Comparison of Particle-Associated Bacteria from a Drinking Water Treatment Plant and Distribution Reservoirs with Different Water Sources

Abstract

This study assessed the characteristics of and changes in the suspended particles and the associated bacteria in an unchlorinated drinking water distribution system and its reservoirs with different water sources. The results show that particle-associated bacteria (PAB) were present at a level of 0.8–4.5 × 103 cells ml−1 with a biological activity of 0.01–0.04 ng l−1 ATP. Different PAB communities in the waters produced from different sources were revealed by a 16S rRNA-based pyrosequencing analysis. The quantified biomass underestimation due to the multiple cells attached per particle was ≥ 85%. The distribution of the biologically stable water increased the number of cells per particle (from 48 to 90) but had minor effects on the PAB community. Significant changes were observed at the mixing reservoir. Our results show the characteristics of and changes in suspended PAB during distribution, and highlight the significance of suspended PAB in the distribution system, because suspended PAB can lead to a considerable underestimation of biomass, and because they exist as biofilm, which has a greater mobility than pipe-wall biofilm and therefore presents a greater risk, given the higher probability that it will reach the customers’ taps and be ingested.

Long-term spatial and temporal microbial community dynamics in a large-scale drinking water distribution system with multiple disinfectant regimes

“…Here the authors concluded that the observed seasonal changes in richness indicate that the most common and dominant bacterial taxa have temporally defined niches. Ling et al (2016) also suggested that the observed seasonal variation influenced the dynamics of several core populations identified in DWDS biofilms and was main driver in the overall variation in the biofilm community.…”

“…The increase of bacterial richness during distribution may be associated with regrowth, particularly in large distribution systems where bulk water is transported over long distances. Microbial growth in drinking water has been observed in the form of higher particle counts and increased turbidity (Liu et al, 2016), higher cell counts (Hammes et al, 2008) and increase in the presence of indicator organisms in the final tap water compared to the water leaving the treatment plant (van der Wielen et al, 2016). The water leaving the treatment plant may therefore be impacted by the distribution system itself through processes such as pipe corrosion (Sun et al, 2014), the detachment of biofilms (Chaves Simões and Simões, 2013) and suspension of loose deposits (Liu et al, 2013;Liu et al, 2017;Liu et al, 2018).…”

“…Although seasonal changes in DWDS microbial communities have previously been investigated VanBriesen, 2012 and2014;Pinto et al, 2014;Ling et al, 2016;Zlatanovic et al, 2017), long-term, in-depth investigations of spatial and temporal dynamics of DWDS microbial communities are rare. Temporal dynamics of DWDSs cannot be accurately described without an extensive, long-term and high-frequency sampling strategy (Prest et al, 2016b).…”

Deconvolution models for a better understanding of natural microbial communities enumerated by flow‐cytometry

“…In particular, there is a call for rapid detection of phenotypically diverse aquatic microbes, comprising small protists, hetero and autotrophic prokaryotes, and viruses, all showing a vast range of different sizes and morphologies (Fig. This can lead to a dramatic underestimation of the true microbial cell number in complex environmental settings (e.g., waters rich in particulate matter and microbial aggregates), bringing to misleading understanding of the system composition (12,13). The ecological interactions among microorganisms (e.g., predation, competition for resources, mutualism, and parasitism) and their metabolic performances may modulate their growth at very short space and time scales.…”

“…In general, the problem is overcome by a strong simplification of the system, considering each detected particle as a single event, independently by its size and actual composition. This can lead to a dramatic underestimation of the true microbial cell number in complex environmental settings (e.g., waters rich in particulate matter and microbial aggregates), bringing to misleading understanding of the system composition (12,13). In other cases, complex gating supported by epifluorescence microscopy was implemented to recognize specific morphologies like microcolonies or single cells (7), and the number of clustered cells (at least for size classes of aggregates) (13)(14)(15).…”