Spatially Resolved Source Apportionment of Industrial VOCs Using a Mobile Monitoring Platform
Bibliographic record
Abstract
Industrial emissions of volatile organic compounds (VOCs) directly impact air quality downwind of facilities and contribute to regional ozone and secondary organic aerosol production. Positive matrix factorization (PMF) is often used to apportion VOCs to their respective sources using measurement data collected at fixed sites, for example air quality monitoring stations. Here, we apply PMF analysis to high time-resolution VOC measurement data collected both while stationary and while moving using a mobile monitoring platform. The stationary monitoring periods facilitated the extraction of representative industrial VOC source profiles while the mobile monitoring periods were critical for the spatial identification of VOC hotspots. Data were collected over five days in a heavily industrialized region of southwestern Ontario containing several refineries, petrochemical production facilities and a chemical waste disposal facility. Factors associated with petroleum, chemical waste and rubber production were identified and ambient mixing ratios of selected aromatic, unsaturated and oxygenated VOCs were apportioned to local and background sources. Fugitive emissions of benzene, highly localized and predominantly associated with storage, were found to be the dominant local contributor to ambient benzene mixing ratios measured while mobile. Toluene and substituted aromatics were predominantly associated with refining and traffic, while methyl ethyl ketone was linked to chemical waste handling. The approach described here facilitates the apportionment of VOCs to their respective local industrial sources at high spatial and temporal resolution. This information can be used to identify problematic source locations and to inform VOC emission abatement strategies.
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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".