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Article

Seasonal Variations in the Occurrence of SARS-CoV-2 RNA Recovered from Wastewater Treatment Facilities (WWTFs) Within South Africa

by
Kingsley Ehi Ebomah
1,2,*,
Luyanda Msolo
1,2,
Velisa Vuyolwethu Qongwe
1,2,
Okuhle Mayoyo
1,2,
Piwe Athi Ntlati
1,2,
Balisa Ngqwala
1,
Nolonwabo Nontongana
1,2,
Renee Street
3,4,
Rabia Johnson
5,6 and
Anthony Ifeanyi Okoh
1,2
1
SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice 5700, South Africa
2
DSTI/NRF SARChI in Water Quality and Environmental Genomics, University of Fort Hare, Alice 5700, South Africa
3
Environment and Health Research Unit, South African Medical Research Council, Cape Town 7505, South Africa
4
Environmental Health Department, Faculty of Health Sciences, University of Johannesburg, P.O. Box 524, Johannesburg 2028, South Africa
5
Biomedical Research Innovation Platform (BRIP), South African Medical Research Council (SAMRC), Tygerberg 7505, South Africa
6
Cardio-Metabolic Research in Africa, Division of Medical Physiology, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg 7505, South Africa
*
Author to whom correspondence should be addressed.
Microorganisms 2026, 14(2), 495; https://doi.org/10.3390/microorganisms14020495
Submission received: 8 December 2025 / Revised: 9 February 2026 / Accepted: 10 February 2026 / Published: 18 February 2026
(This article belongs to the Special Issue Pathogen Surveillance in Wastewater)
Browse Figures
Versions Notes

Abstract

Several researchers have documented the occurrence of the unfamiliar severe acute respiratory syndrome coronavirus 2 ribonucleic acid (also known as SARS-CoV-2 RNA) in various raw wastewater (WW) samples analyzed globally. The efficiency of strategic WW-based epidemiology (WBE) approach as a timely cautioning tool for human coronavirus disease-2019 (COVID) and other similar outbreaks is highly promising. This strategy offers a cost-effective, population-wide surveillance tool that can detect rising case trends, from days to weeks before clinical reports, thus enabling proactive public health interventions. This study aimed to detect the occurrence of the viral genome in WW over four seasons, which contributes to the database for multi-plant surveillance research in South Africa. About 480 WW influent samples were amassed from ten sampling points situated in nine wastewater treatment facilities (WWTFs) in Amathole District Municipality (ADM) located in the Province of Eastern Cape (EC), South Africa (SA). The study was carried out for a period of one year. Quantitative real-time polymerase chain reaction (i.e., RT-PCR) was operated to identify the viral genomes in the respective total RNA samples. Of the 480 extracted RNA samples, 210 (44%) were positive with viral genome copies (gc) that ranged from 700 to 40,000 GC/mL. Our results were contrasted with existing COVID-19-positive cases throughout the COVID omicron wave in the ECP. Variations in gc were observed across different seasons, with the highest GC observed in winter. In contrast, there were significant inconsistencies in the existing data of COVID-19 clinical cases, thus indicating no connection between both data. However, with more similar studies, advanced innovative WBE strategies could possibly act as prompt warning tools to signal public health officials about potential future outbreaks.

