Comparison of Exhaust Emissions, Including Toxic Air Contaminants, from School Buses in Compressed Natural Gas, Low Emitting Diesel, and Conventional Diesel Engine Configurations
Bibliographic record
Abstract
In the United States, most school buses are powered by diesel engines. Some have advocated replacing diesel school buses with natural gas school buses, but little research has been conducted to understand the emissions from school bus engines. This work provides a detailed characterization of exhaust emissions from school buses using a diesel engine meeting 1998 emission standards, a low emitting diesel engine with an advanced engine calibration and a catalyzed particulate filter, and a natural gas engine without catalyst. All three bus configurations were tested over the same cycle, test weight, and road load settings. Twenty-one of the 41 “toxic air contaminants” (TACs) listed by the California Air Resources Board (CARB) as being present in diesel exhaust were not found in the exhaust of any of the three bus configurations, even though special sampling provisions were utilized to detect low levels of TACs. Although there were no significant differences between the low emitting diesel and the natural gas bus configurations for 14 TACs, the low emitting diesel bus configuration had significantly different and lower emission levels than the natural gas bus configuration for aldehydes, ketones, and benzene. Overall, the results demonstrate that low-emitting diesel technology had the lowest level of both EPA regulated emissions and TACs.
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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 | 0.000 |
| 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".