Wastewater treatment plant (WWTP) influent sampling is commonly used in wastewater-based disease surveillance to assess the circulation of pathogens in the population aggregated in a catchment area. However, the signal can be lost within the sewer network due to adsorption, degradation, and dilution processes. The present work aimed to investigate the dynamics of SARS-CoV-2 concentration in three sub-catchments of the sewer system in the city of Hildesheim, Germany, characterised by different levels of urbanisation and presence/absence of industry, and to evaluate the benefit of sub-catchment sampling compared to WWTP influent sampling. Our study shows that sampling and analysis of virus concentrations in sub-catchments with particular settlement structures allows the identification of high concentrations of the virus at a local level in the wastewater, which are lower in samples collected at the inlet of the treatment plant covering the whole catchment. Higher virus concentrations per inhabitant were found in the sub-catchments in comparison to the inlet of the WWTP. Additionally, sewer sampling provides spatially resolved concentrations of SARS-CoV-2 in the catchment area, which is important for detecting local high incidences of COVID-19.

  • Higher concentrations of SARS-CoV-2 per inhabitant were found in the sub-catchments compared to the inlet of the treatment plant.

  • Normalising the SARS-CoV-2 concentration with the COD or the electrical conductivity of the wastewater slightly improves the correlation with the 7-day incidence of COVID-19 circulation in the population.

  • The effort of sampling the sewer system is outweighed by the additional information obtained.

Urban wastewater provides full information about people's lifestyles and health. People excrete drugs, medicines, and also viruses and bacteria into the sewer system. Wastewater-based disease surveillance for public health dates back to the 19th century with the isolation of Salmonella enterica serovar typhi from water, the causative agent of a typhoid fever outbreak, and continued to be used in the 1900s to investigate typhoid fever outbreaks (Budd 1873; Leal 1899). In 1854, John Snow described wastewater-based cholera surveillance in London and linked contaminated drinking water to a nearby sewage pit (Cameron & Jones 1983). Subsequent studies of wastewater-based disease surveillance for public health assessment report on the monitoring of poliovirus circulation in populations in the Netherlands and Israel (van der Avoort et al. 1995; Manor et al. 1999) and illicit drug use in Europe (Thomas et al. 2012). Since the first outbreak of the novel coronavirus disease (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in China, which spread rapidly around the world and placed a heavy burden on healthcare facilities and sanitation, wastewater treatment has again become a focus of research (Ahmed et al. 2020; World Health Organization 2020, 2022; Prado et al. 2021; Haak et al. 2022; Hsu et al. 2022). Since the onset of the pandemic, several countries have implemented national wastewater surveillance for SARS-CoV-2 as an additional tool to assess the spread of the disease within the community (e.g. Australia, Canada, the Netherlands, and the USA). An advantage of wastewater-based disease surveillance is that it targets symptomatic and asymptomatic individuals (Ahmed et al. 2020; La Rosa et al. 2020; Medema et al. 2020; Randazzo et al. 2020). The European Commission published the Recommendation 2021/472 in March 2021, calling on Member States to establish a wastewater surveillance system for SARS-CoV-2 (Recommendation (EU) 2021/472 2021).

To date, most wastewater-based disease surveillance studies have sampled the influent of wastewater treatment plants (WWTPs). Sampling is usually done with an automated sampler (24-h composite samples) and is part of the daily routine at a WWTP. This type of sampling involves a large averaging of data over the whole catchment, making it impossible to identify hotspots within a catchment and apply localised measures (Zdenkova et al. 2022). While influent sampling is useful for monitoring trends, sewer sampling provides data that can be used to make decisions about public health intervention. Few studies have focused on sampling within the sewer system, upstream of the WWTP. Among these, Haak et al. (2022) investigated the spatial patterns of SARS-CoV-2 distribution within the Reno-Sparks metropolitan area and identified neighbourhoods leading waves of infection, as well as demographic parameters with important effects on the COVID-19 dynamics. Zdenkova et al. (2022) sampled sewer systems in Prague, comparing concentrations of SARS-CoV-2 in wastewater with the number of COVID-19 cases. Correlation between viral concentration in wastewater and clinical test data was good for residential areas with more than 7,000 inhabitants. Moreover, Wurtzer et al. (2022) studied the emergence of variants of concern (from alpha to omicron BA.2) in the Paris area by monitoring samples from the WWTP and its contributing sewers. Other studies analysing SARS-CoV-2 in the sewer system have been carried out in Brazil (Prado et al. 2021) and India (Kumar et al. 2023), showing that the concentration in the sewer system could be an effective monitoring and management tool as sampling the sewer network can provide a 2-week lead time to the citywide active cases reported. For Germany, there is only one study from Munich which addressed the sampling of sewer systems (Rubio-Acero et al. 2021). The city of Munich, with a population of around 1.5 million, and the sub-catchments selected in the study (minimum population of 6,800, maximum population of 160,000) are quite large compared to the city of Hildesheim and its sub-catchments. Therefore, the results reported for Munich, although very important, are less representative for the majority of cities in Germany with a population between 20,000 and 100,000.

