S P Keely1,2, N E Brinkman1,2, B D Zimmerman1,3, D Wendell3, K M Ekeren4, S K De Long4, S Sharvelle4, J L Garland1. 1. National Exposure Research Laboratory, United States Environmental Protection Agency, Cincinnati, OH, USA. 2. McMicken College of Arts and Sciences, Department of Biological Sciences, University of Cincinnati, Cincinnati, OH, USA. 3. Department of Energy, Environmental, Biological & Medical Engineering, University of Cincinnati, Cincinnati, OH, USA. 4. Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, USA.
Abstract
AIMS: Development of efficacious grey water (GW) treatment systems would benefit from detailed knowledge of the bacterial composition of GW. Thus, the aim of this study was to characterize the bacterial composition from (i) various points throughout a GW recycling system that collects shower and sink handwash (SH) water into an equalization tank (ET) prior to treatment and (ii) laundry (LA) water effluent of a commercial-scale washer. METHODS AND RESULTS: Bacterial composition was analysed by high-throughput pyrosequencing of the 16S rRNA gene. LA was dominated by skin-associated bacteria, with Corynebacterium, Staphylococcus, Micrococcus, Propionibacterium and Lactobacillus collectively accounting for nearly 50% of the total sequences. SH contained a more evenly distributed community than LA, with some overlap (e.g. Propionibacterium), but also contained distinct genera common to wastewater infrastructure (e.g. Zoogloea). The ET contained many of these same wastewater infrastructure-associated bacteria, but was dominated by genera adapted for anaerobic conditions. CONCLUSIONS: The data indicate that a relatively consistent set of skin-associated genera are the dominant human-associated bacteria in GW, but infrastructure-associated bacteria from the GW collection system and ET used for transient storage will be the most common bacteria entering GW treatment and reuse systems. SIGNIFICANCE AND IMPACT OF THE STUDY: This study is the first to use high-throughput sequencing to identify the bacterial composition of various GW sources. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
AIMS: Development of efficacious grey water (GW) treatment systems would benefit from detailed knowledge of the bacterial composition of GW. Thus, the aim of this study was to characterize the bacterial composition from (i) various points throughout a GW recycling system that collects shower and sink handwash (SH) water into an equalization tank (ET) prior to treatment and (ii) laundry (LA) water effluent of a commercial-scale washer. METHODS AND RESULTS: Bacterial composition was analysed by high-throughput pyrosequencing of the 16S rRNA gene. LA was dominated by skin-associated bacteria, with Corynebacterium, Staphylococcus, Micrococcus, Propionibacterium and Lactobacillus collectively accounting for nearly 50% of the total sequences. SH contained a more evenly distributed community than LA, with some overlap (e.g. Propionibacterium), but also contained distinct genera common to wastewater infrastructure (e.g. Zoogloea). The ET contained many of these same wastewater infrastructure-associated bacteria, but was dominated by genera adapted for anaerobic conditions. CONCLUSIONS: The data indicate that a relatively consistent set of skin-associated genera are the dominant human-associated bacteria in GW, but infrastructure-associated bacteria from the GW collection system and ET used for transient storage will be the most common bacteria entering GW treatment and reuse systems. SIGNIFICANCE AND IMPACT OF THE STUDY: This study is the first to use high-throughput sequencing to identify the bacterial composition of various GW sources. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
Entities:
Keywords:
bacteria; bioinformatics; grey water; infrastructure; metagenomics; water quality; water reuse
Authors: Kaitlin J Mattos; Laura Eichelberger; John Warren; Aaron Dotson; Millie Hawley; Karl G Linden Journal: Environ Eng Sci Date: 2021-05-24 Impact factor: 1.907