Browsing by Author "Jeffrey, Paul J."

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    Assessing microbial growth in drinking water using nucleic acid content and flow cytometry fingerprinting
    (Elsevier, 2024-12-20) Claveau, Leïla; Hudson, Neil; Jeffrey, Paul J.; Hassard, Francis
    This study utilizes flow cytometry (FCM) to evaluate the high nucleic acid (HNA) and low nucleic acid (LNA) content of intact cells for monitoring bacterial dynamics in drinking water treatment and supply systems. Our findings indicate that chlorine and nutrients differently impact components of bacterial populations. HNA bacteria, characterized by high metabolic rates, quickly react to nutrient alterations, making them suitable indicators of growth under varying water treatment and supply conditions. Conversely, LNA bacteria adapt to environments with stable, slowly degradable organics, reflecting distinct physiological characteristics. Changes in water treatment and supply conditions, such as chlorine dosing and nutrient inputs, significantly impact the ratio between HNA and LNA. FCM fingerprinting combined with cluster analysis provides a more sensitive evaluation of water quality by capturing a broader range of microbial characteristics compared to using only HNA/LNA ratios. This work advocates for multi-parameter data analysis to advance monitoring techniques for water treatment and supply processes.
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    Evaluating flow cytometric metrics for enhancing microbial monitoring in drinking water treatment processes
    (Elsevier, 2025-01-01) Claveau, Leïla; Hudson, Neil; Jeffrey, Paul J.; Hassard, Francis
    Flow cytometry (FCM) offers a rapid method for bacterial detection in drinking water but faces challenges in terms of data analysis, particularly gating subjectivity. This study evaluates three metrics derived from the Intact Cell Count (ICC): High/Low Nucleic Acid (HNA/LNA) ratios, Bray–Curtis Dissimilarity Index (BCDI), and FCM fingerprints—to enhance microbial monitoring approaches across different water treatment and distribution stages. ICC provided a direct assessment of microbial load in high cell count scenarios, while HNA/LNA ratios were valuable during low microbial levels. BCDI effectively tracked microbial population changes throughout treatment processes. A lead–lag analysis revealed that ICC changes often precede or coincide with BCDI changes and lead changes in HNA/LNA ratios. FCM fingerprinting visualized spatial and temporal variations in microbial communities. Combining these FCM metrics improved microbial water quality assessment and supports approaches to optimise water treatment strategies from a microbial perspective.