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. 2023 Jun 5;11(3):110289. doi: 10.1016/j.jece.2023.110289

Establishment of wastewater-based SARS-CoV-2 monitoring system over two years: Case studies in South Korea

Lan Hee Kim a, Viktorija Mikolaityte a, Sungpyo Kim a,b,⁎
PMCID: PMC10240911  PMID: 37292384

Abstract

With the global COVID-19 pandemic, wastewater surveillance has received a considerable attention as a method for the early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater treatment plant (WWTP) and sewer systems. For the first time in Korea, this study utilized the wastewater surveillance technique to monitor the COVID-19 outbreak. Sampling efforts were carried out at the WWTPs in the capital city of Korea, Seoul, and Daegu the place where the first severe outbreak was reported. The RNA of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been extracted from the collected wastewater influent and primary sewage sludge samples. The outcomes were contrasted with the COVID-19 cases in the WWTPs served area. Additionally, whole transcriptome sequencing was used to compare the microbial community alterations before and after the COVID-19 outbreak and SARS-CoV-2 variations. The results demonstrated that the changes in SARS-CoV-2 RNA concentrations in the influent and sludge matched the trends of reported COVID-19 cases, especially sludge showed high-resolution data, which is well-matched when fewer COVID-19 cases (0−250) are reported. Interestingly, one month before the clinical report, we found that the SARS-CoV-2 Beta variant (South Africa, B.1.351) in the wastewater. In addition, the Aeromonas bacterial species was dominated (21.2%) among other bacterial species in wastewater after the COVID-19 outbreak, suggesting a potential indirect microbial indicator of the COVID-19 outbreak.

Keywords: Wastewater surveillance, COVID-19, RT-qPCR, RT-ddPCR, Aeromonas, SARS-CoV-2 variants

1. Introduction

The emergence of new infectious diseases is of notable concern because there is no countermeasure to control them. The WHO reported that almost 40 new contagious diseases had occurred since the 1970 s, including severe acute respiratory syndrome (SARS), Ebola, H1N1 flu, Zika virus, and Coronavirus disease 19 (COVID-19) [1]. The COVID-19 outbreak occurred by transferring the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between host species [2]. The enveloped SARS-CoV-2 virus, including a positive-sense, single-stranded RNA genome of ∼30 kb, recognizes a receptor of angiotensin-converting enzyme (ACE2) and infects humans [2], [3]. Even with stringent quarantine measures, the SARS-CoV-2 virus was difficult to control compared to other viruses because COVID-19 patients who did not exhibit any symptoms might still spread the illness [4]. An alternative approach for surveillance of non-symptomatic patients should be considered to enable early countermeasures.

Wastewater surveillance (WS) has been applied to monitor drugs, pharmaceuticals, population markers, industrial chemical markers, and biological markers [5]. Following the COVID-19 outbreak in 2019, WS systems were highlighted throughout the world to enable early detection or to monitor the infected, symptomatic, and asymptomatic people in the community without violating a person's right to privacy [6], [7]. SARS-CoV-2 RNA shedding in stool has also been observed in cases without gastrointestinal but with other symptoms, as well as in pre-and asymptomatic cases, and lasts four weeks after symptoms cease [8], [9]. Interestingly, SARS-CoV-2 RNA genomes could be detected in feces several weeks after not being detected in oral swabs, suggesting that viral excretion in stools may be longer than oral secretion [10].

The WS studies for the detection of SARS-CoV-2 RNA shed in the wastewater have mainly focused on the (1) optimization of the analytical methods (e.g., pretreatment of the wastewater samples, RNA quantification) [11], [12], [13], [14], (2) development of high-throughput detection methods (e.g. reverse transcription-quantitative PCR (RT-qPCR), RT-digital droplet PCR (ddPCR)) [15], and (3) detection and monitoring of the SARS-CoV-2 variants [16], [17], [18].

However, Pecson et al. (2021) showed that pretreatment methods may not be a significant factor in quantifying SARS-CoV-2 RNA extracted [19]. Most (80%) of the samples showed similar recovery-corrected SARS-CoV-2 RNA concentrations within the band of 1 log GC/L[19]. Regarding the RNA quantification method, the RT-(d)dPCR platform showed greater sensitivity compared to RT-qPCR, resulting in possible to detect low levels of SARS-CoV-2 in wastewater samples [20]. Therefore, in WS research, improving the detection sensitivity may be more important than concentrating on pretreatment procedure optimization.

