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. 2022 Jan:203:111877.
doi: 10.1016/j.envres.2021.111877. Epub 2021 Aug 11.

Effect of selected wastewater characteristics on estimation of SARS-CoV-2 viral load in wastewater

Isaac Dennis Amoah  1 , Taher Abunama  1 , Oluyemi Olatunji Awolusi  1 , Leanne Pillay  1 , Kriveshin Pillay  1 , Sheena Kumari  2 , Faizal Bux  1
Affiliations

Effect of selected wastewater characteristics on estimation of SARS-CoV-2 viral load in wastewater

Isaac Dennis Amoah et al. Environ Res. 2022 Jan.

Abstract

Wastewater-based epidemiology has been used as a tool for surveillance of COVID-19 infections. This approach is dependent on the detection and quantification of SARS-CoV-2 RNA in untreated/raw wastewater. However, the quantification of the viral RNA could be influenced by the physico-chemical properties of the wastewater. This study presents the first use of Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine the potential impact of physico-chemical characteristics of wastewater on the detection and concentration of SARS-CoV-2 RNA in wastewater. Raw wastewater samples from four wastewater treatment plants were investigated over four months. The physico-chemical characteristics of the raw wastewater was recorded, and the SARS-CoV-2 RNA concentration determined via amplification with droplet digital polymerase chain reaction. The wastewater characteristics considered were chemical oxygen demand, flow rate, ammonia, pH, permanganate value, and total solids. The mean SARS-CoV-2 RNA concentrations ranged from 648.1(±514.6) copies/mL to 1441.0(±1977.8) copies/mL. Among the parameters assessed using the ANFIS model, ammonia and pH showed significant association with the concentration of SARS-CoV-2 RNA measured. Increasing ammonia concentration was associated with increasing viral RNA concentration and pH between 7.1 and 7.4 were associated with the highest SARS-CoV-2 concentration. Other parameters, such as total solids, were also observed to influence the viral RNA concentration, however, this observation was not consistent across all the wastewater treatment plants. The results from this study indicate the importance of incorporating wastewater characteristic assessment into wastewater-based epidemiology for a robust and accurate COVID-19 surveillance.

Keywords: Adaptive neuro-fuzzy inference system model; Droplet digital PCR; SARS-CoV-2; Wastewater; Wastewater-based epidemiology.

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Conflict of interest statement

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.

Figures

Fig. 1
Fig. 1
A typical ANFIS architecture, adopted from (Abunama et al., 2018).
Fig. 2
Fig. 2
Modeled impact of ammonia and pH on the detection of SARS-CoV-2 in wastewater from Central (A), Darvil (B), Isipingo(C) and Howick (D) wastewater treatment plants.
Fig. 3
Fig. 3
Modeled impact of flow rate and COD on the detection of SARS-CoV-2 in wastewater from Central (A), Darvil (B) and Howick (C) wastewater treatment plants.
Fig. 4
Fig. 4
Modeled impact of permanganate value (PV4) and total solids on the detection of SARS-CoV-2 in wastewater from Central (A) and Isipingo (B) wastewater treatment plants.
Fig. 5
Fig. 5
Correlation plots for the inputs: (A) ammonia and pH (B) Flow rate and COD, and (C) PV4 and Total solids.
Fig. 6
Fig. 6
Relative errors (%) plots for the inputs: (A) ammonia and pH (B) Flow rate and COD, and (C) PV4 and Total solids.
See this image and copyright information in PMC

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