1. Introduction

It has been well recognized that the previous human coronavirus disease-2019 (aka COVID-19) outbreak originated from peculiar SARS-CoV-2 infections. As of 18 November 2022, the outbreak caused about 600 million (M) clinical cases and more than 6 M fatalities, with 4,036,623 cases in South Africa [1]. Numerous reports focused on the existence of the SARS-CoV-2 genome in raw WW have been investigated in different nations including Brazil, China, India, Italy, Japan and United States (US) [2,3,4,5,6,7,8], thus highlighting the role that the gut plays in the spread of microbes into the environment [9,10,11,12,13]. The reported number of clinical cases can be influenced by various factors, such as testing capacity, healthcare access, health-seeking behavior and the accuracy of a country’s disease surveillance, to name a few [14].
Despite the implementation of effective polymerase chain reaction (PCR)-based SARS-CoV-2 assessments in many regions [15,16], transmission by undiagnosed individuals undeniably hindered containment efforts. In another research by Omori et al. [17], it was argued that COVID-positive cases were miscalculated in Japan, due to minimal capability to perform PCR assays. Both asymptomatic and pre-symptomatic individuals can defaecate SARS-CoV-2 viral RNA, which is subsequently detected in wastewater at wastewater treatment facilities (WWTFs). Even though the pandemic provided more opportunities for scientists to explore microbial diversity in environmental samples, several reports that investigated WW influent samples preceded the identification of the first positive case of COVID-19 in the community [18,19].
Currently, several molecular techniques for quantification of SARS-CoV-2 are available. Besides WW-based epidemiology (WBE) studies, there are a few microbiological insights that have been used as advanced molecular techniques like next-generation sequencing (NGS) and the creation of biosensors for real-time monitoring (RTM). In addition, some methods, particularly high-resolution imaging microscopy, could assist in visualizing the spatial organization of microorganisms within extreme ecosystems that are crucial for understanding complex microbial interactions and their influence on environmental health.
Beyond detection of viral RNA, WW surveillance also facilitates the identification and monitoring of disseminating SARS-CoV-2 strains within communities. Similarly, factors like temperature and rainfall play significant roles in seasonal variations and the dissemination of different enteric strains into the aquatic environment, with recent studies pointing out the effects of these parameters on the occurrence of pathogens [20,21]. Various works have reported the occurrence of emerging variants, whereas there are few existing environmental studies on investigating WW for the presence of coronavirus mutant strains in Sub-Saharan Africa.
This present study was aimed at examining the incidence of SARS-CoV-2 genetic fragments in selected wastewater treatment plants (WWTPs) throughout the four seasons (summer, autumn, winter, spring). As arguably the first longitudinal multi-plant dataset for the Amathole District Municipality (ADM) in the Eastern Cape Province (ECP), this report is part of a broader objective in the ongoing nationwide wastewater coronavirus surveillance project.

2. Materials and Method

2.1. Brief Description of Sampling Sites in the Study Area

The ADM is among the 7 District Municipalities (DMs) in the ECP, South Africa (SA). It is located 32°30′ S and 27°30′ E; bounded by the Sarah Baartman DM (SBDM) to the west, the Chris Hani DM (CHDM) to the north, the OR Tambo district to the north-east, and the Indian Ocean to the south-east. The municipality is further divided into 6 Local Municipalities and covers a geographical area of approximately 23 577 km2 [22].

2.2. Sample Collection

Briefly, 500 mL raw influent WW grab sample was collected (between June 2021 and May 2022) at the untreated inlet from each of the 10 WWTF sampling points sited in nine plants distributed across the ADM (Figure 1). S7 is the code name for plants FB-A and FB-B, both located in the same WWTP but have influent flows from 2 different sources. FB-A represents influent directly from a hospital with specialized tuberculosis and Anti-Retroviral (ARV) treatment services, and FB-B is the inlet from the main community. Both influents flow into the secondary stage. The reason for inclusion of both influents is to compare the quality of these influents to ascertain if there is a link between the circulation of SARS-CoV-2 genomic particles among the hospital and the community at large. Only influent samples were considered because this is a community-focused WBE project, and grab samples were collected between 7 a.m. and 10 a.m. on the same weekday as part of the research design. Temporal and hydraulic fluctuations were left out because of the conditions of the facilities (either broken down or under construction). Sampling was carried out once a week even during rainy days (sampler uses protective gears and raincoats), after which collected grab samples were placed in ice and transported to the laboratory to be processed immediately. Averaging was used to aggregate weekly data into monthly values. Table 1 summarizes the specifications of the selected sampling points in the study area.

2.3. Wastewater Influent Concentration and Viral Total RNA Extraction

The process of nucleic acid (NA) extraction was carried out using 100 mL of sewage sample after mixing, which was then centrifuged with speed and time conditions 2500× g for 20 min, after which 2.5 mL of the centrifuged pellet was subsequently utilized for viral total RNA extraction applying a procedure initially defined by Johnson et al. [23]. Concentration and purity of the extracted NA samples were measured employing the NanoDrop, ND-8000-spectrophotometer (Thermo Scientific, Waltham, MA, USA). Extracted samples that showed A260/280 ratios ranging from 1.8 to 2 and A260/230 ratios from 1.8 to 2.1 were considered to pass the quality check (QC) and were subsequently used. Afterwards, the resultant RNA (70 µL) was aliquoted, diluted using 1:10 dilution factor, and preserved in −80 °C freezer for further molecular examination.

