Link-Based Emission Factors for Heavy-Duty Diesel Trucks Based on Real-World Data
H. Christopher Frey, Nagui M. Rouphail, Haibo Zhai
Abstract
H. Christopher Frey, Nagui M. Rouphail, Haibo Zhai
Abstract
Heavy-duty diesel vehicles contribute a substantial fraction of nitrogen oxides (NO x ) and particulate matter (PM) to the on-road vehicle emission inventory. The objectives of this study are to estimate roadway link-based emission rates for heavy-duty trucks for use in emission inventory estimation and to quantify the impact of factors affecting truck emissions. A speed–acceleration modal emission approach is developed from a database gathered via a portable emission measurement system for single rear-axle and tandem-axle dump trucks. Second-by-second real-world truck speed profiles on links are analyzed on the basis of observed patterns of time distributions of speed–acceleration modes. Link-based emission rates are estimated as the product of the fraction of time spent in each mode and the corresponding modal average emission rate. The sensitivity of link-based emission rates to key factors including chassis type, vehicle load, and fuel type is discussed. Single rear-axle trucks have lower emission rates than do tandem-axle trucks for carbon dioxide, PM, nitric oxide (NO), and hydrocarbons (HC) but higher carbon monoxide (CO) emission rates. Loaded trucks have higher fuel use and emissions than unloaded trucks. Replacing diesel fuel with biodiesel fuel for heavy-duty trucks may reduce tailpipe NO exhaust emissions and will reduce emissions of PM, CO, and HC. However, both fuels generate similar CO 2 emissions. Benchmark comparisons for link-based emission rates show that NO emission rates increase with mean speed. However, link-based CO and HC emission rates were not as sensitive to speed variation as NO emissions. The link-based emission rate approach is recommended to couple heavy-duty vehicle emission inventory estimation with transportation demand models.
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Heavy-duty diesel vehicles contribute a substantial fraction of nitrogen oxides (NO x ) and particulate matter (PM) to the on-road vehicle emission inventory. The objectives of this study are to estimate roadway link-based emission rates for heavy-duty trucks for use in emission inventory estimation and to quantify the impact of factors affecting truck emissions. A speed–acceleration modal emission approach is developed from a database gathered via a portable emission measurement system for single rear-axle and tandem-axle dump trucks. Second-by-second real-world truck speed profiles on links are analyzed on the basis of observed patterns of time distributions of speed–acceleration modes. Link-based emission rates are estimated as the product of the fraction of time spent in each mode and the corresponding modal average emission rate. The sensitivity of link-based emission rates to key factors including chassis type, vehicle load, and fuel type is discussed. Single rear-axle trucks have lower emission rates than do tandem-axle trucks for carbon dioxide, PM, nitric oxide (NO), and hydrocarbons (HC) but higher carbon monoxide (CO) emission rates. Loaded trucks have higher fuel use and emissions than unloaded trucks. Replacing diesel fuel with biodiesel fuel for heavy-duty trucks may reduce tailpipe NO exhaust emissions and will reduce emissions of PM, CO, and HC. However, both fuels generate similar CO 2 emissions. Benchmark comparisons for link-based emission rates show that NO emission rates increase with mean speed. However, link-based CO and HC emission rates were not as sensitive to speed variation as NO emissions. The link-based emission rate approach is recommended to couple heavy-duty vehicle emission inventory estimation with transportation demand models.
Key concepts: Truck, Diesel fuel, Automotive engineering, Environmental science, Axle, Engineering, Mechanical engineering