Only few studies exist analysing small sub-catchments. The novelty of our study is the assessment of the added value of sampling sub-catchments for the dynamics of SARS-CoV-2 in wastewater, compared to only sampling the influent to the WWTP, which serves as a benchmark. The investigation is carried out for the sewer system of Hildesheim. Within this study we developed and tested a strategy for wastewater sampling in the sewer system and monitored the concentration of SARS-CoV-2 at the influent of the WWTP and the sewer network comparing the trend in SARS-CoV-2 concentrations in wastewater with local COVID-19 cases. Furthermore, we analysed different normalisation factors to improve the relationship between SARS-CoV-2 concentrations and COVID-19 cases. We addressed the following research questions with our study:

  • Can the regional dynamics of COVID-19 cases be identified by regional sampling within the sewer system at sub-catchment scale?

  • Can we identify an information loss due to decreasing RNA concentration from sub-catchment to the WWTP?

  • Does the normalisation of RNA concentration using wastewater parameters and biomarkers improve the relationship between wastewater surveillance and COVID-19 cases?

Study area and settlement structure

The monitored WWTP serves the city of Hildesheim and the community of Diekholzen. The drainage area covers a population of ∼100,000 in Hildesheim and ∼6,500 in Diekholzen, both located in Lower Saxony, Germany. The sewer consists of 129 km combined sewer, 270 km wastewater sewer, and 304 km rainwater sewer. In 2021, circa 6.3 billion litres of wastewater and 3.3 billion litres of stormwater from sealed surfaces flowed to the WWTP. There are several industries discharging into the sewer system in the catchment area. Three sub-catchments in Hildesheim, namely Drispenstedt (5,433 inhabitants), Himmelsthür (3,366 inhabitants), and Itzum (3,882 inhabitants), were selected for sampling because of their distinct settlement structure (Figure 1). The sewer system within these three watersheds is a separate system, with wastewater and stormwater being collected and transported separately. The Drispenstedt and Himmelsthür sub-catchments contain both, residential and commercial areas, but only Drispenstedt has industry with significant wastewater contributions to the sewer. Itzum is a residential area with one-family houses and some apartment blocks.
Figure 1

Sampling locations (red diamonds) and sub-catchments in the city of Hildesheim, Germany (LGLN-Landesamt für Geoinformation und Landesvermessung Niedersachsen 2022).

Figure 1

Sampling locations (red diamonds) and sub-catchments in the city of Hildesheim, Germany (LGLN-Landesamt für Geoinformation und Landesvermessung Niedersachsen 2022).

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Sample collection programme

The monitoring study took place between October 2021 and December 2022. At the inlet to the WWTP, 24-h time-proportional composite samples were collected twice weekly by the WWTP operators after the grit chamber. Four-hour time-proportional composite wastewater samples were collected weekly by the research team using automated samplers (NEMO 1 M PP, ORI, Germany) at the three manholes in the sewer system and at the inlet to the WWTP (before the grit chamber) from 6 am to 10 am. This time window corresponds to the highest chemical oxygen demand (COD) and ammonium-nitrogen loads at the sampling locations (Look et al. 2023). After collection, the samples were transported directly to the laboratory at 4 °C.

Workflow from sample collection and processing to molecular analysis

The wastewater samples arrived at the laboratory in the afternoon and were stored overnight in the refrigerator at 4 °C. The next day, the samples were concentrated and the nucleic acids were extracted and stored overnight at −80 °C. The Droplet Digital Polymerase Chain Reaction (ddPCR) was performed the following day, so that the results were available 1.5 days after the samples arrived at the laboratory (Figure 2).
Figure 2

Schematic diagram of the workflow from sampling to ddPCR results.

Figure 2

Schematic diagram of the workflow from sampling to ddPCR results.

