Formatted Title
Data Evaluation Framework for Refining PFAS Conceptual Site Models
Background/Objectives
Understanding the chemical patterns of per- and polyfluoroalkyl substances (PFAS) at their source, and along routes of migration for all relevant environmental media, is important for effective characterization, risk assessment, and remediation. However, a standardized approach has yet to be proposed or adopted for how to evaluate and communicate PFAS data when developing conceptual site models (CSMs).
This presentation is based on a manuscript expected to be published by the time of the conference, which will introduce a series of simplifying PFAS chemical metrics that rely on commonly available PFAS data. Combined, these metrics create a PFAS data evaluation framework (PFAS Framework) that incorporates increasing tiers of analysis based on data availability and site complexity.
Approach/Activities
Currently accepted PFAS physical and chemical behaviors from established literature have been incorporated into metrics that can be evaluated geospatially, and/or over time, to build a weight-of-evidence approach to refine PFAS CSMs. Some of the key metrics within the PFAS Framework include:
- Compound ratios (e.g., PFOS/PFHxS, PFOA/PFHxA, PFOS/PFOA, PFHxS/PFOA, etc.).
- Relative percent of perfluoroalkyl acids (PFAAs) that are C6 or less, and their precursors.
- PFAS nominal oxidation state of carbon (NOSC) divided by molecular weight, normalized to all PFAS in a sample.
- Sum of the total molar concentration of PFAS, and as subdivided for each class of PFAAs and their precursors.
- A unique data graphing approach using a PFAS Family Tree is proposed for more consistent interpretation of PFAS source patterns and trends along routes of migration.
Through this structured approach, PFAS sources and differing product patterns can be more confidently identified via multiple lines of evidence.
Results/Lessons Learned
Two case studies from real field sites will be presented to demonstrate how relying on “risk-driving” PFAS alone (e.g., PFOS, PFHxS, PFOA, etc.) can lead to inaccurate conclusions when identifying source zones. One case study will identify a non-AFFF source area downgradient of an AFFF source, that may have been missed using traditional data evaluation approaches. The second case study will identify a 3M LightWater and Ansulite source area, with a second 3M LightWater source downgradient, that may have been missed using traditional data evaluation approaches.