2.4. Quantification of SARS-CoV-2 RNA by RT-PCR Technique

Briefly, a one-step RT-PCR was performed using 1 μL of total RNA (200 ng/μL) in a 10 μL PCR. The diluted RNA or undiluted RNA (with low concentrations) were subjected to RT-PCR using TaqMan 2019-nCoV Analysis Kit v1 (BioRad Laboratories, Richmond, CA, USA) as per manufacturer’s instructions, targeting the nucleocapsid (N1 and N2) genes. All RT-PCR techniques were conducted according to the QC standards established by Michael Pfaffl [24]. Molecular quantification of the viral RNA was performed utilizing about 200 ng of viral RNA, and the procedures were carried out with the operation of an Applied Biosystems™ QuantStudio™ 5 Flex RT-PCR System (ABI Technologies, Waltham, MA, USA). With the use of 200,000 copies/μL-2019-nCoV-N-Positive plasmid as the first standard in serial dilutions of 1:10 (Qauntabio, Beverley, MA, USA), the RNA copy number (CN) of the virus was measured applying the standard curve (SC) procedure [24]. To ensure quality, all positive controls (PCs), negative controls (NCs) for total RNA extraction, and RT-PCR were performed in duplicates. The PCs and NCs for RNA extraction were formerly positive samples and nuclease-free water (NFW), correspondingly. RT-PCR control consisted of a non-template control that contained NFW rather than RNA as the NC and 200 copies/μL standard was incorporated as unknown sample for PC. RNA purity was assessed prior to amplification using A260/280 and A260/230 ratios to minimize PCR inhibition, and samples not meeting quality thresholds were excluded from downstream analysis. The limits of detection (LOD) and limits of quantification (LOQ) were estimated in accordance with assay performance and amplification consistency following CDC-recommended RT-qPCR protocols.
Data generated from the QuantStudio 5 RT-PCR system and the pre-installed software, Design & Analysis 2.4 (Applied Biosystems®, Waltham, MA, USA) were transferred to Excel spreadsheet, after which the average GC from the amplified samples were calculated. The average CT, SD, and quantity mean values were obtained from the generated excel data and used to quantify the viral GC/mL of WW from the extracted sample volume.

2.5. Normalization

Employing IBM SPSS statistics 27, the respective gc per mL were subsequently normalized to the population estimates retrieved from statistics, South Africa (statssa.gov.za) [25], served by the different WWTPs. Parameters including mean, standard deviation, Skewness and Kurtosis statistics, in addition to Kolmogorov–Smirnov and Shapiro–Wilk statistics, were considered for statistical analysis. The tests for normality provide the assumption that the data was normally distributed in the study. Clinical cases data for ECP was extrapolated from National Institutes for Communicable Diseases (NICD) [26]. Averaging was the procedure used to aggregate all weekly data into monthly values.

3. Results

3.1. Total RNA Concentration and Average Viral Copies

The extracted viral total RNA concentrations recovered from the selected WWTPs was measured with the NanodropTM One Microvolume UV-Vis spectrophotometer (Thermo Scientific, Waltham, MA, USA). Out of ten sampling points of the 9 plants, two (namely FB-A & MID) had high total RNA concentrations. Other plants including ADE, BED, SEY and STU were observed to have relatively moderate viral RNA concentrations.

3.2. Detection and Molecular Quantification of SARS-CoV-2 RNA Using RT-PCR

Figure 2 denotes the graphical representation of the genome copies normalized to the estimated population served by the respective WWTF, while Figure 3, Figure 4, Figure 5 and Figure 6 show the average GC per mL of the viral RNA for the selected WWTPs throughout the seasons of winter, spring, summer, and autumn, respectively. The specific N1 and N2 oligonucleotide target probes encoding the Nucleocapsid protein were assembled for the molecular quantification of coronavirus RNA. The standard error was as low as 0.2. Figure 7 illustrates the temporal analysis of the average GC/mL across selected WWTPs in relation to the average COVID-19-positive cases in ECP for that period.

