Climate and Health Relevant Emissions from in-Use Indian Three-Wheelers Fueled by Natural Gas and Gasoline
Bibliographic record
Abstract
Auto-rickshaws in India use different fuels and engine technologies, with varying emissions and implications for air quality and climate change. Chassis dynamometer emission testing was conducted on 30 in-use auto-rickshaws to quantify the impact of switching from gasoline to compressed natural gas (CNG) in spark-ignition engines. Thirteen test vehicles had two-stroke CNG engines (CNG-2S) and 17 had four-stroke CNG engines (CNG-4S), of which 11 were dual-fuel and operable on a back-up gasoline (petrol) system (PET-4S). Fuel-based emission factors were determined for gaseous pollutants (CO(2), CH(4), NO(X), THC, and CO) and fine particulate matter (PM(2.5)). Intervehicle variability was high, and for most pollutants there was no significant difference (95% confidence level) between "old" (1998-2001) and "new" (2007-2009) age-groups within a given fuel-technology class. Mean fuel-based PM(2.5) emission factor (mean (95% confidence interval)) for CNG-2S (14.2 g kg(-1) (6.2-26.7)) was almost 30 times higher than for CNG-4S (0.5 g kg(-1) (0.3-0.9)) and 12 times higher than for PET-4S (1.2 g kg(-1) (0.8-1.7)). Global warming commitment associated with emissions from CNG-2S was more than twice that from CNG-4S or PET-4S, due mostly to CH(4) emissions. Comprehensive measurements and data should drive policy interventions rather than assumptions about the impacts of clean fuels.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".