Abstract

Slow sand filters (SSFs) are widely applied to treat potable water; the removal of contaminants (e.g., particles, organic matter, and microorganism) occurs primarily in the top layer. However, the development of the microbial community and its metabolic function is still poorly understood. In the present study, we analyzed the microbial quantity and community of the influents sampled from the effluent of the last step (rapid sand filtration) and of the top layers of SSFs (Schmutzdecke, 0–2 cm, 4–6 cm, 8–10 cm) sampled near terminal head loss when the Schmutzdecke (SCM) was most developed in two full-scale drinking water treatment plants (DWTPs). The two DWTPs use the same artificially recharged groundwater source. The biomass in the filter, quantified by flow cytometric intact cell counts (ICC) and adenosine triphosphate (ATP), decreased rapidly along the depth till 8–10 cm (>1 log TCC; >75% ATP); the decrease was most pronounced from the SCM to the surface sand layer (0–2 cm), after which the biomass stabilized quickly at lower depths (2–10 cm). Remarkably, beta diversity showed that SSFs layers of the same depth in two DWTPs with distinctive filter age and plant location clustered together, which indicated their insignificant effects in shaping microbial communities in SSFs. The alpha diversity indices followed the trend of the biomass, suggesting more active and diverse communities in SCM layer. PICRUSt-based function prediction revealed significant over-representation of metabolism and degradation of complex organic matters (e.g., butanoate, propanoate, xenobiotic, D-Alanine, chloroalkene, and bisphenol) in SCM layer, the functional importance of which was confirmed by the co-occurrence patterns of the dominant taxa and metabolic functions. Using an island biogeography model, we found that microbial communities in SSFs were strongly assembled by selection (68 OTUs, 50.0% sequences), rather than by simple accumulation of the microbial communities in the influents (120 OTUs, 44.8% sequences). Our findings enhance the understanding of microbial community assembly and of metabolic function in the top layers of SSFs, and constitute a valuable contribution to optimizing the design and operation of biofilters in full-scale DWTPs.

Highlights

  • Efforts have been made to increase understanding of the microbial ecology and biological processes in SSFs

  • The biomass was quantified by adenosine triphosphate (ATP) and intact cell counts (ICC), and 16S rRNA sequencing (Illumina MiSeq) combined with a modified neutral community model (NCM) was performed to investigate the microbial community assembly

  • As quantified by ATP and ICC, the SCM harbored the highest active biomass (1.1e1.9 Â 109 cells gÀ1, 80.1e154.1 ng ATP gÀ1). For both drinking water treatment plants (DWTPs), the biomass distribution showed the same trend along depth; the biomass decreased sharply from the SCM to depth 0e2 cm (D0e2 cm), gradually to D4e6 cm and D8e10 cm

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Summary

Introduction

Efforts have been made to increase understanding of the microbial ecology and biological processes in SSFs. Studies addressing the microbial communities and their metabolic capacities in SSFs are still limited, which is attributed to the limitations on the application of advanced molecular technologies to conventional biological processes in the field and on the accessibility of full-scale filters. Studies of the vertical distribution of the top layers, and multiple-plant studies, which are important for understanding the development of microbial communities in SSFs, are still lacking. Multi-layer sampling from full-scale operational sand filters is still needed to study the development and assembly of microbial communities in SSFs and its metabolic functions. The objective is to investigate the vertical distribution of the quantity and communities of microbes in the top layers, and to explore the assembly of the microbial communities and their metabolic functions. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) based on 16S rRNA sequencing data was used to predict the metabolic functions

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