Dataset Information

Monitoring of Nitrification in Chloraminated Drinking Water Distribution Systems With Microbiome Bioindicators Using Supervised Machine Learning.

ABSTRACT: 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).

SUBMITTER: Gomez-Alvarez V

PROVIDER: S-EPMC7526508 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Monitoring of Nitrification in Chloraminated Drinking Water Distribution Systems With Microbiome Bioindicators Using Supervised Machine Learning.

Gomez-Alvarez Vicente V Revetta Randy P RP

Frontiers in microbiology 20200916

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 DWD ...[more]

PMID: 33042076

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Project description:Water-based opportunistic pathogens (OPs) are a leading cause of drinking-water-related disease outbreaks, especially in developed countries such as the United States (US). Physicochemical water quality parameters, especially disinfectant residuals, control the (re)growth, presence, colonization, and concentrations of OPs in drinking water distribution systems (DWDSs), while the relationship between OPs and those parameters remain unclear. This study aimed to quantify how physicochemical parameters, mainly monochloramine residual concentration, hydraulic residence time (HRT), and seasonality, affected the occurrence and concentrations of four common OPs (Legionella, Mycobacterium, Pseudomonas, and Vermamoeba vermiformis) in four full-scale DWDSs in the US. Legionella as a dominant OP occurred in 93.8% of the 64 sampling events and had a mean density of 4.27 × 105 genome copies per liter. Legionella positively correlated with Mycobacterium, Pseudomonas, and total bacteria. Multiple regression with data from the four DWDSs showed that Legionella had significant correlations with total chlorine residual level, free ammonia concentration, and trihalomethane concentration. Therefore, Legionella is a promising indicator of water-based OPs, reflecting microbial water quality in chloraminated DWDSs. The OP concentrations had strong seasonal variations and peaked in winter and/or spring possibly because of reduced water usage (i.e., increased water stagnation or HRT) during cold seasons. The OP concentrations generally increased with HRT presumably because of disinfectant residual decay, indicating the importance of well-maintaining disinfectant residuals in DWDSs for OP control. The concentrations of Mycobacterium, Pseudomonas, and V. vermiformis were significantly associated with total chlorine residual concentration, free ammonia concentration, and pH and trihalomethane concentration, respectively. Overall, this study demonstrates how the significant spatiotemporal variations of OP concentrations in chloraminated DWDSs correlated with critical physicochemical water quality parameters such as disinfectant residual levels. This work also indicates that Legionella is a promising indicator of OPs and microbial water quality in chloraminated DWDSs.

Project description:Ammonia availability due to chloramination can promote the growth of nitrifying organisms, which can deplete chloramine residuals and result in operational problems for drinking water utilities. In this study, we used a metagenomic approach to determine the identity and functional potential of microorganisms involved in nitrogen biotransformation within chloraminated drinking water reservoirs. Spatial changes in the nitrogen species included an increase in nitrate concentrations accompanied by a decrease in ammonium concentrations with increasing distance from the site of chloramination. This nitrifying activity was likely driven by canonical ammonia-oxidizing bacteria (i.e., Nitrosomonas) and nitrite-oxidizing bacteria (i.e., Nitrospira) as well as by complete-ammonia-oxidizing (i.e., comammox) Nitrospira-like bacteria. Functional annotation was used to evaluate genes associated with nitrogen metabolism, and the community gene catalogue contained mostly genes involved in nitrification, nitrate and nitrite reduction, and nitric oxide reduction. Furthermore, we assembled 47 high-quality metagenome-assembled genomes (MAGs) representing a highly diverse assemblage of bacteria. Of these, five MAGs showed high coverage across all samples, which included two Nitrosomonas, Nitrospira, Sphingomonas, and Rhizobiales-like MAGs. Systematic genome-level analyses of these MAGs in relation to nitrogen metabolism suggest that under ammonia-limited conditions, nitrate may be also reduced back to ammonia for assimilation. Alternatively, nitrate may be reduced to nitric oxide and may potentially play a role in regulating biofilm formation. Overall, this study provides insight into the microbial communities and their nitrogen metabolism and, together with the water chemistry data, improves our understanding of nitrogen biotransformation in chloraminated drinking water distribution systems.IMPORTANCE Chloramines are often used as a secondary disinfectant when free chlorine residuals are difficult to maintain. However, chloramination is often associated with the undesirable effect of nitrification, which results in operational problems for many drinking water utilities. The introduction of ammonia during chloramination provides a potential source of nitrogen either through the addition of excess ammonia or through chloramine decay. This promotes the growth of nitrifying microorganisms and provides a nitrogen source (i.e., nitrate) for the growth for other organisms. While the roles of canonical ammonia-oxidizing and nitrite-oxidizing bacteria in chloraminated drinking water systems have been extensively investigated, those studies have largely adopted a targeted gene-centered approach. Further, little is known about the potential long-term cooccurrence of complete-ammonia-oxidizing (i.e., comammox) bacteria and the potential metabolic synergies of nitrifying organisms with their heterotrophic counterparts that are capable of denitrification and nitrogen assimilation. This study leveraged data obtained for genome-resolved metagenomics over a time series to show that while nitrifying bacteria are dominant and likely to play a major role in nitrification, their cooccurrence with heterotrophic organisms suggests that nitric oxide production and nitrate reduction to ammonia may also occur in chloraminated drinking water systems.

Dataset Information

Monitoring of Nitrification in Chloraminated Drinking Water Distribution Systems With Microbiome Bioindicators Using Supervised Machine Learning.

Publications

Monitoring of Nitrification in Chloraminated Drinking Water Distribution Systems With Microbiome Bioindicators Using Supervised Machine Learning.

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