Sewer Transport Conditions and Their Role in the Decay of Endogenous SARS-CoV-2 and Pepper Mild Mottle Virus from Source to Collection

41 This study presents a comprehensive analysis of the decay patterns of endogenous SARS-CoV-2 and Pepper mild 42 mottle virus (PMMoV) within wastewaters spiked with stool from infected patients expressing COVID -19 symptoms, 43 and hence explores the decay of endogenous SARS -CoV-2 and PMMoV targets in wastewaters from source to 44 collection of the sample . Stool samples from infected patients were used as endogenous viral material to more 45 accurately mirror real-world decay processes compared to more traditionally used lab-propagated spike-ins. As such, 46 this study includes data on early decay stages of endogenous viral targets in wastewaters that are typically overlooked 47 when performing decay studies on wastewaters harvested from wastewater treatment plants that contain already-48 degraded endogenous material. The two distinct sewer transport conditions of dynamic suspended sewer transport 49 and bed and near-bed sewer transport were simulated in this study at temperatures of 4°C, 12°C and 20°C to elucidate 50 decay under these two dominant transport conditions within wastewater infrastructure. The dynamic suspended sewer 51 transport was simulated over 35 hours, representing typical flow conditions, whereas bed and near -bed transport 52 extended to 60 days to reflect the prolonged settling of solids in sewer systems during reduced flow periods. In dynamic 53 suspended sewer transport, no decay was observed for SARS-CoV-2, PMMoV, or total RNA over the 35-hour period, 54 and temperature ranging from 4°C to 20°C had no noticeable effect. Conversely, experiments simulating bed and near-55 bed transport conditions revealed significant decreases in SARS -CoV-2 and total RNA concentrations by day 2, and 56 PMMoV concentrations by day 3. Only PMMoV exhibited a clear trend of increasing decay constant with higher 57 temperatures, suggesting that while temperature influences decay dynamics, its impact may be less significant than 58 previously assumed, particularly for endogenous RNA that is bound to dissolved organic matter in wastewater. First 59 order decay models were inadequate for accurately fitting decay curves of SARS -CoV-2, PMMoV, and total RNA in 60 bed and near-bed transport conditions. F-tests confirmed the superior fit of the two -phase decay model compared to 61 first order decay models across temperatures of 4°C to 20°C . Finally, and most importantly, total RNA normalization 62 emerged as an appropriate approach for correcting the time decay of SARS -CoV-2 exposed to bed and near -bed 63 transport conditions. These findings highlight the importance of considering decay from the point of entry in the sewers, 64 sewer transport conditions, and normalization strategies when assessing and modelling the impact of viral decay rates 65 in wastewater systems. This study also emphasizes the need for ongoing research into the diverse and multifaceted 66 factors that influence these decay rates, which is crucial for accurate public health monitoring and response strategies. 67

Decay rate ; persistence testing; d egradation testing; stool spike-in; wastewater-based surveillance; bed 68 transport. 69 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 2 1. Introduction 70 In late 2019, SARS-CoV-2 virus was first detected in the Hubei province of China (Huang et al., 2020; Xiantian 71 et al., 2020) . The pandemic saw a global scientific advancement of wastewater-based surveillance (WBS), the 72 surveillance of wastewater to detect and quantify pathogens in wastewaters and to assess transmission of disease in 73 communities (Brouwer et al., 2018; Diamond et al., 2022; Michael -Kordatou et al., 2020; O’Reilly et al., 2020; Park et 74 al., 2020; Peccia et al., 2020; Xu et al., 2020). Since the advancement of WBS, strong correlations have been reported 75 between measured SARS -CoV-2 viral RNA concentrations in municipal wastewater s and traditional public health 76 metrics used to monitor the pandemic’s progress, such as new daily reported clinical cases, percent positivity of clinical 77 tests, hospitalization admissions and deaths caused by COVID-19 complications (Ahmed et al., 2020a; D’Aoust et al., 78 2022, 2021a; Hegazy et al., 2022; Keshaviah et al., 2023; Kumar et al., 2020; La Rosa et al., 2020; Randazzo et al., 79 2020; Thompson et al., 2020). As a result, SARS-CoV-2 WBS has since been applied at scale in over 4,648 sites, in 80 at least 72 countries worldwide (Naughton et al., 2023), to help governmental agencies and public health organizations. 81 Significant efforts and developments have been made to improve the measurement of SARS -CoV-2 in 82 wastewaters and specifically to increase the sensitivity of assays (Pecson et al., 2021). While several comprehensive 83 reviews and analyses on laboratory methodologies have been performed , resulting in improved and robust testing 84 methodologies, there still exists a lack of fundamental understanding of the impacts of sewershed -induced decay on 85 viral signal measurements. In particular, the effects of short, moderate and long sewer transport, and the presence of 86 industrial waste or chemical disinfectants on measured viral titers of SARS -CoV-2 have not yet been well elucidated, 87 and limited literature currently exists on the se topics (Parra-Arroyo et al., 2023) . Furthermore, at the time of writing, 88 only a limited number of studies ( 9) have attempted to elucidate decay kinetics of SARS -CoV-2 viral signal in 89 wastewaters (Ahmed et al., 2020b; Babler et al., 2023; Bivins et al., 2020; de Oliveira et al., 2021; Hart et al., 2023; 90 Hokajärvi et al., 2021; Roldan-Hernandez et al., 2022; Sala-Comorera et al., 2021; Weidhaas et al., 2021; Yang et al., 91 2022), with the reported decay kinetics of the existing studies investigating SARS-CoV-2 viral decay shown in Table 1. 92 Six out of ten studies employed spiked-in SARS-CoV-2 viral particles as opposed to studying endogenous SARS-CoV-93 2 already present in wastewater. This distinction is significant as the decay rates between endogenous , fecally shed, 94 SARS-CoV-2 RNA in wastewater samples, and lab -propagated SARS-CoV-2 virions, remain largely unexplored and 95 not well understood (Kantor et al., 2021). It is hypothesized that spiked-in SARS-CoV-2 viral particles may decay faster 96 than their endogenous counterparts, as endogenous SARS -CoV-2 has been observed to bind to dissolved organic 97 matter in wastewater matrices leading to slower degradation and increased persistence in the environment (Chatterjee 98 et al., 2023) . To best elucidate the decay kinetics of SARS -CoV-2 viral signal in sewers and wastewater s, an 99 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 3 endogenous material spike-in approach using infected stool from patients is likely best to limit confounding factors from 100 inherent differences between endogenous SARS-CoV-2 RNA and lab-propagated SARS-CoV-2 virions that impact the 101 interpretation of results. Further, a spike-in approach using infected stool would enable decay to be quantified from the 102 time that the viral titers enter the wastewater matrix, enabling the true decay profile to be analyzed from source to point 103 of collection. 104 The current range of endogenous decay rates from SARS-CoV-2 positive wastewaters reported in literature 105 is broad, with T 90 ranging from 2.4 to 154.9 days at 4°C, Table 1 (Roldan-Hernandez et al., 2022; Weidhaas et al., 106 2021; Yang et al., 2022). While this wide range of values could be somewhat explained by the variating type of starting 107 material, it calls for further investigation into factors such as wastewater matrix composition, titer of the target, and the 108 type of transport the target is subjected to in the sewer system. Additionally, it’s important to note that existing studies 109 using endogenous samples have primarily focused on the decay of endogenous viruses in wastewater samples 110 collected at treatment plants. This approach introduces a significant bias, as these samples have already undergone 111 decay during their residence time in the sewer system, which can extend to more than a day depending on the system, 112 in addition to the holding time prior to the experiment. Consequently, current decay studies may not accurately include 113 the initial decay of the virus titers from the source of entry in the system (such as a toilet flush), leading to a biased 114 understanding of the decay kinetics. Furthermore, existing studies do not incorporate considerations for different flow 115 dynamics existing in the sewersheds, as all studies to date on SARS-CoV-2 viral signal decay in wastewater have been 116 performed in a manner that most closely mimics bed and near -bed sewer transport, which could impact the 117 interpretation of results, and might be limiting WBS applications such as public health reporting, or modelling. 