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A total of 55 mL of each sample was used for concentration and further recovery of nucleic acids. The samples were first centrifuged at 8,000 g for 15 min at room temperature without any resting period to remove large particles and bacteria. The supernatant was then adjusted to a pH between 7.0 and 7.5, and concentrated by PEG/NaCl precipitation as described by Farkas et al. (2021), with slight modifications. Briefly, 10% (w/v) PEG 6000 (VWR, Radnor, PA, USA) and 2% NaCl (w/v) (VWR) were added to the sample and mixed in a shaking incubator at 40 rpm for 3 h at room temperature. The samples were then centrifuged at 10,000 g for 1 h at 4 °C, the supernatant was discarded, and the pellet was resuspended in 500 μL of lysis buffer (Macherey & Nagel, Düren, Germany) and stored at −80 °C until RNA extraction.

Viral nucleic acid was extracted using the NucleoMag DNA/RNA Water kit according to the manufacturer's instructions (Machery & Nagel, Düren, Germany). Briefly, magnetic beads and MWA2 buffer were added to the lysate from the PEG precipitation and incubated for 5 min at room temperature. After the washing steps, the nucleic acid was eluted in 50 μL of DNAse/RNAse-free water (Biozym, Hessisch Oldendorf, Germany). To control the virus recovery, 1 mL of raw wastewater was mixed with 1 mL of lysis buffer after first centrifugation (7,000 g for 15 min at room temperature) and incubated for 5 min. Magnetic beads and 2 mL of MWA2 buffer were then added and the procedure was followed according to the manufacturer's instructions (Machery & Nagel). To remove PCR inhibitors, the nucleic acid was purified using the OneStep PCR Inhibitor Removal Kit according to the manufacturer's instructions (Zymo research, Freiburg, Germany). RNA concentration was measured using the NanodropTM One (VWR). Finally, the nucleic acid samples were stored at −80 °C.

Three SARS-CoV-2-specific sequences (N1, N2, and RdRp), PMMoV, and crAssphage were quantified by one-step reverse transcription (RT)-ddPCR (Table 1). All primers and probes were purchased from biomers.net GmbH (Ulm, Germany). The ddPCR was performed on the QX200 Droplet Digital PCR System (Bio-Rad, Hercules, CA, USA) using the One-step RT-ddPCR Advanced Kit for Probes (Bio-Rad). For the analysis of SARS-CoV-2, 5 μL of nucleic acid was used in a final volume of 20 μL reaction mixture according to the manufacturer's instructions. For the analysis of crAssphage and PMMoV, 3 μL of a 1:100 dilution of the nucleic acid was used in a final volume of 20 μL of reaction mixture according to the manufacturer's instructions. The reaction mixture consisted of One-step RT-ddPCR Supermix (Bio-Rad): 20 units/μL reverse transcriptase (Bio-Rad), 15 mM DTT (Bio-Rad), 900 nM primer (forward and reverse), 250 nM probe and RNase-free water. The mixture was filled into the DG8 cartridge along with 70 μL of droplet generation oil, covered with the DG8 gaskets, and placed in the Droplet Generator (Bio-Rad). The resulting droplets were transferred to a ddPCR™ 96-Well plate (Bio-Rad) for PCR cycling. Cycling conditions were followed according to the manufacturer's instructions. Briefly: 3 min at 25 °C, 60 min RT at 42 °C, 10 min enzyme activation at 95 °C, 30 s denaturation at 95 °C/1 min annealing/extension cycle at 55 °C (50 cycles), 10 min enzyme deactivation at 98 °C, and a hold step at 4 °C until reading on the QX100 droplet reader (Bio-Rad). A no-template control and a positive control (single-stranded RNA fragments of SARS-CoV-2, EURM-019, Joint Research Centre, Geel, Belgium) were included in each ddPCR assay. QX Manger v1.2 Standard Edition (Bio-Rad) was used for manual thresholding and data export. The minimum acceptable droplet count was 11,000. Analyses with less than 11,000 droplets were repeated. Copies per μL were converted to copies per mL of wastewater. All detections were performed in the same laboratory using the same methods. The protocol was written according to the digital MIQE guidelines (Huggett et al. 2013).

