Impact of Solvent Emissions on Reactive Aromatics and Ozone in the Great Lakes Region
Bibliographic record
Abstract
While transportation emissions have declined over the past several decades, volatile organic compound (VOC) emissions from solvent use applications have increased as urban areas expand. In this work, the Canadian air quality model (GEM-MACH-TEB) is used to assess the importance of solvent emissions during the Michigan Ontario Ozone Source Experiment (MOOSE). Model predictions are compared to ozone and total mono-substituted aromatics (TOLU) observations collected in Windsor, Ontario. For summer 2018, model estimates of TOLU from solvent emissions are smaller (30% for an 8 h daytime average) in Windsor than estimates from positive matrix factorization (44% for a 24 h average). The use of updated U.S. solvent emissions from the EPA’s VCPy (Volatile Chemical Product framework) for summer 2021 simulations increases the solvent use source contribution over Detroit/Windsor (30–50% for an 8 h daytime average). This also provides a more uniform spatial distribution across the U.S./Canada border (30–50% for an 8 h daytime average). Long-chain alkanes are the dominant speciation in the model’s air pollutant emission inventory and in the observation-derived solvent use factor. Summertime 8 h daytime ozone decreased by 0.4% over Windsor for a 10% solvent use VOC emission reduction scenario. A 10% mobile NOx emission reduction scenario resulted in a 0.6% O3 decrease over Windsor and more widespread changes over the study region.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".