Contamination of surface and groundwater resources by conventional agricultural practices is becoming increasingly prevalent. In recent years, development of small footprint, low-maintenance, onsite woodchip bioreactors have helped to curb nitrate discharge from tile-drainage collection to downstream receiving waterways. Determining best practices to jumpstart microbial degradation of pesticides in similar reactors could help address pesticide pollution from agricultural areas. Chapter 1 of my dissertation provides the motivation for the following chapters, outlining the respective research objectives. Chapter 2 of my dissertation assesses pesticide removal in woodchip bioreactor systems at three scales: field-scale, bench-scale, and micro-scale batch reactors. I constructed a bench-scale continuous-flow woodchip bioreactor and operated the reactor under field-like conditions to evaluate joint pesticide and nitrate removal. The continuous-flow reactor achieved 83.5 ± 8% diuron removal and 61.6 ± 11.9% imidacloprid removal with a 24 h hydraulic retention time (HRT). I then designed a sequencing-batch reactor configuration to evaluate the impact of an aerobic phase on denitrification and pesticide removal performance. The sequencing-batch reactor achieved 89.2 ± 8.8% nitrate removal with an HRT of 12 h, while the continuous-flow design achieved 55.6 ± 9.1% nitrate removal with a 12 h HRT. There was no significant difference between pesticide removal between sequencing-batch and continuous-flow reactor types. Kinetic batch tests revealed sorption, not microbial degradation as the main mechanisms of removal for both diuron and imidacloprid under denitrifying conditions. Chapter 3 of my dissertation describes a combined ex-vivo and in silico screening approach I developed to understand and predict the catalytic competency of esterases for ester hydrolysis of pyrethroid pesticides and structurally related compounds. I hypothesized that esterases active toward pyrethroids would also interact with structurally similar esters. I screened fourteen representative, wild-type microbial esterases against four pyrethroids, four alternative esters, and a suite of designed/known esterase and lipase substrates. I assayed each enzyme/substrate pair at different concentrations and analyzed assay extracts for specific esterase transformation products to confirm esterase activity. Phenyl salicylate, an ester compound with low aquatic toxicity, was found to be degradable by some of the same enzymes that were found to degrade pyrethroids. In silico induced fit docking of target compounds with esterases of interest highlighted amino acid residues important to substrate binding. For some enzymes, interaction fingerprint data revealed residue interaction patterns that are indicative of catalytic competency (i.e., enzymatic activity). Findings from these in silico experiments could be used as constraints in future molecular docking simulations. Finally, Chapter 4 investigates the biodegradation potential of autochthonous microbial communities enriched on target pyrethroids as well as non-toxic, structurally similar substrates. I hypothesized that phenyl salicylate (identified in Chapter 3 as sharing enzyme activity with pyrethroids) stimulates enzymatic activity effective towards degradation of the two pyrethroids, bifenthrin and cypermethrin. I isolated a native microbial community from a functional woodchip bioreactor, and grew aliquots on phenyl salicylate, bifenthrin, cypermethrin, or glucose as the sole carbon substrate. I compared the growth patterns and metabolic characteristics. I found that the microbial consortia grew similarly on phenyl salicylate and glucose, while the growth curves of the bifenthrin and cypermethrin enrichments were characteristic of a more recalcitrant substrate (i.e., longer lag phase). I also employed a suspect screening approach to track the formation of commonly found metabolites of bifenthrin, cypermethrin, and phenyl salicylate. Results demonstrated that esterase activity occurred in each of these enrichments using the respective substrates. Results also provided evidence that phenyl salicylate-enriched communities may share enzyme profiles with bifenthrin- or cypermethrin-enriched communities and suggest that phenyl salicylate may act as a low-toxicity biostimulant for pyrethroid degradation.

