Formatted Title

How High-Resolution Mass Spectral Tools Can Help with PFAS Forensic Analysis

Background/Objectives

Per- and polyfluoroalkyl substances (PFAS) are widely used for many commercial, and industrial applications, including their use in AFFF by DoD, oil and gas industry, manufacturing facilities, chrome plating operations, etc. Given PFAS are ubiquitously present in the environment, understanding the distinct chemical signatures of different sources is very important. As the terminal transformation products resulting from different PFAS precursors used for different applications are similar, it is very challenging to delineate different sources using routine analytical techniques.  As the number of PFAS contaminated sites identified globally are on the rise, the need for chemical forensic approach to understand the fate and tracking the PFAS sources has grown in the past decade.

Approach/Activities

Using a combination of high-resolution mass spectrometry techniques and machine learning tools, Battelle has developed a chemical forensic technique, PFAS Signature^® for PFAS source differentiation and tracking. PFAS Signature^® is a forensic approach based on the concentration and composition trends of different PFAS from various contamination scenarios to find correlation between PFAS profiles and their sources. The study was designed by collecting samples from different known source scenarios and AFFF materials. All the samples were being analyzed using the high-resolution mass spectrometry techniques, (ultra-performance liquid chromatography coupled to a quadrupole time-of-flight (QTOF) mass spectrometry) in combination with PFAS targeted analysis. A library of suspect list was developed, which is a structure-based library containing over 520 PFAS compounds. Data generated by QTOF is being investigated using both the suspect screening and the non-targeted methods. Multivariate statistical analysis was performed on the filtered data to correlate the PFAS profiles to the respective sources.

Results/Lessons Learned

The results have shown that the high-resolution mass spectral data of different sources shows unique precursor signatures which helps differentiate different sources. Applying PFAS Signature^® approach, we were able to correlate the sources of some of the samples collected from contaminated sites. Statistical analysis of features filtered using mass defect and the suspect list showed good correlation between the both the data. The data collected on a broad range of analytes using a combination of high-resolution mass spectrometry techniques and advanced machine learning tools along with multiple lines of evidence, provide information to identify PFAS source attribution and help manage contaminated sites.