Characterization of scrubber water discharges from ships using comprehensive suspect screening strategies based on GC-APCI-HRMS
Bibliographic record
Abstract
An extended suspect screening approach for the comprehensive chemical characterization of scrubber discharge waters from exhaust gas cleaning systems (EGCSs), used to reduce atmospheric shipping emissions of sulphur oxides, was developed. The suspect screening was based on gas chromatography coupled with high-resolution mass spectrometry (GC-HRMS) and focused on the identification of polycyclic aromatic hydrocarbons (PAHs) and their alkylated derivatives (alkyl-PAHs), which are among the most frequent and potentially toxic organic contaminants detected in these matrices. Although alkyl-PAHs can be even more abundant than parent compounds, information regarding their occurrence in scrubber waters is scarce. For compound identification, an in-house compound database was built, with 26 suspect groups, including 25 parent PAHs and 23 alkyl-PAH homologues. With this approach, 7 PAHs and 12 clusters of alkyl-PAHs were tentatively identified, whose occurrence was finally confirmed by target analysis using GC coupled with tandem mass spectrometry (GC-MS/MS). Finally, a retrospective analysis was performed to identify other relevant (poly)cyclic aromatic compounds (PACs) of potential concern in scrubber waters. According to it, 18 suspect groups were tentatively identified, including biphenyls, dibenzofurans, dibenzothiophenes and oxygenated PAHs derivatives. All these compounds could be used as relevant markers of scrubber water contamination in heavy traffic marine areas and be considered as potential stressors when evaluating scrubber water toxicity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".