Table 1

Primers and probes used for ddPCR analysis

Gene (NCBI)Primer IDTarget positionSequence (5′–3′)Amplicon sizeReference
N1 (NC_045512) 2019-nCoV_N1-F 28,287 GACCCCAAAATCAGCGAAAT 73 bp Lu et al. (2020)  
2019-nCoV_N1-R 28,335 TCTGGTTACTGCCAGTTGAATCTG 
2019-nCoV_N1-Pa 28,309 ACCCCGCATTACGTTTGGTGGACC 
N2 (NC_045512) 2019-nCoV_N2-F 29,164 TTACAAACATTGGCCGCAAA 67 bp Lu et al. (2020)  
2019-nCoV_N2-R 29,213 GCGCGACATTCCGAAGAA 
2019-nCoV_N2-Pa 29,188 ACAATTTGCCCCCAGCGCTTCAG 
RdRP1 (NC_045512) RdRP_SARSr-F2 15,431 GTGARATGGTCATGTGTGGCGG 100 bp Corman et al. (2020)  
RdRP_SARSr-R1 15,505 CARATGTTAAASACACTATTAGCATA 
RdRP_SARSr-P2 15,470 CAGGTGGAACCTCATCAGGAGATGC 
PMMoV (M81413) PMMV-F 1878 GAGTGGTTTGACCTTAACCGTTGA 67 bp Zhang et al. (2006)  
PMMoV-R 1926 TTGTCGGTTGCAATGCAAGT 
PMMoV-Pb 1906 CCTACCGAAGCAAAT 
crAssphage (JQ995537) 056F1_Crass 14,731 CAGAAGTACAAACTCCTAAAAAACGTAGAG 125 bp Stachler et al. (2017)  
056R1_Crass 14,833 GATGACCAATAAACAAGCCATTAGC 
056P1_Crassa 14,772 AATAACGATTTACGTGATGTAAC 
Gene (NCBI)Primer IDTarget positionSequence (5′–3′)Amplicon sizeReference
N1 (NC_045512) 2019-nCoV_N1-F 28,287 GACCCCAAAATCAGCGAAAT 73 bp Lu et al. (2020)  
2019-nCoV_N1-R 28,335 TCTGGTTACTGCCAGTTGAATCTG 
2019-nCoV_N1-Pa 28,309 ACCCCGCATTACGTTTGGTGGACC 
N2 (NC_045512) 2019-nCoV_N2-F 29,164 TTACAAACATTGGCCGCAAA 67 bp Lu et al. (2020)  
2019-nCoV_N2-R 29,213 GCGCGACATTCCGAAGAA 
2019-nCoV_N2-Pa 29,188 ACAATTTGCCCCCAGCGCTTCAG 
RdRP1 (NC_045512) RdRP_SARSr-F2 15,431 GTGARATGGTCATGTGTGGCGG 100 bp Corman et al. (2020)  
RdRP_SARSr-R1 15,505 CARATGTTAAASACACTATTAGCATA 
RdRP_SARSr-P2 15,470 CAGGTGGAACCTCATCAGGAGATGC 
PMMoV (M81413) PMMV-F 1878 GAGTGGTTTGACCTTAACCGTTGA 67 bp Zhang et al. (2006)  
PMMoV-R 1926 TTGTCGGTTGCAATGCAAGT 
PMMoV-Pb 1906 CCTACCGAAGCAAAT 
crAssphage (JQ995537) 056F1_Crass 14,731 CAGAAGTACAAACTCCTAAAAAACGTAGAG 125 bp Stachler et al. (2017)  
056R1_Crass 14,833 GATGACCAATAAACAAGCCATTAGC 
056P1_Crassa 14,772 AATAACGATTTACGTGATGTAAC 

F, forward primer; R, reverse primer; P, probe.

a Probes labelled at the 5′-end with the reporter molecule hexachloro-fluorescein (Hex) and at the 3′-end with BMN-Q530.

b Probes labelled at the 5′-end with the reporter molecule 6-carboxyfluorescein (FAM) and at the 3′-end with BMN-Q530.

Tests for possible inhibition in ddPCR were performed. For this purpose, 10,000 copies of the single-stranded RNA fragments of SARS-CoV-2 were spiked into the 20 μL final volume of the reaction mixture. No significant inhibition was observed. Using different amounts of SARS-CoV-2 single-stranded RNA fragments the lowest detectable concentration was 100 copies.

Physicochemical analysis of wastewater

For the measurements within the sewer, COD and ammonium-nitrogen concentrations were measured photometrically after sample filtration using the Cuvette test LCK 514 and Cuvette test LCK 514 from Hach-Lange (Germany), respectively. Parameters such as temperature and conductivity were measured in situ with the multiparameter sensor. The volumetric flowrate at the inlet of the WWTP was measured in situ and online after the grit chamber. All other physiochemical wastewater parameters at the WWTP were obtained by the Stadtentwässerung Hildesheim in their laboratories.

