Biomarkers selection for population normalization in SARS-CoV-2 wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has been one of the most cost-effective approaches to track the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) levels in the communities since the coronavirus disease 2019 (COVID-19) outbreak in 2020. Normalizing SARS-CoV-2 concentrations by the population biomarkers in wastewater is critical for interpreting the viral loads, comparing the epidemiological trends among the sewersheds, and identifying the vulnerable communities. In this study, five population biomarkers, pepper mild mottle virus (PMMoV), creatinine (CRE), 5-hydroxyindoleacetic acid (5-HIAA), caffeine (CAF) and its metabolite paraxanthine (PARA) were investigated and validated for their utility in normalizing the SARS-CoV-2 loads through two normalizing approaches using the data from 64 wastewater treatment plants (WWTPs) in Missouri. Their utility in assessing the real-time population contributing to the wastewater was also evaluated. The best performing candidate was further tested for its capacity for improving correlation between normalized SARS-CoV-2 loads and the clinical cases reported in the City of Columbia, Missouri, a university town with a constantly fluctuating population. Our results showed that, except CRE, the direct and indirect normalization approaches using biomarkers allow accounting for the changes in wastewater dilution and differences in relative human waste input over time regardless flow volume and population of the given WWTP. Among selected biomarkers, PARA is the most reliable population biomarker in determining the SARS-CoV-2 load per capita due to its high accuracy, low variability, and high temporal consistency to reflect the change in population dynamics and dilution in wastewater. It also demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater. In addition, the viral loads normalized by the PARA-estimated population significantly improved the correlation (rho=0.5878, p < 0.05) between SARS-CoV-2 load per capita and case numbers per capita. This chemical biomarker complements the current normalization scheme recommended by CDC and helps us understand the size, distribution, and dynamics of local populations for forecasting the prevalence of SARS-CoV2 within each sewershed.

Identification and quantification of bioactive compounds suppressing SARS-CoV-2 signals in wastewater-based epidemiology surveillance.

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.

Biomarkers Selection for Population Normalization in SARS-CoV-2 Wastewater-based Epidemiology.

Wastewater-based epidemiology (WBE) has been one of the most cost-effective approaches to track the SARS-CoV-2 levels in the communities since the COVID-19 outbreak in 2020. Normalizing SARS-CoV-2 concentrations by the population biomarkers in wastewater can be critical for interpreting the viral loads, comparing the epidemiological trends among the sewersheds, and identifying the vulnerable communities. In this study, five population biomarkers, pepper mild mottle virus (pMMoV), creatinine (CRE), 5-hydroxyindoleacetic acid (5-HIAA), caffeine (CAF) and its metabolite paraxanthine (PARA) were investigated for their utility in normalizing the SARS-CoV-2 loads through developed direct and indirect approaches. Their utility in assessing the real-time population contributing to the wastewater was also evaluated. The best performed candidate was further tested for its capacity for improving correlation between normalized SARS-CoV-2 loads and the clinical cases reported in the City of Columbia, Missouri, a university town with a constantly fluctuated population. Our results showed that, except CRE, the direct and indirect normalization approaches using biomarkers allow accounting for the changes in wastewater dilution and differences in relative human waste input over time regardless flow volume and population at any given WWTP. Among selected biomarkers, PARA is the most reliable population biomarker in determining the SARS-CoV-2 load per capita due to its high accuracy, low variability, and high temporal consistency to reflect the change in population dynamics and dilution in wastewater. It also demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater. In addition, the viral loads normalized by the PARA-estimated population significantly improved the correlation ( rho =0.5878, p <0.05) between SARS-CoV-2 load per capita and case numbers per capita. This chemical biomarker offers an excellent alternative to the currently CDC-recommended pMMoV genetic biomarker to help us understand the size, distribution, and dynamics of local populations for forecasting the prevalence of SARS-CoV2 within each sewershed. HIGHLIGHT bullet points: The paraxanthine (PARA), the metabolite of the caffeine, is a more reliable population biomarker in SARS-CoV-2 wastewater-based epidemiology studies than the currently recommended pMMoV genetic marker.SARS-CoV-2 load per capita could be directly normalized using the regression functions derived from correlation between paraxanthine and population without flowrate and population data.Normalizing SARS-CoV-2 levels with the chemical marker PARA significantly improved the correlation between viral loads per capita and case numbers per capita.The chemical marker PARA demonstrated its excellent utility for real-time assessment of the population contributing to the wastewater.

Defining biological and biophysical properties of SARS-CoV-2 genetic material in wastewater.