The type of sample (influent, sludge), as well as the physicochemical features of the samples, might have an impact on the extraction efficiency. According to Larsen and Wigginton (2020), compared to non-enveloped viruses, enveloped viruses displayed a higher affinity for the solid portion of wastewater [30], [39]. To monitor the various viral gene concentrations in the local sewer systems, it may be crucial to choose the sample type (influent or sludge).

This study is the first trial to employ the WS tool to monitor the COVID-19 outbreak in Korea. First, optimization of the analytical methods for the pretreatment of the wastewater samples and quantification of gene concentrations was performed. The pretreatment methods (ultracentrifugation, PEG-NaCl) and quantification methods (RNA extraction kit and PCR platform) with wastewater influent or primary sludge samples were compared. Second, wastewater influent and primary sludge samples were collected by grab sampling and quantified SARS-CoV-2 RNA concentrations using real-time quantitative reverse transcription PCR (RT-qPCR) or droplet digital RT-PCR (RT-ddPCR) to monitor the trends of COVID-19 outbreak. Lastly, changes in microbial communities before and after the COVID-19 outbreak and SARS-CoV-2 variants were analyzed.

2. Materials and methods

2.1. Sampling

2.1.1. Sampling site

A sampling campaign was conducted in WWTPs from Seoul and Daegu in Korea ( Fig. 1). Seoul, the capital of Korea, was chosen because of the huge daily foot traffic and the abundance of hospitals (around 15,000). As a result of the frequent intercity or intracity travel, Seoul is susceptible to the COVID-19 outbreak. The largest WWTP in Seoul (S1), with a treatment capacity of 1,590,000 m3/day, provided the wastewater influent ( Table 1).

Fig. 1.

Fig. 1

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Sampling sites in Seoul and Daegu in Korea. The influent and sludge samples were collected from (a) the largest WWTP (S1) in Seoul and (b) 7 WWTPs (D1, D2, D3, D4, D5, D6, D7) in Daegu.

Table 1.

Populations and treatment capacity of the WWTPs selected in this study.

Region
 WWTP
 Population in the served area (person)
 Treatment capacity (m3/day)
Seoul S1 5348,717a 1,590,000
Daegu D1 847,801b 680,000
D2 743,424b 520,000
D3 229,761b 400,000
D4 215,966b 170,000
D5 83,406b 33,750
D6 134,977b 47,000
D7 69,196b 45,000
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a: Averaged population from April 2020 to April 2021; b: Averaged population in July 2021

The wastewater influent samples were taken from 7 WWTPs at Daegu when none of the COVID-19 cases were reported (29 May 2020) after the first severe outbreak of COVID-19 occurred on 19 February 2020 to confirm the presence of the SARS-CoV-2 virus in the wastewater samples after the outbreak.

2.1.2. Sampling methods

The influent samples from Seoul and Daegu WWTPs were taken every week (from 09 May 2020–04 March 2022) and once (08 May 2020), respectively. The grab sampling was done by collecting 1 L of each influent sample in sterile polyethylene bottles and transporting it within 3 h on ice to the laboratory. On arrival, the samples were stored at −80 ℃ until analyzed.

2.2. Phase I: attempt to optimize analytical methods for quantification of SARS-CoV-2 in wastewater samples

2.2.1. Ultracentrifugation-based pretreatment method

In Phase I (from May 2020 to February 2021), the ultracentrifugation-based [21] method was applied for the wastewater influent and sludge samples taken from the Daegu WWTPs and part of the samples from the Seoul WWTP.

The influent sample was pretreated using an ultracentrifugation method (100,000 ×g, 1 h) [21], and the sludge sample was centrifuged at 4,650 ×g for 30 min. Both total RNA and viral RNA were extracted to evaluate the influent and sludge samples' extraction method. The total RNA and viral RNA were extracted using an easy-spin™ Total RNA extraction kit (iNtRON Biotechnology, Korea) and a QIAamp® viral RNA mini kit (Qiagen, USA).

