Discrimination and geo-spatial mapping of atmospheric VOC sources using full scan direct mass spectral data collected from a moving vehicle
Bibliographic record
Abstract
Volatile and semi-volatile organic compounds (S/VOCs) are ubiquitous in the environment, come from a wide variety of anthropogenic and biogenic sources, and are important determinants of environmental and human health due to their impacts on air quality. They can be continuously measured by direct mass spectrometry techniques without chromatographic separation by membrane introduction mass spectrometry (MIMS) and proton-transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS). We report the operation of these instruments in a moving vehicle, producing full scan mass spectral data to fingerprint ambient S/VOC mixtures with high temporal and spatial resolution. We describe two field campaigns in which chemometric techniques are applied to the full scan MIMS and PTR-ToF-MS data collected with a mobile mass spectrometry lab. Principal Component Analysis (PCA) has been successfully employed in a supervised analysis to discriminate VOC samples collected near known VOC sources including internal combustion engines, sawmill operations, composting facilities, and pulp mills. A Gaussian mixture model and a density-based spatial clustering of application with noise (DBSCAN) algorithm have been used to identify sample clusters within the full time series dataset collected and we present geospatial maps to visualize the distribution of VOC sources measured by PTR-ToF-MS.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".