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Using Monte Carlo Simulation for Vehicle Emission Estimation-A Case Study in Hong Kong

Mak, Kai-Long, Loh, Anthony Wai-Keung (2020) Using Monte Carlo Simulation for Vehicle Emission Estimation-A Case Study in Hong Kong. Journal of Applied Sciences, 20 (3). pp. 119-123. ISSN 1812-5654. E-ISSN 1812-5662. (doi:10.3923/jas.2020.119.123) (KAR id:93629)


Background and Objectives: Air pollution caused by vehicular emission has been a public concern and is more seriously in Hong Kong which is a highly urbanized and congested city. Therefore, a study was conducted to observe the vehicular pollutant emission and concentration. This study intends to update the driving behavior in terms of vehicle speed and vehicle acceleration for heavy diesel vehicles in Hong Kong residential area.

Materials and Methods: A total of over 900 min of data were collected by the car chasing technique for 93 heavy diesel vehicles in Shatin, one of the most crowd residential area in Hong Kong during the period November and December, 2019. A three dimensions speed-acceleration matrix and plot were developed by the sampled result. Project team further applied the Hong Kong based speed-acceleration probability distribution to the tunnel mass balance emission model for CO and NO vehicle emission estimation.

Results: A Hong Kong based three-dimension speed-acceleration-frequency matrix was successfully developed. Monte Carlo simulation were further carried out for 1000 times within obtained driving cycle and then further calculating the CO and NO emission factor. It was observed that in average one heavy diesel vehicles has emitted 6.5 g CO and 7.1 g NO for one trip in residential area (around 10 km).

Conclusion: This research project focuses on the interaction effect between vehicle speed and acceleration and showed there was interaction effect. This method is simple and extremely good under limited budgets. Statistical Monte Carlo simulation further improved the accuracy of the driving cycle modeling.

Item Type: Article
DOI/Identification number: 10.3923/jas.2020.119.123
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Ricky Mak
Date Deposited: 17 Mar 2022 12:23 UTC
Last Modified: 18 Mar 2022 09:27 UTC
Resource URI: (The current URI for this page, for reference purposes)

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