118 Table 1: SARS-CoV-2 decay study temperatures, decay kinetics and associated T90 119 Target virus/pathogen Type of sample utilized for decay studies Temperature at which study was performed (°C) k (day-1) T90 (days) Reference SARS-CoV-2 (N-gene) Spiked-in RNA in influent wastewater 4 0.084 27.8 (Ahmed et al., 2020b) 15 0.114 20.4 25 0.183 12.6 37 0.286 8.0 SARS-CoV-2 (N-gene) Spiked-in RNA in autoclaved influent wastewater 4 0.054 43.2 15 0.077 29.9 25 0.171 13.5 37 0.405 5.7 SARS-CoV-2 (N-gene) Spiked-in virus (high-titer) in frozen, then thawed, influent wastewater 20 1.100 1.6 (Roldan-Hernandez et al., 2022) SARS-CoV-2 (N-gene) Spiked-in virus (low-titer) in frozen, then thawed, influent wastewater 20 1.400 2.1 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 4 SARS-CoV-2 (N-gene) Spiked-in RNA in influent wastewater 4 0.060 36.0 (Hokajärvi et al., 2021) SARS-CoV-2 (N-gene) Spiked-in RNA in influent wastewater 4 0.190 7.7 (de Oliveira et al., 2021) 24 0.830 1.9 SARS-CoV-2 (N-gene) Endogenous SARS-CoV-2 in influent wastewater 4 0.960 2.4 (Weidhaas et al., 2021) 10 2.160 1.1 35 4.320 0.5 SARS-CoV-2 (average of N1-N2- gene) Endogenous SARS-CoV-2 collected from wastewater treatment plant A 4 0.0175 154.9 (Roldan-Hernandez et al., 2022) 22 0.0240 96.6 37 0.0550 43.0 SARS-CoV-2 (average of N1-N2- gene) Endogenous SARS-CoV-2 collected from wastewater treatment plant B 4 0.034 70.0 22 0.076 31.3 37 0.095 24.4 SARS-CoV-2 (N-gene) Endogenous SARS-CoV-2 in influent wastewater 4 0.134 17.2 (Yang et al., 2022) 26 0.274 7.7 SARS-CoV-2 (N-gene) Endogenous SARS-CoV-2 in sterilized river water 4 0.610 3.8 (Sala-Comorera et al., 2021) 20 1.010 2.3 SARS-CoV-2 (N-gene) Endogenous SARS-CoV-2 in sterilized seawater 4 1.070 2.2 20 2.020 1.1 SARS-CoV-2 (N-gene) Endogenous SARS-CoV-2 in influent wastewater 22 0.710 3.2 (Babler et al., 2023) SARS-CoV-2 (average of N1-N2- gene) Endogenous SARS-CoV-2 in influent wastewater 25 0.280 8.2 (Hart et al., 2023) 35 0.349 6.6 * calculated from decay rate data presented by reference 120 121 A common fecal biomarker, pepper mild mottle virus (PMMoV), has been used by approximately 30% of 122 SARS-CoV-2 surveillance systems worldwide to normalize reported viral signal to the quantity of fecal matter in the 123 samples (D’Aoust et al., 2021a; Feng et al., 2021; Haramoto et al., 2013; Kitajima et al., 2018; Naughton et al., 2023). 124 As fecal normalization with PMMoV directly impacts reported normalized SARS-CoV-2 viral signal measurements, it is 125 equally important to understand the effects of decay on PMMoV, if any, throughout the sewershed. The reported decay 126 kinetics of the existing studies investigating PMMoV viral decay are shown below in Table 2. Similar to SARS-CoV-2, 127 the current range of decay rates reported for PMMoV is quite broad, ranging from 57.6 to 237 days at 4°C raising the 128 same concern for further investigation. 129 130 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 5 Table 2: PMMoV decay study temperatures, decay kinetics and associated T90. 131 Target virus/pathogen Type of sample utilized for decay studies Temperature at which study was performed (°C) k (day-1) T90 (days) Reference PMMoV (RAP-gene) Endogenous PMMoV in wastewater/wetland water 4 0.040 57.6* (Rachmadi, 2016) 25 0.050 46.1* 37 0.080 28.8* PMMoV (RAP-gene) Endogenous PMMoV in river water 20 0.228** 10.2 (Sala-Comorera et al., 2021) PMMoV (RAP-gene) Endogenous PMMoV in wastewater (WRRF 1) 4 0.010 237.4 (Roldan-Hernandez et al., 2022) 22 0.040 57.2 37 0.045 51.7 PMMoV (RAP-gene) Endogenous PMMoV in wastewater (WRRF 2) 4 0.059 39.0 22 0.077 30.1 37 0.091 25.3 PMMoV (RAP-gene) Endogenous PMMoV in influent wastewater 22 0.453 5.1* (Babler et al., 2023) WRRF = Water Resource Recovery Facility, * calculated from decay rate data presented by reference, ** calculated from data presented by Sala-Comorera (2021) 132 Other normalizers used in WBS studies include various RNA targets. RNA viruses have commonly been used 133 as spiked-in controls for assessing extraction efficiency in WBS studies (Torii et al., 2022). Additionally, RNA extracts 134 have been employed as spike-in inhibition controls during the qPCR step of sample processing (Ahmed et al., 2020c). 135 Given its established role as a control in extraction and qPCR processes, RNA’s utility in wastewater surveillance is 136 well recognized. This role, coupled with the established prevalence of human-associated viruses and bacteria-infecting 137 viruses contributing to the urban virome in sewage, supports total RNA concentration as a robust candidate for 138 normalizing against decay and temperature effects (Guajardo-Leiva et al., 2020; Nieuwenhuijse et al., 2020; Tisza et 139 al., 2023) . The presence of diverse RNA families in sewage indicates that this normalization technique could be 140 effective for multiple endogenous sewage viral targets, particularly if they exist in sewage primarily as components of 141 broken virions, rather than as intact virions with various structural properties. 142 Numerous concurrent biological processes occur in wastewater sewer s, these processed may include both 143 aerobic and anoxic/anaerobic biochemical reactions, depending on flow conditions. Two of the most common flow 144 conditions associated with constituent transport in sewers are: i) dynamic suspended sewer transport, and ii) bed and 145 near-bed transport. Dynamic suspended sewer transport conditions occur during conventional, dynamic, design-flow 146 conditions within the sewers and are characterized as quasi-fully mixed flows with suspended solids transport . This 147 transport condition occurs during conventional weather and during conventional downstream operation of wastewater 148 treatment facilities. It is distinguished from other modes of sewer transport conditions by the lack of pronounced solids 149 deposition and a high degree of oxygen entrainment into the liquid sewage matrix, creating semi -aerobic to aerobic 150 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 6 conditions, along with significant particle movement (Bertrand-Krajewski et al., 2010; Qteishat et al., 2011) . 151 Wastewaters that are subjected to dynamic suspended sewer transport remain within the sewershed for the design 152 residence time of the sewershed, with conventional sewersheds rarely exceeding past a few days of residence time. 153 Bed and near-bed transport conditions are characterized by an accumulation of deposited wastewater solids within the 154 sewershed and is caused by relatively low flow velocities of the wastewaters, with the flow velocities falling below those 155 required for continued solids suspension (Bertrand-Krajewski et al., 2010). Bed and near-bed transport conditions are 156 cyclical in nature, and most often occur in regions of the sewershed where flow velocities are unable to be constantly 157 maintained in a manner ensuring suspended sewer transport conditions (Ashley and Crabtree, 1992; CRABTREE, 158 1989; Lange and Wichern, 2013) . Notably, this phenomenon occurs in seasonal climate s where yearly cycles of low 159 and high rainfall seasons are present. Similarly, in northern and cold climate countries , precipitation accumulates as 160 snow and ice during colder months, representing periods of low flow and sedimentation for months, until the warmer 161 months bring increased flow, thus reinstating higher velocity conditions in the sewers. Finally, within some 162 municipalities, bed and near-bed transport conditions may be induced when the sewers are used to store wastewaters 163 during maintenance activities at the downstream wastewater treatment facility. 164 A limited understanding of the persistence and degradation of viral material in wastewaters remains due to a 165 lack of decay studies that use infected stool spiked into wastewaters and hence studies that investigate of decay rates 166 from the time of individual contribution s to community -level sampling . This considerable limitation in our current 167 knowledge coupled with the significant variation in reported decay rates of spiked-in SARS-CoV-2 viral particles studies 168 and endogenous SARS-CoV-2 already present in wastewater studies warrants further investigation of the persistence 169 and degradation of endogenous SARS-CoV-2 viral material in wastewaters . Our study addresses these gaps by 170 examining the decay rates of SARS-CoV-2 and PMMoV from the point of entry into the sewer system by using infected 171 stool spiked into wastewater and studying persistence and degradation of the viral material under the two common 172 sewer transport conditions of dynamic suspended transport and bed and near-bed transport. This approach leverages 173 the advantages of spike -in studies for evaluating decay from time zero while benefiting from the use of endogenous 174 infected material while also simulating realistic environments within wastewaters through the replication of common 175 transport conditions . This method hence more accurately mirrors the natural conditions the virus encounters upon 176 entering the sewer system. We hypothesize that the decay dynamics of pathogens within sewer systems are 177 significantly influenced by both the flow conditions and the hydraulic retention time , the latter being intrinsically linked 178 to urban planning and population density, which consequently impact the outcomes of WBS across different 179 municipalities. These factors are critical for interpreting viral signal variations across regions and can facilitate a more 180 global understanding of pathogen prevalence and movement. By studying the decay rates from the initial stages and 181 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 7 within more accurate sewer environments , our research aims to develop a more precise and comprehensive 182 understanding of viral persistence and decay dynamics in wastewater systems, thus contributing to the enhancement 183 of wastewater-based epidemiological models. 