key: cord-0784089-r2htmr16
authors: Bhattarai, Bishav; Sahulka, Sierra Quinn; Podder, Aditi; Hong, Soklida; Li, Hanyan; Gilcrease, Eddie; Beams, Alex; Steed, Rebecca; Goel, Ramesh
title: Prevalence of SARS-CoV-2 genes in water reclamation facilities: From influent to anaerobic digester
date: 2021-07-06
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2021.148905
sha: 56167e7cde0442e7d3e3f072d91ce8aa17ef4b58
doc_id: 784089
cord_uid: r2htmr16

Several treatment plants were sampled for influent, primary clarifier sludge, return activated sludge (RAS), and anaerobically digested sludge throughout nine weeks during the summer of the COVID-19 pandemic. Primary clarifier sludge had a significantly higher number of SARS-CoV-2 gene copy number per liter (GC/L) than other sludge samples, within a range from 1.0 × 105 to 1.0 × 106 GC/L. Gene copy numbers in raw influent significantly correlated with gene copy numbers in RAS in Silver Creek (p-value = 0.007, R2 = 0.681) and East Canyon (p-value = 0.009, R2 = 0.775) WRFs; both of which lack primary clarifiers or industrial pretreatment processes. This data indicates that SARS-CoV-2 gene copies tend to partition into primary clarifier sludges, at which point a significant portion of them are removed through sedimentation. Furthermore, it was found that East Canyon WRF gene copy numbers in influent were a significant predictor of daily cases (p-value = 0.0322, R2 = 0.561), and gene copy numbers in RAS were a significant predictor of weekly cases (p-value = 0.0597, R2 = 0.449). However, gene copy numbers found in primary sludge samples from other plants significantly predicted the number of COVID-19 cases for the following week (t = 2.279) and the week after that (t = 2.122) respectively. These data indicate that SARS-CoV-2 extracted from WRF biosolids may better suit epidemiological monitoring that exhibits a time lag. It also supports the observation that primary sludge removes a significant portion of SARS-CoV-2 marker genes. In its absence, RAS can also be used to predict the number of COVID-19 cases due to direct flow through from influent. This research represents the first of its kind to thoroughly examine SARS-CoV-2 gene copy numbers in biosolids throughout the wastewater treatment process and the relationship between primary, return activated, and anaerobically digested sludge and reported positive COVID-19 cases.

It has been shown that many disinfectants used for tertiary treatment of wastewater in Municipal water reclamation facilities (WRFs), including the most commonly used sodium hypochlorite, are most effective at inactivating SARS-CoV-2 viral particles, reducing the risk of contamination as previously described Ahmed et al., 2021) . However, monitoring the presence of SARS-CoV-2 in municipal WRF influent has been proposed as a tool for community-level outbreak detection and epidemiological study (Ahmed et Wastewater-based epidemiology for COVID-19 mainly relies on detecting and quantifying any of three primary SARS-CoV-2 marker genes in community wastewater influent, N1 and N2, (Xagoraraki and O'Brien, 2020, Lu et al., 2020) . The general strategy for wastewater-based epidemiology for SARS-CoV-2 is to sample the influent of wastewater treatment plants or upstream for identifying local sources when WWTPS catchments are large, extract total RNA from a known volume of influent sample, and then estimate SARS-CoV-2 gene copy numbers per mL of the sample using reverse transcriptomics coupled with reverse transcriptase quantitative PCR (RT-qPCR) (Ahmed et al., 2020) . However, very few studies have examined other wastewater matrices for SARS-CoV-2, including primary clarifier solids, mixed liquor bacterial activated biomass, and anaerobically digested biosolids, otherwise known as "sludges or biosolids" (Kocamemi et al. 2020 ). In a typical wastewater treatment employing an activated sludge process, the incoming raw influent passes through preliminary treatment units such as course screens and grit chambers before passing on to the primary clarifiers. After that, the treated wastewater typically flows through a bioreactor. After a specific hydraulic retention time, the mixture of treated wastewater and mixed liquor sludge is taken to a gravity settler where most of the suspended biosolids settle down (Metcalf and Eddy, 2014) . By nature of these settling processes, particles tend to partition into solids and settle down as well. In one study, nearly 26% of viruses examined were adsorbed to the solids portion of wastewater (Ye et al., 2016) . However, it has been shown that some viral pathogens and SARS-related viruses can remain infectious in sewage samples for days to weeks (Wigginton et al., 2015) . This suggests that SARS-CoV-2 may be detected in sludge samples even with long retention times, and therefore sludge gene copy numbers may account for a time lag (Casanova et al., 2009) From this data, we determined where SARS-CoV-2 is most abundant within WRF sludges. At what point of the treatment train SARS-CoV-2 is removed before the final effluent is discharged into the environment. If sludge samples are comparable to influent samples as predictors of disease burden in a sewershed. This study's working hypothesis is that SARS-CoV-2 tend to partition into the solids phase of sludge samples and that their abundance within sludge samples will be predictive of community-level COVID-19 disease prevalence.