2.2.2. RT-qPCR

SARS-CoV-2 RNA concentrations were quantified using a GoTaq® probe 1-step RT-qPCR (Promega, USA) kit with two SARS-CoV-2 RNA nucleocapsid (N1, N2) primers (IDT Technologies, USA) (Table S1) as following the Center for Disease Control and Prevention (CDC) guidelines (2020) [6], [22]. Human RNase P (RP) primer (IDT Technologies, USA) was used as an internal control. The PCR reaction mix was prepared with the compositions of GoTaq® probe qPCR Master Mix (1 ×), GoScript™ RT Mix for 1-step RT-qPCR (1 ×), forward and reverse primers (500 nM), hydrolysis probe (125 nM), RNA template, and nuclease water up to a final volume of 20 µL. Reverse transcriptase-quantitative real-time PCR (RT-qPCR) was done using a Mic real-time PCR cycler (BioMolecular Systems, Australia). The real-time PCR was conducted at 45 °C for 15 min following the 45 cycles of 95 °C for 2 min, 95 °C for 3 s, and 55 °C for 30 s, based on the CDC guidelines [22]. The concentrations were quantified based on the standard curve with the 2019-nCOV_N_positive control (nCoV PC) for N1 and N2 genes and Hs_RPP30 positive control for RP gene with a concentration of 200,000 copies/µL (IDT Technologies, USA). The positive signal of SARS-CoV-2 in the reactions was considered if the cycle number was below 40 cycles (Ct < 40). The assay limit of detection (ALOD95%) (95% probability) and quantification (ALOQ) were analyzed for N1 and N2 genes according to the MIQE guidelines [9], [23], [24]. The standard curve for the genes is plotted with gene copy numbers (GC/mL) against Ct value by 10-fold serial dilutions (from 10-1 to 10-5) of nCoV PC (200,000 GC/µL) with 8 replicates. Based on the standard curves, the slope, y-axis intercept, and amplification efficiency of qPCR (10-1/slope −1)[23] were obtained. For the N1 gene, the slope, Y-intercept, and PCR efficiency were −3.6268, 41.4 (Ct value), and 0.89, respectively (Fig. S1(a)). The N2 genes showed −3.9021, 43.6, and 0.80, respectively (Fig. S1(b)). The ALOD95% of both genes was 3.1 GC/PCR reaction, and the ALOQ of the N1 and N2 genes were 9.4 and 9.5 GC/PCR reaction, respectively.

2.3. Phase II: optimized method to monitor SARS-CoV-2 in WWTPs

2.3.1. PEG-NaCl precipitation-based pretreatment method

In Phase II (from March 2021 to March 2022), the PEG-NaCl precipitation method [35] was used to monitor the influent sample’s SARS-CoV-2 viral concentrations. A 150 mL of influent was centrifuged at 4,000 ×g for 30 min to remove the large particles. The supernatant was carefully transferred to a beaker and mixed with 4 g of PEG 8000 (Promega, USA) and 0.9 g of NaCl (Sigma-Aldrich, USA). Once the reagents were dissolved, the extractants were centrifuged at 11,400 ×g for 2 h. The supernatants were carefully removed, and the pellet was suspended with a small amount of the supernatant (∼100 µL). The RNA from the suspensions was extracted using a Maxwell® RSC Purefood GMO kit (Promega, USA), following the manufacturer's protocol. The suspensions were mixed with 200 µL of CTAB buffer and 40 µL of proteinase K and vortexed for 10 s. The extractants were incubated for 10 min at 56 °C, and the RNA was extracted using a Maxwell® RSC instrument (Promega, USA).

For the sewage sludge samples, the 0.5 g of sludge was placed in a 1.5 mL microtube and centrifuged at 8,000 ×g for 5 min. The pellet (∼0.1 g) was mixed with 500 µL of CTAB buffer and resuspended. Subsequently, 40 µL of proteinase K was added and vortex for 10 s. The prepared samples were incubated at 56 °C for 10 min and centrifuged at the maximum speed (18,341 ×g) for 5 min. The samples were added to the cartridge with the 300 µL lysis buffer and extracted RNA was extracted using a Maxwell® RSC instrument (Promega, USA). The elution volume of influent and sludge samples was 50 µL.

