Study of Effect of Speed, Acceleration and Deceleration of Small Petrol Car on Its Tail Pipe Emission

Bokare, P.S.; Maurya, Akhilesh Kumar

doi:10.7708/ijtte.2013.3(4).09

Cited by 48 publications

(8 citation statements)

References 13 publications

(19 reference statements)

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“…Moreover, several kinematic parameters such as average speed, instantaneous speed, and acceleration as well as driving condition characteristics, impact fuel consumption and thus emissions [16,45,67,68]. Previous works showed that higher levels of acceleration correspond to an increase in fuel consumption which leads to higher carbon monoxide (CO) and hydrocarbon (HC) emissions [45,69,70]. Though this driving pattern greatly impacts fuel consumption and therefore emissions, this study did not investigate this parameter.…”

Section: Fuel Consumption (C Day ) and Travelling Time (T P )mentioning

confidence: 97%

Emissions from the Road Traffic of West African Cities: Assessment of Vehicle Fleet and Fuel Consumption

et al. 2018

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Traffic source emission inventories for the rapidly growing West African urban cities are necessary for better characterization of local vehicle emissions released into the atmosphere of these cities. This study is based on local field measurements in Yopougon (Abidjan, Côte d’Ivoire) in 2016; a site representative of anthropogenic activities in West African cities. The measurements provided data on vehicle type and age, traveling time, fuel type, and estimated amount of fuel consumption. The data revealed high traffic flow of personal cars on highways, boulevards, and backstreets, whereas high flows of intra-communal sedan taxis were observed on main and secondary roads. In addition, the highest daily fuel consumption value of 56 L·day−1 was recorded for heavy vehicles, while the lowest value of 15 L·day−1 was recorded for personal cars using gasoline. This study is important for the improvement of uncertainties related to the different databases used to estimate emissions either in national or international reports. This work provides useful information for future studies on urban air quality, climate, and health impact assessments in African cities. It may also be useful for policy makers to support implementation of emission reduction policies in West African cities.

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Section: Fuel Consumption (C Day ) and Travelling Time (T P )mentioning

confidence: 97%

Emissions from the Road Traffic of West African Cities: Assessment of Vehicle Fleet and Fuel Consumption

et al. 2018

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“…, n), AX ij is the original value of the jth index in the ith sample and AX ij is the treated value by the Equation (10). The weight of index (EW j ) can be calculated by EWM method using Equation (11).…”

Section: Confirmation Of Weight and Scoring Rulementioning

confidence: 99%

“…GPS can provide vehicle location directly and vehicle speed can be estimated based on the location. The acceleration and deceleration behaviour of drivers were studied using GPS in [10,11]. Video cameras were used to capture the drivers' facial expressions and eye movements to analyse the driving behaviour [12].…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car

Zhang

Tian

et al. 2022

Energies

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Evaluation of driving behaviour is helpful for policy development, and for designing infrastructure and an intelligent safety system for a car. This study focused on a quantitative evaluation method of driving behaviour based on the shared-electrical car. The data were obtained from the OBD interface via CAN bus and transferred to a server by 4G network. Eleven types of NDS data were selected as the indexes for driving behaviour evaluation. Kullback–Leibler divergence was calculated to confirm the minimum data quantity and ensure the effectiveness of the analysis. The distribution of the main driving behaviour parameters was compared and the change trend of the parameters was analysed in conjunction with car speed to identify the threshold for recognition of aberrant driving behaviour. The weights of indexes were confirmed by combining the analytic hierarchy process and entropy weight method. The scoring rule was confirmed according to the distribution of the indexes. A score-based evaluation method was proposed and verified by the driving behaviour data collected from randomly chosen drivers.

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“…Besides, the EC EFs for all vehicles excluding transit in these two networks are not significantly different (P < 0.05). The EF comparison results for each pollutant are different, as the EF for each pollutant has a different association with traffic speed (Bokare and Maurya 2013).…”

Section: Contribution Of Trucks Transit Buses and Other Vehicles To Total Emissionsmentioning

confidence: 99%

Comparing emission rates derived from a model with those estimated using a plume-based approach and quantifying the contribution of vehicle classes to on-road emissions and air quality

Wang

Hilker

et al. 2018

Journal of the Air & Waste Management Association

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This study presents a comparison of fleet average emission factor (s) derived from a traffic emission model with EFs estimated using plume-based measurements, including an investigation of the contribution of vehicle classes to carbon monoxide (CO), nitrogen oxides (NO), and elemental carbon (EC) along an urban corridor. To this end, a field campaign was conducted over one week in June 2016 on an arterial road in Toronto, Canada. Traffic data were collected using a traffic camera and a radar, whereas air quality was characterized using two monitoring stations: one located at ground level and another at the rooftop of a four-story building. A traffic simulation model was calibrated and validated, and second-by-second speed profiles for all vehicle trajectories were extracted to model emissions. In addition, dispersion modeling was conducted to identify the extent to which differences in emissions translate to differences in near-road concentrations. The results indicate that modeled EFs for CO and NO are twice as high as plume-based EFs. Besides, modeled results indicate that transit bus emissions accounted for 60% and 70% of the total emissions of NO and EC, respectively. Transit bus emission rates in g/passenger·km for NO and EC were up to 8 and 22 times, respectively, the emission rates of passenger cars. In contrast, the Toronto streetcars, which are electrically fueled, were found to improve near-road air quality despite their negative impact on traffic speeds. Finally, we observe that the difference in estimated concentrations derived from the two methods is not as large as the difference in estimated emissions due to the influence of meteorology and of the urban background given that the study network is located in a busy downtown area. Implications: This study presents a comparison of fleet average emission factor (s) derived from a traffic emission model with EFs estimated using plume-based measurements, including an investigation of the contribution of vehicle classes to various pollutants. Besides, dispersion modeling was conducted to identify the extent to which differences in emissions translate to differences in near-road concentrations. It was observed that the difference in estimated concentrations derived from the two methods is not as large as the difference in estimated emissions due to the influence of meteorology and of the urban background, as the study network is located in a busy downtown area.

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Study of Effect of Speed, Acceleration and Deceleration of Small Petrol Car on Its Tail Pipe Emission

Cited by 48 publications

References 13 publications

Emissions from the Road Traffic of West African Cities: Assessment of Vehicle Fleet and Fuel Consumption

Emissions from the Road Traffic of West African Cities: Assessment of Vehicle Fleet and Fuel Consumption

Evaluation Method of Naturalistic Driving Behaviour for Shared-Electrical Car

Comparing emission rates derived from a model with those estimated using a plume-based approach and quantifying the contribution of vehicle classes to on-road emissions and air quality

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