Wastewater and biosolids samples were collected twice a week from seven WRFs between May 18 th and July 21 st of 2020, capturing a nine-week period during which national quarantine protocols had been established. Specifically, samples from the Provo City Water Table 1 . Daily flow and solid retention time (SRT) values were determined by calculating the average for the summer sampling period. All facilities treated their wastewater using an activated sludge aeration basin system and were, therefore, able to provide both raw influents and return activated sludge (RAS) samples. As indicated in Table 1 , primary clarifier sludge and anaerobically digested sludge samples were supplied by CVWRF, PCWRF, and NDWRF. SLCWRF and TSSD use industrial pretreatment processes rather than primary clarifiers. Therefore they could J o u r n a l P r e -p r o o f Journal Pre-proof only provide additional digested sludge samples. Influent samples were taken as 1-L subsamples of a 24-hour composite raw influent, after course screening and before the grit chamber, if any.

The RAS, primary sludge, and digested sludge samples were collected as 250-mL to 1-L grab samples from the wasting lines. The locations of each WRF can be seen from the heat maps shown in Figure 1 . the fluid samples were first centrifuged at 4000xg for 20 minutes, and the supernatant was gently removed from the semi-solid pellet. The supernatant was then acidified to the same pH as specified in Method A and vacuum filtered on the same filter paper. The remaining semi-solid pellet was then discarded based on the assumption that most viral particles remain suspended in the liquid fraction at low centrifugation speeds. The sample processing was performed in a separate laboratory space equipped with a biosafety level 2 hood, a temperature incubator, a vacuum pump, and other necessary supplies for aseptic sample processing.

To study the effect of pH on SARs-CoV-2 gene copy numbers, a randomly chosen and previously heat-treated influent sample from one of the treatment plants was divided into three aliquots of 200 mL. Each aliquot was then adjusted to either a pH of 3.5 using 2.0 N hydrochloric acid or a pH of 10 using 0.5 N sodium hydroxide; no adjustment was made to one of the aliquots to serve as a control. The aliquots were then processed using Method A as described earlier, followed by RNA extraction and qPCR. Likewise, to examine the effect of temperature, another randomly chosen influent sample but not heat treated was divided into triplicate aliquots, which were then incubated at 25 o C, 35 o C, 65°C, and 75 o C for 2 hours before being processed using Method A, as described above. All of the experiments were performed as technical triplicates to obtain the average SARS-CoV-2 gene copy number per liter, or gene copies per liter (GC/L), and the corresponding standard deviation.

For the RAS, primary sludge, and digested sludge samples, SARS-CoV-2 viral particles and genetic material was directly extracted from the sludge matrix without transferring them to J o u r n a l P r e -p r o o f the aqueous phase was an adjustment to the sludge processing methods described by Kocamemi et al. 2020 . Biosolid samples were processed by centrifuging 50 mL of well-mixed sludge at 10,000xg for two minutes. The supernatant of each sludge sample was immediately decanted and stored for further processing. Multiple samples were centrifuged to obtain a final supernatant volume of 50 to 100 mL, which were then processed using the same Method A, which was used to process influent samples. The resulting solid sludge pellet was immediately stored at -80 o C for later RNA extraction. 0.5 gram of sludge pallet was used to extract genetic material.

