Route of SARS-CoV-2 in sewerage and wastewater treatment plants

Modelling and optimization of membrane process for removal of biologics (pathogens) from water and wastewater: Current perspectives and challenges

As one of the 17 sustainable development goals, the United Nations (UN) has prioritized "clean water and sanitation" (Goal 6) to reduce the discharge of emerging pollutants and disease-causing agents into the environment. Contamination of water by pathogenic microorganisms and their existence in treated water is a global public health concern. Under natural conditions, water is frequently prone to contamination by invasive microorganisms, such as bacteria, viruses, and protozoa. This circumstance has therefore highlighted the critical need for research techniques to prevent, treat, and get rid of pathogens in wastewater. Membrane systems have emerged as one of the effective ways of removing contaminants from water and wastewater However, few research studies have examined the synergistic or conflicting effects of operating conditions on newly developing contaminants found in wastewater. Therefore, the efficient, dependable, and expeditious examination of the pathogens in the intricate wastewater matrix remains a significant obstacle. As far as it can be ascertained, much attention has not recently been given to optimizing membrane processes to develop optimal operation design as related to pathogen removal from water and wastewater. Therefore, this state-of-the-art review aims to discuss the current trends in removing pathogens from wastewater by membrane techniques. In addition, conventional techniques of treating pathogenic-containing water and wastewater and their shortcomings were briefly discussed. Furthermore, derived mathematical models suitable for modelling, simulation, and control of membrane technologies for pathogens removal are highlighted. In conclusion, the challenges facing membrane technologies for removing pathogens were extensively discussed, and future outlooks/perspectives on optimizing and modelling membrane processes are recommended.

Critical Needs for Integrated Surveillance: Wastewater-Based and Clinical Epidemiology in Evolving Scenarios with Lessons Learned from SARS-CoV-2

During the COVID-19 pandemic, wastewater-based epidemiology (WBE) and clinical surveillance have been used as tools for analyzing the circulation of SARS-CoV-2 in the community, but both approaches can be strongly influenced by some sources of variability. From the challenging perspective of integrating environmental and clinical data, we performed a correlation analysis between SARS-CoV-2 concentrations in raw sewage and incident COVID-19 cases in areas served by medium-size wastewater treatment plants (WWTPs) from 2021 to 2023. To this aim, both datasets were adjusted for several sources of variability: WBE data were adjusted for factors including the analytical protocol, sewage flow, and population size, while clinical data adjustments considered the demographic composition of the served population. Then, we addressed the impact on the correlation of differences among sewerage networks and variations in the frequency and type of swab tests due to changes in political and regulatory scenarios. Wastewater and clinical data were significantly correlated when restrictive containment measures and limited movements were in effect (ρ = 0.50) and when COVID-19 cases were confirmed exclusively through molecular testing (ρ = 0.49). Moreover, a positive (although weak) correlation arose for WWTPs located in densely populated areas (ρ = 0.37) and with shorter sewerage lengths (ρ = 0.28). This study provides methodological approaches for interpreting WBE and clinical surveillance data, which could also be useful for other infections. Data adjustments and evaluation of possible sources of bias need to be carefully considered from the perspective of integrated environmental and clinical surveillance of infections.

A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the model's predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.