2.3.2. RT-droplet digital PCR (RT-ddPCR)

The RT-ddPCR was conducted by following the manufacturer’s procedures. For the one-step RT-ddPCR, the 5.5 µL of RNA samples were mixed with 5.5 µL of one-step RT-ddPCR supermix (2 ×), 1.1 µL of triplex probe assay (20 ×), 2.2 µL of reverse transcriptase, 1.1 µL of 300 mM DTT, and the final volume adjusted to 22 µL with RNase-free water. The reagents were provided by Bio-Rad (USA). The samples were partitioned using the droplet generator, and the droplets were carefully transferred to the 96-well plate. The PCR reaction was performed according to the following conditions: 60 min of reverse transcription at 50 °C, 10 min of enzyme activation at 95 °C, 40 cycles of 30 s of denaturation at 94 °C, and 1 min of annealing at 55 °C, 10 min of enzyme inactivation at 98 °C, and 30 min of droplet stabilization at 4 °C. Each PCR product droplet was analyzed using the QX200™ droplet reader (Bio-Rad, USA). The absolute viral concentrations were quantified using the QuantaSoft™ software (Bio-Rad, USA). Non-template control (NTC), negative control (RP gene-positive plasmid DNA), and positive control (N gene and RP gene-positive plasmid DNA) were run with the samples to help with the interpretation of the results. A “Positive” signal was defined if the N1 or N2 genes were detected above > 2 droplets.

2.4. Identification of SARS-CoV-2 variants

The microbial and viral communities in the samples were analyzed through NGS-based metagenomic sequencing and total RNA-Seq. Total 4 RNA samples were extracted from the wastewater influent in Seoul WWTP on 04 December 2020, 15 January, 11 March, and 29 May 2021, respectively. The RNA samples were converted to complementary DNA (cDNA) using a ProtoScript® II first-strand cDNA synthesis kit (NEB, USA) following the manufacturer’s protocol, and further analysis was done by Theragen Bio (Seongnam-si, Korea).

The total RNA-Seq was performed with the cDNA samples by following the sequencing method with the sequence of (1) construction of sequencing libraries, (2) RNA-Seq using Illumina NovaSeq™ 6000 platform [25], and (3) making the consensus sequences. The sequencing libraries were constructed by DNA size selection and adapter ligation. After the adapter trimming and low-quality filtering by khmer (v0.8.4, https://khmer-protocols.readthedocs.io/en/v0.8.4/mrnaseq/index.html), de novo assembly was performed by IDBA-ud (v1.1.1, Iterative De Bruijin Graph De Novo Assembler) [26]. The gene, tRNA, rRNA, and repeat regions were predicted by Prokka (prokaryotic genome annotation tool) software (v1.10). Gene annotation was performed using UniProt (BLAST+ blastp v2.2.29 +, https://www.uniprot.org/blast), RefSeq (BLAST+ blastp v2.2.29 +) [27], and Pfam (HMMER 3.1b1) [28] databases, and the assembled genome (BLASTn (E < 1e-10) is aligned to the databases from NCBI using the megablast algorithm. Taxonomy profiling was performed for assembled genome using NCBI taxonomy information and Krona tools.

The microbial community analysis was done with the COVID-19-positive sample (11 March 2021) and COVID-19-negative sample (29 May 2021) based on the taxonomy profiling information, and the SARS-CoV-2 variants were identified using the Genome Detective tool (v2.52, https://www.genomedetective.com/app/typingtool/virus/) [29] and compared with the coronavirus genome sequences (Fig. S3).

2.5. Statistical Analysis

The SARS-CoV-2 RNA concentrations (copies/µL) obtained from RT-qPCR and RT-ddPCR were converted to copies/L or copies/g for influent or sludge samples, respectively, by normalization with the volume of elution (µL), PCR reaction (µL), RNA template (µL), and wastewater influent (mL) or sludge (g) sample.

The SARS-CoV-2 gene concentrations of influent (copies/L) and sludge (copies/g) were compared with the new COVID-19 cases in the Seoul WWTP served area at the sampling date. Those two variables were compared using Pearson correlation analysis.

3. Results and discussion

3.1. Accumulated SARS-CoV-2 was detected in the sludge during the COVID-19 post-outbreak period

COVID-19 viral RNA was detected in the influent and primary sewage sludge samples collected from the 7 WWTPs in Daegu ( Table 2, Fig. 2). Overall, the sludge samples (106 copies/g of sludge) had a greater COVID-19 N gene concentration (averaged from N1 and N2 gene concentrations) than the influent samples (101 copies/mL of influent). The greater identification of the SARS-CoV-2 virus in the sludge may be due to the virus's electrostatically induced adsorption to the solids in the wastewater [30]. The isoelectric points (IEPs) of the S (spike protein), E (envelop protein), and M (membrane protein) of the SARS-CoV-2 virus were 6.24, 8.57, and 9.51, respectively [31]. Since the wastewater solids have a negative charge [30] and the average pH of sludge samples in this study was 6.1 ( ± 0.9), the virus could be a positive charge in the sludge and easily absorb the solids by electrostatic interaction.

