Coprostanol as a Population Biomarker for SARS-CoV-2 Wastewater Surveillance Studies

Wastewater surveillance is a cost-effective tool for monitoring SARS-CoV-2 transmission in a community. However, challenges remain with regard to interpretating such studies, not least in how to compare SARS-CoV-2 levels between different-sized wastewater treatment plants. Viral faecal indicators, including crAssphage and pepper mild mottle virus, have been proposed as population biomarkers to normalise SARS-CoV-2 levels in wastewater. However, as these indicators exhibit variability between individuals and may not be excreted by everyone, their utility as population biomarkers may be limited. Coprostanol, meanwhile, is a bacterial metabolite of cholesterol which is excreted by all individuals. In this study, composite influent samples were collected from a large- and medium-sized wastewater treatment plant in Dublin, Ireland and SARS-CoV-2 N1, crAssphage, pepper mild mottle virus, HF183 and coprostanol levels were determined. SARS-CoV-2 N1 RNA was detected and quantified in all samples from both treatment plants. Regardless of treatment plant size, coprostanol levels exhibited the lowest variation in composite influent samples, while crAssphage exhibited the greatest variation. Moreover, the strongest correlations were observed between SARS-CoV-2 levels and national and Dublin COVID-19 cases when levels were normalised to coprostanol. This work demonstrates the usefulness of coprostanol as a population biomarker for wastewater surveillance studies.

Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms.

Designing and Modeling of a Municipal Wastewater Treatment Plant With GPS-X

Wastewater treatment is the process used to remove contaminants from wastewater to produce an effluent suitable for discharge to the environment. Several kinds of wastewater are treated based on the concentration of various parameters. Domestic Wastewater/Municipal Wastewater is treated in Sewage Treatment Plants (STP), which involve various types of processes like physical, chemical and biological to treat the wastewater better. The treatment plant involves five steps of the process – preliminary treatment, primary treatment, secondary treatment, tertiary treatment and sludge treatment to meet the safe disposal guidelines of the department of the environment. In this paper, a municipal wastewater treatment plant for Uttara City is designed and modeled with GPS-X. GPS-X is a wastewater treatment plant modeling software package that is used for verify the treatment process. The design calculation of all unit operations is shown with a specification table. The Process Flow Diagram (PFD), Process Block Diagram (PBD) and mass balance of the process are expressed in the article. The design calculation of the STP is performed with a steady-state process. After each unit operation, the concentration of various parameters – TSS, BOD, VSS, TN is analyzed and the removal efficiency for different equipment is calculated. The STP is designed to maintain the guidelines of the Department of Environment, Bangladesh. At the end of the paper, the designing and modeling steps of a sewage treatment plant will be pursued. Finally, some recommendations are done in the conclusion section.

Genomic Characterization and Annotation of Two Novel Bacteriophages Isolated from a Wastewater Treatment Plant in Qatar

We report the genome sequences of Escherichia phage C600M2 (length, 88,162 bp; G+C content, 38.98%) and Escherichia phage CL1 (length, 87,820 bp; G+C content, 41.32%), which were isolated from a wastewater treatment plant in Qatar. Both Escherichia phage C600M2 and Escherichia phage CL1 genomes contain 128 protein-coding genes and 26 tRNAs.

Financial and Economic Investment Evaluation of Wastewater Treatment Plant

Improved Cost-Benefit Analysis (CBA) analysis requires a broader analytical framework, in order to perceive each project individually from the perspective of potentially measurable and significant effects on the environment and society as a whole. The main goal of our paper is to assess the financial and economic justification for variant V3 (as the most technically optimal) of the wastewater treatment plant (WWTP) construction project in Nov Dojran, North Macedonia, with the purpose of advancing municipal infrastructure and environmental benefits from improved water treatment. Based on the economic analysis conducted, we conclude that the investment in the WWTP project is justified, because the economic internal rate of return is higher than the opportunity cost of capital (EIRR = 16.38%), the economic net present value is higher than 0, and EBCR (benefit-cost ratio) is greater than 1 (EBCR = 2.11). The highest environmental benefit of 49.2% in total environmental benefits is associated with nitrogen, while phosphorus is the next pollutant in the structure of environmental benefits at 46.1%. The environmental benefits of removing biological oxygen demand (BOD) and chemical oxygen demand (COD) are significantly less important, despite the removal of significant amounts of these pollutants during treatment. The situation is similar with suspended particles.