Post-remediation evaluation of a LNAPL site using electrical resistivity imaging

Define the problem BEFORE you try to solve it

At complex sites, remediation performance often hinges on one question: do we truly understand how contaminants are distributed in the subsurface?

In this peer-reviewed article, Aestus’ CTO Dr. Todd Halihan and others examine how subsurface characterization methods influence our understanding of contaminant distribution and transport behavior. The paper evaluates how conventional site investigation approaches can underestimate spatial complexity, particularly in settings where permeability contrasts, hydraulic connectivity, and stratigraphic variability control contaminant migration. It emphasizes the importance of resolving subsurface structural controls rather than relying primarily on interpolated concentration contours derived from discrete sampling locations. For stakeholders working in environmental remediation and site characterization, the implications remain highly relevant.

Electrical resistivity imaging (ERI) techniques have produced an improved capability to map contaminants (especially NAPLs) compared to traditional monitoring wells/borings.  The ERI data (confirmed by drilling):

• Demonstrated that LNAPL contaminants were present outside of a delineated and remediated area in Golden, Oklahoma.
• Identified LNAPL that existed between monitoring and remediation wells which had reported low contaminant levels when sampled.
• Provided the drilling location with the highest concentration of hydrocarbon ever found on the site, even after two phases of remediation work had been performed – see the below graphic where the ERI detected two electrically resistive anomalies (red/orange-colored) that were subsequently sampled and confirmed to have high TPH concentrations while samples outside of those anomalies were less impacted.

The takeaway: Current methods of post-remediation site characterization are inadequate for complete site characterization.  Sparse borings and monitoring wells provide important point data — but they can miss the structural complexity and connectivity that is foundational to building a defensible CSM.  When the CSM is oversimplified, remediation strategies may target the wrong zones, underestimate contaminants, or misjudge migration pathways.

This perspective aligns closely with how Aestus approaches subsurface imaging and data integration. The goal is not to add complexity, but to reduce uncertainty by identifying the features that materially influence contaminant behavior. When the CSM is properly defined, decision risk declines and remediation strategies become more reliable.