Modeled Effects of Traffic Fleet Composition on the Toxicity of Volatile Organic Compound Emissions
Bibliographic record
Abstract
Motor vehicle emissions include many different compounds that have different levels of toxicity in humans. Volatile organic compounds (VOCs) are components of motor vehicle emissions with varying effects on human health and cancer risks. Proportional emission rates among compounds can vary substantially by vehicle and fuel type. This study addressed the question of how traffic fleet composition affects the toxicity of VOC emissions. Using inhalation unit risk estimates from the U.S. Environmental Protection Agency, VOC risk profiles were quantified for vehicle–fuel type combinations, light-duty and heavy-duty vehicle fleets, and roadway facility types (on-network and off-network). Of 14 modeled VOCs, formaldehyde, benzene, naphthalene, and 1,3-butadiene contribute most to the cumulative risk of vehicle emissions. Formaldehyde and naphthalene are mainly emitted by diesel vehicles; benzene and 1,3-butadiene are mainly emitted by gasoline vehicles. Cumulative VOC risk generated per on-network vehicle mile is four times higher for a gasoline heavy-duty vehicle and eight times higher for a diesel heavy-duty vehicle than for a gasoline light-duty vehicle. Off-network, cumulative VOC risk generated per vehicle is twice as high for gasoline heavy-duty vehicles as for diesel heavy-duty vehicles or gasoline light-duty vehicles. A case study of lane management strategies demonstrated how traffic management can change a VOC emissions risk profile and that changes in VOC emissions risk are different from changes in VOC emissions mass.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".