key: cord-0742585-8cjd4hzj
authors: Bayati, M.; Hsieh, H.-Y.; Hsu, S.-Y.; Rogers, E.; Belenchia, A.; Zemmer, S. A.; Blanc, T.; LePage, C.; Klutts, J.; Reynolds, M.; Semkiw, E.; Johnson, H.-Y.; Foley, T.; Wieberg, C. G.; Wenzel, J.; Lyddon, T.; LePique, M.; Rushford, C.; Salcedo, B.; Young, K.; Graham, M.; Suarez, R.; Ford, A.; Lei, Z.; Sumner, L.; Mooney, B. P.; Wei, X.; Greenlief, C. M.; Johnson, M.; Lin, C.-H.
title: Identification and Quantification of Bioactive Compounds Suppressing SARS-CoV-2 Signals in Wastewater-based Epidemiology Surveillance
date: 2022-03-12
journal: nan
DOI: 10.1101/2022.03.09.22272155
sha: def97a108864ab60534f570430fe6a508672486b
doc_id: 742585
cord_uid: 8cjd4hzj

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 X 10^11 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 suppression. 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.

Although a majority of the SARS-CoV-2 viral loads in wastewater are introduced through the 8 8 gastrointestinal tract, SARS-CoV-2 can also be introduced into wastewater (domestic and In general, the main purpose of untargeted metabolomics is to determine which of these features 1 3 4 is dysregulated (upregulated and downregulated) between different sample groups or treatments. 1 3 5 Due to the complexity and the number of features in a dataset, it is challenging to accomplish 1 3 6 this comparison manually [43] . Several software programs for automated processing of 1 3 7 LC/HRMS data have been developed over the past decade. However, most of these programs 1 3 8 have restrictions that limit their utility and applicability to different instrumentation. One widely 1 3 9 applicable program for processing LC/HRMS data is XCMS Online, a web-based platform that 1 4 0 contains all of the tools necessary for the entire untargeted metabolomic workflow, including 1 4 1 signal detection, peak alignment, retention time correction calculations, raw data processing, 1 4 2 statistical analysis, and metabolite assignment [42] [43] [44] . An untargeted metabolomic profiling 1 4 3 approach that utilizes a comprehensive program like XCMS Online is well-suited to the 1 4 4 identification of candidate compounds that suppress the SARS-CoV-2 genetic signal in complex 1 4 5 wastewater matrices. 1 4 6 The objectives of this study are to 1) identify the wastewater treatment facilities in 1 4 7 Missouri, USA that exhibit SARS-CoV-2 suppression and determine their suppression rates, 2) 1 4 8 identify possible active compounds suppressing the SARS-CoV-2 genetic signal through a 1 4 9 ACCGCGCACTTGGTGCATGACGAGAAAGCCCGGGGCTTGA 3') along with a N gene Services (DHSS). To establish the relationship between SARS-CoV-2 viral load and case 2 4 4 number, the total viral loads were calculated according to Eq. (1): was 0.1% formic acid (FA) in water (A) and 100% acetonitrile (B). The gradient elution used 2 8 4 started with a linear gradient of 95%: 5-30%: 70% (eluents A: B) in 30 min. Subsequently, the 2 8 5 separation was followed by a linear wash gradient as follows 70-95% B, 95% B, 95-5% B, and . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted March 12, 2022. ;  0.56 mL/min. Mass spectral data were collected automatically using a scan range from 100 to 2 8 8 1,500 m/z and auto calibrated using sodium formate after data acquisition. Each wastewater centWave mode for feature detection (minimum peak width = 5 s, 1 m/z = 10 ppm, and negative ESI mode . An unpaired parametric Welch t-test was used for the statistical analysis. squares-discriminant analysis (PLS-DA) was performed and heatmap was generated via the web- CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted March 12, 2022. ; The compounds identified through untargeted analysis were quantified using liquid States) coupled with a Waters Acquity TQ triple quadrupole mass spectrometer operated i n 3 mode. Waters IntelliStart optimization software was used to optimize collision and ionization 3 2 4 energy, MRM and SIR (single ion recording) transition ions (molecular and product ions), 3 2 5 capillary and cone voltage, and desolvation gas flow. Waters Empower 3 software was used to 3 2 6 analyze data. Concentrations of the compounds found in wastewater extracts were determined

based on a calibration curve for each analyte generated using standards of these compounds 3 2 8 (purity > 95%, Sigma-Aldrich) at 8 concentrations (0.01, 0.05, 0.1, 0.5, 1.25, 2.5, 5, 10 ppm) in 3 2 9 triplicate. The limit of detection (LOD) and limit of quantification (LOQ) were calculated to 3 3 0 assess the sensitivity of the analytical method. For each compound, the signal-to-noise ratios of three and ten were employed to calculate LOD and LOQ, respectively.