4. Discussion

Out of the selected plants, FB-A had the highest concentration, with an average of 22,000 ng/mL, and MID had the second highest average concentration (20,000 ng/mL) of total RNA extracted; however, there is no risk connection between RNA concentrations and viral gc as RNA extraction measures lab performance. Thus, you cannot directly link RNA extraction efficiency to SARS-CoV-2 risk from the viral load (VL). In a previous study by Xie et al. [27], the authors described similarly high concentrations of viral RNA, and they further screened relevant variants of concern (VOCs), unlike our present study, which is solely dedicated to the frequency of novel SARS-CoV-2 RNA in WW.
According to the statistical analysis (Table 2), our values from the parameters of the GC/mL and population estimates are closer to zero. Therefore, we assume that the data of all variables is normally distributed. In the table of tests of normality (Supplementary Tables S1 and S2), the p ≥ 0.05 in the Kolmogorov–Smirnov test (KST) and Shapiro–Wilk test (SWT) with 95% confidence interval; the assumption of normal distribution was met. Hence, there is no significant difference with respect to both gc and population estimates. Pearson correlation was also carried out, and it represents parametric assumptions in a non-distributive context, testing relationship between variables that are continuous. The analysis shows a correlation of 0.134 between NICD clinical cases and gc with 95% credible interval (Supplementary Table S5 and Figure S3). This is typically interpreted as a positive linear relationship. Nevertheless, this indicates a weak positive linear relationship between clinical data and gc. The wide interval suggests the correlation is statistically insignificant, which may be slightly due to noise in the data. In another study, the correlation values ranged from 0.79 to 0.95, and the authors noted that concentration of the virus can be an excellent foreseeing variable for identifying new cases [28]. From Figure 2, we can deduce that between January and February 2022, the GC/mL/P were highest, with plant and code name FB-B having the highest values. This may perhaps be due to the WWTP linked to a tuberculosis hospital in the community.
In the winter season (June 2021), AL had the highest average GC/mL with about 23,000 average GC/mL followed by FB-A (12,000 average GC/mL) with the highest GC/mL in the month of August 2021 (Figure 3). The population was bigger, so this could have affected viral shedding and the results. Also, reported COVID-19-related cases from ECP in June 2021 reached over 100 cases and then doubled in August 2021 (Figure 7). However, there were many unreported cases, which may not be reflected in the genome copies. Figure 7 represents confirmed COVID-19 cases in the Eastern Cape, where ADM and BCM were the municipalities monitored in this study. Moreover, the cases in the province reduced rapidly in July 2021, and our results showed a decrease in the average GC as ADE had the highest average GC of about 1200 GC/mL.
A recent study by Kumar et al. [29] also reported high total RNA concentrations and high viral copies, and this is in line with our study. However, this is not always the case where high concentrations of the total RNA depict high VL. The total RNA encompasses several other viruses besides coronavirus, and very recent research by Mohapatra et al. [30] also stated a proportionate upsurge in the total RNA concentrations of the virus detected in environmental samples.
Certain environmental factors including rainfall and temperature can define seasonality. For instance, the former dictates the availability of water resources, and the latter directly influences biological processes. But both create the diverse climates and predictable seasonal cycles that characterize different biomes around the world [31,32,33]. Ultimately, there was a spike in COVID-19-related cases in SA between September and October 2021, which was in the spring season. In this period, FB-A had the highest average GC with above 25,000 average GC/mL. In the summer season (between December 2021 and January 2022), there was a deterioration in COVID-19-related cases as well as a reduction in the average GC/mL. Nonetheless, FB-A had the highest with about 40,000 average GC/mL. Results in our study indicate that FB-A had the most consistent viral copies and this can be further observed in the autumn season, with FB-A having moderately average GC/mL. Questionably, this may be due to high precipitation during spring as this favors the proliferation of microbes. Also, the winter season brings about flu and other respiratory-related infections, and this can be a contributing factor.
Interpreting WW data requires a clear understanding of how the local climate and certain factors determine how much virus enters the facility and arrives at the sampling point. Flow rate and dilution, WW characteristics, catchment population, and infrastructure type have been extensively discussed as being significantly influential. Temperature is the most critical environmental driver of RNA decay, resulting in predictable seasonal patterns [34]. A key study reported by Qiu et al. [35] stated that viral RNA degrades faster in warmer WW, while viral genome can persist for more than 15 days at 4 °C. Conversely, no infectious virus was detected after 24 h at 37 °C.
A flattened curve in the clinical data between the months of March 2022 and May 2022 may be due to an extreme reduction in COVID-19-related cases in the previous months. And in our results, there was a decrease in the average GC/mL in the autumn season. It is worth noting that this is one of the few studies that investigated the seasonal variations in the occurrence of the viral genomic materials in raw WW from the region of EC, South Africa. The viral load measured in gc/mL can be useful in understanding of the infectious dose and how it relates to transmission risk.
The effect of the COVID-19 pandemic in South Africa can be regarded as overwhelming to various sectors of life, including the environment. In the present study, we detected variations in viral CNs across the geographically distinct yet rustic study sites. During the winter period, extremely high VL was observed in site AL with over 2.5 × 104 viral gc/mL, contrariwise, site MID had the least VL during this period. Similarly, a peak in COVID-confirmed cases was observed in the Eastern Cape Province (ECP) with over 20,000 positive SARS-CoV-2 reported cases. The smallest viral load was obtained in site KOM with approximately 147 GC/mL. The surveillance during the autumn season revealed high VL in site KOM (S8), a more remote area compared to the other sites (Figure 1), over 2.7 × 104 gc/mL were detected in this area. Generally, our wastewater data does not align with the clinical cases across all plants. This may be because of limited COVID testing as well as certain limiting factors in the processing of WW samples.

