Autonomous system for rapid field quantification of Escherichia coli in surface waters

Automation of on-site microbial water quality monitoring from source to tap: Challenges and perspectives

Ensuring the provision of safe drinking water necessitates thorough monitoring of microbial water quality. While traditional culture-based enumeration of bacterial indicators has served as the gold standard in compliance monitoring since the late 19th century, recent advancements in microbial sensor technology, driven by automation and digitalization, are revolutionizing on-site monitoring capabilities. These innovations offer unparalleled potential for automated, high temporal frequency monitoring with remote, real-time data transmission. With regulatory frameworks increasingly favouring risk-based approaches to microbial risk management throughout the drinking water supply chain, we are witnessing a paradigm shift towards the adoption of microbial sensors. This review offers a comprehensive examination of the latest developments and accomplishments in automated on-site monitoring of microbial water quality. Beginning with an elucidation of key terminology and an overview of available sensor technologies, we explore how these cutting-edge tools can enhance our understanding of microbial dynamics in the sourcing, treatment, and distribution of drinking water, and how this knowledge can be translated into operational management. Despite the promise of microbial sensors, significant challenges remain. Drawing from insights gathered from an international online survey targeting drinking water utilities, we discuss the analytical, economic, and legal barriers that must be overcome for the implementation of automated on-site monitoring of microbial water quality. This review serves as a vital resource for researchers, utilities, and policymakers operating in water microbiology and sensor technology. While it is addressing drinking water more specifically, the presented concepts and tools can be extrapolated to recreational waters or wastewater management, with the shared goal to ensure sustainable management of water resources and protection of public health.

Addressing underestimation of waterborne disease risks due to fecal indicator bacteria bound in aggregates

AIMS

This study aims to identify and address significant limitations in current culture-based regulatory methods used for monitoring microbiological water quality. Specifically, these methods' inability to distinguish between planktonic forms and aggregates containing higher bacterial loads and associated pathogens may lead to a severe underestimation of exposure risks, with critical public health implications.

METHODS AND RESULTS

We employed a novel methodology combining size fractionation with ALERT (Automatic Lab-in-a-vial E.coli Remote Tracking), an automated rapid method for comprehensive quantification of culturable fecal indicator bacteria (FIB). Our findings reveal a substantial and widespread presence of aggregate-bound indicator bacteria across various water matrices and geographical locations. Comprehensive bacterial counts consistently exceeded those obtained by traditional methods by significant multiples, such as an average of 3.4× at the Seine River 2024 Olympic venue, and occasionally up to 100× in irrigation canals and wastewater plant effluent. These results, supported by microscopic and molecular analyses, underscore a systematic bias in global water safety regulatory frameworks.

CONCLUSIONS

Our research demonstrates the inadequacy of traditional culture-based techniques in assessing microbiological risks posed by aggregate-bound FIB and associated pathogens, particularly in water matrices affected by FIB-rich fecal particles from recent sewer overflows or sediment, which can carry higher infectious risks. Incorporating comprehensive FIB analysis techniques, including molecular methods and rapid culture-based approaches as shown in this study, offers a promising and effective solution to these risk assessment limitations.

Traditional and new proposals for environmental microbial indicators-a review

The continuous increment in world population coupled with the greatest natural resource consumption and waste generation has an enormous impact on the environment. To date, using biological indicators (bioindicators) to evaluate the biological quality of natural environments is very common. Nonetheless, selecting those suitable for each ecosystem or contaminant is one of the most important issues for environmental sciences. Bacteria and helminths are mainly related to fecal contamination, while antibiotic-resistant bacteria, fungi, viruses, and microalgae are organisms used to determine deteriorated ecosystems by diverse contaminants. Nowadays, each bioindicator is used as a specific agent of different contaminant types, but detecting and quantifying these bioindicator microorganisms can be performed from simple microscopy and culture methods up to a complex procedure based on omic sciences. Developing new techniques based on the metabolism and physiological responses of traditional bioindicators is shown in a fast environmental sensitivity analysis. Therefore, the present review focuses on analyzing different bioindicators to facilitate developing suitable monitoring environmental systems according to different pollutant agents. The traditional and new methods proposed to detect and quantify different bioindicators are also discussed. Their vital role is considered in implementing efficient ecosystem bioprospection, restoration, and conservation strategies directed to natural resource management.

