Two-layer homolog network approach for PFAS nontarget screening and retrospective data mining

Area of Science:

• Environmental Chemistry
• Analytical Chemistry
• Chemical Analysis

Background:

• The proliferation of novel per- and polyfluoroalkyl substances (PFAS) presents significant environmental and health concerns.
• Accurate identification and quantification of these emerging contaminants are hindered by analytical challenges.

Purpose of the Study:

• To develop and validate a novel two-layer homolog network approach for the non-target screening and identification of PFAS using mass spectrometry.
• To assess the presence and diversity of PFAS, including previously unreported compounds, in consumer products and industrial samples.

Main Methods:

• A two-layer network strategy was implemented: the first layer connects chemical homologs, and the second layer links different PFAS classes.
• Mass spectrometry was employed for the non-target screening of PFAS in waterproof products and industrial sludges.
• Retrospective analysis of historical samples and retrieval from a public database (MassIVE) were conducted to uncover novel PFAS.

Main Results:

• The homolog network approach effectively filtered out 94% of false positive candidates during PFAS screening.
• A total of 94 PFAS were identified in twelve waterproof products and two industrial sludge samples.
• Thirty-six novel PFAS were detected for the first time, alongside fifteen novel PFAS in samples dating back to 2005.

Conclusions:

• The developed network approach significantly enhances the identification of PFAS, including novel compounds.
• Novel PFAS have a historic and global presence, underscoring the need for proactive environmental management and policy development.
• This research provides critical data for understanding the environmental fate and impact of emerging PFAS contaminants.