Complementary Nontargeted and Targeted Mass Spectrometry Techniques to Determine Bioaccumulation of Halogenated Contaminants in Freshwater Species
Bibliographic record
Abstract
Assessing the toxicological significance of complex environmental mixtures is challenging due to the large number of unidentified contaminants. Nontargeted analytical techniques may serve to identify bioaccumulative contaminants within complex contaminant mixtures without the use of analytical standards. This study exposed three freshwater organisms (Lumbriculus variegatus, Hexagenia spp., and Pimephales promelas) to a highly contaminated soil collected from a recycling plant fire site. Biota extracts were analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) and mass defect filtering to identify bioaccumulative halogenated contaminants. Specific bioaccumulative isomers were identified by comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry (GCxGC-HRToF). Targeted analysis of mixed brominated/chlorinated dibenzo-p-dioxins and dibenzofurans (PXDD/PXDFs, X = Br and Cl) was performed by atmospheric pressure gas chromatography tandem mass spectrometry (APGC-MS/MS). Relative sediment and biota instrument responses were used to estimate biota-sediment accumulation factors (BSAFs). Bioaccumulating contaminants varied among species and included polychlorinated naphthalenes (PCNs), polychlorinated dibenzofurans (PCDFs), chlorinated and mixed brominated/chlorinated anthracenes/phenanthrenes, and pyrenes/fluoranthenes (Cl-PAHs and X-PAHs, X = Br and Cl), as well as PXDD/PXDFs. Bioaccumulation potential among isomers also varied. This study demonstrates how complementary high-resolution mass spectrometry techniques identify persistent and bioaccumulative contaminants (and specific isomers) of environmental concern.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".