Long term detection and quantification of SARS-CoV-2 RNA in wastewater in Bahrain.

Wastewater-based epidemiology is a corroborated environmental surveillance tool in the global fight against SARS-CoV-2. The analysis of wastewater for detection of SARS-CoV-2 RNA may assist policymakers to survey a specific infectious community. Herein, we report on a long-term quantification study in Bahrain to investigate the incidence of the SARS-CoV-2 RNA in wastewater during the COVID-19 pandemic. The ∼260,000 population of Muharraq Island in Bahrain is served by a discrete sewerage catchment, and all wastewater flows to a single large Sewage Treatment Plant (STP) with a capacity of 100,000 m3/day. The catchment is predominately domestic, but also serves several hospitals and Bahrain's international airport. Flow-weighted 24-h composite wastewater samples for the period February 2020 to October 2021 were analyzed for the presence of SARS-CoV-2 N1, N2 and E genes. A Spearman rank correlation demonstrated a moderate correlation between the concentration of SARS-CoV-2 N1, N2 and E genes in the wastewater samples and the number of COVID-19 cases reported on the same day of the sampling. SARS-CoV-2 viral genes were detected in wastewater samples shortly after the first cases of COVID-19 were reported by the health authorities in Bahrain by reverse transcription-polymerase chain reaction (RT-qPCR). The viral genes were detected in 55 of 65 samples (84.62%) during the whole study period and the concentration range was found to be between 0 and 11,508 RNA copies/mL across the viral genes tested (in average N1: 518.4, N2: 366.8 and E: 649.3 copies/mL). Furthermore, wastewater samples from two COVID-19-dedicated quarantine facilities were analysed and detected higher SARS-CoV-2 gene concentrations (range 27-19,105 copies/mL; in average N1: 5044, N2: 4833 and E: 8663 copies/mL). Our results highlight the potential use of RT-qPCR for SARS-CoV-2 detection and quantification in wastewater and present the moderate correlation between concentration of SARS-CoV-2 genes with reported COVID-19 cases for a specified population. Indeed, this study identifies this technique as a mechanism for long term monitoring of SARS-CoV-2 infection levels and hence provides public health and policymakers with a useful environmental surveillance tool during and after the current pandemic.

Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation.

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997-2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO2 emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R2 value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R2 value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes.

Reliable prediction of municipal solid waste (MSW) generation rates is a significant element of planning and implementation of sustainable solid waste management strategies. In this study, the multi-layer perceptron artificial neural network (MLP-ANN) is applied to verify the prediction of annual generation rates of domestic, commercial and construction and demolition (C&D) wastes from the year 1997 to 2016 in Askar Landfill site in the Kingdom of Bahrain. The proposed robust predictive models incorporated selected explanatory variables to reflect the influence of social, demographical, economic, geographical and touristic factors upon waste generation rates (WGRs). The Mean Squared Error (MSE) and coefficient of determination (R2) are used as performance indicators to evaluate effectiveness of the developed models. MLP-ANN models exhibited strong accuracy in predictions with high R2 and low MSE values. The R2 values for domestic, commercial and C&D wastes are 0.95, 0.99 and 0.91, respectively. Our results show that the developed MLP-ANN models are effective for the prediction of WGRs from different sources and could be considered as a cost-effective approach for planning integrated MSW management systems.

Quantification of landfill gas generation and renewable energy potential in arid countries: Case study of Bahrain.

Sustainable solid waste management can provide pathways for renewable energy generation. The Kingdom of Bahrain has witnessed burgeoning municipal solid waste (MSW) generation rate due to socio-economic development. The authorities of this Small Island Developing State, which is located in arid environment, plan to produce 5% of the total electricity demand from renewable energy sources by 2025 and then double it to 10% by 2035. The US Environmental Protection Agency's Landfill Gas Emission Model software was used to estimate the generation of biogas from MSW at the Askar Landfill site. Results envisaged that maximum landfill gas (LFG) emission rates will be in 2020 following landfill closure by the end of 2019, as an intentional scenario, with a maximum electricity generation potential of 57.4 GWh that could provide power to 488 households. Revenues from carbon credits and electricity sales were US$97.8 million and US$64.8 million, respectively, for the period 2020-2035. The internal combustion engine exhibited the most viable option based on economic analysis of the cost of alternative LFG energy recovery technologies. Our work highlights the potential to use LFG-to-energy technologies to reduce the carbon footprint in arid climates for developing countries with substantial electricity subsidization.