Identification of Chain Scission Products Released to Water by Plastic Exposed to Ultraviolet Light

Buoyant plastic in the marine environment is exposed to sunlight, oxidants, and physical stress, which may lead to degradation of the plastic polymer and the release of compounds that are potentially hazardous. We report the development of a laboratory protocol that simulates the exposure of plastic floating in the marine environment to ultraviolet light (UV) and nontarget analysis to identify degradation products of plastic polymers in water. Plastic pellets [polyethylene, polypropylene, polystyrene, and poly­(ethylene terephthalate)] suspended in water were exposed to a UV light source for 5 days. Organic chemicals in the water were concentrated by solid phase extraction and then analyzed by ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry using a nontarget approach with a C18 LC column coupled to a Q Exactive Orbitrap HF mass spectrometer. We designed a data analysis scheme to identify chemicals that are likely chain scission products from degradation of the plastic polymers. For all four polymers, we found homologous series of low-molecular weight polymer fragments with oxidized end groups. In total, we tentatively identified 22 degradation products, which are mainly dicarboxylic acids

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

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

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

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