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A Fuel-Based Inventory for Heavy-Duty Diesel Truck Emissions

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  • Dreher, David B.
  • Harley, Robert A.

Abstract

A fuel-based method for estimating heavy-duty diesel truck emissions is described. In this method, emission factors are normalized to fuel consumption; vehicle activity is measured by the amount of diesel fuel consumed. For the San Francisco Bay Area during summer 1996, heavy-duty diesel trucks were estimated to emit 100⋅103 kg/day of NOx and 6.4⋅103 kg/day of exhaust PM. These values were 2.1 and 1.8 times, respectively, the corresponding values predicted by California’s motor vehicle emission inventory model, MVEI 7G. Significant decreases in diesel truck activity and emissions, 70-80% below typical weekday levels, were observed in the Bay Area on weekends. Reductions in diesel NOx and black carbon (BC) particle emissions on weekends may contribute to higher ambient ozone concentrations and higher OC/BC ratios observed on weekends. Heavy-duty truck traffic peaks on weekdays during the middle of the day and falls off before the afternoon rush hour. Therefore, the diurnal pattern of heavy-duty truck travel may contribute to increases in ambient OC/BC ratios observed during late afternoon hours.

Suggested Citation

  • Dreher, David B. & Harley, Robert A., 1998. "A Fuel-Based Inventory for Heavy-Duty Diesel Truck Emissions," University of California Transportation Center, Working Papers qt46t948fp, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt46t948fp
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    Cited by:

    1. Sathaye, Nakul & Harley, Robert & Madanat, Samer, 2010. "Unintended environmental impacts of nighttime freight logistics activities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 642-659, October.
    2. Lam, Hon Loong & Klemeš, Jiří Jaromír & Kravanja, Zdravko, 2011. "Model-size reduction techniques for large-scale biomass production and supply networks," Energy, Elsevier, vol. 36(8), pages 4599-4608.
    3. Feng, Yanbiao & Dong, Zuomin, 2019. "Optimal control of natural gas compression engine hybrid electric mining trucks for balanced fuel efficiency and overall emission improvement," Energy, Elsevier, vol. 189(C).
    4. Shi V. Liu & Fu-Lin Chen & Jianping Xue, 2017. "Evaluation of Traffic Density Parameters as an Indicator of Vehicle Emission-Related Near-Road Air Pollution: A Case Study with NEXUS Measurement Data on Black Carbon," IJERPH, MDPI, vol. 14(12), pages 1-11, December.
    5. Barth, Matthew & Younglove, Theodore & Scora, George, 2005. "Development of a Heavy-Duty Diesel Modal Emissions and Fuel Consumption Model," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt67f0v3zf, Institute of Transportation Studies, UC Berkeley.
    6. W. Scott Wayne & Jairo A. Sandoval & Nigel N. Clark, 2009. "Emissions Benefits from Alternative Fuels and Advanced Technology in the U.S. Transit Bus Fleet," Energy & Environment, , vol. 20(4), pages 497-515, August.

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    Keywords

    Social and Behavioral Sciences;

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