Additionally, the solid sludge pellet volume was recorded, and the volatile suspended solids in the supernatant were measured using the Standard Methods as described earlier. Both values were used to calculate the final GC/L values. The gene copy numbers determined for the supernatant and solid sludge pellet were eventually combined after calculation and reported as a single value.

RNA extraction was performed on both filter membranes and sludge samples manually using AllPrep Power Viral DNA/RNA kit (Qiagen, Hidden, Germany). Prior to extraction, 800 µL of solution PM1 (heated at 55 0 C) and 8 µL of β-mecaptoethanol (MP Biomedicals, Irvine, CA) were added to freezed filter paper or sludge pallet, vortexed and homogenized on a bead ruptor 12 (OMNI International). After bead beating, Qiagen protocol was followed for further RNA extraction. Final purified RNA was transferred to 2 mL low DNA binding tubes and stored at -80 o C until further used for reverse transcription.

For the detection and quantification of SARS-CoV-2 gene copy numbers in extracted RNA samples, reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assay All RT-qPCR reactions were performed in triplicate and any calibration curve with an R 2 value less than 0.98 was discarded. Apart from positive and negative controls in each qPCR 96well plates, other quality controls were also performed. For each WRF, a raw influent sample was spiked with a known number of N1 genes from the stock solution and the spiked sample was treated as unknown to evaluate matric effect on PCR amplification efficiency. As suggested in Ahmed et al. (2020) , RNA extraction and RT-qPCR were conducted in separate laboratories to avoid any potential RT-qPCR contamination. For final gene copy number calculation, C T method was used, in which case amplification efficiencies between 90% to 100% were considered for each sample analyzed.

Service district maps were obtained for each district managing the seven WRFs that were examined in this study. The Utah Department of Health provided public health data related to positive COVID-19 cases. The number of daily cases were simply positive COVID-19 cases recorded each day, with less than five positive COVID-19 cases recorded as "<5" to protect individual anonymity. The daily infection rate was recorded as the number of cases per day per100K people served, which was based on the population of each district. Weekly cases were calculated as the sum of daily cases for the corresponding sampling week, while average daily cases were calculated as the average number of cases per day for the corresponding sampling

week. Each of these parameters were then tested in subsequent statistical analyses, as described later. and population describes the spatial impact and distribution of SARS-CoV-2 gene copy numbers.

As detailed in Ahmed et al., 2020, a recovery ratio of 26% was used to estimate the original GC/L recovered by membrane filtration for influent and sludge supernatant samples (Ahmed et al. 2020 ). To calculate averages and standard deviations of SARS-CoV-2 of GC/L, any non-detect samples were assumed to be equal to zero; however, 100% of influent and sludge samples had an associated detected value. In the analysis of public health data, days with less than five new daily cases were estimated as half the maximum number of cases in that range.

Although the calculation of GC/L was sufficient to make comparisons between treatment train processes within each WRF, to make comparisons between each WRF the raw data was normalized to account for average daily flow and population size by converting GC/L to million viral gene copies per capita per day (MVGC/capita/day) using Equation 3:

Nonparametric regressions of SARS-CoV-2 log gene copy number over time were performed using loess in R version 4.0.3, which performs local least-squares to form smoothed estimates. To evaluate the significance of the differences found between SARS-CoV-2 gene copy numbers in influent and sludge samples, one-way ANOVA tests, and Tukey's HSD tests were performed on each sample type WRF, which represents the entire sampling period using J o u r n a l P r e -p r o o f RStudio software. The bar graphs were visualized in Origin, and the box plots were created using RStudio software. To determine if there were any significant correlations between gene copy numbers and positive COVID-19 cases in influent and RAS, multiple generalized linear regression analyses were performed using each public health parameter for both raw and normalized data, as described above, using R Studio software. Linear mixed-effects models (LME) were also performed in R Studio to determine whether SARS-CoV-2 gene copy numbers measured in primary sludge could predict the number of COVID-19 cases for that day, as well as cases with a one-and two-week time lag. This model allowed for random intercepts, which represent different baseline levels of infection for each sewer shed. The model was considered statistically significant if the t-value is approximately equal to two (t  2.00  0.30). All other analyses were considered significant if they resulted in a p-value less than 0.05 (p-value < 0.05).