Sensors for Coastal and Ocean Monitoring

In situ water monitoring sensors are critical to gain an understanding of ocean biochemistry and ecosystem health. They enable the collection of high-frequency data and capture ecosystem spatial and temporal changes, which in turn facilitate long-term global predictions. They are used as decision support tools in emergency situations and for risk mitigation, pollution source tracking, and regulatory monitoring. Advanced sensing platforms exist to support various monitoring needs together with state-of-the-art power and communication capabilities. To be fit-for-purpose, sensors must withstand the challenging marine environment and provide data at an acceptable cost. Significant technological advancements have catalyzed the development of new and improved sensors for coastal and oceanographic applications. Sensors are becoming smaller, smarter, more cost-effective, and increasingly specialized and diversified. This article, therefore, provides a review of the state-of-the art oceanographic and coastal sensors. Progress in sensor development is discussed in terms of performance and the key strategies used for achieving robustness, marine rating, cost reduction, and antifouling protection.

Molecular testing devices for on-site detection of E. coli in water samples

Escherichia coli (E. coli) cells are present in fecal materials that can be the main source for disease-causing agents in water. As a result, E. coli is recommended as a water quality indicator. We have developed an innovative platform to detect E. coli for monitoring water quality on-site by integrating paper-based sample preparation with nucleic acid isothermal amplification. The platform carries out bacterial lysis and DNA enrichment onto a paper pad through ball-based valves for fluid control, with no need of laboratory equipment, followed by loop-mediated isothermal amplification (LAMP) in a battery-operated coffee mug, and colorimetric detection. We have used the platform to detect E. coli in environmental water samples in about 1 h, with a limit of quantitation of 0.2 CFU/mL, and 3 copies per reaction. The platform was confirmed for detecting multiple E. coli strains, and for water samples of different salt concentrations. We validated the functions of the platform by analyzing recreational water samples collected near the Atlantic Ocean that contain different concentrations of salt and bacteria.

Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort

Exposure to contaminated water during aquatic recreational activities can lead to gastrointestinal diseases. In order to decrease the exposure risk, the fecal indicator bacteria Escherichia coli is routinely monitored, which is time-consuming, labor-intensive, and costly. To assist the stakeholders in the daily management of bathing sites, models have been developed to predict the microbiological quality. However, model performances are highly dependent on the quality of the input data which are usually scarce. In our study, we proposed a conceptual framework for optimizing the selection of the most adapted model, and to enrich the training dataset. This frameword was successfully applied to the prediction of Escherichia coli concentrations in the Marne River (Paris Area, France). We compared the performance of six machine learning (ML)-based models: K-nearest neighbors, Decision Tree, Support Vector Machines, Bagging, Random Forest, and Adaptive boosting. Based on several statistical metrics, the Random Forest model presented the best accuracy compared to the other models. However, 53.2 ± 3.5% of the predicted E. coli densities were inaccurately estimated according to the mean absolute percentage error (MAPE). Four parameters (temperature, conductivity, 24 h cumulative rainfall of the previous day the sampling, and the river flow) were identified as key variables to be monitored for optimization of the ML model. The set of values to be optimized will feed an alert system for monitoring the microbiological quality of the water through combined strategy of in situ manual sampling and the deployment of a network of sensors. Based on these results, we propose a guideline for ML model selection and sampling optimization.

Microbial biosensors for recreational and source waters

Biosensors are finding new places in science, and the growth of this technology will lead to dramatic improvements in the ability to detect microorganisms in recreational and source waters for the protection of public health. Much of the improvement in biosensors has followed developments in molecular biology processes and coupling these with advances in engineering. Progress in the fields of nano-engineering and materials science have opened many new avenues for biosensors. The adaptation of these diverse technological fields into sensors has been driven by the need to develop more rapid sensors that are highly accurate, sensitive and specific, and have other desirable properties, such as robust deployment capability, unattended operations, and remote data transfer. The primary challenges to the adoption of biosensors in recreational and source water applications are cost of ownership, particularly operations and maintenance costs, problems caused by false positive rates, and to a lesser extent false negative rates, legacy technologies, policies and practices which will change as biosensors improve to the point of replacing more traditional methods for detecting organisms in environmental samples.