Untargeted sequencing of total RNA in wastewater, known as metatranscriptomics, has recently garnered interest due to its potential for detecting outbreaks and circulation of pathogens and their variants, especially those of RNA viruses. Both the liquid and settled solids fractions of wastewater influent have been successfully used and compared as starting material for community-level surveillance of select targets such as SARS-CoV-2 using quantitative PCR and to a limited extent, targeted sequencing. We seek to complement such comparative studies using a holistic, i.e. untargeted microbiome sequencing, approach to understand the differences in microbial diversity that can be captured between the two sample types, which may inform our decision surrounding choice of sample type for future routine PCR-based surveillance programs which aim to include a broader range of targets. We present a feasibility study for using total untargeted RNA sequencing for the differentiation of microbial abundance and diversity within liquid influent and settled solids samples. Raw wastewater samples were collected from two wastewater treatment plants—City of Davis and University of California at Davis—in January 2022 and March 2022. Extraction of total nucleic acids from liquid influent and settled solids were performed using protocols optimized for PCR-based surveillance of SARS-COV-2, followed by mRNA capture and untargeted library preparation for Illumina shotgun sequencing. For data analysis, we evaluated two open access tools requiring varying skill levels: Chan Zuckerberg ID (CZID), a cloud-based analysis and visualization pipeline with a web-based interface and Kraken2, a script-based pipeline that requires knowledge of the command line interface. We found no significant differences in overall microbial richness, alpha diversity and relative abundance between influent and settled solids samples, though beta diversity analysis suggested samples within a sample group (combination of sample type and location) were most similar to each other. A greater number of phages, novel viruses, and transcribed AMR genes were reported in UCD samples compared to COD. Overall, Kraken2 and CZID were concordant in their detection of the topmost relatively abundant species across domains. The total number of viral classified reads in the overall dataset was lower than expected, which may be addressed in future studies through the optimization of sample pre-concentration steps and the use of pathogen enrichment panels to perform targeted sequencing.

An increasing numbers of trace organic contaminants (TrOCs) are being produced, used, and discharged into the environment. Over 17,000 pesticides and 4,730 per- and polyfluoroalkyl substances (PFAS) were reported by the Organisation for Economic Co-operation and Development (OECD) to be in circulation. Further TrOC diversity is generated when commercially produced compounds transform through myriad pathways. Understanding the public health impacts of environmental exposures to such a large number and wide structural diversity of TrOCs remains a significant challenge. Targeted chemical assays, the most common approach to bioaccumulation and risk assessment, provide a limited understanding of contaminant profiles in biological tissues and associated risks. More comprehensive analytical pipelines are needed. In this dissertation, I develop and apply new methods to assess and predict bioaccumulation and toxicity of complex mixtures of TrOCs, using in vivo, in vitro and in silico approaches. I present the three complementary strategies that together aim to improve management strategies for broad classes of TrOCs.First, I develop and demonstrate an in vivo screening approach to assess the bioaccumulation and risk of TrOCs in complex mixtures. I apply the approach to assess pesticide and PFAS bioaccumulation in edible insect larvae (i.e., H. illucens) reared on agricultural by-products (i.e., almond hulls). Rather than targeting specific TrOCs from the onset, as is typical in chemical risk assessment, my approach broadly screens substrates and organism tissues for TrOCs using large databases of known contaminants. Multiple chemical extracts, obtained using methods that capture TrOCs with a broad range of physiochemical properties, are subjected to one or more of four analytical pipelines (two operational modes each for liquid and gas chromatography coupled with time-of-flight mass spectrometry). Semi-quantitative analysis of the substrates screening revealed that bioaccumulative, persistent, and toxic chemicals are abundant in the agricultural by-products. Initial substrate screening was also used to guide targeted and non-targeted substrate and tissue analyses from in vivo bioaccumulation assays. Using this approach, I found that bifenthrin and a novel PFAS class bioaccumulate in the larvae tissue, initiating a pathway for contaminant transfer into the food chain. Second, I use in vitro and in silico approaches to assess bioaccumulation potential of PFAS in a commercially available class-B firefighting foam—aqueous film forming foams (AFFF). PFAS accumulation in serum and organ tissues are caused by PFAS precursor metabolisms and protein bindings. It is challenging to identify and quantitatively assess the contribution of each bioaccumulation pathway. However, mechanistic understanding in PFAS bioaccumulation is essential for prediction model and bioaccumulation potential assessment across the chemicals. In this dissertation, I collect binding data from equilibrium dialysis experiments using human serum albumin (HSA), the most abundant serum protein in human blood, and AFFF. This in vitro approach eliminates the influence of biotransformation to bioaccumulation and help discover ultra-strong binding or potentially covalent binding PFAS to HSA. I further use experimental data to test the effectiveness of molecular docking predicted HSA-binding and -nonbinding compounds in AFFF. The combination of in vitro and in silico approaches provide replicable, high-throughput workflows for assessing bioaccumulation potentials of chemicals in commercial products that are structurally diverse like PFAS. Third, I use in silico simulations validated in Part 2 to generate noncovalent binding scores of over 4,760 PFAS structures to human carrier and receptor proteins. In this dissertation, I hypothesize that protein binding scores generated from molecular docking can improve model performance for bioactivity/toxicity prediction of PFAS. Moreover, the prediction model can be used to predict bioactivity/toxicity of PFAS with similar chemical structure. I test this theory by correlating protein binding scores along with 45 other chemical descriptors to negative health endpoints using linear discrimination analysis and machine learning algorithms. This quantitative structure-activity relationship (QSAR) uses state-of-the-art knowledge to predict toxicity of PFAS with known structure. Altogether, the combination of experimental and modeling techniques provided value in assessing the bioaccumulation and toxicity of organic contaminants.

