Tsagkari, Erifyli and Sloan, William (2023) The role of chlorine in the formation and development of tap water biofilms under different flow regimes. Microorganisms, 11 (11): 2680. ISSN 2076-2607

Abstract

Water companies make efforts to reduce the risk of microbial contamination in drinking water. A widely used strategy is to introduce chlorine into the drinking water distribution system (DWDS). A subtle potential risk is that non-lethal chlorine residuals may select for chlorine resistant species in the biofilms that reside in DWDS. Here, we quantify the thickness, density, and coverage of naturally occurring multi-species biofilms grown on slides in tap water with and without chlorine, using fluorescence microscopy. We then place the slides in an annular rotating reactor and expose them to fluid-wall shears, which are redolent of those on pipe walls in DWDS. We found that biofilms in chlorine experiment were thicker, denser and with higher coverage than in non-chlorine conditions under all flow regimes and during incubation. This suggests that the formation and development of biofilms was promoted by chlorine. Surprisingly, for both chlorinated and non-chlorinated conditions, biofilm thickness, density and coverage were all positively correlated with shear stress. More differences were detected in biofilms under the different flow regimes in non-chlorine than in chlorine experiments. This suggests a more robust biofilm under chlorine conditions. While this might imply less mobilization of biofilms in high shear events in pipe networks, it might also provide refuge from chlorine residuals for pathogens.

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Tsagkari, Erifyli
Author

Tsagkari, Erifyli and Sloan, William (2023) The role of chlorine in the formation and development of tap water biofilms under different flow regimes. Microorganisms, 11 (11): 2680. ISSN 2076-2607

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Sloan, William
Author

Cholet, Fabien and Vignola, Marta and Quinn, Dominic and Ijaz, Umer Z. and Sloan, William T. and Smith, Cindy J. (2025) Microbial ecology of drinking water biofiltration based on 16S rRNA sequencing: a meta-analysis. Water Research, 281: 123684. ISSN 0043-1354

Gupta, Rohit and Murray, Cameron and Sloan, William T. and You, Siming (2025) Predicting the methane production of microwave-pretreated anaerobic digestion of food waste: a machine learning approach. Energy. ISSN 0360-5442 (In Press)

Alghafli, Khaled and Shi, Xiaogang and Sloan, William and Ali, Awad M. (2025) Investigating the role of ENSO in groundwater temporal variability across Abu Dhabi Emirate, United Arab Emirates using machine learning algorithms. Groundwater for Sustainable Development, 28: 101389. ISSN 2352-801X

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