Assessing the effectiveness of near real-time flow cytometry in monitoring ozone disinfection in a full-scale drinking water treatment plant

While real-time monitoring of physicochemical parameters has widely been incorporated into drinking water treatment systems, real-time microbial monitoring has lagged behind, resulting in the use of surrogate parameters (disinfectant residual, applied dose, concentration × time [CT]) to assess disinfection system performance. Near real-time flow cytometry (NRT-FCM) allows for automated quantification of total and intact microbial cells but has not been widely implemented in full-scale systems. This study sought to investigate the feasibility of NRT-FCM for full-scale drinking water ozone disinfection system performance monitoring. A water treatment plant with high lime solids turbidity in the ozone contactor influent was selected to evaluate the NRT-FCM in challenging conditions. Total and intact cell counts were monitored for 40 days and compared to surrogate parameters (ozone residual, ozone dose, and CT) and grab sample assay results for cellular adenosine triphosphate (cATP), heterotrophic plate counts (HPC), impedance flow cytometry, and 16S rRNA gene sequencing. NRT-FCM provided insight into the dynamics of the full-scale ozone system, including offering early warning of increased contactor effluent cell concentrations, which was not observed using surrogate measures. A strong correlation between log intact cell removal and CT was also not observed (Kendall’s tau= −0.09, p=0.04). Positive correlations were observed between intact cell counts and cATP levels (Kendall’s tau=0.40, p<0.01), HPC (Kendall’s tau=0.20, p<0.01), and impedance flow cytometry results (Kendall’s tau=0.30, p<0.01). However, 16S rRNA gene sequencing results showed that passage through the ozone contactor significantly changed the microbial community (p<0.05), supporting the hypothesis that regrowth was occurring in the later chambers of the contactor. This study demonstrates the utility of direct, near real-time microbial analysis for monitoring full-scale disinfection systems. ![Figure][1] Highlights ### Competing Interest Statement Sysmex America provided fluorescent stains free of charge. Sysmex did not contribute to the data analysis or writing of this manuscript. Interpretation of results and opinions do not necessarily reflect those of Sysmex or OnCyt. The authors declare no other competing financial interests or personal relationships that could influence or appear to influence this work. ### Funding Statement Funding was provided by the Blue Sky Initiative (College of Engineering, University of Michigan). K.S.D was supported by a National Science Foundation Graduate Research Fellowship [grant number DGE-1256260] and a University of Michigan Rackham Predoctoral Fellowship. This research was supported by work performed by The University of Michigan Microbiome Core. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Raw sequencing data are available from the NCBI Sequence Read Archive (SRA) at BioProject ID PRJNA954821. [1]: pending:yes