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Motorcycle Emission Profiles in Bandung City, Indonesia
Technical Paper
2017-32-0076
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Motorcycles account for almost 80% of private vehicles in Indonesia, with an annual growth rate of 12% per year. This paper aims to investigate the emission profiles of CO2, CO, HC and NOx based on typical fuel and motorcycle types in Indonesia. Questionnaire surveys were undertaken to gather fuel type, engine technology and capacity representing the motorcycle population in Bandung City, Indonesia. Emissions were measured based on six-speed variations on a chassis dynamometer. Questionnaire surveys from 290 respondent show that EURO II and EURO III technology with engine capacity less than 150cc is the most utilized type of motorcycle in Bandung. Most of the users’ chose RON 90 and RON 92 gasoline. Based on the results, four groups of 5 motorcycle of EUROII-RON90, EUROII-RON92, EUROIII-RON90, and EUROIII-RON92 were tested. Emission data showed that the higher the speed, the lower the emission, except for CO and NOx which have a different pattern. The highest CO2 and NOx emission are found in the EUROIII- RON92 group (76.81 and 0.17g/km respectively), EUROII-RON90 for highest CO emission (11.92 g/km), and EUROII-RON92 for highest HC (0.30 g/km). Kruskal Wallis test shows significant differences within the group for all parameters. Good quality fuel on motorcycles will lead to engine performance improvement. It increases CO2 and NOx emission as complete combustion marker and reduces byproduct of incomplete combustion, especially CO emission. This paper shows that to reduce the emission of incomplete combustion product, a proper fuel specification should be used to get expected emission reduction. The results highlight the importance of fuel provision with proper specification in the plan of upgrading motorcycle engine technology.
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Citation
Yudison, A., Driejana, ., Reksowardojo, I., and Sulaeman, A., "Motorcycle Emission Profiles in Bandung City, Indonesia," SAE Technical Paper 2017-32-0076, 2017, https://doi.org/10.4271/2017-32-0076.Data Sets - Support Documents
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