Development, Implementation and Normalization of SARS-CoV-2 Wastewater and Environmental Monitoring

Abstract

Wastewater and environmental monitoring (WEM) has for decades been a valuable resource for monitoring public health trends and providing insights into community-wide substance use, such as pharmaceuticals and illicit drugs, tracking infectious diseases like poliomyelitis and viral hepatitis. Despite its promise, WEM was historically underutilized, mainly limited to targeted studies or research efforts, and rarely integrated into real-time public health systems. The onset of the COVID-19 pandemic transformed WEM into a highly sought epidemiological tool, capable of complementing clinical testing and offering community-level data to better inform public health strategies. The COVID-19 pandemic exposed significant gaps in public health surveillance, emphasizing the need for tools that could monitor SARS-CoV-2 viral spread in real-time. WEM emerged as a promising approach, but early efforts faced several challenges: limited detection of the SARS-CoV-2 viral signal in wastewater during periods of low prevalence (low method sensitivity), pronounced variability in wastewater composition by location and season, the initial difficulty of scaling these methods to underserved, rural communities, and the lack of a universal biomarker or method of normalization to reduce the impact of flow and wastewater composition changes. The overarching hypothesis of this work was that WEM for SARS-CoV-2 could be established and refined through optimized sampling and sample processing methodologies, and that novel normalization strategies could improve the robustness of data interpretation and allow WEM to have predictive power as a public health tool. This dissertation addresses this hypothesis and bridges critical knowledge gaps by conducting six studies, establishing the methodologies, normalization strategies, and applications of normalization to WEM to improve its reliability and utility as a public health tool.

The first objective of the dissertation was to develop methodologies for SARS-CoV-2 detection and quantification in wastewater, and to develop the most effective wastewater sampling approach for quantifying SARS-CoV-2 RNA by comparing post-grit solids (PGS) and primary clarified sludge (PCS) and to evaluate the use various normalization biomarkers for reliable monitoring of infection trends, particularly during periods of low COVID-19 incidence. The results showed that primary clarified sludge (PCS) provided the most reliable and sensitive SARS-CoV-2 RNA measurements, with pepper mild mottle virus (PMMoV) serving as an effective normalization biomarker to track infection trends, particularly during periods of low incidence.

The second objective of the dissertation was to enhance the effectiveness of wastewater surveillance in detecting COVID-19 resurgences during periods of low clinical positivity and assess its predictive value as a complementary tool to traditional diagnostic methods. It aimed to determine whether wastewater measurements could provide early warnings of outbreaks, overcoming limitations such as testing biases and delayed hospitalization data. The results showed that SARS-CoV-2 RNA measurements in wastewater surged over 400% and were detected 48 hours before a 300% rise in positive cases and 96 hours before a 160% increase in hospitalizations, highlighting the predictive power of wastewater surveillance in tracking resurgences.

The third objective of the dissertation was to address the knowledge gap in wastewater surveillance by evaluating its effectiveness in small and rural communities, where reliable monitoring of COVID-19 and other pathogens remains challenging. Specifically, it aimed to compare SARS-CoV-2 RNA concentration from upstream pumping stations and downstream wastewater lagoons to determine the most effective sampling location for accurate community-level surveillance. The results showed that samples from the pumping station provided stronger, more stable RNA signals, while lagoon samples had lower concentrations and greater variability, suggesting that wastewater surveillance in rural or low-income regions should prioritize upstream locations like pumping stations for reliable monitoring. The fourth objective of the dissertation was to investigate the relationship between SARS-CoV-2 wastewater signals and clinical case data through the wastewater-to-clinical-case (WC) ratio, aiming to address challenges in interpreting WEM data as clinical testing declines. It sought to determine whether the WC ratio could serve as a reliable indicator of underreported infections and the emergence of new variants in varying community immunity contexts. The results showed that increases in the WC ratio signaled periods of insufficient clinical testing, while changes in the ratio effectively tracked the spread of Alpha, Delta, and Omicron variants, demonstrating the WC ratio’s potential to complement clinical metrics and enhance epidemiological monitoring.

The fifth objective of the dissertation was to assess the potential of tobamoviruses as fecal biomarkers for improving the accuracy and reliability of WEM, focusing on both their ability to indicate the presence of human fecal biomarkers and normalize viral signals in wastewater. This chapter presents a review aimed at addressing the limited research on tobamoviruses beyond PMMoV and tomato brown rugose fruit virus, as human-associated fecal biomarkers, despite their promising attributes for WEM applications. A critical review of over 240 studies, evaluating 43 tobamoviruses, was conducted. This review identified 15 with significant potential as biomarkers across diverse global wastewater contexts, paving the way for future research to potentially improve future WEM normalization efforts.

Tying all of this work together, the sixth and final objective of the dissertation was to address an important gap in wastewater surveillance caused by the absence of a globally validated assay for PMMoV. It aimed to improve data comparability across WEM programs by identifying stable genomic regions of PMMoV that remain consistent despite variations in diets, wastewater composition, and environmental conditions. Testing 116 samples from 12 countries, the study identified the most stable region of the PMMoV genome between bases 2776-3023. Using this region, the research developed the P17 PMMoV assay, a highly sensitive and specific tool (ALOQ = 4.9 copies/reaction, ALOD = 3.4 copies/reaction) to ensure reliable normalization of WEM data worldwide, providing consistent performance across diverse environments.

This research transformed WEM early on in the pandemic by addressing critical challenges in sampling, normalization, and data interpretation, particularly during periods of low disease prevalence and in rural or underserved communities. By refining methodologies to improve signal reliability and predictive value, introducing the WC ratio for enhanced epidemiological insights, and exploring novel biomarkers to expand normalization strategies, this work significantly enhanced the utility of WEM as a public health tool, and contributed to shaping its adoption in Canada and abroad. Collectively, the findings provide a comprehensive framework for advancing wastewater surveillance, making it a more equitable, reliable, and globally applicable approach to monitoring infectious diseases and informing public health interventions.