Heavy-Duty Diesel Truck In-Use NO<sub>x</sub> Emissions Evaluation Using On-Board Sensors
Notice bibliographique
Résumé
<div class="section abstract"><div class="htmlview paragraph">Governments and regulatory agencies in North America are evaluating the nitrogen oxides (NO<sub>x</sub>) 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.</div><div class="htmlview paragraph">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 NO<sub>x</sub> emissions calculations can be successfully applied to in-use truck data sets. However, the use of the on-board vehicle NO<sub>x</sub> 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 NO<sub>x</sub> 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 NO<sub>x</sub> 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.</div><div class="htmlview paragraph">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.</div></div>
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».