Viruses in wastewater present public-health challenges as well as public-health opportunities. I consider both herein. I begin with a systematic literature review of nearly 300 studies, published from 2000 to 2018, that document applications of flow cytometry (FCM) to ensure microbial water quality and hence facilitate safe and effective water treatment, distribution, and reuse. I find that while there is a large body of evidence supporting widespread adoption of FCM as a routine method for microbial water-quality assessment, key knowledge gaps impede the technique from realizing its full potential. One of these gaps is robust protocols for FCM-based analysis of waterborne viruses. In this dissertation, I hypothesize that a fractional factorial experimental design is a better alternative to the “pipeline” strategy commonly followed for FCM protocol optimization. I then demonstrate my approach, using a fractional factorial experimental design to optimize staining of the bacteriophage T4 prior to FCM analysis. My results yield a specific protocol for reliably identifying and quantifying T4 bacteriophage through FCM.

I also explain why manual gating of FCM data using a series of two-dimensional plots—the typical approach to FCM data analysis—is problematic, especially with respect to applications of FCM to facilitate advanced water treatment and reuse. I suggest that algorithmic clustering approaches could expedite and improve FCM data analysis, and could even help position FCM as a technique for real-time microbial water-quality monitoring. I test this theory by generating FCM data from two solutions: (i) a mixed-target solution containing a variety of environmentally relevant viral surrogates, and (ii) an environmental-spike solution comprising T4 bacteriophage in a wastewater matrix. I first analyze these data through manual gating, and then compare results to results obtained through algorithmic clustering: specifically, by coupling the OPTICS ordering algorithm with either manual or automated identification of clusters from the OPTICS-ordered data. I demonstrate that OPTICS-assisted clustering can in some cases work as well or better than manual gating of FCM data—and is certainly far faster and less labor-intensive. OPTICS-assisted clustering can also point to features in FCM data that are difficult to detect through manual gating alone. However, I also find that more needs to be done to position OPTICS as a reliable tool for automated, objective analysis of FCM data from environmental samples, especially data generated from challenging biological targets like viruses in challenging matrices like wastewater.

