Natural attenuation is a preferred remedy for many sites due to its low cost and limited environmental impacts and human interventions as compared to active remediation. 1,4-Dioxane, a carcinogenic solvent stabilizer, is a widespread groundwater contaminant associated with plumes of chlorinated volatile organic compounds (CVOC). However, the physicochemical and biological processes that potentially lead to the natural attenuation of 1,4-dioxane are strongly impacted by microbial functions, geochemical factors (e.g., oxygen, temperature), and co-occurring CVOCs (e.g., 1,1-dichloroethane [DCA], and 1,2-DCA), especially under low 1,4-dioxane conditions. While several laboratory and field studies of 1,4-dioxane degradation by pure cultures and consortia are reported, a microbiome-based framework to quantitatively evaluate and predict natural attenuation of 1,4-dioxane in complex systems has not yet been adequately developed.

Under this study, multiple omics technologies, including metagenomics, metatranscriptomics, and metabolomics, will be applied as well as stable isotope probing (SIP), in natural and engineered experimental systems, to comprehensively characterize indigenous microbiomes, mechanisms, and kinetics specifically responsible for low concentration 1,4-dioxane biodegradation in the presence of CVOC. With the collective microbial information from taxonomic and functional aspects, as well as hydrological and geochemical parameters, machine learning will be used to produce a rational interactive microbiome-contaminant-geochemistry model to predict contaminant fate and transport by microbial features as well as assess the overall ecological status of contaminated sites. The objectives of this proof-of-concept project are to

• determine the biodegradation kinetics and mechanisms of low level 1,4-dioxane natural attenuation in the presence of 1,1-DCA and/or 1,2-DCA;
• identify the microorganisms assimilating 1,4-dioxane and corresponding functions by deoxyribonucleic acid- and ribonucleic acid-based SIP, and multi-omics in laboratory- and field-based efforts; and
• establish a systems approach to validate and predict the natural attenuation as well as ecosystem functioning by microbiome-based machine learning models.

Multiple omics technologies (including metagenomics, metatranscriptomics, and metabolomics) as well as SIP will be combined to explore the molecular mechanisms of indigenous microorganisms catabolizing carbon-13 (13C) labeled 1,4-dioxane. Field studies will use Bio-Traps with ¹³C-1,4-dioxane loaded Bio-Sep beads, which will be deployed in situ in monitoring wells at an impacted site where the 1,4-dioxane concentration is less than 100 μg/L. Following incubation, the Bio-Sep beads will be retrieved and analyzed using the above-mentioned techniques. Groundwater and soil from the same site spiked with ¹³C 1,4-dioxane will be used in bench scale microcosms to explore natural attenuation by indigenous microorganisms. 1,1-DCA and 1,2-DCA will be added as co-occurring chemicals in a range of concentrations. The concentrations of 1,4-dioxane and DCA over time will be measured by gas chromatography-mass spectroscopy, and the biodegradation kinetics of 1,4-dioxane will be determined and normalized to corresponding microbial biomass. Finally, a microbiome-based model informed by the bench- and field-scale studies will be constructed using biostatistics and machine learning approaches to evaluate and predict the viability of natural attenuation of low concentrations of 1,4-dioxane in the presence of CVOCs. The model output would provide action thresholds weighting feasibility for remedial strategy and guide decision making.

This research will support impacted site risk assessments and sustainable groundwater management strategies for large, dilute plumes of CVOC and 1,4-dioxane. This study will identify the key biological agents and functions that specifically degrade 1,4-dioxane, and also advance the understanding of microbial physiological traits, division of labor, and their responses in the context of complex environments. Results will guide optimization of bioaugmentation and biostimulation in support of short- and long-term remedial strategies for mixed chemical plumes. Furthermore, the new-generation, big data-based model will improve the Department of Defense’s ability to address a wider spectrum of chemicals in groundwater plumes, and promote natural and enhanced bioremediation, as well as predict ecosystem functioning during and after the remediation processes. (Anticipated Project Completion - 2024)