Table 2.

Detection of SARS-CoV-2 RNA concentrations in the wastewater influent and sludge samples (n = 2).

Region WWTP Sampling date COVID-19 cases Total RNA extractiona
 Viral RNA extractionb
Sludge (GC/g
of sludge)		 Influent (GC/mL of influent) Sludge (GC/g
of sludge)		 Influent (GC/mL of influent)
Daegu, Korea D1 29 May 2020 0





 BDLc BDL





 BDL BDL
D2 BDL BDL 4.2 × 10-1( ± 5.9 ×10-1)
D3 2.4 × 106 ( ± 3.4 ×106) BDL BDL
D4 BDL BDL 1.0 × 101( ± 1.4 ×101)
D5 1.2 × 105( ± 1.6 ×105) BDL BDL
D6 3.7 × 105( ± 1.4 ×105) 1.2 × 102( ± 1.6 ×102) 4.2 × 100( ± 5.9 ×100)
D7 3.2 × 104( ± 2.8 ×104) BDL BDL
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a Total RNA was extracted from 0.2 g(w/w) of sludge and 250 mL of influent samples using an easy-spin™ Total RNA extraction kit (iNtRON Biotechnology, Korea).

b Viral RNA was extracted from 41.2 g(w/w) of sludge and 123.6 mL of influent samples using the ultracentrifugation method [21].

c Below detection limit

Fig. 2.

Fig. 2

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(a) COVID-19 cases from 18 February 2020–24 June 2021. (b) Quantification of SARS-CoV-2 N gene in the WWTPs at the Daegu region at the post-outbreak stage when there were no COVID-19 cases (BDL, below detection limit). Data shown are averaged from N1 and N2 gene concentrations (n = 2).

However, the presence of PCR inhibitors (relatively more inhibitors are present in the sludge than influent) [32], sample preparation, storage, and pretreatment inefficiencies, as well as variations in viral RNA distributions between the solids and aqueous phase [33], [34], can all contribute to significant errors being found in both the sludge and influent samples. Therefore, it is crucial to optimize the analytical procedure while taking the features of the sewage sample into account in order to produce accurate results.

After confirming the possibility of SARS-CoV-2 detection in sewage from the Daegu case, the analysis method was optimized by assessing the RNA extraction efficiency, SARS-CoV-2 viral RNA recovery rate, and analysis sensitivity while using various pretreatment techniques, such as ultracentrifugation [21], PEG-NaCl[35], filtration[36]) and PCR platforms (e.g., RT-qPCR, RT-ddPCR)[20](SI-2, Table S2). The experimental results were more accurate thanks to the adjusted procedures based on the type of sample (wastewater influent and sludge) (see Section 2.3), which also markedly decreased the standard deviation from the mean from 100.2(48.1)% to 18.7(18.5)% (data not shown).

It is notable that the COVID-19 patients had not yet been reported when the SARS-CoV-2 virus was found in the sludge on May 29, 2020, in Daegu. This could be caused by absorbed viruses on the sludge being detected or by the excretion from asymptomatic patients. Peccia (2020) reported that the SARS-CoV-2 RNA concentrations in sludge were 0–2 days ahead of SARS-CoV-2 positive test results by the date of specimen collection, 1–4 days ahead of local hospital admissions, and 6–8 days ahead of SARS-CoV-2 positive test results by reporting date [37]. The primary sewage sludge consists of solids settled down when municipal raw wastewater is discharged into WWTPs. The sludge contains diverse human viruses and pathogens [38]. Therefore, many studies monitored SARS-CoV-2 RNA in the primary sewage sludge samples because it is well-mixed and highly concentrated compared to influent samples, providing a high-resolution data set [39].