184 2. Materials and Methods 185 2.1. Stool and wastewater samples 186 SARS-CoV-2 positive stool samples containing endogenous SARS -CoV-2 viral material were spiked into 187 wastewater containing endogenous SARS -CoV-2 to perform the decay experiments , thus simulating the contribution 188 from source (flushed toilet) into wastewater. To determine the decay rate starting at the time of entry in the sewershed, 189 5 anonymous stool samples were obtained from consenting SARS-CoV-2 infected adult patients, combined into a 190 paste, and used to spike the wastewater. The only information obtained from the contributing patients was a confirmed 191 positive COVID-19 test result, with no other personal medical records obtained. Hence the collection of stool samples 192 for this study was exempt from research ethics board review. Stool samples were transported on ice and stored at 4°C 193 in the laboratory. Upon arrival, five biological replicates of stools were immediately extracted and screened for SARS-194 CoV-2 and PMMoV using RT-qPCR, as described below, to quantify the level of endogenous viral signals in the spiking 195 material. 196 An 18.9 L grab sample of post -grit influent wastewater was collected for this study from the City of Ottawa’s 197 Robert O. Pickard Environmental Center, the city’s only wastewater treatment plant , which receives and treats the 198 wastewater of approximately 91% of the residents in Ottawa (Supplemental Table S1). The collected wastewater 199 sample was transported to the laboratory on ice and preserved at 4°C until the start of the decay experiments and was 200 not pasteuri zed or otherwise altered in any way. Upon arrival, five biological replicates of the wastewater were 201 immediately extracted and screened for SARS -CoV-2 and PMMoV using RT -qPCR, as described below, to quantify 202 the level of endogenous viral signals. Decay experiments began within 12 hours of the collection of the wastewater. A 203 grab sample was selected to prioritize the freshness of the sample over representativeness, as the study did not require 204 monitoring of diurnal variations, and using a composite sample would have introduced an additional 24 hours of 205 potential decay before analysis. 206 2.2. Temperature-controlled experimental chambers 207 To conduct decay experiments at various, constant temperatures, three small refrigerators were temperature-208 controlled to specific experimental temperatures using an external Inkbird ITC-308 Digital Temperature Controller to 209 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 8 act as temperature -controlled experimental chambers. The three small refrigerators were maintained at the 210 temperatures of 19.6°C ±1.0°C, 12.5°C ±1.1°C and 4.9°C ± 0.6°C, throughout all decay experiments, respectively. 211 2.3. SARS-CoV-2, PMMoV and total RNA decay experiments simulating dynamic 212 suspended transport conditions at three distinct temperatures 213 214 A series of dynamic suspended transport SARS-CoV-2, PMMoV and total RNA decay experiments were 215 performed to simulate sewer transport of viral material within s mall, medium and large subsections of sewersheds. 216 Hence, short, moderate and long sewer hydraulic retention time times of 0 hours, 2.5 hours, 6.0 hours, 15.0 hours, 24.0 217 hours, and 35.0 hours were used in this study to reflect the varying distances wastewater travels, based on the distance 218 from its source, such as households, to the treatment plant. 17.61 grams of stool were dissolved into 2.40 liters of post-219 grit wastewater, achieving a concentration of approximately 7.34 g/L, and the mixture was carefully agitated at 4°C until 220 fully homogenized. The stool-wastewater mix was then separated into three (3) individual reactors containing magnetic 221 stir bars and placed within temperature-controlled experimental chambers set at target temperature of 4° C, 12° C and 222 20°C, respectively . The lowest temperature, 4° C, was specifically selected to reflect the conditions in sewers of 223 northern climate countries during colder periods, while the 12° C and 20°C settings are representative of a wider range 224 of temperatures commonly encountered in sewer systems across various regions globally (Hart and Halden, 2020; 225 Vialkova et al., 2020; Wilson and Worrall, 2021) . Magnetic stir-plates were installed inside the temperature-controlled 226 experimental chambers and the 3 individual vessels containing the stool-wastewater mix were placed on top of the stir-227 plates, where the magnetic stir plates were used to simulate the well -mixed, dynamic movement and suspension 228 Figure 1: Experiment set up simulating suspended and bed and near-bed transport conditions in sewersheds All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 9 conditions of wastewater -associated solids and fecal material during suspended transport conditions . Five 229 representatives 40 mL samples of well -homogenized stool -wastewater mixtures were harvested from each reactor 230 vessel at 0, 2.5, 6.0, 15.0, 24.0, and 35.0 hours. Each time point had five biological replicates, and at the end of the 231 experiment, half of the initial volume remained in the reactor to ensure representative conditions. SARS-CoV-2 RNA, 232 PMMoV RNA and total RNA were extracted immediately after sampling. Extracted RNA was never frozen and instead 233 kept at 4° C until RT-qPCR analysis was performed within 48 hours of extraction , a timeframe during which literature 234 has confirmed RNA stability(Robinson et al., 2021; Torabi et al., 2023). Control experiments with wastewater samples 235 that did not contain spiked stool were conducted under identical conditions and are shown in Supplemental Figure S1. 236 This approach was to verify that the spiked material from infected patients, although endogenously similar to the 237

found in typical wastewater samples, behaved comparably to samples collected directly from the treatment 238 plant only 12 hours earlier. 239 2.4. SARS-CoV-2, PMMoV and total RNA decay experiments simulating bed and near-240 bed transport conditions at three distinct temperatures 241 A series of SARS-CoV-2, PMMoV and total RNA decay experiments were designed to simulate bed and near-242 bed transport conditions in conventional sewersheds, including sedimentation time representative of wintertime flow 243 conditions in northern and cold climate countries , where precipitation and groundwater infiltration effects on the 244 sewershed are significantly reduced. 48 grams of stool were dissolved in 7 liters of post -grit wastewater, achieving a 245 concentration of approximately 6.86 g/L, and were carefully agitated at 4°C until the stool was fully mixed into the 246 wastewater. This slightly lower stool concentration was due to logistical constraints in measuring and dissolving the 247 stool such as residues adhering to the plastic weighing boats. The stool-wastewater mix was then transferred to forty-248 five (45) individual 50 mL conical centrifuge tubes . Three individual temperature-controlled experimental chambers 249 were set to 4° C, 12° C and 20 °C, respectively, and were used to house the 50 mL conical centrifuge tubes, which 250 were not agitated, simulating quiescent sewershed conditions and sedentary bed and near -bed transport of 251 wastewater-associated solids and fecal material. Five individual 50 mL conical centrifuge tubes containing stool -252 wastewater mixtures were harvested at periodic intervals between 0 and 6 0 days from each temperature -controlled 253 experimental chamber. Extended sampling for simulating bed and near-bed transport conditions was necessary due to 254 these conditions persisting for months. Indeed, sediment layers can remain for months in the sewer system until eroded 255 by environmental factors (Lange and Wichern, 2013) . In colder climates like Ottawa, precipitation solidifies as snow 256 and ice, reducing flow and erosion until warmer months, when snowmelt and rainfall initiate erosion. Samples were 257 extracted immediately after sampling, extracted RNA was never frozen and instead maintained at 4° C until, with RT-258 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 10 qPCR analysis being performed within 48 hours of collection of the sample . Control experiments with wastewater 259 samples that did not contain spiked stool were conducted under identical conditions and are shown in Supplemental 260 Figure S2. This approach was to verify that the spiked material from infected patients, although endogenously similar 261 to the material found in typical wastewater samples, behaved comparably to samples collected directly from the 262 treatment plant only 12 hours earlier. 