Target and Suspect Screening Integrated with Machine Learning to Discover Per- and Polyfluoroalkyl Substance Source Fingerprints

Published

September 11, 2023

Author(s)

Nayantara Joseph, Trever Schwichtenberg, Dunping Cao, Gerrad Jones, Alix Rodowa, Joe Charbonnet, Christopher Higgins, Damian Helbling, Jennifer Field

Abstract

This study elucidates per- and polyfluoroalkyl substance (PFAS) fingerprints for specific PFAS source types. Ninety-two samples were collected from aqueous film-forming foam impacted groundwater (AFFF-GW), landfill leachate, biosolids leachate, municipal wastewater treatment plant effluent (WWTP), and wastewater effluent from the pulp and paper and power generation industries. High-resolution mass spectrometry operated with electrospray ionization in negative mode was used to quantify up to 50 target PFASs and screen and semi-quantify up to 2,266 suspect PFASs in each sample. Machine learning classifiers were used to identify PFASs that were diagnostic of each source type. Four C5–C7 perfluoroalkyl acids and one suspect PFAS (trihydrogen-substituted fluoroethernonanoic acid) were diagnostic of AFFF-GW. Two target PFASs (5:3 and 6:2 fluorotelomer carboxylic acids) and two suspect PFASs (4:2 fluorotelomer-thia-acetic acid and N-methylperfluoropropane sulfonamido acetic acid) were diagnostic of landfill leachate. Biosolids leachates were best classified along with landfill leachates and N-methyl and N-ethyl perfluorooctane sulfonamido acetic acid assisted in that classification. WWTP, pulp and paper, and power generation samples contained few target PFASs, but fipronil (a fluorinated insecticide) was diagnostic of WWTP samples. Our results provide PFAS fingerprints for known sources and identify target and suspect PFASs that can be used for source allocation.

Citation

Environmental Science and Technology

Pub Type

Journals

Download Paper

https://doi.org/10.1021/acs.est.3c03770

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Keywords

PFAS forensics source allocation principal component analysis hierarchical clustering support vector classification logistic regression random forest multivariate analyses

Citation

Joseph, N. , Schwichtenberg, T. , Cao, D. , Jones, G. , Rodowa, A. , Charbonnet, J. , Higgins, C. , Helbling, D. and Field, J. (2023), Target and Suspect Screening Integrated with Machine Learning to Discover Per- and Polyfluoroalkyl Substance Source Fingerprints, Environmental Science and Technology, [online], https://doi.org/10.1021/acs.est.3c03770, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936389 (Accessed April 27, 2024)