Heavy-Duty Diesel Truck In-Use NO<sub>x</sub> Emissions Evaluation Using On-Board Sensors
Bibliographic record
Abstract
Governments and regulatory agencies in North America are evaluating the nitrogen oxides (NOx) emissions of heavy-duty on-road vehicles to effectively regulate these emissions in order to improve public health and meet air quality requirements. This paper provides results from real-world Class 8 tractor-trailer truck activity and emissions data gathering conducted in the Northeast and Mid-Atlantic United States. Unlike some other areas of the United States (US), there is little available data on in-use operation and emissions performance from heavy-duty trucks in this region where temperatures can be consistently cold in winter. The purposes of this study are to add to the literature on real-world truck operation and emissions in the Northeast and Mid-Atlantic regions; to analyze the captured emissions data using recently established calculation methods implemented by the California Air Resources Board (CARB), which have not yet been applied to data from this region; and to assist air quality regulators in identifying priorities for new heavy-duty engine and vehicle emission standards and test procedures. The Northeast States for Coordinated Air Use Management (NESCAUM) conducted this project jointly with Environment and Climate Change Canada (ECCC). Given the proximity of the truck routes evaluated in this project to the Eastern Canadian provinces, the information is of interest to Canadian regulators as well. The results of this data logging and analysis showed that the CARB’s three-bin moving average window (MAW) and sum-over-sum NOx emissions calculations can be successfully applied to in-use truck data sets. However, the use of the on-board vehicle NOx sensors for data logging limited the amount of time that data were able to be captured at low loads. The active percentage time of sensors by truck was between 43% and 80% in low-load conditions (69% over all trucks). Therefore, low-load data was “backfilled” with estimated emissions values in order to compensate for the time the sensors were inactive. Backfilled data showed that a range of 21% to 67% of the mass of NOx emitted by individual trucks occurred in idle and low-load conditions combined, whereas the original data showed values between 10% and 43%. Median daily backfilled sum-over-sum NOx emissions results by truck specimen ranged from 0.32 g/bhp-hr to 0.75 g/bhp-hr in low-load conditions and from 0.02 g/bhp-hr to 0.16 g/bhp-hr in high-load conditions. As part of the study, data on over 150 vehicle and engine parameters during 100 days of truck operation were collected. This data can be used by regulators and researchers to evaluate truck emissions and operations in the region.
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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 | 0.000 |
| 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) | 0.003 | 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".