Polycyclic aromatic compounds (PACs) in the Canadian environment: A review of sampling techniques, strategies and instrumentation
Bibliographic record
Abstract
A wide variety of sampling techniques and strategies are needed to analyze polycyclic aromatic compounds (PACs) and interpret their distributions in various environmental media (i.e., air, water, snow, soils, sediments, peat and biological material). In this review, we provide a summary of commonly employed sampling methods and strategies, as well as a discussion of routine and innovative approaches used to quantify and characterize PACs in frequently targeted environmental samples, with specific examples and applications in Canadian investigations. The pros and cons of different analytical techniques, including gas chromatography – flame ionization detection (GC-FID), GC low-resolution mass spectrometry (GC-LRMS), high performance liquid chromatography (HPLC) with ultraviolet, fluorescence or MS detection, GC high-resolution MS (GC-HRMS) and compound-specific stable (δ13C, δ2H) and radiocarbon (Δ14C) isotope analysis are considered. Using as an example research carried out in Canada’s Athabasca oil sands region (AOSR), where alkylated polycyclic aromatic hydrocarbons and sulfur-containing dibenzothiophenes are frequently targeted, the need to move beyond the standard list of sixteen EPA priority PAHs and for adoption of an AOSR bitumen PAC reference standard are highlighted.
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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 | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".