Factorization methods applied to characterize the sources of volatile organic compounds in Montreal, Quebec
Bibliographic record
Abstract
OBJECTIVES: The study objective was to assemble emission characteristics of the sources of the ambient volatile organic compounds (VOCs) and to elaborate methods of organizing them into the sources' chemical profiles. MATERIAL AND METHODS: The UNMIX--sensor modeling method from the U.S. Environment Protection Agency (EPA)--was used to process the VOC concentration data acquired over the years 2000-2009 for 175 VOC species in 4 air quality monitoring stations in Montreal, Quebec. RESULTS: The method enabled to assess VOC emissions from the typically distributed sources existing in urban environment and VOC occurrences characterizing the local, or point-like, sources. The distributed sources were inextricably associated with hydrocarbons from exhaust, heavier hydrocarbons from contaminated urban soil, fugitive evaporations of gasoline and liquefied petroleum gases (LPG), leakage from the industrial and commercial use of solvents, and the inert, ozone depleting gases permeating urban atmosphere. The sources' profiles were charted involving 60-120 VOC species per source. Spatial distribution of the sources was examined. CONCLUSIONS: The UNMIX application and the source profiling methods, by building robust chemical profiles of VOC sources, provided information that can be used to assign the measured VOC emissions to physical sources. This, in turn, provides means of assessing the impact of environmental policies, on one hand, and of industrial activities on the other hand on VOC air pollution.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".