Rapid and sensitive method for the simultaneous determination of PAHs and alkyl-PAHs in scrubber water using HS-SPME-GC–MS/MS
Bibliographic record
Abstract
Scrubber water, a waste stream generated by ships exhaust gas cleaning systems, may pose a threat when released into the marine environment due to potential contamination with polycyclic aromatic hydrocarbons (PAHs) and their alkyl derivatives (alkyl-PAHs). This study aims to develop a reliable analytical procedure combining headspace solid-phase microextraction (HS-SPME) with gas chromatography coupled to triple quadrupole tandem mass spectrometry (GC–MS/MS) to simultaneously separate and determine target compounds in aqueous samples. Method validation demonstrated good linearity up to 200 ng L −1 (r 2 > 0.996) and low limits of detection (0.33 to 1.67 ng L −1 , except for naphthalene at 3.3 ng L −1 ). The method shows good precision (RSD<20%) and satisfactory analytical recoveries. The methodology was successfully applied to scrubber water samples collected from a container ship and the results highlight the prevalence of naphthalene, phenanthrene, and their alkyl derivatives. • Rapid and reproducible HS-SPME-GC–MS/MS method for the analysis of PAHs and alkyl-PAHs in scrubber water. • The capacity of SPME to analyze both filtered and unfiltered samples was assessed, showing that the more hydrophobic PAHs may be lost during filtration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".