Experimental data analysis

For the data analysis, we calculated SARS-CoV-2 concentration in wastewater by the arithmetic mean of the three concentrations N1, N2, and RdRp. Furthermore a moving average over three data points was applied to smoothen the results; note, that generally equidistant data points are needed for this kind of smoothing, which were not available in our study. The Hildesheim Public Health Department provided 7-day incidence data for COVID-19 in the population on the sub-catchment scale as well as for the whole area. All virus concentration values obtained on dry and wet days were used in the correlation analysis of SARS-CoV-2 concentration with the 7-day incidence.

We used Pearson's correlation coefficient (r) to analyse the assumed linear relationship between SARS-CoV-2 concentration in wastewater and 7-day incidence. The strength of the correlation between two parameters was assessed as follows: r < 0.25 no correlation, 0.25 < r < 0.50 weak correlation, 0.50 < r < 0.75 moderate correlation, and r > 0.75 strong correlation (Han & Kamber 2010).

Simple normalisation of SARS-CoV-2 concentration was performed by dividing the SARS-CoV-2 concentration in wastewater by the concentration of chemical (COD, ammonium-nitrogen, and electrical conductivity) and molecular markers (crAssphage and PMMoV) of faecal contamination. In the case of flow rate, viral load was calculated as the product of concentration times flow rate. To assess the improvement in the fit between normalised SARS-CoV-2 concentration and 7-day COVID-19 incidence, we calculated Pearson's correlation coefficient r for all possible combinations.

Monitoring of hydrological, meteorological, and physicochemical wastewater parameters and their interaction

During the monitoring period, the wastewater flowrate showed a dynamic behaviour with peaks during rainy periods that reached up to three times the average flowrate (Supplementary material, Figure S1). Precipitation showed a strong positive correlation with wastewater flowrate (r = +0.81, n = 401) and a weak negative correlation with electrical conductivity (r = −0.34, n = 50). This behaviour was to be expected since the city of Hildesheim has a partially combined system with stormwater inflow. Regarding the other wastewater parameters, only for ammonium-nitrogen concentration a weak negative correlation was obtained with precipitation (r = −0.49, n = 46), with wastewater flowrate a strong negative correlation (r = −0.75, n = 46), a moderate negative correlation with electrical conductivity (r = −0.70, n = 46), and a weak negative correlation with the COD concentration (r = −0.48, n = 46). A weak positive correlation was obtained between the COD and ammonium-nitrogen concentrations (r = +0.43, n = 67).

The city of Hildesheim encompasses 14 districts each connected to the main sewer that conducts the wastewater to the treatment plant. In the present monitoring study, we included three districts, Himmelsthür, Drispenstedt, and Itzum (Figure 1). The samples collected from the sewer system were 4-h composite samples (from 6 to 10 h), while the ones collected at the inlet of the treatment plant were 24-h composite samples. To assess how the sampling duration affects the wastewater characteristics, 4-h composite samples were additionally taken at the inlet of the treatment plant. In order to compare the average, range, and spread of the results regarding the different parameters at the different sample locations, box and whisker plots were prepared and discussed as follows (Figure 3).
Figure 3

Box and Whisker plots for the wastewater parameters at the inlet of the treatment plant (WWTP) and at several locations in the sewer system. The concentrations of COD and ammonium-nitrogen are expressed in units of mg/L and the concentrations of PMMoV and crAssphage are expressed in units of copies/mL. The electrical conductivity (cond.) is expressed in units of μS/cm.

Figure 3

Box and Whisker plots for the wastewater parameters at the inlet of the treatment plant (WWTP) and at several locations in the sewer system. The concentrations of COD and ammonium-nitrogen are expressed in units of mg/L and the concentrations of PMMoV and crAssphage are expressed in units of copies/mL. The electrical conductivity (cond.) is expressed in units of μS/cm.

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The sampling duration at the inlet of the treatment plant, 24-h versus 4-h, had a minor effect in COD concentration; however, for the ammonium-nitrogen a slightly higher concentration was measured in 24-h composite sample. The districts Himmelsthür and Itzum with a similar population size of 3,366 and 3,882, respectively, but with a different type of settlement (one-family houses and a few apartments in Itzum compared to apartment blocks in Himmelsthür), present different COD and ammonium-nitrogen concentrations. The wastewater at the sampling location Himmelsthür is more diluted regarding both parameters, however it has a higher COD to ammonium-nitrogen ratio. The conductivity measured at Himmelsthür is higher than that at Itzum suggesting that the dilution water has a high salt concentration probably originating from industrial laundry washing. The wastewater collected at the location Drispenstedt has the highest COD concentration and the lowest ammonium-nitrogen concentration which can be explained by the activity of several industries with direct/indirect wastewater discharge into the sewer system (Figure 1). The COD to ammonium-nitrogen concentration ratio in the wastewater at the inlet to the treatment plant varied between 8.6 and 25.1. The same ratio at the sampling point Itzum, a residential area with a separate sewage system dominated by one-family-houses and a few apartment buildings, was in the range of 5.6 to 11.8. The higher COD to ammonium-nitrogen ratio at the inlet of the treatment plant indicates industrial wastewater discharge into the sewage system, as already expected.

