Dissertation Defense Announcements

Wastewater-based epidemiology (WBE) has drawn significant attention as an early warning tool to detect and predict the trajectory of COVID-19 cases in a community, in conjunction with public health data. This means of monitoring for outbreaks has been used at municipal wastewater treatment centers to analyze COVID-19 trends in entire communities, as well as by universities and other community living environments to monitor COVID-19 spread in buildings. Precise and accurate quantification of viral copies in wastewater is a prerequisite for a successful WBE surveillance project. Accurate quantification of SARS-CoV-2 is dependent on the choice of an effective and reliable virus concentration method. Sample concentration is crucial, especially when viral abundance in raw wastewater is below the threshold of detection by RT-qPCR analysis. The first objective of my dissertation is the performance evaluation of a rapid ultrafiltration-based virus concentration method using InnovaPrep Concentrating Pipette (CP) Select and how it compares with the electronegative membrane filtration (HA) method. The criteria of the evaluation were based on the SARS-CoV-2 detection sensitivity, surrogate virus recovery rate, and sample processing time. Results suggested that the CP Select concentrator was more efficient at concentrating SARS-CoV-2 from wastewater compared to the HA method. About 25% of samples that tested SARS-CoV-2 negative when concentrated with the HA method produced a positive signal with the CP Select protocol. The optimization of the CP Select protocol by adding AVL lysis buffer and sonication increased Bovine Coronavirus (BCoV) recovery by 19%, which seems to compensate for viral loss during centrifugation. Filtration time decreased by approximately 30% when using the CP Select protocol, making this an optimal choice for building surveillance applications where quick turnaround time is necessary.

The inherent limitation of most of the current virus concentration methods is capable of processing small volumes of wastewater ranging from 20 – 250 mL. While small volume-based virus concentration methods can be successful for detecting and quantifying SARS-CoV-2 viruses during high community infection, these methods may not be informative, especially during the early stage of community infections. The second objective is to develop a large-volume filtration-based virus concentration method for increased sensitivity of molecular detection of SARS-CoV-2 and application in sequencing techniques. A dead-end hollow fiber ultrafilter (UF) and electronegative membrane filtration (HA) were used as primary and secondary concentration methods for concentrating viruses from wastewater. This study found that a modified UF-HA method, incorporating sonication and centrifugation, showed 100% SARS-CoV-2 positive detection in low COVID-19 infection periods compared to only 9% positive detection with the HA method and 63% with the UF alone. During the high COVID-19 infection period, no significant difference in SARS-CoV-2 detection and quantification was observed among the alternatives. The hollow UF-based primary method showed higher BCoV recovery compared to the combined method and HA method. The combined method (UF-HA_soni) can be used to identify the early stages of COVID-19 infection by detecting SARS-CoV-2 viruses from the low-tittered wastewater which can help prevent future outbreaks. Either the combined method or the UF-based primary method can be used to monitor SARS-CoV-2 viruses during the high COVID-19 infection period.

We also aim to apply digital droplet PCR to track the transmission dynamics of the Omicron variants by assessing the relative proportion of the strains circulating in Charlotte, North Carolina. We applied Digital Droplet Polymerase Chain Reaction (ddPCR) technology to detect and quantify Omicron variants using three different mutation assays targeting the S gene (N764K and N856K). Using these two assays, we first detected the Omicron variants on December 6, 2021, from the wastewater sample of Mecklenburg County which was earlier than the first clinical detection on December 10, 2021. The relative abundance of Omicron VOCs determined by the RT-ddPCR from wastewater was strongly and positively correlated with the clinically reported VOCs (r = 0.98, p = < 0.0001). This surveillance method for the variant analysis can give a near real-time transmission dynamic of the Omicron variants enabling quick administrative intervention such as awareness, preparedness, and control measures.

The increasing threats posed by climate change and urbanization have elevated the importance of addressing Urban Heat Island (UHI) phenomenon, a critical concern impacting cities across the United States. This dissertation comprises three articles that collectively investigate the effectiveness of trees and greenspaces in managing UHI, creating a Heat Health Score (HHS) to identify areas experiencing UHI effects and investigate the perceived effectiveness of policies and programs aimed at reducing UHI in cities, while providing recommendations for Charlotte, NC in particular. Article 1 shows that urban greenspaces consisting of trees can help reduce the UHI effect by creating shade and cooling spaces, potentially reducing energy costs, improving human living conditions, providing food and habitat to wildlife, and improving aesthetics and land values. In Article 2, measures to mitigate the effect of UHI are evaluated from select cities and a ratio of daily average high temperature between locations and the corresponding difference in land cover of tree and shrub areas, create the Heat Health Score (HHS) (a unique metric) which allows municipalities and community groups to gauge the heat health between locations. These results show that most urban locations remain hotter and with lower vegetative cover than their suburban or rural counterparts, however, changes in tree and shrub cover can impact these results in a positive way. Results from Article 3 elucidate the perceived successes and challenges of current policies through a qualitative survey. These responses offer practical recommendations for policymakers across the US but for Charlotte, NC in particular.
These results draw from the real-world experiences and lessons uncovered in the three articles, in aggregate, these provide a valuable resource for city leaders and policymakers striving to create a more sustainable and climate-resilient city. It stresses the importance of urban greenspaces and urban trees in particular, provides community leadership with an easily accessible, not previously defined tool to discern urban heat health through the use of free, open-source data to score heat health, and provides insight into the perceived effectiveness of policies and programs used to mitigate UHI in cities in the United States.

In this dissertation we present new results on the classification of limit distributions of random geometric processes. In particular, that develop on the work of Penrose and Wade, who were the first to document the phenomenon with limited initial variables. In this dissertation we put forth not only new results, but a new method of obtaining results through analyzing the sequence of moments produced by random variables. Additionally we have new results in cycle decomposition of the related Dickman-Goncharov distribution. We present a novel proof of the distribution of the three highest order cycles in a random partition of Sn.

Since the early 1980s, American educational reformers have tried to improve schools through standards, high-stakes tests, and punishments for those schools that failed to meet the mark. In North Carolina, many schools with diverse populations and low socioeconomic status have struggled to succeed, receiving the state performance grade of D or F and the consequent “low-performing” label. Meanwhile, some teachers in these schools have achieved at high levels and attempted to improve not only their classrooms, but their schools and districts. Few researchers have sought the opinions and expertise of high-performing teachers in order to better understand their experiences, their role as change agents, and their recommendations for other so-called “low-performing” schools. This qualitative multiple case study used in-depth interviews with these high-performing teachers in “low-performing” elementary schools in North Carolina. Specifically, this research gathered information about their backgrounds, their actions for school transformation, and their lessons learned about education and equity. Findings from the study indicated that high-performing elementary teachers tried to reform their “low-performing” schools through teacher agency but were blocked by multiple factors. School administrators and district officials reduced teacher agency and opportunities for school improvement. North Carolina’s “low-performing” schools policy harmed children, reinforced school failure, and produced discriminatory and inequitable results. Teacher agency theory provided a promising approach for the state to change course and improve failing schools.