3.2. SARS-CoV-2 RNA was detected earlier in the wastewater

Fig. 3 shows the relationship between the new COVID-19 cases in Seoul WWTP-served area at the sampling date and the SARS-CoV-2 RNA concentrations (N1 gene) from influent and primary sewage sludge. The Average total population in the treatment area is 5,348,717 (from 2020 to 2021) (Table 1). The SARS-CoV-2 RNA concentrations (N1 gene) from the influent samples (n = 43) were analyzed from 29 May 2020–04 March 2022. All the results were shown as averaged concentrations from the duplicate experiment. At first, the RNA concentrations were detected with concentrations ranging from 102 to 103 copies/L (15 July 2021–18 November 2021). On 26 January 2022, the RNA concentrations were increased by 1 log, while the COVID-19 cases were increased by 2.2 times compared to the cases on 18 November 2021. From 26 January 2022, the SARS-CoV-2 RNA was significantly increased with the increase of COVID-19 cases (Fig. 3(a)). The high correlation (r = 0.944) between the SARS-CoV-2 N1 gene concentrations and COVID-19 cases was shown in the influent sample (Fig. S4(a)).

Fig. 3.

Fig. 3

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Relations between the new COVID-19 cases in the WWTP-served area at the sampling date and SARS-CoV-2 concentrations from the (a) influent (29 May 2020 – 04 March 2022) (n = 2) and (b) primary sewage sludge (29 May 2020 – 18 March 2021).

The SARS-CoV-2 RNA extracted from the primary sewage sludge samples (n = 20) showed positive N1 gene signals when the lower COVID-19 cases (∼250 cases) from 29 May 2020–18 March 2021 (Fig. 3(b)), although the much lower correlation (r = 0.303) between the SARS-CoV-2 RNA concentrations and COVID-19 cases (Fig. S4(b)). Therefore, the sludge samples can be utilized when low COVID-19 cases (< 1000 cases; 0.02% of the total number of populations in the served area) are reported. The results of the sludge samples from the Daegu WWTPs, which were previously disclosed, are in line with the study of SARS-CoV-2 RNA concentration in sludge that exhibits high sensitivity. The findings demonstrated that in the WWTP served region, COVID-19 cases could be predicted with influent in the event of high COVID-19 cases and primary sewage sludge in the case of low numbers (< 1000). Peccia et al. (2020) showed that SARS-CoV-2 RNA was detected in the primary sludge 1–4 days and ∼1 week earlier than the reported hospitalizations and clinical testing results, respectively [38]. In addition, the positive sludge results were shown when none of the reported cases were reported, and the RNA concentrations were correlated with reported COVID-19 cases ranging from 0 to 150 [37]. Therefore, the sludge can be used to predict the early stage of COVID-19 outbreaks at the community level. However, it could not be applicable to building-level studies due to the difficulties of obtaining the sludge from the sewer systems, such as dormitories, schools, prisons, and nursing homes [39]. It is crucial to choose the sample type (influent or sludge) in accordance with the goal of the research.

3.3. Early detection of SARS-CoV-2 variants was enabled in wastewater

RNA-Seq was conducted with the positively detected influent samples (04 December 2020 and 11 March 2021). The genotype of the two RNA samples was identified as Alpha_Beta coronavirus and SARS-CoV-2 B.1.351_501Y.V2_20H, respectively as shown in phylogenetic plot ( Fig. 4). It is noteworthy that the South Africa variants (SARS-CoV-2 B.1.351_501Y.V2_20H) were found in wastewater one month before the virus's first infection case in Seoul (05 April 2021). Other study also reported that the SARS-CoV-2 lineage B.1.1.7 variant was found two weeks before the first report in the patient sample from Switzerland [40]. The results of this study proved that the possible earlier detection of variants in the wastewater than the report from clinical diagnosis. The occurrence of variants, such as B.1.1.7 (from the UK) and 501. V2 (from South Africa), has become much interest because they may be associated with increased infectivity and accelerate the spread in the human population [40]. Therefore, early detection and monitoring of the SARS-CoV-2 variants in the wastewater could help to prevent infection outbreaks.

Fig. 4.

Fig. 4

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The phylogenetic plot of the SARS-CoV-2 virus was extracted from Seoul WWTP influent samples on (a) 04 December 2020 and (b) 11 March 2021.

3.4. Aeromonas species were shown as a possible indirect biomarker of concern

In the early phase of the SARS-CoV-2 virus infection, high viral load and reduced inflammatory activity are associated with symptoms of gastrointestinal illness. The biopsy on the COVID-19 patient showed the presence of SARS-CoV-2 protein coat in the stomach, duodenum, and rectum, indicating the SARS-Co-2 can invade the gut and impact its microbiota [41]. However, limited studies on the changes in microbial compositions by SARS-CoV-2 infection was conducted [41], [42].

