Mapping the geospatial distribution of atmospheric BTEX compounds using portable mass spectrometry and adaptive whole air sampling
Bibliographic record
Abstract
Atmospheric environmental monitoring with mobile laboratories is becoming more common as instrumentation evolves and the benefits of taking the lab-to-sample are realized. One of the benefits of this approach is the ability to screen a geographic area for compounds of interest and to use ‘real-time’ data to inform adaptive sampling. We report on the use of a membrane introduction mass spectrometer (MIMS) for continuous monitoring of atmospheric volatile organic compounds (VOCs) associated with hydrocarbon upgrading and refining facilities in northern Alberta, Canada. Field campaigns involved continuous ambient-air sampling from a moving vehicle (collected at ~ 1Hz). Real-time MIMS data were used to monitor chemical concentrations of benzene, toluene, and ethylbenzene/xylene/s (BTEX) and to prompt collection whole air sample (WAS) canisters for laboratory-based, trace-level VOC speciation and quantitation. The MIMS data showed a high degree of spatiotemporal variability, which allowed for near real-time feedback to guide otherwise subjective or random collection of whole air samples. Laboratory based comparisons using lab constructed air samples showed the percent difference in quantitation between MIMS and WAS to be within 20% across targeted analytes in the low ppbv concentration range.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".