Monitoring Approaches for Faecal Indicator Bacteria in Water: Visioning a Remote Real-Time Sensor for E. coli and Enterococci

A comprehensive review was conducted to assess the current state of monitoring approaches for primary faecal indicator bacteria (FIB) E. coli and enterococci. Approaches were identified and examined in relation to their accuracy, ability to provide continuous data and instantaneous detection results, cost, environmental awareness regarding necessary reagent release or other pollution sources, in situ monitoring capability, and portability. Findings showed that several methods are precise and sophisticated but cannot be performed in real-time or remotely. This is mainly due to their laboratory testing requirements, such as lengthy sample preparations, the requirement for expensive reagents, and fluorescent tags. This study determined that portable fluorescence sensing, combined with advanced modelling methods to compensate readings for environmental interferences and false positives, can lay the foundations for a hybrid FIB sensing approach, allowing remote field deployment of a fleet of networked FIB sensors that can collect high-frequency data in near real-time. Such sensors will support proactive responses to sudden harmful faecal contamination events. A method is proposed to enable the development of the visioned FIB monitoring tool.

Near real-time notification of water quality impairments in recreational freshwaters using rapid online detection of β-D-glucuronidase activity as a surrogate for Escherichia coli monitoring

Waterborne disease outbreaks associated with recreational waters continue to be reported around the world despite existing microbiological water quality monitoring frameworks. Most regulations resort to the use of culture-based enumeration of faecal indicator bacteria such as Escherichia coli to protect bathers from gastrointestinal illness risks. However, the long sample-to-result time of standard culture-based assays (minimum 18-24 h) and infrequent regulatory sampling (weekly or less) do not enable detection of episodic water quality impairments and associated public health risks. The objective of this study was to assess the suitability of an autonomous online technology measuring β-D-glucuronidase (GLUC) activity for near real-time monitoring of microbiological water quality in recreational waters and for the resulting beach management decisions. GLUC activity and E. coli concentrations were monitored at three freshwater sites in Quebec, Canada (sites Qc1-3) and one site in New Zealand (site NZ) between 2016 and 2018. We found site-dependent linear relationships between GLUC activity and E. coli concentrations and using confusion matrices, we developed site-specific GLUC activity beach action values (BAVs) matching the regulatory E. coli BAVs. Using the regulatory E. coli BAV as the gold standard, rates of false alarms (unnecessary beach advisories using GLUC activity BAV) and failures to act (failure to trigger advisories using GLUC activity) ranged between 0 and 32% and between 3 and 10%, respectively, which is comparable to the rates reported in other studies using qPCR-defined BAVs. However, a major benefit of the autonomous enzymatic technology is the real-time reporting of threshold exceedances, while temporal trends in GLUC activity can assist in understanding the underlying dynamics of faecal pollution and potential health risks. Our study is the first to describe the applicability of online near real-time monitoring of microbiological water quality as a tool for improved beach management and public health protection.

Capacity challenges in water quality monitoring: understanding the role of human development

Monitoring the qualitative status of freshwaters is an important goal of the international community, as stated in the Sustainable Development Goal (SDGs) indicator 6.3.2 on good ambient water quality. Monitoring data are, however, lacking in many countries, allegedly because of capacity challenges of less-developed countries. So far, however, the relationship between human development and capacity challenges for water quality monitoring have not been analysed systematically. This hinders the implementation of fine-tuned capacity development programmes for water quality monitoring. Against this background, this study takes a global perspective in analysing the link between human development and the capacity challenges countries face in their national water quality monitoring programmes. The analysis is based on the latest data on the human development index and an international online survey amongst experts from science and practice. Results provide evidence of a negative relationship between human development and the capacity challenges to meet SDG 6.3.2 monitoring requirements. This negative relationship increases along the course of the monitoring process, from defining the enabling environment, choosing parameters for the collection of field data, to the analytics and analysis of five commonly used parameters (DO, EC, pH, TP and TN). Our assessment can be used to help practitioners improve technical capacity development activities and to identify and target investment in capacity development for monitoring.