disposal) from various sources (e.g., residences, hospitals). Antibiotic resistant bacteria have caused 2.8 million infections and subsequently 35,000 deaths in the United States each year. Considering treated wastewater can serve as a route of exposure for humans, potential spread of antibiotic resistance by WWTPs is of high priority to mitigate from a public health perspective. In the first study utilizing a technology to assess the full complement of ARGs in a given sample, we observed that the total load of ARGs was removed by approximately 50% across wastewater treatment, on average; total antibiotic load exhibited a similar reduction. The second study demonstrated that antibiotic resistant environmental opportunistic pathogen (i.e., pathogens which take advantage of the "opportunity" to infect an immunocompromised host, especially thriving in low nutrient engineered systems), Acinetobacter baumannii, possesses the ability to regrow following disinfection in the absence of a disinfectant residual. In contrast, antibiotic resistant Enterococcus faecium, an opportunistic pathogen of fecal origin, was successfully inactivated and unable to regrow. The findings of this study emphasize a need to move beyond the framework of assessing treatment efficacy based on the attenuation of fecal pathogens.

As the population in water stressed areas increases, it is critical that wastewater treatment plants (WWTPs) continue to replenish depleted water supplies, and serve as an alternative water source. WWTPs depend on microorganisms in activated sludge to remove pollutants from wastewater and therefore an understanding of how these microorganisms are affected by various conditions and pollutants is needed. Also, as consumer products and industrial processes evolve, so do the pollutants they discharge to wastewater. In order to keep pace with these changes, understanding the effects of emerging contaminants to WWTP processes is essential. The research herein assesses microbial community dynamics of the response of nitrifying microorganisms in activated sludge to variation in ammonia concentration and evaluates the impact of engineered nanoparticles on activated sludge microbial communities and other emerging pollutants, such as antibiotic resistance genes and disinfection by-products. In order to assess microbial community dynamics of the response of nitrifying microorganisms to removal of ammonia in the feed, nitrifying activated sludge reactors were operated at various relevant temperatures and the nitrifying microbial community was characterized using activity assays and bio-molecular techniques. We found that Nitrospira spp. were the dominant nitrifying microorganisms, exhibiting stable relative abundance across multiple trials and over a range of temperatures. These results indicate the possibility of comammox bacteria in the system and highlight the complexity of nitrifying microbial communities in activated sludge relative to past understanding. Both microbial and chemical impacts of engineered nanoparticles on WWTP processes were also investigated. Metagenomic analysis of DNA extracted from activated sludge sequencing batch reactors dosed with gold nanoparticles with varied surface coating and morphology indicated that nanoparticle morphology impacted the microbial community and antibiotic resistance gene content more than surface coating. However, nanoparticle fate was controlled by surface coating more than morphology. Disinfection by-product formation in the presence of nanoparticles during WWTP disinfection was assessed using silver, titanium dioxide, ceria, and zero valent iron nanoparticles. Silver nanoparticles were found to enhance trihalomethane formation, which was attributed to the citrate coating of the nanoparticles. These studies both raise concern over the relationship between engineered nanoparticles and other emerging concerns in WWTPs, and take a step towards informing nanoparticle design in a manner that limits their associated environmental impact. Ph. D.

The purpose of this dissertation was to broadly investigate the fate of antibiotic resistance genes (ARGs) and engineered nanomaterials (ENMs) as representative contaminants of emerging concern in wastewater treatment plants (WWTPs). WWTPs may have their performance impacted by ENMs and may also serve as a reservoir and point of release for both ENMs and ARGs into the environment. Of interest were potential adverse effects of ENMs, such as stimulation of antibiotic resistance in the WWTP, toxicity to microbial communities critical for WWTP performance, and toxicity to humans who may be exposed to effluents or aerosols containing ENMs and their transformation products. Response of nine representative ARGs encoding resistance to sulfonamide, erythromycin and tetracycline to various lab-scale sludge digestion processes were examined, and factors that drove the response of ARGs were discussed. Mesophilic anaerobic digestion significantly reduced sulI, sulII, tet(C), tet(G), and tet(X) with longer solids retention time (SRT) exhibiting a greater extent of removal. Thermophilic anaerobic digesters performed similarly to each other and provided more effective reduction of erm(B), erm(F), tet(O), and tet(W) compared to mesophilic digestion. Thermal hydrolysis pretreatment drastically reduced all ARGs, but they generally rebounded during subsequent anaerobic and aerobic digestion treatments. Bacterial community composition of the sludge digestion process, as controlled by the physical operating characteristics, was indicated to drive the distribution of ARGs present in the produced biosolids, more so than the influent ARG composition. Effects of silver (nanoAg), zero-valent iron (NZVI), titanium dioxide (nanoTiO2) and cerium dioxide (nanoCeO2) nanomaterials on nitrification function and microbial communities were examined in duplicate lab-scale nitrifying sequencing batch reactors (SBRs), relative to control SBRs received no materials or ionic/bulk analogs. Nitrification function was only inhibited by high load of 20 mg/L Ag+, but not by other nanomaterials or analogs. However, decrease of nitrifier gene abundances and distinct microbial communities were observed in SBRs receiving nanoAg, Ag+, nanoCeO2, and bulkCeO2. There was no apparent effect of nanoTiO2 or NZVI on nitrification, nitrifier gene abundances, or microbial community structure. A large portion of nanoAg remained dispersed in activated sludge and formed Ag-S complexes, while NZVI, nanoTiO2 and nanoCeO2 were mostly aggregated and chemically unmodified. Thus, the nanomaterials appeared to be generally stable in the activated sludge, which may limit their effect on nitrification function or microbial community structure. Considering an aerosol exposure scenario, cytotoxicity and genotoxicity of aqueous effluent and biosolids from SBRs dosed with nanoAg, NZVI, nanoTiO2 and nanoCeO2 to A549 human lung epithelial cells were examined, and the effects were compared relative to outputs from SBRs dosed with ionic/bulk analogs and undosed SBRs, as well as pristine ENMs. Although the pristine nanomaterials showed varying extents of cytotoxicity to A549 cells, and gentoxicity was observed for nanoAg, no significant cytotoxic or genotoxic effects of the SBR effluents or biosolids containing nanomaterials were observed. Studies presented in this dissertation provided new insights in the fate of ARGs in various sludge digestion processes and ENMs in nitrifying activated sludge system in lab-scale reactors. The study also yielded toxicity data of ENMs to biological wastewater treatment microbial communities and human lung cells indicated by a variety of toxicity markers. The results will aid in identifying appropriate management technologies for sludge containing ARGs and will inform microbial and human toxicity assessments of ENMs entering WWTPs. Ph. D.