5. Conclusions

The unprecedented occurrence of coronavirus genetic components in WW influent continues to be worrisome, because this threatens public health and obviously highlights the burden in communal health. This calls for an urgent need to carry out more WW surveillance projects even in the developing regions of the country and to invent strategies that tackle and prevent future outbreaks. Thus, this present study sheds light on the significance of consistent surveillance of WW systems for the presence of more enteric pathogens. While historical WBE data assists in developing baselines and trends, its usage for advancing public health decisions is constrained by biological, demographic, and technical changes over time. Our results show arguably high detection during the winter and spring seasons with varying concentrations; however, more studies are mandatory to verify the viability of SARS-CoV-2 GC in other ecological components, including WW effluent, activated sludge, and river water. Advanced microbiological methods like NGS and RTM should be employed as well. The implications of this research for public health include informing the development of more effective testing strategies, such as use of RT-PCR to monitor viral load and inform future treatment decisions. Also, these results have significant consequences for public health policy and practice, as they can guide future public health measures and have provided the necessary platform for more advanced follow-up studies.

Study Limitations

There was inconsistency in accessing some of the WWTPs on certain days, especially during lockdown and very bad weather conditions. Due to broken-down facilities, lack of temporal and hydraulic fluctuations and flow-rate data were limitations. Another limitation was that surrogate virus recovery controls were not employed. Also, in the Pearson correlation analysis, there is insufficient evidence to conclude a meaningful relationship between the variables. There was no consideration of unreported COVID cases as many individuals may have been asymptomatic. The statistical results can be regarded as somewhat inconclusive; thus, a different analysis and more data might be useful for prediction.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/microorganisms14020495/s1. Figure S1: Histogram showing the frequency of the population estimates that serve the different plants. The chart was generated in the test for normality using SPSS. Figure S2: Histogram shows the frequency of the genome copies for the different plants. The chart was generated in the test for normality using SPSS. Table S1: Descriptive statistics for the normality test of population estimates; Table S2: Tests of Normality for population estimates; Table S3: Tests of Normality for GC/mL; Table S4: Descriptive statistics for the normality test of GC/mL; Table S5: Pearson correlation showing possible link between clinical cases and genome copies; Figure S3: Pictorial representation of the Pearson correlation of the genome copies/mL of the different plants in relation to the COVID clinical cases in ECP.