Both biological markers show higher concentrations during the summer months. crAssphage are present in wastewater at higher concentrations compared to PMMoV. The concentrations of crAssphage and PMMoV varied in the range of 6.9 × 104 to 1.2 × 106 DNA-copies/mL and 2.0 × 104 to 3.6 × 105 RNA-copies/mL, respectively. Both parameters had the same variation of 1.3 log10 units during the monitoring period. These ranges are plausible and can be found in literature works. Farkas et al. (2019) reported concentrations of crAssphage in raw wastewater in the range of 2 × 102 to 106 DNA-copies/mL. For PMMoV, the concentration in raw wastewater varied in the range 5.5 × 103 to 7.2 × 103 RNA-copies/mL according to Kuroda et al. (2015) and in the range 105 to 106 RNA-copies/mL according to LaTurner et al. (2021). We did not find correlation between flowrate and PMMoV and flowrate and crAssphage (r < 0.25). No correlation was found between chemical and molecular markers of faecal pollution which can be justified by the presence of industry in the catchment area.

Monitoring of SARS-CoV-2 concentration in wastewater

Monitoring the WWTP (benchmark)

The average concentration of SARS-CoV-2 in the influent of the treatment plant (24-h sampling) shows distinct peaks of different magnitude corresponding to the prevalence of different variants of the virus (Figure 4, panel I and panel II). In period 1 the delta variant dominated, being outcompeted in periods 2 and 3 by the omicron variant – sub-variant BA.1 and BA.2 in period 2, and sub-variant BA.5 in period 3. In the present study, the highest 7-day COVID-19 incidence in the population and SARS-CoV-2 concentration in wastewater were detected during the prevalence of omicron sub-variant BA.2 in period 2. Two consecutive SARS-CoV-2 concentration peaks occurred in period 3, characterised by the dominance of the omicron sub-variant BA.5, with magnitudes between those of periods 1 and 2. We hypothesise that the higher peak concentrations of SARS-CoV-2 in period 2 compared to periods 1 and 3 can be explained by the dominance of the omicron sub-variant BA.2 with a higher faecal shedding rate compared to the delta variant and the other omicron sub-variants. Despite the large number of publications on the occurrence of SARS-CoV-2 in wastewater, studies on the shedding rate of infected patients with different variants and sub-variants of the virus are scarce. Rector et al. (2023) examined faecal samples from SARS-CoV-2-positive patients infected with different variants and reported that the faecal viral load was not as high in patients infected with omicron BA.1 as in those infected with delta, omicron BA.2 or BA.5. Prasek et al. (2022) investigated variant-specific SARS-CoV-2 shedding rates in wastewater and suggested that faecal shedding rates are influenced by the progression of the pandemic in terms of variants and vaccination. Mean faecal shedding rates increased from parental to delta, being the parental stage defined by the wild-type SARS-CoV-2 and the emergence of the alpha variant B.1.1.7, i.e., from 7.6 to 8.5 log10 RNA-copies/g faeces to 8.1–9.2 log10 RNA-copies/g faeces and then decreased with the predominance of the omicron infections, i.e., from 8.1 to 9.2 log10 RNA-copies/g faeces to 7.7–8.2 log10 RNA-copies/g faeces. A direct comparison of our data with the literature is not possible, but it is clear from the Rector et al. (2023) and Prasek et al. (2022) studies that viral shedding rates depend on the prevalent virus variant and sub-variants. Regarding the vaccination coverage in the population, the number of vaccinated persons in the district of Hildesheim increased by 80% in 2022 (Figure 4, panel III). The higher vaccination coverage in period 3 compared to the previous periods could also explain the lower SARS-CoV-2 concentration in the wastewater. Jiang et al. (2022) suggested that vaccines alleviate disease symptoms and reduce viral shedding.
Figure 4

Panel (I) average concentration of SARS-CoV-2 expressed as RNA-copies/mL (green dots, right y-axis) and moving average (green line, moving average = 3) at the inlet of the treatment plant (24-h composite samples) and 7-day incidence (red line, left y-axis) during the monitoring study for the Hildesheim county. Panel (II) delta and omicron sub-variants detected in the German population (in percentage). Panel (III) adult vaccination in Hildesheim County. (Includes only persons vaccinated at vaccination centres. It was not possible to obtain data on vaccinations administered in private practices.)

