Estimation of bus emission models for different fuel types of buses under real conditions

Wang, Chao; Ye, Zhirui; Yu, Yongbo; Gong, Weiping

doi:10.1016/j.scitotenv.2018.05.289

Cited by 52 publications

(14 citation statements)

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“…Jia et al [8] explored the effects of road features and bus lane on bus fuel consumption using second-by-second trajectory data collected from nine bus routes in Shanghai, China. Some researchers also evaluated the effects of fuel types, bus stops, and passenger load on bus emissions using emission data collected by Portable Emissions Measurement System (PEMS) in China [9][10][11]. Furthermore, Li et al [12] calculated the characteristics of bus emissions in Hainan by the COPERT model, which was also calibrated using the data of portable emission measurement system to improve the accuracy of emission predictions.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Urban Bus Emission Characteristics Based on Localized MOVES Using Sparse GPS Data in Shanghai, China

et al. 2019

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Bus emissions have become one of the important contributing factors in urban environmental pollution due to the frequent use of heavy-duty diesel engines in the day-time. Local bus driving cycles have a significant influence on bus emissions under the different traffic conditions. This study investigated the operation mode distributions and emission characteristics for urban buses based on localized MOtor Vehicle Emission Simulator (MOVES) using sparse Global Position System (GPS) data in Shanghai, China. Sparse GPS data from forty-three buses were prepared, and then bus trajectories were reconstructed to calculate local bus driving cycles, including model description, model calibration, and trajectory reconstruction. MOVES localization was conducted for emission estimation mainly focusing on the bus emission inventory comparison between US and China. Bus emission factors were estimated based on the localized MOVES from the aspect of different driving conditions. Results show that with the increase in average traveling speed, the proportion of idling operation mode showed a decreasing trend. Four typical vehicle operation mode distributions were identified with different average speeds to show the impact of traffic conditions. Bus emission factors first rapidly decreased and then slowly declined towards some minimum values. Bus lanes exhibited emission reduction benefits under serious traffic congestion. The findings of this study have great importance for transportation operation management and policy-making to reduce bus emissions, as well as improving air quality.

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Section: Introductionmentioning

confidence: 99%

Evaluating Urban Bus Emission Characteristics Based on Localized MOVES Using Sparse GPS Data in Shanghai, China

et al. 2019

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“…This may be due to the fact that for intersection and bus stop influence areas, vehicles may conduct a complete stop to wait for traffic signals and service boarding and alighting passengers. Thus, the whole process included cruising, deceleration, idling, acceleration, and cruising modes [20]. In addition, results of t-tests (p-values < 0.050) displaying the differences between EURO-IV, EURO-V, CNG, and GEHE buses were all statistically significant.…”

Section: Resultsmentioning

confidence: 93%

“…Although emissions and energy consumption per passenger mile of public transportation vehicles are significantly lower than those of other vehicle types [14], exhaust fumes from public transportation are still one of the most relevant environmental problems [15][16][17]. Most urban transit buses are equipped with heavy-duty diesel engines, which have higher emission factors and pollution levels [18][19][20][21]. To alleviate this shortcoming, there is an increasing number of buses that are switching to use new energy power.…”

Section: Introductionmentioning

confidence: 99%

“…According to the relevant references [40], in China, the percentage of hybrid electric buses will be 40% to 50% in 2030. In addition, according to the relevant references [20,41], the main sources of particulate matter (such as PM2.5) in urban areas are soil dust, coal combustion, biomass burning, industrial pollution, and secondary inorganic aerosol, with contributions of 16%, 14%, 13%, 28%, and 26%, respectively. The particulate matter from traffic and waste incineration emission accounts for merely 3%.…”

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On-Road Bus Emission Comparison for Diverse Locations and Fuel Types in Real-World Operation Conditions

