Performance and Durability Evaluation of Continuously Regenerating Particulate Filters on Diesel Powered Urban Buses at NY City Transit
Bibliographic record
Abstract
Particulate emission from diesel engines is one of the most important pollutants in urban areas. As a result, particulate emission control from urban bus diesel engines using particle filter technology is being evaluated at several locations in the US. A project entitled “Clean Diesel Demonstration Program” has been initiated by NY City Transit under the supervision of NY State DEC and with active participation from several industrial partners. Under this program, several NY City transit buses with DDC Series 50 engines have been equipped with continuously regenerating diesel particulate filter system and are operating with ultra low sulfur diesel (< 30 ppm S) in transit service in Manhattan since February 2000. These buses are being evaluated over a 8-9 month period for operations, maintainability and durability of the particulate filter. In addition, an extensive emissions testing program is being carried out under transient cycles on a chassis dyno to evaluate the emissions reductions obtained with the particle filter. The on-road operational data over eight months showed stable exhaust back pressure with the particle filters, indicating successful filter regeneration. No adverse operational or maintenance issues were observed which can be attributed to the filter system. The emissions results from the first phase of testing exhibited >90% reductions in CO, HC and PM with the particulate filter. In addition, >99% reductions in Carbonyls and up to 80% PAH and 94% NO2-PAH destructions were also achieved. In this paper, the operational and emissions testing data from the NY City Clean Diesel Demonstration Program are discussed in detail.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".