263 2.5. Sample concentration and nucleic acid extraction 264 Representative 40 mL samples of well -homogenized stool -wastewater mix tures were collected from the 265 suspended and bed and near -bed transport temperature-controlled experimental chambers and were immediately 266 processed. Samples were concentrated and SARS-CoV-2, PMMoV, and total RNA were extracted as described by 267 D’Aoust et al. (2021b) . Briefly, s amples were centrifuged at 12,000 x g for 45 minutes, and the supernatant was 268 discarded. Samples were then centrifuged once again at 12,000 x g for an additional 5 minutes, and the resulting 269 supernatant was again discarded. RNA was extracted from the resulting pellet using Qiagen’s RNeasy 270 PowerMicrobiome Kit (Qiagen, Germantown, MD), with the following modifications to the manufacturer’s procedures: 271 i) 250 mg of the resulting solids pellet was extracted instead of a 200 µL liquid sample, and ii) the optional phenol -272 chloroform solution was substituted by Trizol LS reagent (ThermoFisher, ON, Canada). Samples were then eluted in 273 100 µL of RNAse/DNAse-free water. 274 2.6. RT-qPCR SARS-CoV-2 and PMMoV analyses 275 The SARS-CoV-2 viral signal was quantified using a singleplex one-step, RT-qPCR targeting the N1 and N2 276 genomic regions. The PMMoV viral signal was also measured using a singleplex one -step with RT-qPCR targeting a 277 region in the replication -associated protein portion of the genome (Haramoto et al., 2013) . Each PCR reaction was 278 composed of 1.5 µL of RNA template, forward and reverse primers (final concentration of 500 nM each), probe (final 279 concentration of 250 nM) 2.5 µL of 4x TaqMan ® Fast Virus 1-step Mastermix (ThermoFisher, ON, Canada), in a total 280 reaction volume of 10 µL. All primer sequences used in this study are shown in Supplemental Table S2. All samples 281 were run in technical triplicates with non -template controls and 5 -point standard curves prepared with the Exact 282 Diagnostic (EDX) COV019 SARS -CoV-2 RNA standard (Exact Diagnostics, TX, USA). PCR cycling conditions were 283 identical as those described previously (D’Aoust et al., 2021b). The assay’s limit of detection (ALOD) and quantification 284 (ALOQ) for SARS-CoV-2’s N1 region were of approximately 2 copies/reaction and 3.2 copies for reaction, respectively. 285 For the N2 region, they were of approximately 2 copies/reaction and 8.1 copies/reaction, respectively. All cycling 286 conditions used in this study are shown in Supplemental Table S3. 287 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 11 2.7. Total RNA analysis 288 The total RNA concentration of each sample was measured using an Agilent 2100 Bioanalyzer. 2 µL of 289 extracted RNA elution was loaded on an RNA 6000 Pico Chip. Further data analysis and concentration determinations 290 were performed using Agilent’s 2100 Expert software (v. B.02.10.SI764). 291 2.8. Sanger sequencing of amplicons 292 Sanger sequencing was conducted on time point day 21, day 45 and day 60, to ensure that the analyses did 293 not produce false positives. The specificity of the amplicons resulting from RT-qPCR analyses generated for the various 294 targets of this study was evaluated via Sanger sequencing. First, a touchdown PCR (TD-PCR) was performed using 295 Q5® High-Fidelity DNA Polymerase with 1 µL of RT-qPCR amplicons as the starting template. The initial touchdown 296 was performed as follows: [98 °C (30 seconds) + 64 °C → 55 °C, drop of 1 °C/cycle, + 72 °C (30 seconds)] x 10 cycles. 297 Amplification was then performed as follows: [98 °C (30 seconds) + 64 °C → 55 °C, drop of 0.4 °C/cycle, + 72 °C (30 298 seconds)] x 25 cycles. The TD-PCR products were then run on a 3% agarose gel at 100V to separate the amplicons. 299 The amplicon band observed at the appropriate location was then cut and gel extracted using Monarch® DNA Gel 300 Extraction Kit (New England Biolabs, MA, USA) as per the manufacturer’s instructions. After obtaining purified DNA, a 301 novel primer extension strategy for one -step PCR amplification was performed using the Q5 ® High-Fidelity DNA 302 Polymerase. In brief, 1 ng of DNA was used to extend the N1 and N2 amplicon using the oligo adapters (Supplemental 303 Table S2) with partial complementarity to the targeted amplicons. An extension PCR amplification was then performed 304 as follows: 98 °C (30 seconds) + [98 °C (10 seconds) + 50 °C (30 seconds) + 72 °C (30 seconds) x 30 cycles, + 72 °C 305 (2 minutes). The extension-PCR amplified product is then purified using QIAquick® PCR Purification Kit (Qiagen, MD, 306 USA) following the manufacturer’s instructions. The final amplicon product was then sequenced by Sanger Sequencing 307 at the Ottawa Hospital’s Research Institute (OHRI) StemCore Sequencing Facility using an ABI Prism 3730 DNA 308 Sequencer (Applied Biosystems, MA, USA). The sequences were compiled and edited using BioEdit (ver. 7.2) (Hall, 309 1999) and sequence alignment was done by Clustal Omega (Madeira et al., 2022) . Each PCR reaction had a total 310 volume of 25 µL and was composed of the amplicons’ regular reverse primers (500 nM), A target -specific amplicon-311 seq-Stuffer forward primer (50 nM), Stuffer -1 forward primer (50 nM), Stuffer -2 forward primer (500 nM), dNTP (200 312 µM), and 1X of the 5X Q5 reaction buffer and Q5 high fidelity DNA polymerase (0.02U/µL). All primer sequences used 313 in this study are shown in Supplemental Table S2. 314 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 12 2.9. Volume, PMMoV and total RNA normalization 315 Measurements of SARS -CoV-2 in this study were normalized using volume, PMMoV, and total RNA to 316 investigate potential degradation mitigation techniques. Volume normalization was employed instead of flow 317 normalization to accurately reflect the conditions of this controlled experiment, where sampling was conducted from a 318 batch reactor rather than a continuous flow system. As such, volume normalization serves as an extrapolation of flow 319 normalization typically used in wastewater treatment plants with variable daily flow rates. Additionally, since SARS -320 CoV-2 and PMMoV are known to be prevalent in the solids fraction (D’Aoust et al., 2021a) , volume normalization in 321 this study effectively simulates the real -world scenario where, despite consistent sampling volumes, the amount of 322 solids and consequently, the viral signal can vary, even when flow normalization is applied. 323 2.10. Statistical analyses 324 First order decay rate 325 The first order decay rate model was applied to the SARS-CoV-2, PMMoV and total RNA targets under 326 dynamic suspended transport conditions and bed and near-bed transports at temperatures of 4° C, 12° C and 20 °C. 327 The first order decay rate constant (k) was calculated as shown in equation 1 (Chick, 1908) . Here, the term [𝐴]𝑡 328 represents the concentration of the target at time t, while [𝐴]0 is the initial concentration at time zero. The rate was 329 obtained by calculating the slope of the natural logarithm (Ln) of the concentration of the target’s (A) signal versus time 330 (t). The slope was calculated using GraphPad Prism (version 9.3.1). 331 ln[𝐴]𝑡 = −𝑘𝑡+ 𝑙𝑛[𝐴]0 (Eq 1) Significance of first order decay rate constant 332 To assess whether the SARS-CoV-2, PMMoV and total RNA targets exhibited decay, a two-tailed t-test with 333 a significance level (α) of 0.05, was used to determine the significance of the first order decay constant under dynamic 334 suspended transport conditions and bed and near-bed transport conditions at temperatures of 4° C, 12° C, and 20 °C, 335 with the null hypothesis that the decay rate is zero. These tests were performed on k values obtained from the first 336 order decay model for all targets at each temperature to determine if each k was significantly different from zero. 337 Comparison of first order decay rate constant 338 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 13 Differences in decay rate constants between targets at various temperatures were assessed using a two-tailed 339 t-test with a significance level (α) of 0.05, with the null hypothesis that the decay rates of the targets were not significantly 340 different. Specifically, three separate t-tests were conducted for each comparison, always comparing two groups at a 341 time: SARS-CoV-2 vs. PMMoV, SARS-CoV-2 vs. total RNA, and PMMoV vs. total RNA. Additionally, three independent 342 t-tests were performed to compare the normalized signals (volume -normalized vs. PMMoV -normalized, volume -343 normalized vs. total RNA-normalized, and PMMoV-normalized vs. total RNA-normalized) at temperatures of 4°C, 12°C, 344 and 20°C. Finally, three independent t -tests were conducted to compare decay rates between different temperature 345 conditions (4°C vs. 12°C, 4°C vs. 20°C, and 12°C vs. 20°C). All t-tests were performed using GraphPad Prism (version 346 9.3.1). 