Figure 4

Panel (I) average concentration of SARS-CoV-2 expressed as RNA-copies/mL (green dots, right y-axis) and moving average (green line, moving average = 3) at the inlet of the treatment plant (24-h composite samples) and 7-day incidence (red line, left y-axis) during the monitoring study for the Hildesheim county. Panel (II) delta and omicron sub-variants detected in the German population (in percentage). Panel (III) adult vaccination in Hildesheim County. (Includes only persons vaccinated at vaccination centres. It was not possible to obtain data on vaccinations administered in private practices.)

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The dynamics of the moving average (n = 3) of the SARS-CoV-2 concentration in the wastewater collected at the inlet of the treatment plant and the 7-day COVID-19 incidence in the population have a similar trend and a strong positive correlation was obtained between both parameters (r = +0.89, n = 58). An analysis for the individual periods was not carried out due to insufficient data for each period.

Monitoring the sewer system

The concentration of SARS-CoV-2 in the wastewater for the three investigated sub-catchments, Drispenstedt, Himmelsthür and Itzum, and the concentration in the wastewater collected at the inlet of the Hildesheim WWTP, which was used as a benchmark, are shown in Figure 5 (panel II). It is important to note that the virus concentration has been divided by the number of inhabitants connected to each sub-catchment to facilitate comparison. The highest concentrations of SARS-CoV-2 were found in Itzum. This result can be explained by the settlement structure in Itzum, which is characterised by a residential area with single-family houses and a few apartment buildings, and no influence from industrial wastewaters. Despite a similar number of inhabitants, the SARS-CoV-2 concentration detected in the wastewater was lower in Himmelsthür than in Itzum. Based on the results of the physio-chemical analysis, the wastewater in Himmelsthür has a higher conductivity and lower COD and ammonium-nitrogen concentrations, indicating the discharge of high saline wastewater into the sewer, probably from industrial laundry (Zoroufchi Benis et al. 2021). Of the three sub-catchments investigated, Drispenstedt had the lowest concentration of SARS-CoV-2 in its wastewater. This result is explained by the presence of industries with high water consumption/wastewater discharge into the sewer in this sub-catchment. The concentration of SARS-CoV-2 in the wastewater at the inlet of the treatment plant (benchmark) showed the lowest values, indicating a loss of viral signal in the sewer, which can be explained by dilution (storm water and industrial waste water discharges), viral decay and adsorption to sediment particles. Our results show that by sampling sub-catchments separately, infection events in the population can be detected earlier. If the infection is still local, measuring in each sub-catchment allows earlier detection, as the concentration may be above the detection limit of the method. Another major advantage is the immediate localisation of the source of infection. The settlement structure in the sub-catchments may influence the spread of the disease. However, there are not enough data in our study to make a statistically sound statement.
Figure 5

Panel (I) 7-day COVID-19 incidence values for the sub-catchments of Drispensedt, Himmelsthür, and Itzum and for the region of Hildesheim. Panel (II) SARS-CoV-2 concentration in wastewater expressed as RNA-copies/(mL·person).

Figure 5

Panel (I) 7-day COVID-19 incidence values for the sub-catchments of Drispensedt, Himmelsthür, and Itzum and for the region of Hildesheim. Panel (II) SARS-CoV-2 concentration in wastewater expressed as RNA-copies/(mL·person).

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To our knowledge, there have been few studies on the monitoring of SARS-CoV-2 in the sewer system because of the challenges of collecting samples from community manholes. From November to mid-December 2020, Haak et al. (2022) monitored the spatial and temporal variability of SARS-CoV-2 in the sewer system of the Reno-Sparks metropolitan area (Nevada, USA). Their research identified local SARS-CoV-2 hotspots and community patterns of disease spread based on a sampling frequency of two to three samples per week; however, they did not link wastewater monitoring to clinical data on disease incidence. From September 2020 until May 2021, Rector et al. (2023) monitored SARS-CoV-2 concentration at three local sewers that collect wastewater from residential sites in the city of Leuven, Belgium. Based on daily sampling, increasing viral loads were detected at two residential sites, triggering immediate intervention by the contact tracing team.

The wastewater data are less discriminating than the 7-day COVID-19 incidence data for the sub-catchments under study (Figure 5, panel I) due to the data density. To increase the resolution, the sampling frequency needs to be increased. It is important to emphasise that the higher resolution of the 7-day COVID-19 incidence data available in the present study was an additional service provided to the project by the local health authority, which is not done for the regular monitoring of COVID-19 in the population due to the high work capacity involved.