In our previous study, the correlations between the new COVID-19 cases in Seoul WWTP- served area at the sampling date and total cell numbers (Pearson’s r = 0.7169), and enteric pathogen numbers (Pearson’s r = 0.9109) in the sludge (n = 2) were observed (Fig. S5). Therefore, this study tried to observe the changes in dominant enteric pathogens that the COVID-19 outbreak might impact. Therefore, early detection and monitoring of microbial communities in influent samples from Seoul's S1 WWTP with and without the identification of SARS-CoV-2 RNA (11 March 2021 and 29 May 2021, respectively) were examined ( Fig. 5). Gammaproteobacteria (38.1%) dominated the COVID-19-positive sample, whereas Beta-proteobacteria (39.8%) dominated the COVID-19-negative sample, showing a change in the microbial community brought on by the COVID-19 outbreak. Interestingly, the Aeromonas genus was highly dominant (21.2%) for the COVID-19-positive wastewater sample. In particular, Aeromonas media and Aeromonas veronii species accounted for 11.2% and 6.0%, respectively.

Fig. 5.

Fig. 5

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Comparison of the microbial community in COVID-19-positive and COVID-19-negative wastewater samples in Seoul WWTPs. The relative abundance of (a) Class, (b) Order, (c) Family, and (d) Genus level community are shown.

The literature regarding the relations between the microbial community and COVID-19 surveillance [41] also showed that Simpliscira, Prevotella, Bacteroides, Aeromonas, Sulfurospirillum, Arcobacter, Tolumonas, Citrobacter, Zoogloea, and Janthinobacteriums were associated with positive virus detection in wastewater samples.

After SARS-CoV-2 has infected a person through the respiratory system, the organisms in the body trigger the immune systems in the gut and lungs. Gut microbiome alterations may be influenced by immunological responses caused by the excretion of microbial metabolites [42]. According to reports, Aeromonas is an emerging pathogen that affects both immunocompetent and immunocompromised patients and causes illnesses such gastroenteritis, septicemia, and wound infections [43]. The Aeromonas strains were identified in wastewater, drinking water, and the feces of diarrhea individuals [43]. Accordingly, the significant abundance of Aeromonas species in the SARS-CoV-2 virus-positive wastewater sample could be caused by the body's immune system, resulting in diarrhea in COVID-19 patients. Therefore, monitoring the Aeromonas species in wastewater could enable indirect anticipation of an outbreak of respiratory viruses in community. Under given conditions in this study, Aeromonas species were found as the dominant species in COVID-19-positive samples, but it is necessary to select a group of candidates through more case studies in the future.

4. Possibilities and limitations of national-level wastewater surveillance in Korea

At the first COVID-19 outbreak stage, South Korea actively responded to the COVID-19 outbreak by establishing a “K-quarantine model” system such as a drive-thru screening clinical model and real-time gene-amplification-based diagnostic technique (RT-PCR). Clinical diagnostics is a strong and effective way to find COVID-19-confirmed individuals and prevent them from spreading to the community; however, several limitations, such as the overloading of clinical tests, the social stigma of patients, and the limited government budget, should be complemented.

In this study, the authors introduced the WS system application in Korea for the first time as a complementary tool for clinical testing. The findings demonstrated that the WS technique is useful for early COVID-19 outbreak prediction at the community level and the potential detection of variations in sewage. We will talk about the application of the WS system in Korea and future considerations through these research experiences.

Korea has substantial advantages for the application of the WS system in terms of 1) the high coverage rate of sewer systems (94.5% of the total population) and 2) the existence of research institutes affiliated with local governments that enables systematic monitoring from the broad regions at the same time. However, several aspects should be considered to apply the WS system nationally. First, it is necessary to specify the monitoring goal, which can either be to manage the COVID-19 cases at a specific building (decentralized level) or to watch trends in the COVID-19 epidemic (centralized level). The centralized level monitoring aids in tracking community COVID-19 outbreak patterns and serves as a sentinel for the local government's right reaction. For example, managing senior citizens in nursing homes and prisons can be done using the other decentralized level of monitoring to stop the spread of COVID-19. Because the sewage samples are obviously impacted by external conditions (e.g., temperature, disinfectant usage, flow rate, resident numbers), the building level WS should carefully examine the sample collection (sampling duration and frequencies) and analysis procedures (qPCR vs. ddPCR).