347 First order decay rate time needed to achieve 90% reduction (T90) 348 T90, the time required for 90% of the starting target to decay, was calculated for the first order decay model as 349 shown in equation 2. All T 90 values were utilized as a comparative measure for analyzing the SARS-CoV-2, PMMoV 350 and total RNA decay rates at temperatures of 4° C, 12° C and 20 °C and for comparisons with other studies. 351 𝑇90 = − ln(0.1) 𝑘 (Eq 2) 352 Model fit 353 To assess model fit, the coefficient of determination (R2) was calculated for both the first order and two-phase 354 models for all targets and temperatures of the study using GraphPad Prism (version 9.3.1). The R 2 was used to 355 calculate the proportion of the variation in the dependent variable predic table from the variation in the independent 356 variable (Gage, 1988). 357 358 Two-phase decay rate 359 The two-phase decay rate model was applied to the bed and near-bed transport condition data sets to achieve 360 better model fit compared to the first phase decay model . The two -phase decay constants (k fast and k slow) were 361 calculated using a two-phase decay model as defined in equations 3 to 3.2 using GraphPad Prism (version 9.3.1). This 362 composite exponential decay model defines the overall decay rate as the sum of a simultaneous fast and a slow 363 exponential decay as shown in equation 3. The “Percent Fast ” parameter defines the proportion of the initial 364 concentration [𝐴]0 subjected to the fast decay process, characterized by the rate constant kfast. Simultaneously, the 365 remaining fraction (100 - Percent Fast), undergoes decays at a slower rate kslow. The equations 3.1 and 3.2 define the 366 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 14 initial concentrations for the fast and slow decay phases, respectively, which are then incorporated into the two-phase 367 decay model. GraphPad Prism offers the option to include a non-zero plateau, representing the terminal concentration 368 at which the decay stabilizes. Based on the nature of our study and the expectation that the concentration diminishes 369 entirely over time, we set the plateau to zero. 370 [𝐴]𝑡 = [𝐴]0 𝑓𝑎𝑠𝑡 ∗ 𝑒−𝑘𝑓𝑎𝑠𝑡∗𝑡 + [𝐴]0 𝑠𝑙𝑜𝑤 ∗ 𝑒−𝑘𝑠𝑙𝑜𝑤∗𝑡 (Eq 3) [𝐴]0 𝑓𝑎𝑠𝑡 = [𝐴]0 ∗ 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝐹𝑎𝑠𝑡∗ 0.1 [𝐴]0 𝑠𝑙𝑜𝑤 = [𝐴]0 ∗ (100 − 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝐹𝑎𝑠𝑡) ∗ 0.1 (Eq 3.1) (Eq 3.2) 371 Two-phase decay rate time needed to achieve 90% reduction (T90) 372 T90, the time required for 90% of the starting target to decay, was calculated for the two-phase decay model 373 as shown in equation 4. T90 was calculated for two-phase decay model by solving equation 4 for t using a bisection 374 numerical method . This calculation was perform ed using the “uniroot” function from the “rootSolve” library in R 375 programming language. All T90 values were utilized as a comparative measure for analyzing the decay rates of SARS-376 CoV-2, PMMoV and total RNA decay rates at temperatures of 4° C, 12° C and 20 °C. 377 0.1 = (𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝐹𝑎𝑠𝑡∗ 0.01) ∗ 𝑒−𝑘𝑓𝑎𝑠𝑡∗𝑡 + ((100 − 𝑃𝑒𝑟𝑐𝑒𝑛𝑡 𝐹𝑎𝑠𝑡) ∗ 0.01) ∗ 𝑒−𝑘𝑠𝑙𝑜𝑤∗𝑡 (Eq 4) 378 3. Results and Discussion 379 3.1. Decay of SARS-CoV-2, PMMoV and total RNA under conditions simulating toilet 380 flushed stool and dynamic sewer suspended transport 381 The dynamic suspended transport experiments, conducted over a 35-hour period, were designed to mimic the 382 rapid transit conditions typically experienced in sewer systems under normal flow conditions, simulating the movement 383 of viral material from the time of a toilet flush using spiked -in infected stool material. Measurements of SARS -CoV-2, 384 PMMoV and total RNA (Figure 2A to 2C), as well as the volume -normalized, PMMoV -normalized and total RNA -385 normalized SARS-CoV-2 signal (Figure 2D to 2F), are presented over a simulated sewer transport time of 35 hours. 386 The volume-normalized results are calculated by volume normalizing the study data with the reactor volumes, which is 387 analogous to flow-normalized data collected from a full -scale, continuous flow operating system. No significant decay 388 was observed in the SARS-CoV-2, PMMoV and total RNA measurements within 35 -hour period. Consequently, there 389 were also no changes observed in the volume-normalized, PMMoV-normalized and total RNA-normalized SARS-CoV-390 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 15 2 signals. These observations, particularly for SARS -CoV-2 and PMMoV, are consistent with previous studies. While 391 the previous studies did not directly replicate dynamic flow conditions, they assessed decay at temperatures between 392 4°C and 20°C using spiked -in viral materials and endogenous viral material already present in wastewater. Sala -393 Comorera et al. (2021) employed spiked -in viral material in river water and seawater at 4°C and 20°C, observing no 394 decay in river water at either temperature and no decay in seawater at 4°C, with significant decay occurring only in 395 seawater at 20°C. Similarly, Roldan -Hernandez et al. (2022) used endogenous material from wastewater that had 396 already been in the sewer system for 17 hours and observed little decay during the first 24 hours at temperatures 397 ranging from 4°C to 22°C. Interestingly, there are also contracting findings in the current literature, with studies by 398 Weidhaas et al. (2021) reporting significant decay of endogenous material during studies on sample storage at 4, 10 399 and 35 C, hence indicating that there may exist other factors influencing decay. As such, this study shows that 400 endogenous SARS-CoV-2 viral material, PMMoV viral material and total RNA released from stool do not significantly 401 decay under suspended sewer transport conditions during conventional sewer travel times from the point of entry in 402 sewersheds to the sampling point at temperatures between 4°C and 20°C. 403 To confirm that no statistically significant differences existed between the viral signal trends over the 35 -hour 404 simulated transport period, we conducted a series of independent student’s t -tests. For each target s (3) and 405 normalization methods (3), we assessed the effect of temperature using three separate tests: 4°C vs. 12°C, 4°C vs. 406 20°C, and 12°C vs. 20°C, resulting in a total of 18 tests. Additionally, we performed t -tests to compare the different 407 0.0 0.5 1.0 1.5 2.0 0 10,000 20,000 30,000 Time (Days) Average N1 & N2 copies/g 4°C 12°C 20°C A 0.0 0.5 1.0 1.5 2.0 0.000 0.004 0.008 0.012 Time (Days) Average N1 & N2 copies/copy PMMoV 4°C 12°C 20°C E 0.0 0.5 1.0 1.5 2.0 0 1.8×106 3.6×106 5.4×106 Time (Days) PMMoV copies/g 4°C 12°C 20°C B 0.0 0.5 1.0 1.5 2.0 0.000 0.002 0.004 0.006 Time (Days) Average N1 & N2 copies/total RNA g 4°C 12°C 20°C F 0.0 0.5 1.0 1.5 2.0 0 6×106 1.2×107 1.8×107 Time (Days) Total RNA g/g 4°C 12°C 20°C C 0.0 0.5 1.0 1.5 2.0 0 40,000 80,000 120,000 Time (Days) Average N1 & N2 copies/L 4°C 12°C 20°C D Figure 2: Observed measurements across 35 hours under conditions simulating conventional sewer flow conditions of A SARS-CoV-2; B PMMoV; C total RNA signal. Observed measurements of SARS-CoV-2 across 35 hours under conventional sewer flow setting normalized by D volume; E PMMoV; F total RNA. Starting signal levels are represented by the gray line. Mean and standard deviation are display ed for three temperatures, 4°C, 12°C and 20°C. Where the standard deviation is too small, the error bars are not displayed. Each measurement was performed using 6 technical triplicates from three biological replicates (n=5). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 16 normalization methods (volume-normalized vs. PMMoV-normalized, volume-normalized vs. total RNA-normalized, and 408 PMMoV-normalized vs. total RNA -normalized). The resulting p-values were all above the significance threshold (α = 409 0.05), indicating that neither temperature nor normalization method had a significant effect on persistence or 410 degradation, which was expected given the nonsignificant decay trends observed across all measured targets . These 411 findings are consistent with to those reported by a recent endogenous PMMoV and SARS -CoV-2 decay study by 412 Roldan-Hernandez et al. (2022) that found limited decay when subjected to temperature of 4°C and 22°C for a 10-day 413 period. In contrast, several other studies that use spiked virus with testing conditions that more closely simulate bed 414 and near -bed transport that report an increasing decay rate constant with increasing temperature , especially with 415 temperatures above 25°C (Ahmed et al., 2020b; de Oliveira et al., 2021; Roldan-Hernandez et al., 2022; Weidhaas et 416 al., 2021; Yang et al., 2022) . Our study hence addresses gaps in current knowledge and contradictory findings in the 417 literature by demonstrating that SARS-CoV-2, PMMoV and total RNA do not significantly decay under suspended sewer 418 transport conditions after the flush event at temperatures between 4°C and 20°C. 419 3.2. Decay of SARS -CoV-2, PMMoV and total RNA under conditions simulating toilet 420 flushed stool and bed and near-bed sewer transport 421 The bed and near-bed transport experiments extended up to 6 0 days to represent the longer retention times 422 of sedimented solids associated with lower flow conditions in the sewer system, particularly during colder months when 423 flow rates decrease. As with the dynamic transport experiments, spiked-in infected stool material was used to simulate 424 the transport of viral material from the time of a toilet flush. The concentrations of SARS-CoV-2, PMMoV and total RNA, 425 Figure 3A to 3, as well as the volume-normalized, PMMoV-normalized and total RNA-normalized SARS-CoV-2 viral 426 signal, Figures 3D to 3F , throughout the 60 -day experimental period is shown below. Sanger sequencing was 427 conducted at time points on day 21, day 45, and day 60 to ensure that the analyses did not produce false positives, 428 with all tests exhibiting a homology greater than 95% to the SARS-CoV-2 reference sequence (Severe acute respiratory 429 syndrome coronavirus 2 genome assembly, chromosome: 1, GenBank: OV387455.1) A statistically significant , 430 unexpected, increase in the measurements of SARS-CoV-2 (121% ± 21%; Figure 3A), PMMoV (75% ± 14%; Figure 431 3B) and total RNA (248% ± 6%; Figure 3C) at all temperatures are observed at the very beginning of the experiment 432 (between day 0 and day 1 as shown between the grey colo ur data point at time zero and the subsequent data points 433 shown in blue ( T=4°C), orange ( T=12°C) and red ( T=20°C)). Specifically, increases in SARS-CoV-2 (15% ± 4%), 434 PMMoV (63% ± 10%) and total RNA (160% ± 12%) were recorded across all temperatures . Similarly, an increase at 435 all temperatures for the volume-normalized dataset (25% ± 10%) were also observed while no significant changes were 436 noted for the PMMoV or RNA normalized signals . This increase was also seen in the non -stool-spiked wastewater 437 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 17 control, ruling out the possibility this increase was caused solely by the use of spiked-in stool material in this study 438 (Supplementary Figure S2). As further exploration of this increase was beyond the scope of this study, we herein limit 439 the discussion of this phenomen on to a few brief statements that this change may be caused by the stool and 440 wastewater being exposed to the specific simulated bed and near-bed transport conditions in this study, as this same 441 increase was not observed when the stool or the wastewater was exposed to the dynamic suspended transport 442 conditions (i.e. were well mixed throughout the experimental phase). As such, it is possible that the microenvironments 443 within the wastewater mixed with stool created by non-mixed conditions of the vessels throughout the experimental 444 phase may have become anaerobic or anoxic and hence have increased the accessibility to the measurement targets 445 within the wastewater matrix. Indeed, as shown in Figure 4A, a drop of 1.52 ± 0.27 in pH is observed between time 0 446 and day 1 under bed and near-bed transport conditions while Figure 4B shows that pH remains relatively constant after 447 1 day of exposure to suspended transport conditions. The measured decrease in pH under simulated bed and near-448 bed transport conditions supports the potential of anaerobic conditions and a related shift in microenvironments within 449 the wastewater matrix which could in turn lead to a decrease in pH and a change in the partitioning of the target material 450 that may result in an increase of the accessibility of targets during the concentration and extraction analytical processes 451 used in this study (Espinosa et al., 2022). Further work is needed to continue to investigate this unique behaviour of an 452 increase in target material during exposure to unmixed transport conditions. 453 0 10,000 20,000 30,000 5 20 35 50 65 Time (Days) Average N1 & N2 copies/g 4°C 12°C 20°C 1 2 30 * ** * *** *** *** ** A ** 0 1.8×106 3.6×106 5.4×106 5 20 35 50 65 Time (Days) PMMoV copies/g 4°C 12°C 20°C 0 1 2 3 * * *** * B 0.000 0.004 0.008 0.012 5 20 35 50 65 Time (Days) Average N1 & N2 copies/copy PMMoV 4°C 12°C 20°C 0 1 2 3 ** * * ** ** E ** 0.000 0.002 0.004 0.006 5 20 35 50 65 Time (Days) Average N1 & N2 copies/total RNA g 4°C 12°C 20°C 0 1 2 3 * F 0 6×106 1.2×107 1.8×107 5 20 35 50 65 Time (Days) Total RNA g/g 4°C 12°C 20°C 0 1 2 3 ** * * * * * ** C 0 40,000 80,000 120,000 5 20 35 50 65 Time (Days) Average N1 & N2 copies/L 4°C 12°C 20°C 0 1 2 3 ** *** ** * * *** ** D Figure 3: Observed measurements across 60 days under conditions simulating bed and near-bed transport conditions of A SARS-CoV-2; B PMMoV; C total RNA signal. Observed measurements of SARS -CoV-2 across 60 days under conditions simulating bed and near-bed transport conditions normalized by: D volume; E PMMoV; F total RNA. Mean and standard deviations are displayed for three temperatures, 4°C, 12°C and 20°C. Where the standard deviation is too small, the error bars are not displayed. Each measurement is performed in 6 technical triplicates from five biological replicates (n=5). Asterisks indicate which data points are statistically different from the previous one based on p-value cut-off of minimum <0.05. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 18 Decay rates were investigated from day 1 to day 60 of the simulated bed and near-bed transport conditions, 454 following the exclusion of the initial 24 -hour increase in signal, which was beyond the scope of this study (Figure 3). 455 Statistically significant decay was observed in SARS-CoV-2 and total RNA signal at all temperature s at day 2 while 456 PMMoV measurements only began showing signs of statistically significant decay at day 3. The heightened stability of 457 PMMoV may be attributed to its robust rod-shaped structure (Kitajima et al., 2014), while SARS-CoV-2 is hypothesized 458 to be present in wastewater primarily as fragmented virions (Kantor et al., 2021). For volume-normalized and PMMoV-459 normalized signals, statistically significant change was observed on day 3 at 4°C and 12°C, but not 20°C. Total RNA -460 normalized signal shows no significant changes except on day 2 at 4°C, 12°C and 20°C, due to the unexpected increase 461 described before, and again only on day 60 at 12°C. SARS-CoV-2, PMMoV and total RNA signals as well as volume-462 normalized signals demonstrated a rapid decay in measured signal between day 1 to 3, followed by a marked tapering 463 of the decay for the remainder of the study ( days 7 to 60). During this experiment, PMMoV-normalized viral signal 464 showed a constant decrease between days 1 to 45, while total RNA-normalized viral signal showed almost no change 465 from days 1 to 60. A first order decay model was first investigated to model the experimental data. The mean first order 466 decay rate constants at temperature of 4°C to 20°C for SARS -CoV-2, PMMoV and total RNA ranged from of 0.045 to 467 0.053 day-1, 0.014 to 0.025 day -1 and 0.040 to 0.050 day -1 respectively. The subsequent calculated decay rates at 468 temperature of 4°C to 20°C for the volume-normalized and PMMoV-normalized signal ranged from, 0.037 to 0.042 day-469 1 and 0.032 to 0.027 day -1 respectively. The mean first order decay rate of total RNA-normalized signal was non-470 calculatable because there as no decay present of this normalized signal. 471 472 The T90 values ranged from 43.4 to 51.2 days for SARS-CoV-2, 92.1 to 164.5 days for PMMoV and 46.1 to 473 57.6 days for total RNA (Table 3). At all temperatures, the SARS-CoV-2 T90 observed in this study is larger than the 474 Figure 4: pH variation across time in: A conditions simulating dynamic suspended transport ; B conditions simulating bed and near-bed transport conditions. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 19 values reported by studies investigating decay rates of lab propagated spiked SARS-CoV-2 material in wastewater (1.6 475 – 36 days) (Ahmed et al., 2020b; de Oliveira et al., 2021; Hokajärvi et al., 2021). However the reported range 476 significantly broadens (0.5 – 154.9 days) when considering studies investigated decay with endogenous SARS-CoV-2 477 (Babler et al., 2023; Hart et al., 2023; Roldan -Hernandez et al., 2022; Weidhaas et al., 2021; Yang et al., 2022) , with 478 this range now including the T90 values measured in this study. The T90 values of this study in combination with the 479 findings of previously reported value suggest that endogenous SARS -CoV-2 is more persistent and hence more 480 resistant to decay then lab propagated spiked material. The PMMoV T90 observed in this study falls within the range of 481 reported values from other studies investigating endogenous PMMoV material (25.3 – 237.4 days) (Rachmadi, 2016; 482 Roldan-Hernandez et al., 2022; Sala -Comorera et al., 2021) . The variability in the reported T 90 values in current 483 literature suggests that there are additional, unreported factors influencing decay rates. Our study highlights the 484 knowledge gap of the influence of decay from the point of entry into the sewer system and also the influence of various 485 sewer flow conditions on endogenous signal decay. This further emphasizes the need for more research to identify 486 other potential sources of variation, such as wastewater matrix composition, target titer and sewer infrastructure. 