Author Contributions

Conceptualization, K.E.E., L.M., R.S., R.J., and A.I.O.; methodology, K.E.E., L.M., V.V.Q., O.M., P.A.N., and B.N.; software, K.E.E.; validation, K.E.E., L.M., N.N., R.S., R.J., and A.I.O.; formal analysis, K.E.E.; investigation, K.E.E. and L.M.; resources, N.N., R.S., R.J., and A.I.O.; data curation, K.E.E. and L.M.; writing—original draft preparation, K.E.E.; writing—review and editing, K.E.E., L.M., N.N., R.S., R.J., and A.I.O.; visualization, K.E.E., L.M., V.V.Q., O.M., N.N., R.S., R.J., and A.I.O.; supervision, K.E.E., L.M., N.N., R.S., R.J., and A.I.O.; project administration, N.N., R.S., R.J., and A.I.O.; funding acquisition, N.N., R.S., R.J., and A.I.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by grant from South African Medical Research Council (SAMRC) with grant number SAMRC/UFH/P790, and the National Research Foundation of South Africa (grant number RCHDI241119283812) for financial support.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors acknowledge the South African Medical Research Council; the South African Department of Science, Technology and Innovation; and the National Research Foundation for their support.

Conflicts of Interest

The authors declare no conflicts of interest.

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Microorganisms 14 00495 g001
Figure 1. Map of the Eastern Cape Province (ECP) including sampling points around the Amathole District Municipality (ADM). The map was constructed using ArcGIS 10.6.1. Abbreviation: S, Study sites.
Figure 1. Map of the Eastern Cape Province (ECP) including sampling points around the Amathole District Municipality (ADM). The map was constructed using ArcGIS 10.6.1. Abbreviation: S, Study sites.
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Figure 2. Graphical representation of the genome copies/mL normalized to the estimated population served by the respective WWTFs. The figure suggests that the most predominant plant is FB-A. Respective color signifies the mean genome copies/mL/population across all WWTFs during sampling period by month.
Figure 2. Graphical representation of the genome copies/mL normalized to the estimated population served by the respective WWTFs. The figure suggests that the most predominant plant is FB-A. Respective color signifies the mean genome copies/mL/population across all WWTFs during sampling period by month.
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Figure 3. Temporal analysis of the average GC/mL of SARS-CoV-2 across selected WWTFs during the winter season. Data suggests that the most predominant plant is AL. Data is characterized as a clustered bar chart. Respective color signifies the average genome copies/mL across all WWTFs during sampling period by weeks.
Figure 3. Temporal analysis of the average GC/mL of SARS-CoV-2 across selected WWTFs during the winter season. Data suggests that the most predominant plant is AL. Data is characterized as a clustered bar chart. Respective color signifies the average genome copies/mL across all WWTFs during sampling period by weeks.
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Figure 4. Temporal analysis of the average GC/mL (SARS-CoV-2) across selected WWTFs during the spring season. Data suggests that the most predominant plant is FB-A. Clustered bar chart is used to represent data. Each color denotes the average gc/mL across all WWTFs during sampling period by weeks.
Figure 4. Temporal analysis of the average GC/mL (SARS-CoV-2) across selected WWTFs during the spring season. Data suggests that the most predominant plant is FB-A. Clustered bar chart is used to represent data. Each color denotes the average gc/mL across all WWTFs during sampling period by weeks.
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Figure 5. Temporal analysis of the average SARS-CoV-2 GC/mL across selected WWTFs during the summer season. Data suggests that the most predominant plant is FB-A. Data is represented as a clustered bar chart. Every color signifies the average gc/mL across all WWTFs during sampling period by weeks.
Figure 5. Temporal analysis of the average SARS-CoV-2 GC/mL across selected WWTFs during the summer season. Data suggests that the most predominant plant is FB-A. Data is represented as a clustered bar chart. Every color signifies the average gc/mL across all WWTFs during sampling period by weeks.
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Figure 6. Temporal analysis of the average SARS-CoV-2 GC/mL across selected WWTFs during the autumn season. Data suggests that the most predominant plant is STU. Data is characterized as a clustered bar chart. All colors represent the average gc/mL across all WWTFs during sampling period by weeks.
Figure 6. Temporal analysis of the average SARS-CoV-2 GC/mL across selected WWTFs during the autumn season. Data suggests that the most predominant plant is STU. Data is characterized as a clustered bar chart. All colors represent the average gc/mL across all WWTFs during sampling period by weeks.
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Figure 7. Sequential analysis of the average genome copies of SARS-CoV-2 RNA across selected WWTFs in relation to the average COVID-19-positive cases for ECP. Data suggests that the clinical cases align mostly with the genome copies. Data is characterized as a clustered bar chart. All colors represent the average gc/mL across all WWTFs, and line graph represents the average number of positive COVID-19 cases.
Figure 7. Sequential analysis of the average genome copies of SARS-CoV-2 RNA across selected WWTFs in relation to the average COVID-19-positive cases for ECP. Data suggests that the clinical cases align mostly with the genome copies. Data is characterized as a clustered bar chart. All colors represent the average gc/mL across all WWTFs, and line graph represents the average number of positive COVID-19 cases.
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Table 1. Specifications of the respective study sites.
Table 1. Specifications of the respective study sites.
SitesCodeCapacity (mL/d)Disinfection ProcessTechnology UsedServed Population (Inhabitants)Catchment River
ADES11.4ChlorinationActivated sludge12,191Koonap River
ALS2N/ANo treatmentActivated sludge15,142Thyme River
BEDS30.5No treatmentWastewater ponds8769Nyara River
CATS53.4ChlorinationActivated sludge7360Unnamed tributary of Thorn River
FBA/FBBS7N/AChlorine tabletActivated sludge25,668Kat River
KOMS80.7Chlorine tabletActivated sludge8090Komga River (Tributary of Kei River)
MIDS9N/ANo treatment Bio/trickling filters 2140Keiskamma River
SEYS1112.3Chlorine gasBio/trickling filters2466Gesie River (Tributary of Kat River Dam)
STUS123.0Chlorine gas Rotating biological contractors 24,672Cumakala River (Tributary of Kubusie River)
Table 2. Overall descriptive statistics for the test of normality.
Table 2. Overall descriptive statistics for the test of normality.
N StatisticMean StatisticStd. Deviation StatisticSkewness
Statistic Std. Error		Kurtosis
Statistic Std. Error
Population estimates 10 13,216.60 9217.67 0.42 0.69 −1.44 1.33
Genome copies/mL 10 1508.81 771.27 1.59 0.69 1.37 1.33
Valid N (listwise) 10
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Ebomah, K.E.; Msolo, L.; Qongwe, V.V.; Mayoyo, O.; Ntlati, P.A.; Ngqwala, B.; Nontongana, N.; Street, R.; Johnson, R.; Okoh, A.I. Seasonal Variations in the Occurrence of SARS-CoV-2 RNA Recovered from Wastewater Treatment Facilities (WWTFs) Within South Africa. Microorganisms 2026, 14, 495. https://doi.org/10.3390/microorganisms14020495

