Modelling of future year emissions control scenarios for the Lower Fraser Valley: Impacts of natural gas and propane vehicle technologies
Bibliographic record
Abstract
The MC2–CALGRID photochemical modeling system is used to simulate the impact of two fuel substitution scenarios on ozone levels for a future year in the Lower Fraser Valley of British Columbia, Canada. The relative impacts of selected natural gas and propane vehicle technologies are compared for the year 2005. The chosen natural gas technology imposes large reductions in nonmethane hydrocarbon emissions with moderate reductions in nitrogen oxide emissions, while the propane technology greatly lowers nitrogen oxide emissions with only small changes to nonmethane hydrocarbon emissions. \n \nThe model results showed that replacing the entire light-duty gasoline car and truck fleet with the selected natural gas vehicle technology in the year 2005 in the Canadian portion of the Lower Fraser Valley yielded significant benefits in terms of reducing potential exposures to elevated ozone levels in suburban and rural areas. Sites closer to the urban core were less affected. For the propane fuel substitution, benefits were realized in terms of lowering ozone concentrations and ozone exposures in the rural areas. Within the urban and suburban areas, ozone exposures tended to increase. The exposures to peroxyacetyl nitrate were universally smaller in the alternative fuel scenarios. \n \nThe nature of an effective control strategy for the Lower Fraser Valley is discussed, and it is suggested that in addition to the propane fuel substitution, moderate controls on the primary NOx sources in conjunction with moderate nonmethane hydrocarbon controls could be the preferred route to lower ozone exposures.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".