SARS-CoV-2 genetic material has been detected in raw wastewater around the world throughout the COVID-19 pandemic and has served as a useful tool for monitoring community levels of SARS-CoV-2 infections. SARS-CoV-2 genetic material is highly detectable in a patient's feces and the household wastewater for several days before and after a positive COVID-19 qPCR test from throat or sputum samples. Here, we characterize genetic material collected from raw wastewater samples and determine recovery efficiency during a concentration process. We find that pasteurization of raw wastewater samples did not reduce SARS-CoV-2 signal if RNA is extracted immediately after pasteurization. On the contrary, we find that signal decreased by approximately half when RNA was extracted 24-36 h post-pasteurization and ~90% when freeze-thawed prior to concentration. As a matrix control, we use an engineered enveloped RNA virus. Surprisingly, after concentration, the recovery of SARS-CoV-2 signal is consistently higher than the recovery of the control virus leading us to question the nature of the SARS-CoV-2 genetic material detected in wastewater. We see no significant difference in signal after different 24-hour temperature changes; however, treatment with detergent decreases signal ~100-fold. Furthermore, the density of the samples is comparable to enveloped retrovirus particles, yet, interestingly, when raw wastewater samples were used to inoculate cells, no cytopathic effects were seen indicating that wastewater samples do not contain infectious SARS-CoV-2. Together, this suggests that wastewater contains fully intact enveloped particles.

RadBall Technology Testing in the Savannah River Site's Health Physics Instrument Calibration Laboratory.

The United Kingdom's National Nuclear Laboratory (NNL) has developed a radiation-mapping device that can locate and quantify radioactive hazards within contaminated areas of the nuclear industry. The device, known as RadBall(™), consists of a colander-like outer collimator that houses a radiation-sensitive polymer sphere. The collimator has over two hundred small holes; thus, specific areas of the polymer sphere are exposed to radiation becoming increasingly more opaque in proportion to the absorbed dose. The polymer sphere is imaged in an optical-CT scanner that produces a high resolution 3D map of optical attenuation coefficients. Subsequent analysis of the optical attenuation data provides information on the spatial distribution of sources in a given area forming a 3D characterization of the area of interest. The RadBall(™) technology has been deployed in a number of technology trials in nuclear waste reprocessing plants at Sellafield in the United Kingdom and facilities of the Savannah River National Laboratory (SRNL). This paper summarizes the tests completed at SRNL Health Physics Instrument Calibration Laboratory (HPICL).

RadBall Technology Testing and MCNP Modeling of the Tungsten Collimator.

The United Kingdom's National Nuclear Laboratory (NNL) has developed a remote, non-electrical, radiation-mapping device known as RadBall(™), which can locate and quantify radioactive hazards within contaminated areas of the nuclear industry. RadBall(™) consists of a colander-like outer shell that houses a radiation-sensitive polymer sphere. The outer shell works to collimate radiation sources and those areas of the polymer sphere that are exposed react, becoming increasingly more opaque, in proportion to the absorbed dose. The polymer sphere is imaged in an optical-CT scanner, which produces a high resolution 3D map of optical attenuation coefficients. Subsequent analysis of the optical attenuation matrix provides information on the spatial distribution of sources in a given area forming a 3D characterization of the area of interest. RadBall(™) has no power requirements and can be positioned in tight or hard-to reach locations. The RadBall(™) technology has been deployed in a number of technology trials in nuclear waste reprocessing plants at Sellafield in the United Kingdom and facilities of the Savannah River National Laboratory (SRNL). This study focuses on the RadBall(™) testing and modeling accomplished at SRNL.

Assessment of radionuclide databases in CAP88 mainframe version 1.0 and Windows-based version 3.0.

In this study the radionuclide databases for two versions of the Clean Air Act Assessment Package-1988 (CAP88) computer model were assessed in detail. CAP88 estimates radiation dose and the risk of health effects to human populations from radionuclide emissions to air. This program is used by several U.S. Department of Energy (DOE) facilities to comply with National Emission Standards for Hazardous Air Pollutants regulations. CAP88 Mainframe, referred to as version 1.0 on the U.S. Environmental Protection Agency Web site (http://www.epa.gov/radiation/assessment/CAP88/), was the very first CAP88 version released in 1988. Some DOE facilities including the Savannah River Site still employ this version (1.0) while others use the more user-friendly personal computer Windows-based version 3.0 released in December 2007. Version 1.0 uses the program RADRISK based on International Commission on Radiological Protection Publication 30 as its radionuclide database. Version 3.0 uses half-life, dose, and risk factor values based on Federal Guidance Report 13. Differences in these values could cause different results for the same input exposure data (same scenario), depending on which version of CAP88 is used. Consequently, the differences between the two versions are being assessed in detail at Savannah River National Laboratory. The version 1.0 and 3.0 database files contain 496 and 838 radionuclides, respectively, and though one would expect the newer version to include all the 496 radionuclides, 35 radionuclides are listed in version 1.0 that are not included in version 3.0. The majority of these has either extremely short or long half-lives or is no longer in production; however, some of the short-lived radionuclides might produce progeny of great interest at DOE sites. In addition, 122 radionuclides were found to have different half-lives in the two versions, with 21 over 3 percent different and 12 over 10 percent different.