In matrix spiked samples, as such, we did not see any inhibition, and the gene copy numbers were in the proximity of expected gene copy numbers within a range of 95% to 105%.

The three WRFs that provided influent samples that were tested in this experiment were Salt Lake City WRF (SLCWRF), Timpanogas Sewage District (TSSD), and Central Valley Water

Reclamation Facility (CVWRF). The average SARS-CoV-2 GC/L for these WRF samples were 5.06x10 4 ± 1.14x10 3 , 3.95x10 4 ± 9.70x10 3, and 7.57x10 4 ± 4.35x10 3 , respectively, using Method A. For Method B, these same influent samples' results were as follows: 2.4x10 4 ± 0.64x10 4 , 1.34x10 4 ± 1.31x10 3, and 4.4x10 4 ± 1.22x10 4 , in the same order of WRF as described above.

Hence, Method A produced significantly higher SARS-CoV-2 GC/L than using Method B (pvalue <0.05). Therefore, for all further sample processing, Method A was employed. The influent samples' results treated to a pH of 3.5 and 7.5 exhibited gene copy numbers of 2.15x104 ± 0.99x104 and 2.72x104 ± 1.18x103 GC/L, respectively. A one-way ANOVA test shows that the difference in gene copy number between these two treatments was not significantly different. However, all samples treated to a pH of 10 resulted in a non-detect gene copy number for all samples tested, resulting in a substantially lower recovery efficiency at a basic, rather than neutral or acidic, pH value.

All The results of Tukey's HSD analysis on combined data are summarized in Table 2 . It was found that for PCWRF, primary sludge had significantly greater gene copy numbers than other sample types. NDWRF showed a similar significant difference in gene copy number between primary sludge and other sample types, but with p-values slightly above 0.05. Similar to PCWRF sludges, the abundance of SARS-CoV-2 gene copy numbers in CVWRF primary sludge samples was significantly higher than influent and RAS. However, for SLCWRF, only influent was significantly higher than RAS, but not digested sludge samples. Figure 3 shows 

Supplementary Figures Table 3 . In summary, TSSD had the highest average GC/L in influent while ECWRF had the lowest, and SCWRF had the highest average in RAS, while SLCWRF had the lowest. After normalization for the population size of the sewershed and daily average flow rate, SLCWRF had the highest average MVGC per capita per day in influence, while PCWRF had the lowest. However, Table 3 shows that the RAS data normalization did not change which WRF had the highest average gene copy number, which remained as SCWRF as the highest and SLCWRF as the lowest. 

In this study, two methodologies and two environmental parameters were evaluated for their 

Overall, SARS-CoV-2 gene copy numbers were detected in 100% of influent and RAS In total, these data support the idea that primary sedimentation is the most effective treatment process in the removal of SARS-CoV-2 in wastewater treatment. The data in 

According to the CDC and published reports, the SARS-CoV-2 virus is present in human feces by infected individuals (CDC, 2020). Although it has a low potential to be infectious for SARS-CoV-2 might be improved by the examination of primary clarifier, return activated, or anaerobic digester sludges. Regardless of the matrix that is examined, wastewater surveillance for SARS-CoV-2 will inform and influence public policies that will ultimately save an unknown number of human lives.

We truly appreciate all the support provided by participating WRFs. Sampling at different locations of WRFs was critical and personnel from different WRFs unselfishly helped us with sampling. Most of the field sampling and student support was funded through The United States J o u r n a l P r e -p r o o f National Foundation's Rapid funding mechanism (Project # 2029515). However, the views presented in this manuscript are those of authors and not necessarily reflect on the funding agency. Partial funding through the University of Utah's I3 program provided partial support for student salary and molecular supplies. Tables   Table 1. Water reclamation facility metadata and sample types. Table 2 . Significance of differences in gene copy number between different sludge sample types for PCWRF, NDWRF, CVWRF, SLCWRF, and TSSD. Figures 6a-c. Significant results of linear mixed effects models with randomized intercepts for a) primary sludge gene copy number and the daily rate of cases that week, b) primary sludge gene copy number and the daily rate of cases in one week, and (c) digested sludge gene copy number and the daily rate of cases in two weeks (t  2.00). 

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☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:J o u r n a l P r e -p r o o f