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

Cazals, Margot; Stott, Rebecca; Fleury, Carole; Proulx, François; Prévost, Michèle; Servais, Pierre; Dorner, Sarah; Burnet, Jean-Baptiste

doi:10.1016/j.scitotenv.2020.137303

Cited by 14 publications

(8 citation statements)

References 51 publications

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“…Both sites 1 and 2 are impacted by several combined sewer overflow (CSO) discharges located within a <1 km to >10 km stretch upstream from both sampling stations. Temporal series of online GLUC activity measurements have been recently reported for both sites [22,32] and demonstrate vulnerability of the latter to intermittent fecal pollution inputs.…”

Section: Sampling Sitesmentioning

confidence: 94%

“…Three 40 L samples were collected during this event and analysed in the laboratory for E. coli, protozoan parasites and Bacteroides. The threshold for peak sampling mode activation was set at 10 mMFU/100 mL based on the GLUC activity measured under dry weather conditions (in absence of any contamination event) during the previous bathing season [32].…”

Section: Field Validation Of the Triggering Modesmentioning

confidence: 99%

“…Peak contamination sampling was carried out at site 2, following the definition of a threshold of 10 mMFU/100 mL −1 . The latter was based on online GLUC activity monitoring data collected under dry and wet weather conditions (in absence of contamination events or following rainfall-induced untreated sewage discharges, respectively) during the 2018 bathing season [32]. Using the algorithm developed for the peak sampling mode, a 40 L sample was collected by the autosampler at the GLUC activity peak.…”

Section: Peak Samplingmentioning

confidence: 99%

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Automated Targeted Sampling of Waterborne Pathogens and Microbial Source Tracking Markers Using Near-Real Time Monitoring of Microbiological Water Quality

Burnet

Habash

Hachad

et al. 2021

Water

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Waterborne pathogens are heterogeneously distributed across various spatiotemporal scales in water resources, and representative sampling is therefore crucial for accurate risk assessment. Since regulatory monitoring of microbiological water quality is usually conducted at fixed time intervals, it can miss short-term fecal contamination episodes and underestimate underlying microbial risks. In the present paper, we developed a new automated sampling methodology based on near real-time measurement of a biochemical indicator of fecal pollution. Online monitoring of β-D-glucuronidase (GLUC) activity was used to trigger an automated sampler during fecal contamination events in a drinking water supply and at an urban beach. Significant increases in protozoan parasites, microbial source tracking markers and E. coli were measured during short-term (<24 h) fecal pollution episodes, emphasizing the intermittent nature of their occurrence in water. Synchronous triggering of the automated sampler with online GLUC activity measurements further revealed a tight association between the biochemical indicator and culturable E. coli. The proposed event sampling methodology is versatile and in addition to the two triggering modes validated here, others can be designed based on specific needs and local settings. In support to regulatory monitoring schemes, it should ultimately help gathering crucial data on waterborne pathogens more efficiently during episodic fecal pollution events.

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Section: Sampling Sitesmentioning

confidence: 94%

Section: Field Validation Of the Triggering Modesmentioning

confidence: 99%

Section: Peak Samplingmentioning

confidence: 99%

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Automated Targeted Sampling of Waterborne Pathogens and Microbial Source Tracking Markers Using Near-Real Time Monitoring of Microbiological Water Quality

Burnet

Habash

Hachad

et al. 2021

Water

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“…As with other methods, the presence of other bacteria and substances that can provide a response within the criteria of the test, can lead to an erroneous inference (Fiksdall & Tryland, 2008). The rapidity of enzyme‐based biosensors makes them an attractive option and an increasing number of methodologies have been developed to use these tests either in the field (Gunda & Mitra, 2016; Hesari et al, 2016) or as an online, autonomous, installation (Burnet et al, 2019; Cazals et al, 2020).…”

Section: Synthesis Of Current Conventional Methods To Assess Microbia...mentioning

confidence: 99%

Advances in quantifying microbial contamination in potable water: Potential of fluorescence‐based sensor technology

et al. 2022

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Improved monitoring of potable water is essential if we are to achieve the UN Sustainable Development Goals (SDGs), specifically SDG6: to make clean water and sanitation available to all. Typically monitoring of potable water requires laboratory analysis to detect indicators of fecal pollution, such as thermotolerant coliforms (TTCs), Escherichia coli (E. coli), or intestinal enterococci. However, these analyses are time‐consuming and expensive, and recent advances in field deployable sensing technology offer opportunities to investigate both the spatial and temporal dynamics of microbial pollution in a more resolved and cost‐effective manner, thus advancing process‐based understanding and practical application for human health. Fluorescence offers a realistic proxy for monitoring coliforms in freshwaters with potential for quantification of potable water contamination in near real‐time with no need for costly reagents. Here, we focus on E. coli to provide a state‐of‐the‐art review of potential technologies capable of delivering an effective real‐time E. coli sensor system. We synthesize recent research on the use of fluorescence spectroscopy to quantify microbial contamination and discuss a variety of approaches (and constraints) to relate the raw fluorescence signal to E. coli enumerations. Together, these offer an invaluable platform to monitor drinking water quality which is required in situations where the water treatment and distribution infrastructure is degraded, for example in less economically developed countries; and during disaster‐relief operations. Overall, our review suggests that the fluorescence of dissolved organic matter is the most viable current method—given recent advances in field‐deployable technology—and we highlight the potential for recent developments to enhance approaches to water quality monitoring. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > Methods Human Water > Methods

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“…One strategy for monitoring the bathing water quality and deciding when to open or close a bathing site is to use online measurement systems that detect Beta-D-Galactosidase or Beta-D-Glucuronidase activity. For instance, the ColiMinder automated measurement system (Vienna Water Monitoring, VWM GmbH) [48], ALERT system (Fluidion) [49], Colifast ALARM™ [50], and TECTA-B16 (Endetec, Veolia) [51] have been demonstrated to be useful to monitor E. coli in rivers, but the price of these devices may be economically prohibitive for numerous cities, as one unit may cost up to tens of thousand of euros. Alternatively, the use of sensing technologies to measure proxies or surrogate parameters procures high frequency, precise, and accurate data.…”

Section: Automated Data Collectionmentioning

confidence: 99%

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

Naloufi

Lucas

Souihi

et al. 2021

Water

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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.

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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

Cited by 14 publications

References 51 publications

Automated Targeted Sampling of Waterborne Pathogens and Microbial Source Tracking Markers Using Near-Real Time Monitoring of Microbiological Water Quality

Automated Targeted Sampling of Waterborne Pathogens and Microbial Source Tracking Markers Using Near-Real Time Monitoring of Microbiological Water Quality

Advances in quantifying microbial contamination in potable water: Potential of fluorescence‐based sensor technology

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

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