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October 19, 2025Environmental Science & Technology

Discovery of Comprehensive Sets of Chemical Constituents as Markers of PFAS Sources through a Nontarget Screening and Machine Learning Approach

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Authors

NJNayantara T. JosephBDBoris DrozTSTrever Schwichtenberg

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Overview

Nontarget analysis improves classification of six PFAS sources, highlighting novel markers and implications.

Key Points

  • Improved source classification of PFAS using an integrated nontarget analysis and machine learning approach.
  • Analysis revealed 21,815 chemical features from one acquisition mode and 114,660 from another, enhancing identification.
  • Inclusion of diverse non-PFAS markers significantly boosted classification performance compared to PFAS-only classifiers.
  • This framework offers critical tools for environmental monitoring and remediation of PFAS contamination.

Cite This Study

Joseph et al. (2025) studied this question.

synapsesocial.com/papers/68f43f03854d1061a58ac2c8https://doi.org/10.1021/acs.est.5c07560
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