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Research

Current Projects

Development of an Integrated Field Test/Modeling Protocol for Efficient In Situ Bioremediation Design and Performance Uncertainty Assessment
Sponsor: Strategic Environmental Research and Development Program (SERDP)
Project ID: ER-2311

Objective
Widespread use of chlorinated solvents, such as tetrachloroethene (PCE) and trichloroethene (TCE), in dry cleaning and degreasing operations has resulted in groundwater contamination at thousands of industrial facilities and government installations throughout the United States and abroad. It is now widely acknowledged that both the quantity and spatial distribution of immobile contaminant mass will ultimately control the long-term performance (i.e., mass removal or transformation rates) of most remedial technologies, particularly those that require the delivery of chemical additives or amendments. Despite significant advances in the understanding of chlorinated solvent source zones and the maturation of several in situ remediation technologies (e.g., bioremediation), the ability to provide a priori predictions of the performance of remediation technologies in the field remains severely limited.

The objective of this project is to develop and demonstrate a remediation design and performance protocol that couples careful characterization of the contaminant source with down-hole treatability testing and mathematical modeling to efficiently assess the suitability of a remediation technology, either alone or in combination, and to estimate remedial performance (e.g., mass removal/destruction) and the uncertainty associated with such predictions. Although the project will utilize microbial reductive dechlorination (i.e., bioaugmentation and/or biostimulation) as a representative in situ remediation technology, the developed protocol and associated modeling tools will be applicable to other remediation technologies, such as monitored natural attenuation and chemical oxidation.

Technical Approach
The approach involves field measurements, laboratory validation, and numerical simulations of a representative TCE-contaminated field site. The project is structured around three phases that combine: (i) source and plume characterization, (ii) upscaled mass transfer and transformation rates, and (iii) field-scale reactivity and predictions of remedial performance. In Phase I, protocols and software tools will be developed to efficiently estimate key source characteristics that govern remediation technology selection, design, and performance. A key goal of this work is to understand and couple the spatial distribution of permeability, contaminant mass, and microbial populations. In Phase II, research activities will focus on laboratory-scale batch and aquifer cell testing to support contaminant source zone characterization, provide kinetic data for testing and refinement of upscaled mathematical models, and guide the design and implementation of field-scale reactivity tests. In Phase III, down-hole treatability (DHT) tests will be conducted at the field site to obtain rate parameters that can be utilized in numerical models to predict remedial performance, and more importantly, to estimate the uncertainty associated with these predictions. Upscaled models and uncertainty analysis will be implemented in widely-used platforms, including MT3DMS and MODFLOW, to facilitate adoption by practitioners and site managers.

Benefits
The protocols and software tools will enable site managers, regulatory officials, and the scientific community to efficiently characterize site conditions, obtain relevant reaction rates and develop upscaled models, and predict remedial performance and associated uncertainty. Although developed for microbial reductive dechlorination, the methodologies and tools will be designed with sufficient flexibility to allow for implementation with other remediation technologies or combinations of remedies. Other types of remedial performance data, such as isotopic analysis and proteomics, also could be incorporated into the upscaled models to allow for refined predictions of remedial performance and optimization of remediation strategies in near real-time. (Anticipated Project Completion - 2017)