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Microbial water quality investigation through flow cytometry fingerprinting: from source to tap.

Created on 11 Aug 2026

Authors

Leila Claveau, Neil Hudson, Peter Jarvis, Paul Jeffrey, Francis Hassard

Published in

Sustainable microbiology. Volume 1. Issue 1. Pages qvae003. Epub Feb 14, 2024.

Abstract

Ensuring the quality of treated drinking water is crucial for preventing potential health impacts, regulatory fines, and reputation damage. Traditional culture-based microbiological methods often fail to capture the heterogeneity of the bacterial communities in drinking water. This study employed daily interstage monitoring and flow cytometry (FCM) analysis over a period of one year to investigate the dynamics of water treatment processes and service reservoirs. The objective of this study was to test the utility of FCM fingerprints for aiding in microbial event detection. We found that the chlorine concentration contact time was pivotal for microbial log reduction across the treatment works. FCM fingerprints exhibited significant deviations during operational events, such as process interruptions, but did not correlate with the presence of bacterial indicator organisms in the distributed and tap water. Furthermore, the diversity of bacterial fingerprints, quantified by the Bray-Curtis dissimilarity index, served as an indicator for identifying potentially poor microbial water quality. In chlorinated waters with low cell counts, the background signal shows potential as a metric to differentiate between different water sources, thereby offering the possibility to characterize breakthrough events in these circumstances that challenge most other microbial analytical methods. Interestingly, groundwater from simpler treatment works showed a higher occurrence of bacterial indicators, whereas surface water works had a lower incidence. These findings underpin the importance of appropriate disinfection even for "low-risk" source waters and the added value that the statistical interpretation of FCM data can offer objective decision making.

PMID:
42576867
Bibliographic data and abstract were imported from PubMed on 11 Aug 2026.

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