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

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.

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.

<p>Natural Language Processing (NLP), empowered by the most recent developments in Deep Learning, demonstrates its potential effectiveness for handling texts. Urban water research  benefits from both subfields of NLP, namely, Natural Language Understanding (NLU) and Natural Language Generation (NLG). In this work, we present three recent studies that use NLP for: (1) automated processing and responding to registered customer complaint within Dutch water utilities, (2) automated collection of up-to-date water-related information from the Internet, (3) extraction of key information about chemical compounds and pathogen characteristics from scientific publications. These applications, using the latest NLP models and tools (e.g., Rasa, Spacy), take into account studies on both water quality and quantity for the water sector. According to our findings, NLU and rule-based text mining are effective in extracting information from unstructured texts. In addition, NLU and NLG can be integrated to build a human-computer interface, such as a value-based Chabot to understand and address the demands made by customers of water utilities.</p>

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.