487 488 Table 3: Mean and 95% confidence interval of first order decay constant (k) and T90 under conditions simulating toilet 489 flushed stool and bed and near-bed sewer transport. 490 n.c. indicate non-calculatable, where the model was unstable because no decay was present. 491 First Order Decay Model Measurement Temperature (°C) k (d-1) (Mean) [95% CI] T90 (days) (Mean) [95% CI] R2 Average N1 & N2 (copies/g) 4 0.045 [0.048 to 0.042] 51.2 [48.0 to 54.8] 0.48 12 0.049 [0.052 to 0.046] 47.0 [44.3 to 50.1] 0.54 20 0.053 [0.056 to 0.049] 43.4 [41.1 to 47.0] 0.63 PMMoV (copies/g) 4 0.014 [0.016 to 0.012] 164.5 [143.9 to 191.9] 0.32 12 0.022 [0.025 to 0.019] 104.7 [92.1 to 121.2] 0.40 20 0.025 [0.028 to 0.022] 92.1 [82.2 to 104.7] 0.38 total RNA (μg/g) 4 0.040 [0.047 to 0.034] 57.6 [49.0 to 67.7] 0.63 12 0.041 [0.047 to 0.035] 56.2 [49.0 to 65.8] 0.67 20 0.050 [0.058 to 0.043] 46.1 [39.7 to 53.5] 0.77 Average N1 & N2 (copies/L) 4 0.037 [0.040 to 0.035] 62.2 [57.6 to 67.7] 0.53 12 0.037 [0.040 to 0.033] 62.2 [57.6 to 69.8] 0.42 20 0.042 [0.046 to 0.038] 54.8 [50.1 to 60.6] 0.59 Average N1 & N2 (copies/copies PMMoV) 4 0.032 [0.034 to 0.030] 72.0 [67.7 to 76.8] 0.54 12 0.025 [0.027 to 0.023] 62.2 [57.6 to 69.8] 0.50 20 0.027 [0.029 to 0.025] 85.3 [79.4 to 92.1] 0.51 Average N1 & N2 (copies/total RNA μg) 4 n.c. n.c. n.c. 12 n.c. n.c. n.c. 20 n.c. n.c. n.c. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 20 The three normalization strategies applied in this research, volume-normalized, PMMoV-normalized and total 492 RNA-normalized SARS -CoV-2, demonstrate that the total RNA normalization strategy effectively corrects for time 493 decay of SARS -CoV-2 under bed and near -bed transport conditions, when decay of the viral measurement is 494 significant. An evaluation of whether decay constants were significantly non -zero showed in this study that only the 495 total RNA normalization yielded nonsignificant first order decay constants, with p-values of 0.9588, 0.0511, and 0.1640 496 at 4°C, 12°C, and 20°C, respectively (Supplemental Table S4). This is to be expected as the decay rate of SARS-CoV-497 2 was distinct from the decay rate of PMMoV (p<0.001 at all temperatures) while, when compared with total RNA, no 498 significant difference was seen between the decay rate of SARS-CoV-2 and total RNA (p-value of 0.2166, 0.0738 and 499 0.6824 at 4°C, 12°C and 20°C respectively) (Supplementary Table S5). This suggests that the measurement of SARS-500 CoV-2 decays at a similar rate to the measurement of the total RNA of stool and wastewaters. Hence, total RNA is 501 identified in this study as an important normalizing marker for sewer decay during bed and near-bed transport conditions 502 and hence also as a potential important indicator of sewer flushing events that are known to re-suspend settled solids 503 from within sewer infrastructure. 504 3.2.1. Temperature effect 505 To investigate the effect of temperature on decay rate constants, comparisons were made between the rates 506 at different temperatures : 4°C versus 12°C, 4°C versus 20°C, and 12°C versus 20°C. These comparisons were 507 conducted for measurements of SARS-CoV-2, PMMoV, total RNA, and their normalized values (Supplemental Table 508 S6). A significant temperature effect was observed between the decay rate of SARS -CoV-2 at 4°C and 20°C (p -509 value=0.0021). The decay rate of PMMoV differed significantly between 4°C and 12°C and between 4°C and 20°C with 510 both showing p-values of less than 0.0001. A significant difference was only observed for the decay rate of total RNA 511 between 4°C and 20°C , with a p-value of 0.0433, which is close to the conventional threshold for significance. This 512 suggests that additional data would be required to more accurately interpret the influence of temperature on total RNA 513 decay. The volume-normalized signal datasets also indicated significant temperature effects, with significant 514 differences observed between 4°C and 20°C (p-value=0.0330), as well as between 12°C and 20°C (p -value=0.0387). 515 Similarly, PMMoV-normalized measurements showed significant differences between 4°C and 12°C, and 4°C and 20°C 516 (p-values<0.0001 and 0.0016, respectively), which could be partially driven by the temperature effect on the normalizer, 517 PMMoV itself. Finally, no temperature effect was observed on the total RNA-normalized signal, as there was no decay 518 detected, which aligns with the finding that total RNA would be a sui table normalizer for sewer decay under bed and 519 near-bed transport conditions and as an identifier of sewer flushing events that re-suspends settled solids. Despite the 520 statistical significance observed in temperature -related differences in decay rates, the actual magnitude of these 521 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 21 changes is minimal. When we assess the impact of temperature on the decay constant by linearizing the relationship 522 through a Log 10 transformation of the mean first order decay rate against temperature, as shown in Figure 5, only 523 PMMoV demonstrates a discernible trend of increasing decay constant with rising temperature. In contrast, SARS-524 CoV-2, total RNA and volume-normalized Log10 linearize decay versus temperature display a flat trend, and the 525 PMMoV-normalized data even shows a slight inverse correlation. This deviates from what is typically reported in the 526 literature for spiked viruses (Ahmed et al., 2020b) where temperature impact is significant. However, this could be 527 attributed to the enhanced persistence of RNA when bound with dissolved organic matter in wastewater (Roldan-528 Hernandez et al., 2022), which may impede its biodegradation in natural systems, potentially offering protection against 529 temperature effects (Chatterjee et al., 2023) . This suggests that the persistent measurement of endogenous SARS -530 CoV-2, PMMoV, and total RNA is primarily driven by transport conditions and travel time within the sewer system, 531 rather than by temperature. This conclusion is reinforced by the alignment of this study’s decay rates with findings from 532 modelling of endogenous signals, which suggests that time spent in the sewer system has a greater impact on 533 degradation than temperature (Guo et al., 2023; McCall et al., 2022). 534 3.2.1. Two-phase decay 535 In addition to applying a first order decay model to the experimental data (excluding the initial 24-hour period), 536 a two-phase decay model was also evaluated to describe the decay dynamics observed from day 1 to day 60. The fit 537 of the model was assessed using an extra sum -of-squares F test. For SARS-CoV-2, PMMoV, total RNA signals and 538 the normalized signals, results showed that the F test values exceeded the critical threshold, indicating that the two-539 phases decay model had a better fit compared to the first order model. This finding implies that decay time might be 540 underestimated by the first order model, with the decrease in measurements being more accurately expressed in the 541 two-phases decay model. The p-values for each test were less than 0.0001, suggesting that the F -statistic results are 542 not likely due to random variability. The total RNA -normalized signal had a p -value of 0.0018, which is significantly 543 higher than other results and because no decay was observable in the RNA-normalized data. 544 0 4 8 12 16 20 24 -2.0 -1.5 -1.0 -0.5 0.0 Temperature (°C) Log10 k Average N1 & N2 (copies/g) PMMoV (copies/g) Total RNA (g/g) Average N1 & N2 (copies/L) Average N1 & N2 (copies/copy PMMoV) Figure 5: Log10 linearization of the mean first order decay rate constant against temperature All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 22 Table 4: Mean and 95% confidence interval of two-phase decay constant (k) and T90 under conditions simulating 545 toilet flushed stool and bed and near-bed sewer transport. 546 n.c. indicate non-calculatable, where the model was unstable because no decay was present. 547 The mean second order decay rates at temperatures ranging from 4°C to 20°C for SARS -CoV-2, PMMoV, 548 and total RNA ranged respectively from 0 .660 to 0.842 day-1, 0.798 to 1.343 day-1, and 0.539 to 0.934 day-1 for kfast. 