Second, guidelines for the countermeasures based on detected SARS-CoV-2 concentrations in the sewage should be prepared considering monitoring size. At the large city, the changes in detected SARS-CoV-2 concentrations in the wastewater influent can provide helpful information about the “real increase (or decrease)” of COVID-19 cases in a community. This data is important to confirm the suitability of a large city's general pandemic illness management plans. On the other hand, since the transmission of SARS-COV-2 is not as significant as in large cities, the more crucial role of WS in small-sized rural region cities may protect the high-risk COVID-19 individuals. Therefore, it might be more appropriate to sample wastewater at sewage pipes that are connected to establishments (such as hospitals). The regional government should focus more on the local human resources and budget for sustainable management when WS is adopted as a regular monitoring method. Table S3 shows WS operation strategies in the different monitoring areas (large city, small city, and rural area).

Third, the critical discussion point is the standardization of analytical methods for WS, including sampling, pretreatment, and measurement of target microorganisms (or microcontaminants) in wastewater. Some researchers worldwide are looking for a standard (golden) method that can be applied to any site [34]. The complicated wastewater matrix properties and varying extraction efficiencies depending on the researcher, the extraction kit or method utilized, and the instruments make it difficult to create the gold standard [34]. In a recent study, the reproducibility and sensitivity of 36 standard operating procedures (SOPs) were assessed by combining eight different methodologies [19]. Briefly stated, 32 laboratories in the USA received raw wastewater samples tainted with beta-coronavirus OC43 and examined them for analytical repeatability. Surprisingly, replicate sample results showed solid reproducibility across the 36 SOPs; 80% of the recovery-corrected results fell within a band of ± 1.15 log copies/L within a single SOP (standard deviation of 0.13 log copies/L). A solids removal step's inclusion and the concentration method chosen did not appear to have a discernible, systematic effect on the outcomes after recovery correction. In comparison to other causes of variability, other technical variables (such as pasteurization, primer set choice, and use of RT-qPCR or RT-ddPCR platforms) typically produced negligible variances. These results imply that a variety of techniques can yield repeatable outcomes. If a facility chooses a certain approach, it is more crucial that it should be preserved for monitoring SARS-CoV-2 developments. As a result, rather than focusing on developing a single perfect approach, it may be more practicable to characterize several alternatives through wastewater characterization.

As an additional clinical monitoring tool, the Korea Centers for Disease Control (KCDC) is implementing national-level surveillance. Research institutes in the 17 domestic areas first implemented the WS approach and kept track of infectious viruses in partnership with academic institutions and KCDC. Technical advancement (sampling, analytical methodologies), proper monitoring strategy selection based on monitoring scale, and political support, such as legislation, are needed for the WS program in Korea to operate successfully.

5. Conclusion

The first WS case studies in the Seoul and Daegu WWTPs were conducted. The WS tool would enable early countermeasures and risk evaluation of the wastewater samples to stop further outbreaks, according to the results. During the post-COVID-19 outbreak, the Daegu case suggested the potential existence of viral RNA genetic materials in the sludge. The Seoul example also demonstrated the possibility of an early warning 14 days before to a rise in COVID-19 cases and the potential for an early detection of variations in the vicinity of WWTPs. It was proposed that Aeromonas sp. was a sign of COVID-19 instances in the area, but additional case studies with plenty of samples should be conducted. The creation of analytical techniques, technological advancements, and the adoption of regulations and policies are all necessary for the national WS system to advance.

CRediT authorship contribution statement

Lan Hee Kim: Conceptualization, Literature Data collection, Methodology and Formal analysis, Data curation, Writing, Writing-original draft preparation. Viktorija Mikolaityte: Literature Data collection, Methodology, Data curation. Sungpyo Kim: Conceptualization, Writing – review & editing, Supervision, Revised the draft.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research has been performed as Project Open Innovation R&D (OTSK_2022_011) and supported by K-water, Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (2021R1A2C2011852), Basic Research Program through the National Research Foundation of Korea(NRF) funded by the MSIT(2021R1A4A1032746), Korea Ministry of Environment (2019002950004), and the Korea University grant. The authors also thank the Daegu Environmental Corporation for their help with the sampling.

Editor: P. Fernández−Ibáñez

Footnotes

Appendix A

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jece.2023.110289.

Appendix A. Supplementary material

Supplementary material.

mmc1.docx (680.9KB, docx)

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