Development and Optimization of a Gas Chromatography–Mass Spectrometry Analytical Method for Detecting Sulfolane and Benzene Toluene, Ethylbenzene, and Xylenes in Water Samples
Bibliographic record
Abstract
Sulfolane, an organic solvent widely used in the petrochemical industry, has raised concerns due to its potential health risks and environmental mobility. Toxicological studies suggest that it may negatively affect human and ecological health, highlighting the need for risk assessments. Alongside sulfolane, BTEX compounds (benzene, toluene, ethylbenzene, and xylenes) are commonly present in petrochemical operations, and their migration may be influenced by sulfolane. This study developed a gas chromatography–mass spectrometry (GC-MS) method for simultaneous analyses of sulfolane and BTEX in water. The sample preparation was designed for simplicity to allow for easy implementation without specialized equipment. The method was characterized, validated, and its ruggedness was tested through experimental design. The method was then applied to evaluate the stability of water samples under various storage conditions, and to analyze 97 real water samples collected from a contaminated site in Alberta, Canada. The results identified 17 samples with sulfolane concentrations exceeding the maximum limits for aquatic life preservation, and three samples with detectable toluene levels. These findings highlight the need for further research to better understand contamination profiles and assess associated risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".