« Previous AbstractDetection of volatile organic compounds released by wood furniture based on a cataluminescence test system    Next Abstract"Microbial inhibitors of the fungus Pseudogymnoascus destructans, the causal agent of white-nose syndrome in bats" »

Water Res

Title:		"Monitoring, assessment, and prediction of microbial shifts in coupled catalysis and biodegradation of 1,4-dioxane and co-contaminants"
Author(s):		Miao Y; Johnson NW; Phan T; Heck K; Gedalanga PB; Zheng X; Adamson D; Newell C; Wong MS; Mahendra S;
Address:		"Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, United States. Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, 77005, United States. Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, United States; Department of Public Health, California State University, Fullerton, CA, 92834, United States. Department of Statistics, University of California, Los Angeles, CA, 90095, United States. GSI Environmental Inc., Houston, TX, 77098, United States. Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, United States. Electronic address: mahendra@seas.ucla.edu"
Journal Title:		Water Res
Year:		2020
Volume:		20200125
Issue:
Page Number:		115540 -
DOI:		10.1016/j.watres.2020.115540
ISSN/ISBN:		1879-2448 (Electronic) 0043-1354 (Linking)
Abstract:		"Microbial community dynamics were characterized following combined catalysis and biodegradation treatment trains for mixtures of 1,4-dioxane and chlorinated volatile organic compounds (CVOCs) in laboratory microcosms. Although a few specific bacterial taxa are capable of removing 1,4-dioxane and individual CVOCs, many microorganisms are inhibited when these contaminants are present in mixtures. Chemical catalysis by tungstated zirconia (WO(x)/ZrO(2)) and hydrogen peroxide (H(2)O(2)) as a non-selective treatment was designed to achieve nearly 20% 1,4-dioxane and over 60% trichloroethene and 50% dichloroethene removals. Post-catalysis, bioaugmentation with 1,4-dioxane metabolizing bacterial strain,Pseudonocardia dioxanivorans CB1190, removed the remaining 1,4-dioxane. The evolution of the microbial community under different conditions was time-dependent but relatively independent of the concentrations of contaminants. The compositions of microbiomes tended to be similar regardless of complex contaminant mixtures during the biodegradation phase, indicating a r-K strategy transition attributed to the shock experienced during catalysis and the subsequent incubation. The originally dominant genera Pseudomonas and Ralstonia were sensitive to catalytic oxidation, and were overwhelmed by Sphingomonas, Rhodococcus, and other catalyst-tolerant microbes, but microbes capable of biodegradation of organics thrived during the incubation. Methane metabolism, chloroalkane-, and chloroalkene degradation pathways appeared to be responsible for CVOC degradation, based on the identifications of haloacetate dehalogenases, 2-haloacid dehalogenases, and cytochrome P450 family. Network analysis highlighted the potential interspecies competition or commensalism, and dynamics of microbiomes during the biodegradation phase that were in line with shifting predominant genera, confirming the deterministic processes guiding the microbial assembly. Collectively, this study demonstrated that catalysis followed by bioaugmentation is an effective treatment for 1,4-dioxane in the presence of high CVOC concentrations, and it enhanced our understanding of microbial ecological impacts resulting from abiotic-biological treatment trains. These results will be valuable for predicting treatment synergies that lead to cost savings and improve remedial outcomes in short-term active remediation as well as long-term changes to the environmental microbial communities"
Keywords:		"Biodegradation, Environmental Catalysis Dioxanes *Hydrogen Peroxide *Water Pollutants, Chemical Bioremediation Deterministic process Machine learning Metagenome Predictive functions Synergistic treatments;"
Notes:		"MedlineMiao, Yu Johnson, Nicholas W Phan, Thien Heck, Kimberly Gedalanga, Phillip B Zheng, Xiaoru Adamson, David Newell, Charles Wong, Michael S Mahendra, Shaily eng England 2020/02/06 Water Res. 2020 Apr 15; 173:115540. doi: 10.1016/j.watres.2020.115540. Epub 2020 Jan 25"