Dataset Information

Impact of Distribution and Network Flushing on the Drinking Water Microbiome.

ABSTRACT: We sampled the tap water of seven unique, full-scale drinking water distribution systems at different locations as well as the corresponding treatment plant effluents to evaluate the impact of distribution and the potential presence of a core drinking water microbiome. The water was also sampled during network flushing to examine its effect on the microbial ecology. While a core microbiome dominated by Gammaproteobacteria was found using 16S rRNA gene pyrosequencing, an increase in biomass was detected in the networks, especially during flushing. Water age did not significantly impact the microbiology. Irrespective of differences in treatment plants, tap water bacterial communities in the distinct networks converged and highly resembled the flushed water communities. Piping biofilm and sediment communities therefore largely determine the final tap water microbial quality, attenuating the impact of water source and treatment strategy and highlighting the fundamental role of local physicochemical conditions and microbial processes within infrastructure micro-niches.

SUBMITTER: El-Chakhtoura J

PROVIDER: S-EPMC6157312 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Impact of Distribution and Network Flushing on the Drinking Water Microbiome.

El-Chakhtoura Joline J Saikaly Pascal E PE van Loosdrecht Mark C M MCM Vrouwenvelder Johannes S JS

Frontiers in microbiology 20180919

We sampled the tap water of seven unique, full-scale drinking water distribution systems at different locations as well as the corresponding treatment plant effluents to evaluate the impact of distribution and the potential presence of a core drinking water microbiome. The water was also sampled during network flushing to examine its effect on the microbial ecology. While a core microbiome dominated by <i>Gammaproteobacteria</i> was found using 16S rRNA gene pyrosequencing, an increase in biomas ...[more]

PMID: 30283424

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Project description:Biofilms are the dominant form of microbial loading (and organic material) within drinking water distribution systems (DWDS), yet our understanding of DWDS microbiomes is focused on the more easily accessible bulk-water. Disinfectant residuals are commonly provided to manage planktonic microbial activity in DWDS to safeguard water quality and public health, yet the impacts on the biofilm microbiome are largely unknown. We report results from a full-scale DWDS facility used to develop biofilms naturally, under one of three chlorine concentrations: Low, Medium, or High. Increasing the chlorine concentration reduced the bacterial concentration within the biofilms but quantities of fungi were unaffected. The chlorine regime was influential in shaping the community structure and composition of both taxa. There were microbial members common to all biofilms but the abundance of these varied such that at the end of the Growth phase the communities from each regime were distinct. Alpha-, Beta-, and Gamma-proteobacteria were the most abundant bacterial classes; Sordariomycetes, Leotiomycetes, and Microbotryomycetes were the most abundant classes of fungi. Mechanical cleaning was shown to immediately reduce the bacterial and fungal concentrations, followed by a lag effect on the microbiome with continued decreases in quantity and ecological indices after cleaning. However, an established community remained, which recovered such that the microbial compositions at the end of the Re-growth and initial Growth phases were similar. Interestingly, the High-chlorine biofilms showed a significant elevation in bacterial concentrations at the end of the Re-growth (after cleaning) compared the initial Growth, unlike the other regimes. This suggests adaptation to a form a resilient biofilm with potentially equal or greater risks to water quality as the other regimes. Overall, this study provides critical insights into the interaction between chlorine and the microbiome of DWDS biofilms representative of real networks, implications are made for the operation and maintenance of DWDS disinfectant and cleaning strategies.

Project description:Many drinking water utilities in the United States using chloramine as disinfectant treatment in their drinking water distribution systems (DWDS) have experienced nitrification episodes, which detrimentally impact the water quality. Identification of potential predictors of nitrification in DWDS may be used to optimize current nitrification monitoring plans and ultimately helps to safeguard drinking water and public health. In this study, we explored the water microbiome from a chloraminated DWDS simulator operated through successive operational schemes of stable and nitrification events and utilized the 16S rRNA gene dataset to generate high-resolution taxonomic profiles for bioindicator discovery. Analysis of the microbiome revealed both an enrichment and depletion of various bacterial populations associated with nitrification. A supervised machine learning approach (naïve Bayes classifier) trained with bioindicator profiles (membership and structure) were used to classify water samples. Performance of each model was examined using the area under the curve (AUC) from the receiver-operating characteristic (ROC) and precision-recall (PR) curves. The ROC- and PR-AUC gradually increased to 0.778 and 0.775 when genus-level membership (i.e., presence and absence) was used in the model and increased significantly using structure (i.e., distribution) dataset (AUCs = 1.000, p < 0.01). Community structure significantly improved the predictive ability of the model beyond that of membership only regardless of the type of data (sequence- or taxonomy-based model) we used to represent the microbiome. In comparison, an ATP-based model (bulk biomass) generated a lower AUCs of 0.477 and 0.553 (ROC and PR, respectively), which is equivalent to a random classification. A combination of eight bioindicators was able to correctly classify 85% of instances (nitrification or stable events) with an AUC of 0.825 (sensitivity: 0.729, specificity: 0.894) on a full-scale DWDS test set. Abiotic-based model using total Chlorine/NH2Cl and NH3 generated AUCs of 0.740 and 0.861 (ROC and PR, respectively), corresponding to a sensitivity of 0.250 and a specificity of 0.957. The AUCs increased to > 0.946 with the addition of NO2 - concentration, which is indicative of nitrification in the DWDS. This research provides evidence of the feasibility of using bioindicators to predict operational failures in the system (e.g., nitrification).

Dataset Information

Impact of Distribution and Network Flushing on the Drinking Water Microbiome.

Publications

Impact of Distribution and Network Flushing on the Drinking Water Microbiome.

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