Direct atmospheric pressure chemical ionization-tandem mass spectrometry for the continuous real-time trace analysis of benzene, toluene, ethylbenzene, and xylenes in ambient air
Bibliographic record
Abstract
Real-time monitoring of benzene, toluene, ethylbenzene, and xylenes (BTEX) in ambient air is essential for the early warning detection associated with the release of these hazardous chemicals and in estimating the potential exposure risks to humans and the environment. We have developed a tandem mass spectrometry (MS/MS) method for continuous real-time determination of ambient trace levels of BTEX. The technique is based on the sampling of air via an atmospheric pressure inlet directly into the atmospheric pressure chemical ionization (APCI) source. The method is linear over four orders of magnitude, with correlation coefficients greater than 0.996. Low limits of detection in the range 1-2 microg/m(3) are achieved for BTEX. The reliability of the method was confirmed through the evaluation of quality parameters such as repeatability and reproducibility (relative standard deviation below 8% and 10%, respectively) and accuracy (over 95%). The applicability of this method to real-world samples was evaluated through measurements of BTEX levels in real ambient air samples and results were compared with a reference GC-FID method. This direct APCI-MS/MS method is suitable for real-time analysis of BTEX in ambient air during regulation surveys as well as for the monitoring of industrial processes or emergency situations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".