Monitoring biofilm function in new and matured full-scale slow sand filters using flow cytometric histogram image comparison (CHIC)

Abstract

While slow sand filters (SSFs) have produced drinking water for more than a hundred years, understanding of their associated microbial communities is limited. In this study, bacteria in influent and effluent water from full-scale SSFs were explored using flow cytometry (FCM) with cytometric histogram image comparison (CHIC) analysis; and routine microbial counts for heterotrophs, total coliforms and Escherichia coli. To assess if FCM can monitor biofilm function, SSFs differing in age and sand composition were compared. FCM profiles from two established filters were indistinguishable. To examine biofilm in the deep sand bed, SSFs were monitored during a scraping event, when the top layer of sand and the schmutzdecke are removed to restore flow through the filter. The performance of an established SSF was stable: total organic carbon (TOC), pH, numbers of heterotrophs, coliforms, E. coli, and FCM bacterial profile were unaffected by scraping. However, the performance of two newly-built SSFs containing new and mixed sand was compromised: breakthrough of both microbial indicators and TOC occurred following scraping. The compromised performance of the new SSFs was reflected in distinct effluent bacterial communities; and, the presence of microbial indicators correlated to influent bacterial communities. This demonstrated that FCM can monitor SSF performance. Removal of the top layer of sand did not alter the effluent water from the established SSF, but did affect that of the SSFs containing new sand. This suggests that the impact of the surface biofilm on effluent water is greater when the deep sand bed biofilm is not established.

Mapping Dynamics of Bacterial Communities in a Full-Scale Drinking Water Distribution System Using Flow Cytometry

“…FCM fingerprints, including ratios of LNA and HNA bacteria, were compared and analyzed using the single cell analysis software FlowJo (Treestar, Inc., San Carlos, CA, USA). CHIC analysis (CHIC: Cytometric Histogram Image Comparison) was used for pattern analyses using R packages flowCHIC and flowCore [23][24][25]. FCM scatterplots were converted into 300 × 300 pixel images with 64-channel gray scale resolution for image comparison.…”

“…FCM scatterplots were converted into 300 × 300 pixel images with 64-channel gray scale resolution for image comparison. The values generated by CHIC describing the differences between water samples were visualized using a non-metric multidimensional scale (NMDS) plot based on Bray-Curtis dissimilarity to capture changes in bacterial community structure [23,24]. To simplify correlations, the envfit function for environmental vectors was applied using R software [26].…”

Coupling Riverbank Filtration with Reverse Osmosis May Favor Short Distances between Wells and Riverbanks at RBF Sites on the River Danube in Hungary

“…In order to assess microbiological activity and the biomass, other emerging methods are available such as ATP or RNA analysis, measurement of certain enzymes, etc. [18]. Due to the low number of bacteria in the river water, only less than 1 log removal values could be observed during the time of the experiment.…”

Molecular Analyses of Fecal Bacteria and Hydrodynamic Modeling for Microbial Risk Assessment of a Drinking Water Source

“…in the infiltrated water. Biofils consisting of bacterial communities also residing in sand filters and might be a source of contamination depending on their composition of microorganisms [48]. Other studies have detected Salmonella in treated effluent wastewater [49].…”