The Application of Cyclic Ion Mobility to Non- targeted Analysis of Per- and Polyfluoroalkyl substances (PFAS) in Environmental Samples

Significance of the Topic

Per- and polyfluoroalkyl substances (PFAS) persist in the environment due to strong C–F bonds and pose health and regulatory challenges. Emerging PFAS variants and byproducts of remediation require robust analytical strategies for both known and novel compounds.

Objectives and Study Overview

This study aimed to implement a non-targeted analysis (NTA) workflow combining liquid chromatography, high-resolution mass spectrometry (HRMS), and cyclic ion mobility spectrometry (IMS) to:

• Identify legacy and emerging PFAS in environmental water samples
• Discover previously unreported PFAS structures
• Evaluate the benefits of IMS separation and collision cross section (CCS) metrics

Instrumentation Used

The analytical platform comprised:

• ACQUITY UPLC I-Class PLUS with PFAS solution kit to minimize background contamination
• Atlantis Premier BEH C18 AX mixed-mode columns for enhanced PFAS retention
• SELECT SERIES™ Cyclic™ IMS mass spectrometer operating in negative electrospray ionization
• Data acquisition in HDMSE mode (m/z 50–1200) and software control via MassLynx™ and UNIFI™ on waters_connect™

Methodology

Environmental surface water, influent, and effluent wastewater were prepared per EPA Method 1633. Chromatographic separation used a water/ammonium acetate to methanol/ammonium hydroxide gradient. Ion mobility parameters were tuned for short-drift-time halogenated compounds. Data processing involved automated 4D peak picking, high/low collision energy alignment, and suspect screening against an HRMS PFAS library and theoretical databases.

Main Results and Discussion

• Known PFAS (PFCAs, PFSAs, FTS) were identified with ppm-level mass accuracy, retention time error <0.2 min, diagnostic fragments, and CCS deviation <1%.
• IMS alignment improved spectral clarity, isolating co-eluting fragments by drift time.
• Extended screening revealed five additional PFAS not in EPA 1633 target lists (e.g., FBSA, diPAPs).
• A drift-time vs. m/z filter reduced unidentified features (>10 000) to <300 candidates per injection, enriching polyfluorinated ions.
• Tentative identification of N-methyl perfluorobutane sulfonamidoacetic acid (MeFBSAA) and homologues was supported by homologue series CCS trends and fragmentation evidence.

Benefits and Practical Applications

• Automated workflows in waters_connect™ streamline target and non-target PFAS screening
• IMS-derived CCS adds a reliable orthogonal identification metric
• Drift-time filtering accelerates discovery of low-abundance PFAS in complex matrices

Future Trends and Potential Applications

• Integration of machine learning to predict PFAS CCS and fragmentation patterns
• Expansion of suspect libraries with computationally derived structures
• Application to other halogenated pollutants and biotransformation products
• Miniaturization of IMS-MS for in-field PFAS monitoring

Conclusion

Combining LC-IMS-HRMS with advanced data processing delivers a powerful NTA strategy for PFAS, enhancing confidence in known analyte identification and enabling the discovery of emerging and unreported substances. CCS-drift-time filtering offers a targeted approach to mine complex environmental samples and supports regulatory and remediation efforts.

References

1. Schymanski et al., Environ. Sci. Technol., 2023.
2. US EPA CompTox Dashboard, 2022.
3. US EPA Method 1633, 2024.
4. ASTM D8421-22, 2022.
5. Liu et al., Trends Anal. Chem., 2019.
6. McCullagh et al., Waters Application Note, 2014.
7. Dodds et al., Anal. Chem., 2020.
8. Organtini et al., Waters Application Note, 2023.
9. Organtini et al., Waters Application Note, 2023.
10. Twohig et al., Waters Application Note, 2021.
11. Charbonnet et al., Environ. Sci. Technol. Lett., 2022.
12. Mullin et al., Anal. Chim. Acta, 2020.
13. Foster et al., Environ. Sci. Technol., 2022.
14. MacNeil et al., Anal. Chem., 2022.
15. Huset et al., Chemosphere, 2011.
16. Newton et al., Environ. Sci. Technol., 2017.