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The copyright holder for this preprint this version posted March 12, 2022. ;  The compounds that could not be ionized or detected by the Waters Acquity TQ triple   3  3  3   quadrupole, including 4-octylphenol, sodium tetradecyl sulfate, diethylene glycol, netilmicin and   3  3  4 dicyclopentadiene, were quantified by a Waters Xevo TQ-S triple quadrupole mass spectrometer 3 3 5 coupled to UHPLC system. A symmetry C18 column (2.1×100mm, 3.5µm, WAT058965) was 3 3 6 used and compounds separated by gradient delivery (0.5 mL/min) of solvent. Initial conditions 3 3 7 were 95%A and 5%C (Solvent A: 0.1% FA, 2mM ammonium acetate, in water; solvent B: 3 3 8 acetonitrile with 0.1% FA; solvent C:0.1% FA, 2mM ammonium acetate, in methanol), which 3 3 9 ramped to 30% B and 70% C over 3 min, and held at 30%B and 70%C over 3 min, followed by 3 4 0 going back to the initial composition within 0.1 min, and being held at the initial conditions for 0.9 min. The total run time was 7 min. The column was heated to 30 °C and the samples were 

Stepwise-Regression Analysis for Identifying the Molecules Suppressing SARS- 3 4 5 CoV-2 Signals in Wastewater 3 4 6 Stepwise linear regression models and least absolute shrinkage and selection operator 3 4 7 (LASSO) regression models were utilized to identify the compounds that are positively 3 4 8 correlated with the SARS-CoV-2 suppression rates. In all models, chemical signal intensities Four different statistical approaches were used to determine the positive correlation between the relative intensities of the compounds and suppression rate. The four approaches 3 5 3 included: forward stepwise regression, backward stepwise regression, best subset linear 3 5 4 regression, and LASSO. The regsubsets( ) function in the R package leaps (https://cran.r-

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The copyright holder for this preprint this version posted March 12, 2022. ; https://doi.org/10.1101/2022.03.09.22272155 doi: medRxiv preprint project.org/web/packages/leaps/leaps.pdf) were utilized for forward, backward and best subset 3 5 6 stepwise regression models. The forward selection began with a model without any predictor 3 5 7 variables. The predictors were added to the model one by one until all of them were in the model. 3 5 8 Conversely, backward selection began with the model with all predictors, followed by leaving 3 5 9 one out at a time until no predictor was in the model [48] . The best subset regression model criteria [49] . In the end, the subset model with the highest adjusted R 2 out of all tree approaches To avoid overfitting the model, LASSO regression model was also used to examine the 3 6 5 predictors. The glmnet( ) function in the R package glmnet package (https://cran.r-3 6 6 project.org/web/packages/glmnet/glmnet.pdf) was used to build the model. The shrinkage 3 6 7 penalty (λ) was determined by cross validation. The coefficients of insignificant predictors with 3 6 8 λ were shrunk to zero [50] . The suppression experiments were carried out to investigate the effect of the identified identified compound were prepared with commercially available standards in 100% methanol at concentrations was mixed with 20 mL ultrapure water (MilliQ system,

Synergy® Water Purification System, MA, USA). The mixture was stirred gently for 5 min and . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted March 12, 2022. ; https://doi.org/10.1101/2022.03.09.22272155 doi: medRxiv preprint transferred to 50 mL polypropylene tubes (SARSTEDT, Newton, NC, USA). Then, the diluted 3 7 8 wastewater samples were spiked with 200 µL of 10,000 mg/L of each target compound to reach 3 7 9 a final concentration of 50 mg/L. Another set of the control samples were spiked with 200 µL of 3 8 0 methanol. The tubes were sealed, shaken, and sit on the bench at ambient temperature for 24 h. After 24 h, RNA was extracted immediately from raw samples, and viral concentrations were The suppression rates (SR) were calculated using Eq. (2):

where [C] is the concentration of reactant C, k is the first-order rate constant, and t is time. 3 9 9 Rearranging the rate law and solving the integral using initial conditions of t = 0 and C = C 0 , the 4 0 0 following expression can be found:

Subsequently, this expression can be written as

Plotting the natural resulting line yields the rate constant k. The half-life ( 1 2 t ) of the reaction is given by:

If the reaction is greater than first-order, the rate law is expressed as:

After integrating, the following equation can be obtained:

For the second-order reaction (n= 2), both with respect to C and overall, the rate law is expressed

as:

The half-life ( 1 2 t ) of the reaction is given by:

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For a second-order reaction involving a reactant, the rate constant k can be determined by

to yield a straight line with a slope of k. 

Between July 2020 and December 2020, more than fifty-seven wastewater treatment rates, along with RT-qPCR results, the average quantity of SARS-CoV-2 load per patient that 4 3 2 contributing to the sewershed was calculated (Figure 1) . The results showed that on average, . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted March 12, 2022. ; https://doi.org/10.1101/2022.03.09.22272155 doi: medRxiv preprint Figure 2 presents the average SARS-CoV-2 viral load per diagnosed case among all the 4 3 8 facilities included in this study. According to the results, sewersheds can be divided into three wastewater could explain the results of Ahmed et al [51] , in which no correlation was found 4 4 4 between viral genetic material and daily reported cases. major reasons for average SARS-CoV-2 gene copies being higher than the corresponding case including symptoms and close contacts with a positive case [51] . From these results, among the is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The total ion chromatograms as well as the spectra of active compounds in the 4 6 2 wastewater extracts were captured from liquid chromatography-high resolution MS (LC-HRMS) 4 6 3 studies. The raw data were processed with the XCMS online platform and the features were 4 6 4 annotated using the METLIN library, which resulted in the putative identification of 30 4 6 5 compounds ( Table 2 ). These compounds are used for a variety of products such as surfactants, 4 6 6 bleaching agents, emulsifiers, and stabilizers ( Table 3) . Heatmap visualization of the clustering 4 6 7 of chemical profiles is based on the 30 most significant compounds identified by using a t-test (p 4 6 8 < 0.001) (Figure 4) . Twenty-three compounds exhibited higher relative intensities in suppressed 4 6 9 facilities compared to control facilities, contributing significantly to the distinction between dodecylbenzenesulfonic acid (DCBS) were identified as important compounds that significantly 4 7 6 contributed to both control and suppression facilities ( Figure 5A ). To further characterize the 4 7 7 differences in the relative intensities, partial least squares-discriminant analysis (PLS-DA), a 4 7 8 supervised regression technique for classifying groups from multidimensional data, was 4 7 9 performed using MetaboAnalyst. PLS-DA analysis with two principal components (PCs) 4 8 0 covered 85% of the total variability of the data (Figure 5B) , indicating significant differences in CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted March 12, 2022. ;  explained 63.9% of the data variability, whereas the second principal component (PC2) 4 8 3 accounted for 21.1% of the total variability of the data set. The molecules tentatively identified through global metabolomic profiling analysis were compounds out of thirty were detected and quantified ( summarizes the molecular ions, product ions, retention times, and ionization modes for targeted 4 9 3 LC-MSMS analysis of these compounds. The results showed that most of the bioactive signal suppression than the control facilities. Four compounds had much higher concentrations in 4 9 6 the suppression facilities than the control facilities. In particular, 4-nonylphenol, palmitelaidic 4 9 7 acid, sodium oleate, and polyethyleneglycol dioleate exhibited concentrations that were 73.3%, 4 9 8 35.3%, 54%, and 58.8% higher in the suppression facilities than the control facilities, The concentrations of 4-nonylphenol in the urban wastewaters were determined in Japan, To further characterize the findings from the metabolomic approach, stepwise regression 5 3 0 models and LASSO regression models were used to determine the significant predictor variables The relationships among chemical signal intensities (generated from UPLC-MS positive 5 3 5 ion mode analysis) and SARS-CoV-2 RNA suppression rate were examined using four different 5 3 6 statistical approaches. According to the forward and backward stepwise regression models, the (Table S2) . Best subsets regression also identified the signal intensities of 13 out of 21 5 3 9 compounds as being positively correlated with the viral suppression rate ( Table S3 ). The signal (Table S4) . Palmitelaidic acid, 4-nonylphenol, dicyclopentadiene, (Table S2-S4) . Furthermore, using 5 4 4 the same statistical approaches, polyoxyethylene glycol dioleate and 4-nonylphenol appeared to 5 4 5 be positive correlated to vial suppression rate among all four approaches when the signal 5 4 6 intensities from negative ion mode were analyzed (Table S5 and S6) . In conclusion, only the is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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hydrophobic interaction plays an important role in the interaction between surfactants and lipid 5 7 3 bilayers.