I explore wastewater-borne viruses as a public-health opportunity through the lens of the COVID-19 pandemic. Wastewater-based epidemiology (WBE) quickly became recognized as a useful complement to clinical testing following the pandemic’s onset. However, little is known about sub-community relationships between wastewater and clinical data. I present a novel framework for probabilistically aligning wastewater and clinical data with high spatial granularity. I use this framework to uncover clear sub-regional (i.e., sub-city) and building/neighborhood-scale correlations between wastewater and clinical data collected through the Healthy Davis Together (HDT) pandemic-response initiative in Davis, CA. In addition, I hypothesize that multiple imputation (using an expectation maximization-Markov chain Monte Carlo (MCMC) approach) of non-detects in wastewater qPCR data is less likely to bias results than more commonly used non-detect handling methods (e.g., censoring or single imputation). I use the HDT data to test this hypothesis. I find that while results obtained using different non-detect handling methods are similar, they are not the same—indicating the importance of specifying non-detect handling method in WBE studies. I also find that the EM-MCMC method yields somewhat better agreement between clinical and wastewater data than do the other non-detect handling methods examined. Refinements to the algorithm, tuning parameters, and variable groupings used in this dissertation could further recommend the EM-MCMC method for wastewater-data analysis in the future.

I conclude the dissertation with a discussion of lessons learned from my experience helping launch, grow, and manage the HDT WBE program. Conducting WBE requires significant investments of time, money, labor, and expertise. Given that much information gleaned from wastewater is not directly actionable, and/or duplicates information from other sources, it is prudent to consider whether these investments are worth it. I present seven recommendations for end users seeking to incorporate WBE into COVID-19 response: (1) avoid redundancy between clinical testing and WBE; (2) emphasize statistical thinking, data analysis, and data management; (3) define action thresholds; (4) monitor fewer sites more frequently; (5) build on existing infrastructure and programs for wastewater collection and analysis; (6) be prepared to adapt as the pandemic evolves; and (7) keep an eye on the future, including by proactively searching for emerging variants of concern.

Monitoring wastewater for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—whether performed at a building, neighborhood, or city level—has emerged as a viable way to track the prevalence of Coronavirus Disease-2019 (COVID-19) in a population. This study assesses health equity implications for wastewater sampling paradigms at a sub-city (or sub-sewershed) scale. Many wastewater-based disease surveillance efforts established during the COVID-19 pandemic relied on convenient points of access to sampling locations within the sewer network and/or voluntary participation within a city or region. Sampling in this way may generate public health data that does not equitably represent diverse populations within the area of interest. In preparation for future pandemics, better strategies are needed to design wastewater sampling frameworks. We help address this knowledge gap by: (1) developing a geospatial analysis tool that probabilistically assigns demographic data for subgroup populations aggregated by race and age (which were cited as major risk factors for severe COVID-19 outcomes) from census blocks to sub-sewershed sampling zones; (2) evaluating the representativeness of subgroup populations and sub-sewershed wastewater data in Davis, California, within the sampling framework employed for COVID-19 disease surveillance; and (3) demonstrating a scenario planning strategy in which adaptive sampling prioritizes vulnerable populations (in this case, populations >65 years old). Extensive sub-sewershed wastewater monitoring data was collected in Davis from November 2021 through September 2022, with wastewater samples collected three times per week from 15 maintenance holes (nodes) and daily from the influent of the city’s centralized wastewater treatment plant (WWTP). The sub-city scale sampling achieved near complete coverage of the population, with spatial resolution that informed public health communication initiatives within the city. Wastewater data aggregated from the sub-city scale as a population-weighted mean correlated strongly with wastewater data collected from the centralized treatment plant (Spearman’s Rank correlation coefficient 0.909). We considered four down-scaling scenarios for a reduction in the number of sampling zones from baseline by 25% and 50%, chosen either randomly or by prioritizing maintenance of coverage of the >65-year-old population. Prioritizing representation of this vulnerable population in zone selection increased coverage of >65-year-olds from 51.1% to 67.2% when removing half of the sampling zones, while simultaneously increasing coverage of Black or African American populations from 67.5% to 76.7%. Downscaling the number of sampling zones had little effect overall on the correlation between the sub-sewershed zone wastewater data and centralized WWTP data (Spearman’s Rank correlations ranged from 0.875 to 0.917), but the strongest correlations were obtained when maintaining sampling zones to maximize coverage of the >65-year-old population. When resource constraints necessitate downscaling the number of sampling sites, the approach demonstrated herein can inform decisions in ways that help preserve spatial representation of vulnerable populations, thereby promoting more inclusive, region-specific, and sustainable wastewater monitoring in the future.