New insights into drinking water treatment, storage and distribution systems using Flow Cytometry.

Excessive microbial regrowth in drinking water distribution systems (DWDS) signifies compromised biostability. In chlorinated DWDS, diminished chlorine residual and substantially elevated water age or transit times can pose risks to water safety. This study delves into microbial community dynamics within DWDS by analysing samples from 119 service reservoirs and 41 water towers across various water sources for six months (March-September 2021). Using Flow Cytometry (FCM) to directly measure microbial populations, surface water exhibited 4-10 times higher microbial loading compared to groundwater and mixed sources. Among these sites, two distinct microbial water quality compliance events (detection of culturable coliform bacteria) were identified through FCM data, each presenting different microbial trends. Factors influencing regrowth in DWDS, notably water age and free chlorine, were scrutinized. Elevated intact cell counts were noted with chlorine levels <0.50 mg/L and water ages surpassing 4 days. Multiple linear regression highlighted temperature as the prime factor affecting cell counts variability in surface and mixed waters. For groundwaters, water age was significant, likely due to decreased disinfectant residuals and minimal treatment of these sources. The Bray-Curtis similarity index, derived from FCM fingerprints, emerged as a potential metric for detecting biological instability in drinking water microbiomes. The findings underscore the necessity of optimally managed DWDS and emphasize the significance of maintaining chlorine levels, especially at higher water ages and temperatures – particularly relevant considering climate change. Through FCM and its fingerprint analysis, a more detailed view of DWDS dynamics is attainable, promoting possibility for enhanced system control. The implications of this research offering potential for safeguarding public health, ensuring consistent water quality, and pathways for more resilient and sustainable water distribution practices. As a prospective direction for future research, machine learning models could be developed to predict and classify microbial community dynamics in DWDS using the rich dataset provided by FCM fingerprints.