549 Two-Phase Decay Model Measurement Temperature (°C) k (day-1) (Mean) [95% CI] T90 (days) [95% CI] Fast phase Percentage (%) R2 Average N1 & N2 (copies/g) 4 kfast : 0.842 [0.712 to 1.000] 340.7 [464.6 to 255.6] 81.7 [78.8 to 84.2] 0.79 kslow : 0.015 [0.011 to 0.020] 12 kfast : 0.700 [0.603 to 0.812] 363 [564.6 to 267.4] 82.4 [79.9 to 84.7] 0.84 kslow : 0.014 [0.009 to 0.019] 20 kfast : 0.660 [0.576 to 0.756] 307.2 [491.6 to 223.4] 85.7 [83.3 to 87.8] 0.86 kslow : 0.016 [0.010 to 0.022] PMMoV (copies/g) 4 kfast : 1.343 [0.751 to 3.594] 1388.8 [2777.5 to 793.6] 63.2 [47.2 to 90.7] 0.54 kslow : 0.004 [0.002 to 0.007] 12 kfast : 0.875 [0.634 to 1.213] 1088.2 [2720.6 to 680.1] 70.6 [63.6 to 76.7] 0.76 kslow : 0.005 [0.002 to 0.008] 20 kfast : 0.789 [0.556 to 1.108] 1079.9 [2699.7 to 599.9] 72.6 [65.6 to 78.4] 0.74 kslow : 0.005 [0.002 to 0.009] total RNA (μg/g) 4 kfast : 0.934 [0.707 to 1.269] 327.6 [476.5 to 238.3] 78.3 [73.6 to 82.4] 0.92 kslow : 0.016 [0.011 to 0.022] 12 kfast : 0.922 [0.536 to 1.831] 299.8 [539.6 to 199.9] 72.7 [62.1 to 84.1] 0.89 kslow : 0.018 [0.010 to 0.027] 20 kfast : 0.539 [0.389 to 0.746] 282.8 [1272.8 to 149.7] 82.2 [74.6 to 88.1] 0.63 kslow : 0.018 [0.004 to 0.034] Average N1 & N2 (copies/L) 4 kfast : 0.449 [0.317 to 0.626] 394.2 [613.2 to 275.9] 66.1 [59.6 to 71.4] 0.68 kslow : 0.014 [0.009 to 0.020] 12 kfast : 0.590 [0.445 to 0.775] 416.3 [676.5 to 284.8] 71.9 [65.7 to 77.0] 0.61 kslow : 0.013 [0.008 to 0.019] 20 kfast : 0.260 [0.189 to 0.350] 357.4 [1340.2 to 206.2] 74.2 [64.8 to 82.2] 0.68 kslow : 0.015 [0.004 to 0.026] Average N1 & N2 (copies/copies PMMoV) 4 kfast : 0.475 [0.325 to 0.667] 427.7 [695 to 308.9] 62.7 [56.5 to 67.9] 0.69 kslow : 0.013 [0.008 to 0.018] 12 kfast : 0.319 [0.217 to 0.463] 562.7 [937.8 to 401.9] 50.1 [42.2 to 56.8] 0.61 kslow : 0.010 [0.006 to 0.014] 20 kfast : 0.220 [0.138 to 0.334] 697.9 [5582.9 to 398.8] 60.4 [51.3 to 69.3] 0.63 kslow : 0.008 [0.001 to 0.014] Average N1 & N2 (copies/total RNA μg) 4 kfast : n.c. n.c. n.c. n.c. kslow : n.c. 12 kfast : n.c. n.c. n.c. n.c. kslow : n.c. 20 kfast : n.c. n.c. n.c. n.c. kslow : n.c. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 23 For kslow, these rates were 0.014 to 0.015 day-1, 0.004 to 0.005 day-1, and 0.016 to 0.018 day-1 (Table 4). The calculated 550 decay rates at the same temperature range for the volume-normalized and PMMoV -normalized signals ranged 551 respectively from 0.260 to 0.590 day-1 and 0.220 to 0.475 day-1 for kfast. For kslow, these rates were 0.013 to 0.015 day-552 1 and 0.008 to 0.013 day-1 (Table 4). The model was unstable and could not fit the RNA-normalized signals as no decay 553 was present. The T90 values ranged from 307.2 to 340.7 days for SARS-CoV-2, 1079.9 to 1388.8 days for PMMoV, 554 and 282.8 to 372.6 days for total RNA ( Table 4 ). These value s fall outside the range observed in studies using 555 endogenous SARS -CoV-2 (0.5 – 154.9 days) and PMMoV (25.3 – 237.4 days) which could be due to an 556 underestimation of the reported decay rate when modelled with a fir st order decay model (Roldan-Hernandez et al., 557 2022; Weidhaas et al., 2021; Yang et al., 2022) . This discrepancy may also stem from our study ’s approach to 558 measuring persistence and degradation, including the examination of decay from the point of entry into the system, a 559 factor often overlooked in other studies and potentially leading to an underestimation of T 90 values, and also our 560 approach to simulate common transport conditions within sewer systems. Hence , the use of spiked -in stool samples 561 and assessing the impact of common flow conditions on endogenous SARS-CoV-2 and PMMoV decay could contribute 562 to these observed differences, bridging key knowledge gaps in achieving a more accurate representation of the decay 563 dynamics of these target materials in real-world sewer systems. Once again, the kfast and kslow of SARS-CoV-2 and 564 PMMoV were statistically different, with PMMoV decaying much slower than the SARS -CoV-2 target. On the other 565 hand, the kfast and kslow of total RNA was similar to that of SARS-CoV-2. As a result, both flow and PMMoV were shown 566 not to be adequate normalizers of targets exposed to bed and near-bed transport conditions. These findings suggest 567 that while PMMoV is a good fecal marker normalizer, its slower de cay rate makes it unsuitable for normalizing decay 568 occurring in bed and near-bed transport. In addition the study shows that total RNA-normalized signal is an appropriate 569 biomarker to normalize for bed and near -bed transport and in turn is a potential important indicator of sewer flushing 570 events. 571 4. Conclusions 572 Our study offers insights into the endogenous decay of SARS -CoV-2, PMMoV and total RNA from point of 573 entry in the sewer system, the toilet flush event , and during two predominant sewer transport conditions . Simulated 574 dynamic suspended transport over 35 hours period revealed good persistence and minimal degradation of the 575 measurement of SARS-CoV-2, PMMoV, and total RNA throughout short, moderate and long sewer transport conditions 576 that simulate small, medium and large sewersheds subsections. The observed decay rates showed no significant decay 577 rate for SARS -CoV-2, PMMoV or total RNA which appears to be independent of temperature effects within the 578 temperature range of 4°C to 20°C. This finding indicates negligible decay in dynamic suspended transport. 579 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 24 In contrast, the experiments simulating bed and near -bed transport conditions for 60 days demonstrated an 580 initial unexpected increase in the measurement of SARS-CoV-2, PMMoV, and total RNA. This could be attributed to 581 microenvironment shifts caused by the simulated t ransport conditions, a phenomenon that warrants further 582 investigation. Due to the complexity of this initial phase, the data from day 0 to 1 were excluded from the decay analysis. 583 Subsequently, decay was computed from day 1 to 60, where significant decay rates were observed with differing decay 584 patterns for SARS -CoV-2, PMMoV, and total RNA being observed. Temperature effect was minimal, suggesting the 585 decay is primarily driven by transport conditions and travel time within the sewer system, rather than by temperature. 586 The decay rates of the simulated bed and near-bed transport were observed to be within the range of previous studies 587 on endogenous targets. Although within the range, the variability in reported decay patterns suggests the potential 588 influence of other parameters like wastewater matrix composition, viral titers, or sewer system dynamics, which need 589 further exploration. To that end, our research particularly highlighted the previously overlooked impacts on endogenous 590 signals of decay from point of entry into the system and the role of different flow conditions in this process. While our 591 first order decay model fell short in predicting the decay rates, a two-phases decay model significantly improved the fit 592 during bed and near-bed transport. Total RNA normalization emerged as the most effective strategy for correcting time 593 decay in sewer systems experiencing bed and near -bed transport conditions . The outcomes of our study have 594 implications for understanding and modelling of SARS-CoV-2 WBS in sewersheds, especially systems that undergo 595 bed and near -bed transport conditions followed by sudden resuspension and mixing events, such as large rainfall 596 events, where flushing of the sewer infrastructure causes the re-suspension of SARS-CoV-2, PMMoV and total RNA 597 gene targets. This study also underlines the need for further investigation into time zero, toilet flush de cay studies 598 performed under different sewer transport conditions. 599 5. Declaration of competing interests 600 The authors declare that no known competing financial interests or personal relationships could appear to 601 influence the work reported in this manuscript. 602 6. Acknowledgements 603 The authors wish to acknowledge the help and assistance of the University of Ottawa, the Ottawa Hospital, 604 the Children’s Hospital of Eastern Ontario, the Children’s Hospital of Eastern Ontario’s Research Institute, Public Health 605 Ontario and all their employees involved in the project. Most specifically, the authors wish to thank Jessica Haines, 606 Rebecca Porteous, Irene Watpool. Their time, facilities, resources, and feedback are greatly appreciated. 607 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted October 2, 2024. ; https://doi.org/10.1101/2024.10.01.24314490doi: medRxiv preprint 25 7. Funding 608 This research was supported by the Province of Ontario’s Wastewater Surveillance Initiative (WSI). This 609 research was also supported by a CHEO (Children’s Hospital of Eastern Ontario) CHAMO (Children’s Hospital 610 Academic Medical Organization) grant, awarded to Dr. Alex E. MacKenzie. This research was supported by the CIHR 611 Applied Public Health Research Chair in Environment, Climate Change and One Health, awarded to Dr. Robert 612 Delatolla. The funding source had no involvement in the study design, data collection, data analysis, data interpretation, 613 nor the writing or decision to submit the paper for publication. 614 615 8. 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