The overall objective of this work was to develop a comprehensive remediation performance and cost database using results from numerous actual remediation projects. The project sought to expand the breadth and depth of the remediation performance and cost database compiled as part of a previous SERDP project (ER-1292) to provide a more powerful and reliable dataset. Several characteristics of remediation projects were evaluated to provide insights into factors that may affect remediation outcomes. In addition, several key focus areas were studied to provide insights on sustained treatment vs. rebound, performance of “treatment trains,” and performance at “remediation done right” sites as described in the peer-reviewed literature.
The project resulted in a performance database of 235 remediation projects. The dataset suggests that concentration reductions of 0.5 to 2.0 orders of magnitude are typical when using the most common in-situ remedial technologies for groundwater treatment of chlorinated solvents.
The DoD and private sector have invested billions in environmental restoration, with thousands of sites in the United States requiring some type of groundwater remediation. In the process of remediating these sites, large amounts of monitoring data are collected, including prior to the start of clean-up, during the active remediation phase, and after remediation efforts have been completed. To make this large investment in groundwater remediation technologies more effective, end-users need quantitative, accurate, and reliable performance and cost data for commonly used remediation technologies. While the data from an individual site are valuable in guiding site-specific decisions, the real value for the remediation community as a whole is in compiling and analyzing data from a range of sites to provide insight on the overall performance of technologies
The project consisted of two primary components:
The final products of this project include numerous charts and graphics that are intended to help inform the remedial decision-making process at sites, as well as an electronic Decision Support System that allows the user to select various site parameters and remedial technologies to see the actual remediation performance data for sites with the selected characteristics. In no case is the dataset intended to replace a thorough technology screening, design, and/or feasibility or pilot testing. Furthermore, the dataset is not intended to predict precisely what remediation outcome might be achieved at a specific site, but rather to provide a range of expectations based on levels of performance that were achieved at other sites with similar characteristics.
The dataset developed under this project is expected to have a tiered relevance as part of the remedial decision-making process, where the data will be very useful for technology screening, supportive for the conceptual design, and less useful at the detailed design stage. For sites that are already undergoing active remediation, the project team envisions that the dataset could be particularly useful for transition assessments at complex sites and for Five-Year Reviews at federal cleanup sites