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. 2024 Feb 6;58(5):2458-2467.
doi: 10.1021/acs.est.3c07220. Epub 2024 Jan 25.

Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification

Mélanie Z Lauria  1 , Helen Sepman  1   2 , Thomas Ledbetter  1   2 , Merle Plassmann  1 , Anna M Roos  3 , Malene Simon  4 , Jonathan P Benskin  1 , Anneli Kruve  1   2
Affiliations

Closing the Organofluorine Mass Balance in Marine Mammals Using Suspect Screening and Machine Learning-Based Quantification

Mélanie Z Lauria et al. Environ Sci Technol. .

Abstract

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening has identified a growing number of novel per- and polyfluoroalkyl substances (PFASs) in the environment. However, without analytical standards, the fraction of overall PFAS exposure accounted for by these suspects remains ambiguous. Fortunately, recent developments in ionization efficiency (IE) prediction using machine learning offer the possibility to quantify suspects lacking analytical standards. In the present work, a gradient boosted tree-based model for predicting log IE in negative mode was trained and then validated using 33 PFAS standards. The root-mean-square errors were 0.79 (for the entire test set) and 0.29 (for the 7 PFASs in the test set) log IE units. Thereafter, the model was applied to samples of liver from pilot whales (n = 5; East Greenland) and white beaked dolphins (n = 5, West Greenland; n = 3, Sweden) which contained a significant fraction (up to 70%) of unidentified organofluorine and 35 unquantified suspect PFASs (confidence level 2-4). IE-based quantification reduced the fraction of unidentified extractable organofluorine to 0-27%, demonstrating the utility of the method for closing the fluorine mass balance in the absence of analytical standards.

Keywords: Combustion ion chromatography; cetaceans; dolphins; high resolution mass spectrometry; ionization efficiency-based quantification; suspect screening.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Workflow for obtaining predicted concentrations for suspects detected in liver samples using predicted log IE values. The 33 target PFASs were used as calibrants for converting the predicted log IE of the suspect chemicals to the predicted response factor, which was used in concentration estimations.
Figure 2
Figure 2
Fluorine mass balance for dolphins from Sweden (SD), pilot whales from East Greenland (PW), and dolphins from West Greenland (GD) in decreasing order of EOF concentrations from top to bottom. Model error is not plotted here, error bars are based on relative standard deviation observed for triplicate measurements of GD3 (for dolphins) and PW-3 (for pilot whales), and the standard deviations of ∑PFAS and ∑Suspects are combined. GD1 and -5 have EOF < LOQ; therefore, the LOQ value (29.3) was plotted here.
Figure 3
Figure 3
Training (dark blue) and test (orange) sets of the ESI negative mode prediction model for A) non-PFAS chemicals and B) PFASs. The correlation plots for experimental log IE values for PFASs compared to C) predicted log IE values using a model without added PFASs and D) predicted log IE values obtained from the leave-one-out approach. The correlation between spiked concentrations and E) concentration estimations using the response factor of a homologue chemical that is one −CF2– unit smaller (light blue) or larger (dark blue) compared to the respective suspect and F) concentration estimations using predicted ionization efficiency for the same set of chemicals with a leave-one-out approach.
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