Kristi Diller is geologist with over 12 years of environmental consulting experience focused on site characterization and remediation. She specializes in complex site assessments, remedy selection, remedial design, and remedial actions at industrial and Department of Defense sites for a range of media (groundwater, soil, soil gas) and contaminants (chlorinated solvents, petroleum, metals, per- and polyfluoroalkyl substances, etc.). Ms. Diller earned a B.S. in Geological Sciences from the University of Idaho and a M.S. in Geological Sciences from Arizona State University. She is a registered geologist in the States of California and Oregon.
A Dynamic and Data-Driven Approach to an Expanded Site Inspection for PFAS
An expanded site inspection (ESI) for per- and polyfluoroalkyl substances (PFAS) was conducted at a site where a previous site inspection had documented the presence of PFAS in groundwater, soil, surface water, and sediment. The focus of the ESI was at six areas of concern (AOCs) where eight carbon chain based Aqueous Film Forming Foam (AFFF) may have been used or spilled during historical firefighting operations. The primary objectives of the ESI were to assess potential for PFAS migration pathways between the AOCs and downgradient receptors and to determine if there are upgradient sources that may be contributing PFAS mass to groundwater and surface water near the site. The ESI was designed to augment the data collected during the previous site inspection. The ESI investigation followed a phased approach wherein key source area data was collected and submitted for a short laboratory turnaround time. Field data were collected using electronic forms on tablet computers connected to project databases which allowed for real-time quality assurance and data interpretation. Field and analytical data were reviewed, combined with historic datasets, and interpreted in real time allowing a dynamic approach to the investigation. Rapid decision-making regarding where to collect additional contingency step-out samples produced a streamlined dataset optimized to meet project objectives. Advanced data analytical techniques, including PFAS proportional analysis, were applied to reassess and revise the underlying decision-making model, which is used to set priorities for further actions.