Our team applied ESS, a technical approach recognized by the U.S. Environmental Protection Agency and the U.S. Air Force as a best practice in developing CSMs for complex contaminated groundwater sites. This approach focuses on the geology and understanding depositional systems that formed the subsurface to accurately define the subsurface heterogeneities and permeability architecture, which is the primary control on fluid migration and contaminant distribution and transport. Equipped with ESS enhanced CSMs, remediation professionals are able to design more effective groundwater treatment systems that achieve project goals in less time, at significantly lower cost.

Our team applied the geology-based ESS approach by examining the existing subsurface data and applying scientifically proven stratigraphic analysis. The analysis incorporated knowledge of the regional geology and depositional environment to identify and correlate genetically related lithology units (or lithofacies), resulting in a more refined understanding of the site’s stratigraphic connectivity.

Detailed geologic cross sections and maps were constructed to define the 3D hydrostratigraphic units that controlled contaminant transport and storage. The ESS framework was then used to evaluate groundwater elevation data and contaminant chemistry data to target the location of additional groundwater extraction wells and maximize the extraction of impacted groundwater and contaminant mass removal.

This approach improved the subsurface CSM, allowing the Air Force to better understand the fate and transport of groundwater contaminants to establish optimum extraction well locations based on the geologic conditions at this site. The ESS analysis identified and mapped an intermediate clay layer that proved to be key in targeting and designing new groundwater extraction well location and screened interval beneath the clay (see Well #1).

The second new extraction well (Well #2) also targeted stream channel features identified by the ESS analysis. The new extraction wells were brought online without increasing the overall capacity of the pump-and-treat system. Thus, a significant increase in contaminant mass removal was achieved without increasing operational costs (see Before ESS and After ESS graphics).

Implementation of the recommendations and enhancements to the 3D fate and transport model resulted in a shorter estimated timeline to site closure. Comparison between the original and optimized performance curves reveals cost avoidance and shows that the system should achieve GCTLs by 2022 — 10 years earlier than the system performance prior to applying ESS. This relatively small investment in analysis and data interpretation helped produce significant project life cycle cost savings, substantial risk reduction and the ability to meet remediation performance goals on a timely basis.