The suppression of SARS-CoV-2 RNA in wastewater over time was also investigated. The experiments were conducted at room temperature. Spiked wastewaters (with 50 mg/L of 5 7 6 each compound) were collected at the following times: 0, 3, 6, 12, 24, 48, and 96 hrs. Samples 5 7 7 were immediately extracted and processed by RT-qPCR. Figure 8 shows the kinetic existence of these two compounds at 50 mg/L will dramatically decrease the COVID-19 signals 5 8 4 in wastewater. It is therefore critical to determine the real concentrations of the compounds that concentrations, correction factors may be developed to achieve more reliable and unbiased 5 8 7 surveillance results for wastewater treatment facilities receiving wastewater from industries. 5 8 8 In order to calculate the rate constant of the reaction (k) and the half-life of the viral RNA (t 1/2 ), the data from Figure 8 was used to determine the order of the reaction. Zero-order, first- order, and second-order were tested and the results showed that all the data fit the second-order (Figure 9 ). This meant that the rate of the reaction increases by the square of the were compared to the results from 24 h (Figure 7) . The SARS-CoV-2 RNA were suppressed by 5 9 4 78.2% and 57.2% when adding PEGD and PAMA, respectively. The calculated t 1/2 (the time 5 9 5 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted March 12, 2022. ;  when SARS-CoV-2 concentrations drop to its half value) were 8.5 h and 2.2 h for PEGD and 5 9 6 PAMA respectively (Figure 9) . In an effort to evaluate the role that well-shaking plays in the 5 9 7 half-life calculation, another experiment was conducted on the rocker at room temperature. 5 9 8 Samples were continuously agitated during the experiment period. The constant agitation on the mixing experiments were also fit the second-order reaction (Figure 11) . The 24 h results showed CoV-2, leading to longer times to reach t 1/2 . It is also important to mention that SARS-CoV-2 in the control samples was less stable and was about 69 h when the control samples were continuously agitated (Figure 11 ). The main 6 1 2 reason that could explain this observation is that the agitating process will allow the chemicals Robinson et al [68] (Missouri team), the Ohio State [69] , and the team at University of Notre is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted March 12, 2022. ;  rapid degradation of the SARS-CoV-2 signal following a first order decay constant at both 4 C, 6 1 9 10 C, or 35 C within 24 h, with the virus signal not being detectable after 12 h of storage at 6 2 0 35 °C. Similar susceptibility to decay and degradation of SARS-CoV-2 RNA by increasing 6 2 1 temperature in wastewater were also reported by Ahmed et al. [71] . The accelerated transfer of 6 2 6 Approximately 20% of our currently tested wastewater treatment facilities (WWTFs) in 6 2 7 Missouri, USA receive some input from industries. Several classes of molecules released by 6 2 8 these regional industries and manufacturing facilities, particularly the food processing 6 2 9 industry, significantly suppressed the signals of SARS-CoV-2 in wastewater by breaking 6 3 0 down the lipid-bilayer of viral membranes. By taking advantage of recent advancements in 6 3 1 mass spectrometry, metabolomics algorithms, computational capacity and mass spectral 6 3 2 reference databases, we have successfully identified and quantified several bioactive 6 3 3 chemicals that suppress the signals of the SARS-CoV-2 in wastewater. The chemical 6 3 4 suppressors include active ingredients in surfactants, detergents, lubricants, preservatives, 6 3 5 degreasers, and disinfection products. Based on the concentrations of these bioactive 6 3 6 molecules that significantly reduce the stability of the SARS-CoV-2 genetic markers signals 6 3 7 in wastewater (e.g., 4-nonylphenol, palmitelaidic acid, sodium oleate, and polyethylene 6 3 8 glycol dioleate), correction factors could be developed to achieve more reliable and unbiased 6 3 9 surveillance results for wastewater treatment facilities receiving wastewater from industries. 6 4 0 In addition, our findings from the suppression kinetics experiments suggest that the stability