Wang

Sun

2020

Sustainability

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Urban buses have energy and environmental impacts because they are mostly equipped with heavy-duty diesel engines, having higher emission factors and pollution levels. This study proposed a mean distribution deviation (MDD) method to identify bus pollutant emissions including CO, CO 2 , HC, and NO X at road sections, intersections, and bus stops for different fuel types; and explore the changes in emissions for different locations in the road sections, bus stops, and intersection influence areas. Bus speed, acceleration, and emissions data were collected from four fuel types in China. For different locations and fuel types, the differences in emissions were all statistically significant. MDD values for different locations indicated that there were more obvious differences in emissions between road sections and intersections. In addition, heat maps were applied in this study to better understand changes in bus emissions for different locations in the bus stop influence areas, intersection influence areas, and road sections. the proportion of new energy vehicles in buses is largely increasing, it is necessary to re-estimate the emissions characteristics of public transport vehicles, especially for new energy buses. In particular, intersection and bus stop areas can create congestion and further increase emission rates due to speed changes and stop-and-go operations [24][25][26][27]. In light of these considerations, measuring and comparing bus emissions at different locations are one of the emphases of this study.The first category of vehicle emission analysis methods is aimed at analyzing exhaust fumes for large areas and regions. This kind of emission analysis is regarded as a "macro" scale and is combined with the average values of influencing variables [28]. The second category aims to develop models to provide more precise knowledge about vehicle emissions in easily identifiable environments, which are called "micro" or "local" scale models. This approach offers the chance to acquire vehicle emissions data in real-world conditions [28,29]. Zhang et al. investigated the emission characteristics of transportation vehicles using real data [30]. From the results, the problem of NO X emissions is still confronted with severe challenges. For different locations, the performance of pollutant emissions may vary. In the relevant reference [18], approximately half of bus emissions were produced in and around intersection/stop areas. In addition, the use of alternative fuels, such as compressed natural gas, biodiesel, and hybrid technology (diesel/electric) reduced carbon emissions, leading to improved air quality [31][32][33][34]. For instance, Dreier et al. found an absolute reduction of 1115 g CO 2 per kilometer when using plug-in hybrid-electric city buses [35]. It should be noted that if the available data and information about the application system and technologies (such as bus emissions) are incomplete, imprecise, and uncertain, then technology identification could be very difficult. Liu et al. introduced a simpl...

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“…[4] The use of this type of vehicle has produced quite an impact on the environment by emitting pollutants into the air. [5]. The transportation sector has a direct impact on the accelerated growth of greenhouse gas (GHG) emissions, which cause climate change.…”

Section: Introductionmentioning

confidence: 99%

Transport externalities of bus stations produced by Greenhouse Gas (GHG)

et al. 2020

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It is established that the interprovincial transportation in bus terminals of the Cities such as Ambato, Riobamba, Salcedo, Latacunga and Guaranda have contributed to the build-up of external costs of Greenhouse Gases (GHG) The climate change costs are calculated by multiplying the carbon emissions by the cost factor. To quantify the GHG emissions, this study has taken into account of both the direct and indirect sources of the Greenhouse Gas Protocol (GHG), as well as the ISO 14064.1: 2006 standard. In view thereof, it was found that the 11 bus terminals of the five cities, namely Latacunga, Riobamba Salcedo, Ambato, Guaranda-which accounts for around 3225 buses, had accounted for the emissions of 25,746.8 tCO2eq, 37,404.6 tCO2eq, 8,762.7 tCO2eq, 92,364.9 tCO2eq, 31,990.3 tCO2eq, respectively. Simply, the average load of such pollution produced per vehicle was 60.8 tCO2eq. and the total emissions were 196,269.3 tCO2eq with an estimated GHG contamination cost of €27,477,702 per year.

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Estimation of bus emission models for different fuel types of buses under real conditions

Cited by 52 publications

References 51 publications

Evaluating Urban Bus Emission Characteristics Based on Localized MOVES Using Sparse GPS Data in Shanghai, China

Evaluating Urban Bus Emission Characteristics Based on Localized MOVES Using Sparse GPS Data in Shanghai, China

On-Road Bus Emission Comparison for Diverse Locations and Fuel Types in Real-World Operation Conditions

Transport externalities of bus stations produced by Greenhouse Gas (GHG)

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