Emission rates of regulated pollutants from on-road heavy-duty diesel vehicles

Shah, Sandip D.; Johnson, Kent C.; Miller, John W.; Cocker, David R.

doi:10.1016/j.atmosenv.2005.09.033

Cited by 38 publications

(31 citation statements)

References 9 publications

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“…This implies that, compared to cruising on freeways, HDDVs driving at low speeds and frequent acceleration in congested traffic conditions may result in higher NO x emissions, which is similar to the result by other researchers (17).…”

Section: Modal Average Emission Rates Estimatessupporting

confidence: 87%

Link-Based Emission Factors for Heavy-Duty Diesel Trucks Based on Real-World Data

Frey

Rouphail

Zhai

2008

Transportation Research Record: Journal of the Transportation R

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Heavy-duty diesel vehicles contribute a substantial fraction of nitrogen oxides and particulate matter to 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 emissions approach is developed from a database gathered via a portable emissions measurement system for single rear axle and tandem dump trucks. Second-by-second real-world truck speed profiles on links are analyzed based on observed patterns of time distributions of speedacceleration 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 presented. Single rear axle trucks have lower emission rates than tandems for CO 2 , PM, NO and HC, but higher 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 rates approach is recommended to couple heavy-duty vehicle emission inventory estimation with transportation demand models.Frey, Rouphail and Zhai 2 INTRODUCTION Heavy-duty diesel vehicles (HDDVs) contribute a substantial fraction of nitrogen oxides (NO x ), and particulate matter (PM) released to the atmosphere (1). In 2002, heavy-duty diesel vehicles accounted for approximately 46% of NO x and 54% of PM 10 of the nationwide on-road vehicle emission inventory (2). Therefore, close attention should be paid to HDDV emissions characteristics and mitigation as they relate to vehicle duty cycles.Emission measurement methods for HDDVs typically include engine and chassis dynamometer tests, tunnel studies, and remote sensing (3-6). Many engine dynamometer test cycles are based upon steady-state modal profiles that are not likely to be representative of real world vehicle activity patterns. Chassis dynamometer tests are expensive and there are few such dynamometers. Tunnel studies are limited in their ability to discriminate among specific vehicle types. With remote sensing, each measurement is only a snap shot of the vehicle activity at a single location, and thus does not characterize the entire duty cycle (7). The U.S. Environmental Protection Agency (EPA) has developed an on-road transportable diesel emissions characterization facility for measuring real-world emissions of heavy-duty combination trucks (8). Portable emissions measurement system (PEMS) and transportabl...

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Section: Modal Average Emission Rates Estimatessupporting

confidence: 87%

Link-Based Emission Factors for Heavy-Duty Diesel Trucks Based on Real-World Data

Frey

Rouphail

Zhai

2008

Transportation Research Record: Journal of the Transportation R

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“…The variability of emissions on vehicle speed and engine load has been found to be higher when emissions are expressed by distance travelled as compared to fuel consumed. A similar dependency of the effects of driving conditions (categorized for example by idling, acceleration and cruising modes) on vehicle emissions has been found in a more limited number of on-road measurement studies (Tong et al, 2000;Shah et al, 2006;Durbin et al, 2008).…”

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confidence: 67%

Comparison of emissions from on-road sources using a mobile laboratory under various driving and operational sampling modes

et al. 2009

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Abstract. Mobile sources produce a significant fraction of the total anthropogenic emissions burden in large cities and have harmful effects on air quality at multiple spatial scales. Mobile emissions are intrinsically difficult to estimate due to the large number of parameters affecting the emissions variability within and across vehicles types. The MCMA-2003 Campaign in Mexico City has showed the utility of using a mobile laboratory to sample and characterize specific classes of motor vehicles to better quantify their emissions characteristics as a function of their driving cycles. The technique clearly identifies "high emitter" vehicles via individual exhaust plumes, and also provides fleet average emission rates. We have applied this technique to Mexicali during the Border Ozone Reduction and Air Quality Improvement Program (BORAQIP) for the Mexicali-Imperial Valley in 2005. We analyze the variability of measured emission ratios for emitted NO x , CO, specific VOCs, NH 3 , and some primary fine particle components and properties by deploying a mobile laboratory in roadside stationary sampling, chase and fleet average operational sampling modes. The measurements reflect various driving modes characteristic of the urban fleets. The observed variability for all measured gases and particle emission ratios is greater for the chase and roadside stationary sampling than for fleet average measurements. The fleet Correspondence to: M. Zavala (miguelz@mce2.org) average sampling mode captured the effects of traffic conditions on the measured on-road emission ratios, allowing the use of fuel-based emission ratios to assess the validity of traditional "bottom-up" emissions inventories. Using the measured on-road emission ratios, we estimate CO and NO x mobile emissions of 175±62 and 10.4±1.3 metric tons/day, respectively, for the gasoline vehicle fleet in Mexicali. Comparisons with similar on-road emissions data from Mexico City indicated that fleet average NO emission ratios were around 20% higher in Mexicali than in Mexico City whereas HCHO and NH 3 emission ratios were higher by a factor of 2 in Mexico City than in Mexicali. Acetaldehyde emission ratios did not differ significantly whereas selected aromatics VOCs emissions were similar or smaller in Mexicali. Nitrogen oxides emissions for on-road heavy-duty diesel truck (HDDT) were measured near Austin, Texas, as well as in both Mexican cities, with NO y emission ratios in Austin < Mexico City < Mexicali.

