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

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 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 flow cytometry fingerprints for aiding in microbial event detection. We found that the chlorine concentration contact time (Ct) was pivotal for microbial log reduction across the treatment works. Flow cytometry 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 characterise 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 flow cytometry data can offer objective decision making.

Microbial drinking water monitoring now and in the future

“…Over recent years there has been a focus on computational and statistical advancements as well, which has led to higher resolution flow cytometric fingerprints and thus more tools to analyse the microbiology (Claveau, Hudson, Jeffrey, & Hassard, 2024 ). The latter has proven to be useful in operational event detection and disinfection monitoring (Claveau, Hudson, Jarvis, et al., 2024 ). The collection of this big data (high‐resolution flow cytometry and other online data) in combination with machine learning will lead to a more precise monitoring and possibly even event prediction based on several online parameters (Sadler et al., 2020 ).…”