Short-term microbial dynamics in a drinking water plant treating groundwater with occasional high microbial loads

Besmer, Michael D.; Hammes, Frederik

doi:10.1016/j.watres.2016.10.041

Cited by 55 publications

(84 citation statements)

References 34 publications

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“…Source water contamination: FCM analysis of temporal fluctuations in source water was reported previously (Besmer et al, 2014(Besmer et al, , 2016a(Besmer et al, , 2016bKötzsch and Sinreich, 2014;Besmer and Hammes, 2016) and is illustrated in Figure 2 and Example S1. In many of these examples, high frequency online FCM was used over extended time periods to quantify dry weather baseline values and microbiological changes caused by regional precipitation events with respect to their frequency and magnitude.…”

Section: Fcm Provides Relevant Quantitative Process Informationmentioning

confidence: 89%

“…Recent developments in fully automated online technology allow continuous FCM measurements for several subsequent weeks Brognaux et al, 2013, Besmer et al, 2014. For example, Besmer et al (2016aBesmer et al ( , 2016b and Besmer and Hammes (2016) characterised precipitation-induced fluctuations in raw water and operationally induced fluctuations in treated water at high temporal resolution, resulting in total sample numbers in the thousands -far in excess of what is remotely possible with conventional sampling and analysis tools (Example S1). This enabled a detailed characterization of the bacterial baseline concentrations and fluctuations in specific systems on a level of detail not previously possible, and also demonstrated the potential for early warning systems and strategic sampling strategy design (Besmer et al, 2016a(Besmer et al, , 2016bBesmer and Hammes, 2016).…”

Section: Fcm Speed Automation and Online Analysis Potentialmentioning

confidence: 99%

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Flow cytometric bacterial cell counts challenge conventional heterotrophic plate counts for routine microbiological drinking water monitoring

et al. 2017

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Drinking water utilities and researchers continue to rely on the century-old heterotrophic plate counts (HPC) method for routine assessment of general microbiological water quality. Bacterial cell counting with flow cytometry (FCM) is one of a number of alternative methods that challenge this status quo and provide an opportunity for improved water quality monitoring. After more than a decade of application in drinking water research, FCM methodology is optimised and established for routine application, supported by a considerable amount of data from multiple full-scale studies. Bacterial cell concentrations obtained by FCM enable quantification of the entire bacterial community instead of the minute fraction of cultivable bacteria detected with HPC (typically < 1% of all bacteria). FCM measurements are reproducible with relative standard deviations below 3% and can be available within 15 min of samples arriving in the laboratory. High throughput sample processing and complete automation are feasible and FCM analysis is arguably less expensive than HPC when measuring more than 15 water samples per day, depending on the laboratory and selected staining procedure(s). Moreover, many studies have shown FCM total (TCC) and intact (ICC) cell concentrations to be reliable and robust process variables, responsive to changes in the bacterial abundance and relevant for characterising and monitoring drinking water treatment and distribution systems. The purpose of this critical review is to initiate a constructive discussion on whether FCM could replace HPC in routine water quality monitoring. We argue that FCM provides a faster, more descriptive and more representative quantification of bacterial abundance in drinking water.

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Section: Fcm Provides Relevant Quantitative Process Informationmentioning

confidence: 89%

Section: Fcm Speed Automation and Online Analysis Potentialmentioning

confidence: 99%

Flow cytometric bacterial cell counts challenge conventional heterotrophic plate counts for routine microbiological drinking water monitoring

et al. 2017

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“…Online FCM with an automatic system for sampling, staining, and cleaning has quickly gained attention and consideration throughout the water sector over the past years to quantify both total and intact bacterial cell counts 22,36,[38][39][40] . The real-time monitoring of bacterial cell numbers through online FCM can be helpful to indicate locations of concern throughout the distribution network.…”

Section: Introductionmentioning

confidence: 99%

Online characterization of bacterial processes in drinking water systems

2020

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The use of traditional drinking water microbial quality monitoring methods, including heterotrophic plate counts (HPCs) and total coliform counts, are not only laborious and time-consuming but also do not readily allow identification of risk areas in the network. Furthermore, if areas of concern are identified, and mitigation measures are taken, it takes days before the effectiveness of these measures is known. This study identified flow cytometry (FCM) as an online sensor technology for bacterial water quality monitoring in the distribution network. We monitored the total bacterial cell numbers and biodiversity in a drinking water distribution system (DWDS) using an online FCM. Two parallel online FCM monitoring systems were installed on two different locations at a drinking water treatment plant (DWTP; Saudi Arabia) supplying chlorinated water to the distribution and in the network 3.6 km away from the DWTP. The FCMs were operated at the same time in parallel to assess the biological stability in DWDSs. The flow cytometric data was compared with the conventional water quality detection methods (HPC and total coliforms). HPC and total coliforms were constantly below the detection limits, while the FCM provided detectable total cell count data and enabled the quantification of changes in the drinking water both with time and during distribution. Results demonstrate the value of FCM as a tool for compliance monitoring and risk assessment of DWDSs.

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“…PhenoGMM allows to infer diversity metrics efficiently, both in an unsupervised and supervised setting. Technological advancements have enabled an automation of the data acquisition, resulting in a detailed characterization of the microbial community on-line (i.e., samples are measured at routine intervals between 5-15 min) or even in real-time (i.e., near-continuous measurements) (Hammes et al, 2012;Besmer and Hammes, 2016). Therefore we see great potential to use FCM as a monitor technique to rapidly investigate microbial community dynamics.…”

Section: Discussionmentioning

confidence: 99%

PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

Rubbens

Props

Kerckhof

et al. 2019

Preprint

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Motivation:Microbial flow cytometry allows to rapidly characterize microbial community diversity and dynamics. Recent research has demonstrated a strong connection between the cytometric diversity and taxonomic diversity based on 16S rRNA gene amplicon sequencing data. This creates the opportunity to integrate both types of data to study and predict the microbial community diversity in an automated and efficient way. However, microbial flow cytometry data results in a number of unique challenges that need to be addressed. Results: The results of our work are threefold: i) We expand current microbial cytometry fingerprinting approaches by using a model-based fingerprinting approach based upon Gaussian Mixture Models, which we called PhenoGMM. ii) We show that microbial diversity can be rapidly estimated by PhenoGMM. In combination with a supervised machine learning model, diversity estimations based on 16S rRNA gene amplicon sequencing data can be predicted. iii) We evaluate our method extensively by using multiple datasets from different ecosystems and compare its predictive power with a generic binning fingerprinting approach that is commonly used in microbial flow cytometry. These results confirm the strong connection between the genetic make-up of a microbial community and its phenotypic properties as measured by flow cytometry.

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Short-term microbial dynamics in a drinking water plant treating groundwater with occasional high microbial loads

Cited by 55 publications

References 34 publications

Flow cytometric bacterial cell counts challenge conventional heterotrophic plate counts for routine microbiological drinking water monitoring

Flow cytometric bacterial cell counts challenge conventional heterotrophic plate counts for routine microbiological drinking water monitoring

Online characterization of bacterial processes in drinking water systems

PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

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