Development of gas chromatographic and mass spectrometric techniques for the analysis of Polycyclic Aromatic Compounds (PACs) in environmental samples
Bibliographic record
Abstract
Concerns emanating from the presence of polycyclic aromatic compounds (PACs) in the environment especially as a yardstick for overall environmental health have been a challenge for several decades. Polycyclic aromatic compounds are a complex and structurally diverse class of compounds many of which can exist as isomers. Approaches to measure these compounds in environmental samples have relied on techniques based on gas chromatography coupled to mass spectrometry (GC/MS) at unit mass resolution. For example, the quantitation of the 16 US Environmental Protection Agency priority polycyclic aromatic hydrocarbons (PAHs) uses GC/MS and selected ion monitoring (SIM) of diagnostic m/z values of individual PAHs. Although this approach works well for the 16 priority PAHs, for more complex PACs, GC/MS in SIM mode is largely unreliable. Reasons for this include low selectivity of the SIM mode, insufficient chromatographic and mass resolution and the lack of commercially available authentic analytical standards. The overarching hypothesis of my thesis, therefore, is that more sophisticated chromatographic and MS techniques will provide more accurate measurements of PACs in the environment. The first advancement I made to the field of PAC research was the development of a one-dimensional GC coupled to MS method in the multiple reaction monitoring (MRM) to measure a suite of PACs. The method I developed was fully validated according to the EURACHEM guide - The Fitness for Purpose of Analytical methods and represents a significant improvement in the current SIM approach to quantitation of PACs in environmental samples. The second major advancement I made to the field of PAC research was the validation of a comprehensive 2D GC (GC×GC) high resolution time-of-flight (HR-TOF) MS method for the separation and quantitation of PACs. In this study I was able to exploit the increased peak capacity of the GC×GC system (relative to 1D) and the specificity of the HR-TOF/MS to quantify individual isomers of PAC compounds that were not attempted before. The methods I developed were paramount to the success of 2 other studies: (i) the GC/MS MRM method was used to understand the kinetics of PAH absorption to a highly sorptive medium that is used by industry to remediate the aquatic environment during/after an oil-spill and, (ii) the GC×GC-HR-TOF/MS was used in the detection of novel halogenated PACs in biological samples from the Alberta Oil Sands Region. The results of my work will have an impact and influence on future studies on source apportionment and chemical fingerprinting of crude-oil and will support much needed toxicological and monitoring studies of individual PAC isomers in the environment.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".