Dataset Information

Partitioning of Antibiotic Resistance Genes and Fecal Indicators Varies Intra and Inter-Storm during Combined Sewer Overflows.

ABSTRACT: Combined sewer overflows (CSOs) degrade water quality through the release of microbial contaminants in CSO effluent. Improved understanding of the partitioning of microbial contaminants onto settleable particles can provide insight into their fate in end-of-pipe treatment systems or following release during CSO events. Sampling was performed across the hydrograph for three storm events as well as during baseflow and wet weather in three surface waters impacted by CSO. qPCR was performed for select antibiotic resistance genes (ARG) and a marker gene for human fecal indicator organisms (BacHum) in samples processed the partitioning of microbial contaminants on settleable particles versus suspended in the aqueous phase. Amplicon sequencing was performed on both fractions of storm samples to further define the timing and partitioning of microbial contaminants released during CSO events. Samples collected at the CSO outfall exhibited microbial community signatures of wastewater at select time points early or late in the storm events. CSOs were found to be a source of ARG. In surrounding surface waters, sul1 was higher in samples from select locations during wet weather compared to baseflow. Otherwise, ARG concentrations were variable with no differences between baseflow and wet weather conditions. The majority of ARG at the CSO outfall were observed on the attached fraction of samples: 64-79% of sul1 and 59-88% of tet(G). However, the timing of peak ARG and human fecal indicator marker gene BacHum did not necessarily coincide with observation of the microbial signature of wastewater in CSO effluent. Therefore, unit processes that remove settleable particles (e.g., hydrodynamic separators) operated throughout a CSO event would achieve up to (0.5-0.9)-log removal of ARG and fecal indicators by removing the attached fraction of measured genes. Secondary treatment would be required if greater removal of these targets is needed.

SUBMITTER: Eramo A

PROVIDER: S-EPMC5655003 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Partitioning of Antibiotic Resistance Genes and Fecal Indicators Varies Intra and Inter-Storm during Combined Sewer Overflows.

Eramo Alessia A Delos Reyes Hannah H Fahrenfeld Nicole L NL

Frontiers in microbiology 20171020

Combined sewer overflows (CSOs) degrade water quality through the release of microbial contaminants in CSO effluent. Improved understanding of the partitioning of microbial contaminants onto settleable particles can provide insight into their fate in end-of-pipe treatment systems or following release during CSO events. Sampling was performed across the hydrograph for three storm events as well as during baseflow and wet weather in three surface waters impacted by CSO. qPCR was performed for sele ...[more]

PMID: 29104562

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Project description:BackgroundCombined sewer overflows (CSOs) discharge untreated sewage into surface and recreational water, often following heavy precipitation. Given projected increases in frequency and intensity of precipitation due to climate change, it is important to understand the health impacts of CSOs and mediating effects of sewerage systems.ObjectivesIn this study we estimate associations of CSO events and emergency department (ED) visits for gastrointestinal (GI) illness among City of Atlanta, Georgia, residents and explore how these associations vary with sewerage improvements.MethodsWe estimate associations using Poisson generalized linear models, controlling for time trends. We categorized CSOs by overflow volume and assessed effects of CSO events prior to ED visits with 1-, 2- and 3-wk lags. Similarly, we evaluated effects of weekly cumulative precipitation greater than the 90th percentile at the same lags. We also evaluated effect modification by ZIP Code Tabulation Area (ZCTA)-level poverty and infrastructure improvement period using interaction terms.ResultsOccurrence of a large volume CSO in the previous week was associated with a 9% increase in daily ED visits for GI illness. We identified significant interaction by ZCTA-level poverty, with stronger CSO-GI illness associations in low than high poverty areas. Among areas with low poverty, we observed associations at 1-wk and longer lags, following both large and lower volume CSO events. We did not observe significant interaction by infrastructure improvement period for CSO- nor precipitation-GI illness associations; however, the number of CSO events decreased from 2.31 per week before improvements to 0.49 after improvements.DiscussionOur findings suggest that CSOs contribute to acute GI illness burden in Atlanta and that the magnitude of this risk may be higher among populations living in areas of low poverty. We did not find a protective effect of sewerage system improvements. Nonetheless, observed reductions in CSO frequency may lower the absolute burden of GI illness attributable to these events. https://doi.org/10.1289/EHP10399.

Project description:The sewer environment is a potential hotspot for the proliferation of antibiotic resistance genes (ARGs) and other hazardous microbial agents. Understanding the potential for ARG proliferation and retardation and/or accumulation in sewer sediments is of interest for protecting the health of sewage workers and the broader community in the event of sewer overflows as well as for interpreting sewage epidemiology data. To better understand this understudied environment for antibiotic resistance, a field survey was conducted to identify the factors that may control ARGs in sewer sediments and sewage. qPCR was performed for select ARGs and amplicon sequencing was performed for paired samples from combined and separate sanitary sewer systems. Metagenomic sequencing was performed on combined sewer sediments. The relative abundances of sul1, tet(O), tet(W), ermF, and vanA were higher in wastewater compared to sewer sediments, while NDM-1 was greater in sewer sediment and ermF was similar between the two matrices. NDM-1 was observed in sewer sediment but rarely above detection in wastewater in this study. This may indicate that larger/more frequent wastewater samples are needed for detection and/or that retardation and/or accumulation in sewage sediment may need to be considered when interpreting wastewater-based epidemiology data for ARGs. Random forest analyses indicated that season and conductivity were important variables and to a lesser extent so were pH, TSS, heavy metals, and sewer type for explaining the variance of the ARGs. These variables explained the 19-61% of the variance of sul1, tet(O), tet(G), and tet(W) quantified in wastewater. These variables performed less well for explaining the variance in sewer sediments (0.2-24%). Sewer sediment and wastewater had distinct microbial community structures and biomarkers for each are described. Metagenomics indicated that a high diversity of ARGs, including several of medical importance, were observed in the combined sewer sediment. This work provides insight into the complex sewer microbiome and the potential hazard posed by different sewer matrices.

Dataset Information

Partitioning of Antibiotic Resistance Genes and Fecal Indicators Varies Intra and Inter-Storm during Combined Sewer Overflows.

Publications

Partitioning of Antibiotic Resistance Genes and Fecal Indicators Varies Intra and Inter-Storm during Combined Sewer Overflows.

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