Wastewater-Based Epidemiology (WBE) is widely used to monitor the progression of SARS-CoV-2 pandemic. While there is a clear correlation between the number of COVID patients in a sewershed and the viral load in the wastewater, there is notable variability across different treatment plants. In particular, some facilities consistently exhibit higher viral content per diagnosed patient, implying a potential underestimation of the number of COVID patients, while others show a low viral load per diagnosed case, indicating potential attenuation of genetic material from the sewershed. In this study, we investigated the impact of nonylphenol ethoxylate (NPHE), linear alkylbenzene sulfonic acid (LABS), bisoctyl dimethyl ammonium chloride (BDAC), and didecyldimethylammonium chloride (DDAC), the surfactants that have been commonly used as detergents, emulsifiers, wetting agents on the stability of SARS-CoV-2 in wastewater. The results showed multiple and dynamic mechanisms, including degradation and desorption, can occur simultaneously during the interaction between SARS-CoV-2 and different chemicals depending on the physicochemical properties of each chemical. Through the elucidation of the dynamic interactions, the findings from this study could help the state health organizations and scientific community to optimize the SARS-CoV-2 wastewater-based epidemiology strategies.

Recent SARS-CoV-2 wastewater-based epidemiology (WBE) surveillance have documented a positive correlation between the number of COVID-19 patients in a sewershed and the level of viral genetic material in the wastewater. Efforts have been made to use the wastewater SARS-CoV-2 viral load to predict the infected population within each sewershed using a multivariable regression approach. However, reported clear and sustained variability in SARS-CoV-2 viral load among treatment facilities receiving industrial wastewater have made clinical prediction challenging. Several classes of molecules released by regional industries and manufacturing facilities, particularly the food processing industry, can significantly suppress the SARS-CoV-2 signals in wastewater by breaking down the lipid-bilayer of the membranes. Therefore, a systematic ranking process in conjugation with metabolomic analysis was developed to identify the wastewater treatment facilities exhibiting SARS-CoV-2 suppression and identify and quantify the chemicals suppressing the SARS-COV-2 signals. By ranking the viral load per diagnosed case among the sewersheds, we successfully identified the wastewater treatment facilities in Missouri, USA that exhibit SARS-CoV-2 suppression (significantly lower than 5 × 1011 gene copies/reported case) and determined their suppression rates. Through both untargeted global chemical profiling and targeted analysis of wastewater samples, 40 compounds were identified as candidates of SARS-CoV-2 signal suppressors. Among these compounds, 14 had higher concentrations in wastewater treatment facilities that exhibited SARS-CoV-2 signal suppression compared to the unsuppressed control facilities. Stepwise regression analyses indicated that 4-nonylphenol, palmitelaidic acid, sodium oleate, and polyethylene glycol dioleate are positively correlated with SARS-CoV-2 signal suppression rates. Suppression activities were further confirmed by incubation studies, and the suppression kinetics for each bioactive compound were determined. According to the results of these experiments, bioactive molecules in wastewater can significantly reduce the stability of SARS-CoV-2 genetic marker signals. Based on the concentrations of these chemical suppressors, a correction factor could be developed to achieve more reliable and unbiased surveillance results for wastewater treatment facilities that receive wastewater from similar industries.