We propose a minimum set of meta-information to accompany the reporting of SARS-CoV-2 occurrence in wastewater for improved data interpretation.

Most water utilities have to handle a substantial number of customer complaints every year. Traditionally, complaints are handled by skilled staff who know how to identify primary issues, classify complaints, find solutions, and communicate with customers. The effort associated with complaint processing is often great, depending on the number of customers served by a water utility. However, the rise of natural language processing (NLP), enabled by deep learning, and especially the use of deep recurrent and convolutional neural networks, has created new opportunities for comprehending and interpreting text complaints. As such, we aim to investigate the value of the use of NLP for processing customer complaints. Through a case study about the Water Utility Groningen in the Netherlands, we demonstrate that NLP can parse language structures and extract intents and sentiments from customer complaints. As a result, this study represents a critical and fundamental step toward fully automating consumer complaint processing for water utilities.

Over the last 30 years, constructed wetlands (CWs) have been used as an alternative, cost-efficient way of treating wastewater, often in combination with conventional wastewater technologies. When CWs are attached at the end of conventional wastewater treatment plants, they treat the effluent and thus provide a polishing step. However, recent studies have shown that when CWs are used as the main wastewater treatment method for the agricultural reuse of effluents, they perform poorly on meeting the accepted limit of microbial contamination. Moreover, CWs are increasingly used within the scope of the circular economy and water reuse applications. Therefore, there is a need for a comprehensive exploration of the performance of CWs on pathogen removal. This paper explores relevant case studies regarding pathogen removal from constructed wetlands to create a comprehensive dataset that provides a complete overview of CWs performance under various conditions. After a systematic literature review, a total of 48 case studies were qualified for both qualitative and quantitative analyses. From the dataset, the general performance, optimal conditions, and knowledge gaps were identified. The review confirmed that constructed wetlands (as a standalone treatment) cannot meet the accepted limits of pathogen removal. However, they can be a credible choice for wastewater polishing when they are combined with conventional wastewater treatment systems. Regarding the most common indicators that were recorded, the removal of Escherichia coli ranged between 0.01–5.6 log; the removal of total and fecal coliforms was 0.2–5.32 log and 0.07–6.08 log, respectively; while the removal of fecal streptococci was 0.2–5.2 log. The great variability of pathogen removal indicates that the complexity of CWs makes it difficult to draw robust conclusions regarding their removal efficiency. Potential correlations were identified between influent and effluent concentrations, as well as between log removal and hydraulic characteristics. Additionally, no correlations between pathogen removal and temperature/climatic zones were found since average pathogen removal per country showed high variation throughout the various climatic zones. The dataset can be used as a benchmark of CWs’ performance as a barrier against the spreading of pathogens in the environment. The knowledge gaps identified in this review can provide direction for further research. Finally, a potential meta-analysis of the dataset using statistical analysis can pave the way for a better understanding of the design and operational parameters of CWs in order to fine-tune and quantify the factors that influence the performance of these systems.

Emergencies and disasters (such as earthquakes and floods), may contaminate drinking water systems with pathogens, that can affect the health of both First Responders and Citizens. As part of the Horizon 2020 “Pathogen Contamination Emergency Response Technologies” (PathoCERT) project, we are developing a Digital Twin tool (PathoINVEST) to assist First Responders and Water Authorities in investigating and responding efficiently to drinking water contamination events. In this paper, we present preliminary work on PathoINVEST, its architecture, and how it operates with the PathoCERT ecosystem of technologies. Moreover, using an illustrative case study, we demonstrate how PathoINVEST will process data and produce useful insights for the First Responders during a realistic contamination event. This work demonstrates how different research results can be integrated into a holistic water contamination emergency management system, in accordance with the needs of First Responders who need to make decisions within a limited time frame and to reduce the impact of a contamination event.

Health risk assessment of environmental exposure to pathogens requires complete and up to date knowledge. With the rapid growth of scientific publications and the protocolization of literature reviews, an automated approach based on Artificial Intelligence (AI) techniques could help extract meaningful information from the literature and make literature reviews more efficient. The objective of this research was to determine whether it is feasible to extract both qualitative and quantitative information from scientific publications about the waterborne pathogen Legionella on PubMed, using Deep Learning and Natural Language Processing techniques. The model effectively extracted the qualitative and quantitative characteristics with high precision, recall and F-score of 0.91, 0.80, and 0.85 respectively. The AI extraction yielded results that were comparable to manual information extraction. Overall, AI could reliably extract both qualitative and quantitative information about Legionella from scientific literature. Our study paved the way for a better understanding of the information extraction processes and is a first step towards harnessing AI to collect meaningful information on pathogen characteristics from environmental microbiology publications.