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RationaleSilicone wristbands have emerged as valuable passive samplers for monitoring of personal exposure to environmental contaminants in the rapidly developing field ofexposomics. Once deployed, silicone wristbands collect and hold a wealth of chemical information that can be interrogated using high‐resolution mass spectrometry (HRMS) to provide a broad coverage of chemical mixtures. MethodsGas chromatography coupled to Orbitrap™ mass spectrometry (GC/Orbitrap™ MS) was used to simultaneously perform suspect screening (using in‐house database) and unknown screening (using vendor databases) of extracts from wristbands worn by volunteers. The goal of this study was to optimize a workflow that allows detection of low levels of priority pollutants, with high reliability. In this regard, a data processing workflow for GC/Orbitrap™ MS was developed using a mixture of 123 environmentally relevant standards consisting of pesticides, flame retardants, organophosphate esters, and polycyclic aromatic hydrocarbons as test compounds. ResultsThe optimized unknown screening workflow using a search index threshold of 750 resulted in positive identification of 70 analytes in validation samples, and a reduction in the number of false positives by over 50%. An average of 26 compounds with high confidence identification, 7 level 1 compounds and 19 level 2 compounds, were observed in worn wristbands. The data were further analyzed via suspect screening and retrospective suspect screening to identify an additional 36 compounds. ConclusionsThis study provides three important findings: (1) a clear evidence of the importance of sample cleanup in addressing complex sample matrices for unknown analysis, (2) a valuable workflow for the identification of unknown contaminants in silicone wristband samplers using electron ionization HRMS data, and (3) a novel application of GC/Orbitrap™ MS for the unknown analysis of organic contaminants that can be used in exposomics studies.
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This content will become publicly available on February 1, 2026
Non-targeted analysis and suspect screening of organic contaminants in temperate snowfall using liquid chromatography high-resolution mass spectrometry
Contaminants released into the atmosphere that undergo regional and long-range transport can deposit back to Earth through snowfall. When snow melts, these contaminants re-enter the environment, sometimes far from their original emission sources. Here we present the first comprehensive characterization of organic contaminants in snow from North America. Fresh snowfall samples were collected in the central United States over a three-year period and measured by liquid chromatography high-resolution mass spectrometry for suspect screening and non-targeted analysis. The resulting data set was screened against experimental MS/MS libraries and underwent supplemental in silico MS/MS analysis. In total, 91 possible compounds were tentatively identified in snow, and 17 were successfully confirmed and semi-quantified with reference standards. These contaminants were mostly anthropogenic in origin and included six herbicides, three insect repellants, one insecticide metabolite, and one fungicide. The most prominent compounds present in all samples were N-cyclohexylformamide (known contaminant in tire leachate), DEET (insect repellent), and dimethyl phthalate (plasticizer), with median deposition fluxes of 4032, 284, and 262 ng m-2, respectively. Three additional compounds were detected in 100% of samples: coumarin (phytochemical and fragrance additive), 5-methylbenzotriazole (antifreeze component), and quinoline (heterocyclic aromatic). The Peto-Peto test revealed statistically significant differences in deposition fluxes for these six contaminants (p < 0.05), with weak but statistically significant positive associations between coumarin and DEET and between coumarin and quinoline according to a Kendall’s tau correlation analysis. These findings demonstrate the utility of in silico analysis to complement MS/MS matching with experimental databases. Even so, thousands of unidentified features remained in the data set, highlighting the limitations of current strategies in non-targeted analysis of environmental samples.
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- PAR ID:
- 10596580
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Environmental Research
- Volume:
- 266
- Issue:
- C
- ISSN:
- 0013-9351
- Page Range / eLocation ID:
- 120494
- Subject(s) / Keyword(s):
- non-targeted analysis suspect screening snow high-resolution mass spectrometry environmental contaminants pesticides SIRIUS
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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