The dynamics of the SARS-CoV-2 concentration in the wastewater and the 7-day COVID-19 incidence in the population show a similar trend; however, the goodness of fit decreased in the following order: Himmelsthür (r = +0.76), inlet WWTP 4-sampling (r = +0.62), Drispenstedt (r = +0.42) and Itzum (r = +0.40). The best correlation was obtained at Himmelsthür, a sub-catchment containing residential and commercial areas, but no industry.

Evaluation of SARS-CoV-2 normalisation parameters

The concentration of SARS-CoV-2 in wastewater is influenced by, among other things, precipitation and the discharge of wastewater from industries connected to the sewer system. Under these circumstances, the dynamics of the virus in wastewater may differ from the spread of the disease among humans. To compensate for the effect of these events, the concentration of SARS-CoV-2 in wastewater was normalised with several wastewater parameters, namely flow rate, COD, ammonium-nitrogen, PMMoV and crAssphage concentrations. The quality of the correlation between the normalised virus concentration and the 7-day COVID-19 incidence was assessed using the Pearson correlation coefficient (r) and the results are presented in Table 2. For all locations except Himmelsthür, the best correlation between SARS-CoV-2 concentration and 7-day incidence was obtained when the virus concentration was normalised to COD concentration. A strong correlation was obtained for the 24-h WWTP and Drispenstedt, whereas a moderate correlation was obtained for the 4-h WWTP and Itzum. In the case of Himmelthür, normalisation with the parameter conductivity gave the best results. For the inlet WWTP (24-h sampling), the normalisation with the flow rate gave the best fit. However, we cannot compare the normalisation with the flow rate between the different locations because this parameter was not measured in the sewer system due to the long time needed to install the measuring equipment in situ.

Table 2

Classification of the suitability of chemical and molecular markers of faecal contamination based on the values of the correlation coefficient (r) for the normalisation of SARS-CoV-2 concentrations in wastewater

 
 

aWithout normalisation.

Strong correlation is indicated in dark green: r > 0.75; moderate correlation is indicated in bright green: 0.50 < r < 0.75; weak correlation is indicated in yellow: 0.25 < r < 0.50.

In this study, we analysed the benefit of wastewater-based disease surveillance on a sub-catchment scale. Therefore, we took in addition to the influent of the WWTP of Hildesheim, samples for three sub-catchments within the sewer network. Our main question was: is it worth the extra effort to sample the sewer system for wastewater-based disease surveillance? Our key conclusions are:

  • SARS-CoV-2 monitoring in selected sub-catchments provides more detailed information on the spread of COVID-19 in the population than monitoring at the WWTP inlet. Higher concentrations of SARS-CoV-2 per inhabitant were found in the sub-catchments in comparison to the inlet of the WWTP. The selection of strategic sampling points in the sewer with settlement structures more prone to virus spread-out provide valuable information that is attenuated at the inlet of the WWTP due to mixing of wastewaters originating from all sub-catchments.

  • Normalisation of the SARS-CoV-2 concentration in the wastewater with the chemical oxygen demand or the electrical conductivity of the wastewater slightly improves the correlation with the 7-day incidence of COVID-19 circulation in the population.

  • Correlating the trend of SARS-CoV-2 in the sewer system and in the population requires in-depth knowledge of the sub-catchments in terms of hydraulic infrastructure, presence of industry and social structure, as well as 7-day incidence values discriminated to the sub-catchment level. Despite the availability of this information, it is time consuming to compile the information due to lack of human resources in the different agencies.

  • In view of the valuable information obtained on the dynamics of SARS-CoV-2 in selected sub-catchments, it is necessary to put more effort into reducing the time and resources required to obtain composite wastewater samples by using other sampling methods, such as passive samplers.

Although sampling within the sewer system improves the spatial resolution of COVID-19 distribution in the population, it is very time and resource intensive. Automated samplers must be transported and set up on site for sampling. A promising alternative is the use of passive samplers in the sewer system. Schang et al. (2021) showed a positive correlation between the concentrations of SARS-CoV-2 in wastewater and the levels found on the passive samplers.

The authors would like to thank Stadtentwässerung Hildesheim (SEHi) and the Screening Team for their support, and the European Regional Development Fund (ERDF/ZW 7-85094959/ZW 7-85094949) for funding. This research is part of the project SCREENING from Ostfalia University of Applied Sciences in Germany.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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