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

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.

RT-LAMP: A Cheaper, Simpler and Faster Alternative for the Detection of SARS-CoV-2 in Wastewater.

Reverse transcription loop-mediated isothermal amplification (RT-LAMP) has the potential to become a cheaper and faster option for monitoring COVID-19 infections through wastewater-based epidemiology. However, its application in COVID-19 surveillance has been limited to clinical testing only. We present in this paper two optimized RT-LAMP protocols based on colour change and fluorescence detection and application of these protocols for wastewater monitoring from four wastewater treatment plants over 4 weeks. The optimized RT-LAMP protocols have a limit of detection of 10 copies/25 µl reaction with positive amplification within 35 minutes. Over the 4 weeks of monitoring, the colorimetric protocol detected a prevalence of 12.5%, when 1 µl of extracted RNA with 92.7(± 28.2) ng/µl concentration was analysed. When the RNA template was increased by fivefold, the prevalence increased to 44%. The fluorescent RT-LAMP had a prevalence of 31% and 47% for starting templates of 92.7(± 28.2) ng/µl and 480(± 134.5) ng/µl of the extracted RNA, respectively. All samples were positive for SARS-CoV-2 when analysed with droplet digital PCR, with viral loads ranging from 18.1 to 195.6 gc/ml of wastewater. The RT-ddPCR, therefore, confirms the presence of the viral RNA in the wastewater samples, albeit at low concentrations. Additionally, the RT-LAMP protocols positively detected SARS-CoV-2 in wastewater samples with copies as low as 20.7 gc/ml. The results obtained in our study show the potential application of RT-LAMP for the detection of SARS-CoV-2 in wastewater, which could provide a cheaper and faster alternative to RT-qPCR or RT-ddPCR for wastewater-based epidemiological monitoring of COVID-19 and other viral infections.

Fuzzy inference optimization algorithms for enhancing the modelling accuracy of wastewater quality parameters.

To ensure the safe discharge of treated wastewater to the environment, continuous efforts are vital to enhance the modelling accuracy of wastewater treatment plants (WWTPs) through utilizing state-of-art techniques and algorithms. The integration of metaheuristic modern optimization algorithms that are natlurally inspired with the Fussy Inference Systems (FIS) to improve the modelling performance is a promising and mathematically suitable approach. This study integrates four population-based algorithms, namely: Particle swarm optimization (PSO), Genetic algorithm (GA), Hybrid GA-PSO, and Mutating invasive weed optimization (M-IWO) with FIS system. A full-scale WWTP in South Africa (SA) was selected to assess the validity of the proposed algorithms, where six wastewater effluent parameters were modeled, i.e., Alkalinity (ALK), Sulphate (SLP), Phosphate (PHS), Total Kjeldahl Nitrogen (TKN), Total Suspended Solids (TSS), and Chemical Oxygen Demand (COD). The results from this study showed that the hybrid PSO-GA algorithm outperforms the PSO and GA algorithms when used individually, in modelling all wastewater effluent parameters. PSO performed better for SLP and TKN compared to GA, while the M-IWO algorithm failed to provide an acceptable modelling convergence for all the studied parameters. However, three out of four algorithms applied in this study proven beneficial to be optimized in enhancing the modelling accuracy of wastewater quality parameters.

Monitoring changes in COVID-19 infection using wastewater-based epidemiology: A South African perspective.

Monitoring of COVID-19 infections within communities via wastewater-based epidemiology could provide a cost-effective alternative to clinical testing. This approach, however, still requires improvement for its efficient application. In this paper, we present the use of wastewater-based epidemiology in monitoring COVID-19 infection dynamics in the KwaZulu-Natal province of South Africa, focusing on four wastewater treatment plants for 14 weeks. The SARS-CoV-2 viral load in influent wastewater was determined using droplet digital PCR, and the number of people infected was estimated using published models as well as using a modified model to improve efficiency. On average, viral loads ranged between 0 and 2.73 × 105 copies/100 ml, 0-1.52 × 105 copies/100 ml, 3 × 104-7.32 × 105 copies/100 ml and 1.55 × 104-4.12 × 105 copies/100 ml in the four wastewater treatment plants studied. The peak in viral load corresponded to the reported COVID-19 infections within the districts where these catchments are located. In addition, we also observed that easing of lockdown restrictions by authorities corresponded with an increase in viral load in the untreated wastewater. Estimation of infection numbers based on the viral load showed that a higher number of people could potentially be infected, compared to the number of cases reported based on clinical testing. The findings reported in this paper contribute to the field of wastewater-based epidemiology for COVID-19 surveillance, whilst highlighting some of the challenges associated with this approach, especially in developing countries.

Artificial Intelligence for the Evaluation of Operational Parameters Influencing Nitrification and Nitrifiers in an Activated Sludge Process.

Nitrification at a full-scale activated sludge plant treating municipal wastewater was monitored over a period of 237 days. A combination of fluorescent in situ hybridization (FISH) and quantitative real-time polymerase chain reaction (qPCR) were used for identifying and quantifying the dominant nitrifiers in the plant. Adaptive neuro-fuzzy inference system (ANFIS), Pearson's correlation coefficient, and quadratic models were employed in evaluating the plant operational conditions that influence the nitrification performance. The ammonia-oxidizing bacteria (AOB) abundance was within the range of 1.55 × 10(8)-1.65 × 10(10) copies L(-1), while Nitrobacter spp. and Nitrospira spp. were 9.32 × 10(9)-1.40 × 10(11) copies L(-1) and 2.39 × 10(9)-3.76 × 10(10) copies L(-1), respectively. Specific nitrification rate (qN) was significantly affected by temperature (r 0.726, p 0.002), hydraulic retention time (HRT) (r -0.651, p 0.009), and ammonia loading rate (ALR) (r 0.571, p 0.026). Additionally, AOB was considerably influenced by HRT (r -0.741, p 0.002) and temperature (r 0.517, p 0.048), while HRT negatively impacted Nitrospira spp. (r -0.627, p 0.012). A quadratic combination of HRT and food-to-microorganism (F/M) ratio also impacted qN (r (2) 0.50), AOB (r (2) 0.61), and Nitrospira spp. (r (2) 0.72), while Nitrobacter spp. was considerably influenced by a polynomial function of F/M ratio and temperature (r (2) 0.49). The study demonstrated that ANFIS could be used as a tool to describe the factors influencing nitrification process at full-scale wastewater treatment plants.