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The copyright holder for this preprint this version posted March 12, 2022. ;  of SARS-CoV-2 in wastewater is also strongly influenced by the sample preparation process 6 4 2 (i.e., agitating vs. sitting still), which might account for the conflicting findings reported 6 4 3 among different studies. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted March 12, 2022. ; Figure 1 . Average SARS-CoV-2 gene copies per diagnosed case. Each data point represents a Missouri wastewater treatment facility (WWTF) from our study. Y-axis is the calculated RNA in the sewershed over the testing period using Eq. (1). X-axis equals the total number of COVID-19 patients identified in each sewershed over the same period.

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The copyright holder for this preprint this version posted March 12, 2022. ; https://doi.org/10.1101/2022.03.09.22272155 doi: medRxiv preprint Figure 2 . Average SARS-CoV-2 gene copies /case among wastewater treatment facilities (WWTFs). Zone 1 represents facilities with signal suppression; Zone 2 represents the facilities within the average SARS-CoV-2 gene copies/case; Zone 3 represents the facilities with underestimated case number. The abbreviation for each facility are listed in the Table S1 .

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The copyright holder for this preprint this version posted March 12, 2022. ; https://doi.org/10.1101/2022.03.09.22272155 doi: medRxiv preprint . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted March 12, 2022. ; https://doi.org/10.1101/2022.03.09.22272155 doi: medRxiv preprint Figure 4 . Heatmap of the relative intensities of the identified bioactive found in different locations. Blue represents low relative intensity, whereas red represents high relative intensity. Heatmap features the top thirty metabolite features as identified by t-test analysis (p < 0.001 and intensity ≥ 10,000). Distance measure is by Euclidean correlation and clustering is determined using the Ward algorithm. The abbreviations of the chemicals are listed in the Table 2 .

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. In the VIP score plot, the colored boxes indicate the relative intensities of the corresponding compounds in the control and suppression samples. Red represents higher relative abundance, while blue represents lower relative abundance in the VIP score plot. In the PLS-DA plot, the same-colored circles represent replicates of metabolic profiles for each group. The colored ellipses indicate 95% confidence regions of each group.

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The copyright holder for this preprint this version posted March 12, 2022. ; Figure 7 . Chemical effect on the SARS-CoV-2 signals in the wastewater. Samples from different batches were treated with 50 mg/L PEGD (polyethylene glycol dioleate), NOPH (4nonylphenol), SOOE (sodium oleate), and PAMA (palmitelaidic acid). Wastewater samples were reacted with each chemical individually for 24 h at room temperature. Error bars represent standard deviation.

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The copyright holder for this preprint this version posted March 12, 2022. ;  https://doi.org/10.1101/2022.03.09.22272155 doi: medRxiv preprint Figure 10 . Kinetic experiments for control samples. No chemicals were spiked. The wastewaters were from two different batches. The data were normalized by the gene copies/µL at time zero. All the experiments were conducted at room temperature. The collected samples were immediately extracted and processed by RT-qPCR.

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All the experiments were conducted at room temperature. All the samples were continuously agitated on the rocker.

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The copyright holder for this preprint this version posted March 12, 2022. ; Table 1 . SARS-CoV-2 gene suppression rates for the facilities included in this study.

Suppression Rate (%) . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted March 12, 2022 is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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Determination of Sodium Dodecyl Sulfate in Toothpastes by a PVC Matrix Membrane Sensor

Identification of Non-ionic Surfactants in Elements of the Aquatic Environment

A novel method for the rapid determination of polyethoxylated tallow amine surfactants in water and sediment using large volume injection with high performance liquid chromatography and tandem mass spectrometry

Surfactants -Compounds for inactivation of SARS-CoV-2 and other enveloped viruses

Fatty Acid Binding Proteins from Different Tissues Show Distinct Patterns of Fatty Acid Interactions

Thermodynamics of Sodium Dodecyl Sulfate Partitioning into Lipid Membranes

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

Wastewater SARS-CoV-2 monitoring as a community-level COVID-19 trend tracker and variants in Ohio, United States

Persistence of SARS-CoV-2 in Water and Wastewater

Decay of SARS-CoV-2 and surrogate murine hepatitis virus RNA in untreated wastewater to inform application in wastewaterbased epidemiology