AMA Style

Ebomah KE, Msolo L, Qongwe VV, Mayoyo O, Ntlati PA, Ngqwala B, Nontongana N, Street R, Johnson R, Okoh AI. Seasonal Variations in the Occurrence of SARS-CoV-2 RNA Recovered from Wastewater Treatment Facilities (WWTFs) Within South Africa. Microorganisms. 2026; 14(2):495. https://doi.org/10.3390/microorganisms14020495

Chicago/Turabian Style

Ebomah, Kingsley Ehi, Luyanda Msolo, Velisa Vuyolwethu Qongwe, Okuhle Mayoyo, Piwe Athi Ntlati, Balisa Ngqwala, Nolonwabo Nontongana, Renee Street, Rabia Johnson, and Anthony Ifeanyi Okoh. 2026. "Seasonal Variations in the Occurrence of SARS-CoV-2 RNA Recovered from Wastewater Treatment Facilities (WWTFs) Within South Africa" Microorganisms 14, no. 2: 495. https://doi.org/10.3390/microorganisms14020495

APA Style

Ebomah, K. E., Msolo, L., Qongwe, V. V., Mayoyo, O., Ntlati, P. A., Ngqwala, B., Nontongana, N., Street, R., Johnson, R., & Okoh, A. I. (2026). Seasonal Variations in the Occurrence of SARS-CoV-2 RNA Recovered from Wastewater Treatment Facilities (WWTFs) Within South Africa. Microorganisms, 14(2), 495. https://doi.org/10.3390/microorganisms14020495

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