From source to tap: tracking of drinking water bacteria using flow cytometry

Understanding how water microbial quality changes occur in distributed water is crucial for ensuring water quality, safeguarding public health, optimizing treatment processes, and predicting the impacts of environmental and anthropogenic changes on microbial ecosystems. However, current monitoring tools, such as heterotrophic plate count, have limitations due to their inability to capture the full diversity of microbial communities, low sensitivity to non- culturable microorganisms, and delayed results that hinder real-time decision- making. Flow Cytometry (FCM) has emerged as a promising alternative offering high- throughput and real-time analysis of microbial cells. Through metrics such as Total Cell Count (TCC), Intact Cell Count (ICC)High Nucleic Acids (HNA), Low Nucleic Acids (LNA), and Bray and Curtis Dissimilarity Index (BCDI) resulting from the CHIC analysis of cell’s fluorescence histogram, FCM provides valuable insights into microbial abundance, viability, and metabolic activity. Despite its potential, the application of these metrics lacks standardised guidance, with metrics such as HNA and LNA not fully comprehended leading to risks of incorrect application potentially resulting in misinterpretation of water quality data and suboptimal treatment decisions. Therefore, there is a need for addressing the research question of how to appropriately use these metrics to monitor water microbial quality. This EngD study aimed to address this gap by identifying the conditions under which the FCM metrics are most valuable and evaluating their potential as early warning indicators for unwanted water microbial quality. A mixed-methods approach was employed, combining the monitoring of 35 Water Treatment Works (WTW)s and 231 associated Service Reservoirs (SR) with in-depth interstage monitoring of one individual WTW. The first case evaluated the relevance of the metrics across different WTW designs, while the second focused on their relevance at different treatment process stages within a single WTW. The findings revealed that cell counts are most effective in high-cell environments, BCDI is particularly useful in low-cell ones, and HNA/LNA metrics are most relevant in treatment stages involving chemicals. These results highlight the need for a standardised framework to guide FCM users in selecting and interpreting metrics to prevent misinterpretations and support effective water quality management.