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“…As shown in Table 1, the EFs of PM 2.5 in the WZS tunnel was 70.1 ± 15.9 mg·veh −1 ·km −1 , whereas it was 22.2 ± 6.52 mg·veh −1 ·km −1 in the KXL tunnel. ; It is known that, the PM 2.5 EFs of vehicles could be influenced by many factors, such as vehicle type (Cheng et al, 2010;He et al, 2008), fuel quality (Yue et al, 2015), model year of the vehicles (Liu et al, 2009;Shah et al, 2006), vehicle speed (Gillies et al, 2001), and the features of the road (Pio et al, 2013). EFs of PM 2.5 in this study were compared with those from other tunnel studies in China (Table 1).…”

Section: Emission Factors Of Pm 25 and Comparison With That From Othmentioning

confidence: 99%

Chemical composition of PM 2.5 from two tunnels with different vehicular fleet characteristics

Cui

Chen

Tian

et al. 2016

Science of The Total Environment

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• Composition profiles of PM 2.5 were obtained in smaller city, Yantai.• Characteristics of PM 2.5 were changed by emission standards and fuel policy.• Source profiles of PM 2.5 for gasoline vehicles and diesel vehicles were given.• The diesel vehicles play a major role in PM 2.5 emissions in Yantai. The chemical compositions of PM 2.5 including OC, EC, water soluble ions, elements, and organic components such as polycyclic aromatic hydrocarbons (PAHs), hopanes, and steranes, emitted in Wuzushan (WZS) and Kuixinglou (KXL) tunnels were determined. WZS tunnel is a major route for diesel vehicles traveling, while KXL tunnel has limited to diesel vehicles. The results showed that the proportions of the different constituents of PM 2.5 in the Wuzushan (WZS) tunnel were OC (27.7%), EC (32.1%), elements (13.9%), and water soluble ions (9.2%). Whereas the chemical profile of PM 2.5 in the Kuixinglou (KXL) tunnel was OC (17.7%), EC (10.4%), elements (8.90%), and water soluble ions (8.87%). The emission factors (EFs) of PM 2.5 and proportions of SO 4 2− and Pb were decreased by vehicle emission standards and fuel quality policy in China, and the higher molecular weight PAHs (4 + 5 + 6 rings) were more abundant than the lower molecular weight PAHs (2 + 3 rings) in the two tunnels. The proportions of 17A(H)-21B(H)-30-Norhopane and 17A(H)-21B(H)-Hopane in the hopane and sterane were not dependent on the vehicles types. In addition, specific composition profiles for PM 2.5 from gasoline-fueled vehicles (GV) G R A P H I C A L A B S T R A C T a b s t r a c t a r t i c l e i n f o Contents lists available at ScienceDirectScience of the Total Environment j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / s c i t o t e n v and diesel-fueled vehicles (DV) emissions were drafted, which indicated that OC (0.974 mg·veh −1 ·km −1 ) was the most abundant component in PM 2.5 , followed by Fe, Cl − , and Mg for GV. The relative proportions of the different constituents in the PM 2.5 for DV were EC (35.9%), OC (27.2%), elements (12.8%), and water soluble ions (11.7%). Both the PM 2.5 EFs and EC proportions in DV were higher than those in GV, and the HMW PAHs were the dominant PAHs for both GV and DV. The PM 2.5 emissions from the vehicles in Yantai were 581 ± 513 tons to 1353 ± 1197 tons for GV, and 19,627 ± 2477 tons to 23,042 ± 2887 tons for DV, respectively.

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Emission rates of regulated pollutants from on-road heavy-duty diesel vehicles

Cited by 38 publications

References 9 publications

Link-Based Emission Factors for Heavy-Duty Diesel Trucks Based on Real-World Data

Link-Based Emission Factors for Heavy-Duty Diesel Trucks Based on Real-World Data

Comparison of emissions from on-road sources using a mobile laboratory under various driving and operational sampling modes

Chemical composition of PM 2.5 from two tunnels with different vehicular fleet characteristics

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