Analysis of motorcycle fleet in Hanoi for estimation of air pollution emission and climate mitigation co-benefit of technology implementation
Highlights
► Motorcycle fleet technologies and driving activities in Hanoi were analyzed. ► Questionnaire, GPS and video camera survey were conducted to collect relevant data. ► IVE2.0 was used to generate the base case emission for 2008. ► Faster EURO2 and EURO3 technology intrusion scenarios were considered. ► Air quality and climate co-benefit under the scenarios were quantified.
Introduction
Motorcycles (MC) have the largest share (>90%) of the road transport fleet in Hanoi, Vietnam (Truc and Kim Oanh, 2007). From 2000 to 2008, the number of MC in the city increased at a rate of 16% per year gaining a total of over 2 million vehicles in 2008, compared to 150 thousand of passenger cars, 24 thousand of trucks and 8 thousand of buses (HPC, 2008). This fast growing MC fleet contributes significantly to air pollution in the city but has not been properly quantified. The available data are insufficient to identify the contributions from MC as well as other types of vehicle to the air pollution in Hanoi. ADB (2002) suggested that MC fleet contributed about 54% of CO and HC pollution at a roadside in Hanoi during morning rush hours. More update information on emission source contribution is required which should be based on an accurate emission inventory (EI) to develop appropriate emission reduction strategies for the fleet. Such EI requires detail information on the MC fleet and the emission factors (EFs) for specific vehicle/fuel technology and local driving conditions in the city. In the past, a few attempts were made to prepare the traffic EI for Hanoi (Nhung, 2003; Hue, 2005) which mainly relied on the top down approach using the fuel consumption and the EFs developed in other countries.
Accurate EF data are the foremost requirement for any EI effort but collection of such data is always resource demanding. The traditional chassis dynamometer test method, which measures the vehicle exhaust emission on laboratory simulated road conditions, is straightforward but expensive therefore can be performed for only a relatively small number of vehicles (Kim Oanh et al., 2010). In Hanoi recently some chassis dynamometer tests were conducted but only for a few MC (Tuan, 2008) which cannot produce a representative EF database of the large fleet in the city. Other direct measurement techniques, such as on-board portable emission monitoring system (US EPA, 2005a), on-road mobile laboratories (Zavala et al., 2006) or on-road remote sensing (Chan et al., 2004), have the advantage of being able to characterize emissions over a full range of real-world driving patterns for a larger number of vehicles. Alternatively, indirect methods for determination of fleet emission, such as inverse modeling of roadside air pollution levels (Gramotnev et al., 2003; Kim Oanh et al., 2008) and tunnel modeling (Chiang and Huang, 2009), can also examine hundreds of in-use vehicles in near real-world driving conditions. In general, the modeling approach is less resource consuming and has been effectively used in a number of studies (Gramotnev et al., 2003; Burón et al., 2004; Kim Oanh et al., 2008).
Traffic emission inventory is normally done using the emission modeling approach. This approach can handle a large number of vehicles with diverse technologies of engine, fuel and exhaust control devices commonly present in a traffic fleet. The models normally incorporate a range of default EFs for various technologies types. A series of US EPA MOBILE (US EPA, 2004a) or California Air Resources Board EMFAC (CARB, 2009), applied worldwide, contain the base EFs (BEFs) drawn from chassis dynamometer tests made in the US. The latest version of the COPERT series of vehicle emission models, commonly used in Europe (Burón et al., 2004), contained rather detailed vehicle sub-classes and is considered to be more appropriate for applications in countries where EURO standards are adopted. Nevertheless, developing countries with specific vehicle technologies, fuels and road conditions may have different EFs than those default values of the models.
The International Vehicle Emissions (IVE) model has been developed as a joint effort of University of California at Riverside, College of Engineering – Center for Environmental Research and Technology (CE-CERT), Global Sustainable System Research (GSSR) and the International Sustainable System Research Center (ISSRC) which incorporates various vehicle technologies and on-road real driving operations (ISSRC, 2010). This model uses the Vehicle Specific Power (VSP) as an indicator of the second-to-second driving pattern. The VSP data account for instantaneous velocity, road grade and acceleration, which are believed to relate to vehicle emissions better than the average speed considered in COPERT and MOBILE (Wang et al., 2008). IVE has been applied in some polluted cities of developing countries, e.g. Mexico City (Mexico), Pune (India), Beijing and Shanghai (China) with promising results (ISSRC, 2010). The model considers three emission types: tailpipe emission produced during a hot-stabilized engine operation (hot/running emission), tailpipe emission during cold engine start (cold start emission), and VOC evaporative running losses. The default values of the running BEFs, g km−1, and the cold start BEFs, g start−1 in the model have been derived from emission tests conducted mainly in USA and Europe, but a few values were also taken from developing countries such as China, India and Mexico. IVE incorporates over 150 MC technologies which are defined based on engine technology, vehicle weight, mileage, fuel used, air/fuel control, and exhaust control devices. To obtain the EFs appropriate to MC technologies for a study area, an adjustment of BEF is done using a series of correction factors to reflect the local conditions/variables (meteorology, altitude, I/M program, etc.), fuel quality, and power and driving characteristics (VSP, road grade, air condition usage, and start distribution).
In this study, we analyzed the MC fleet and driving patterns in Hanoi for the year 2008 using the data collected by a questionnaire survey, vehicle counting and GPS survey. The collected data were processed and input into the IVE model to generate the adjusted EFs for different MC technologies in Hanoi. The obtained EFs were compared with the limited available measurement data for MC in the city. EI was produced for the base year 2008 (base case). To estimate the impacts on air pollution and climate forcer emission of “early actions”, two “what-if” scenarios of faster EURO2 and EURO3 technology intrusions for the MC fleet were also examined.
Section snippets
Data collection and processing
The data collection was conducted in Hanoi during the period from 20 October 2008 to 22 November 2008 using the questionnaire survey, traffic video recording, GPS (Global Positioning System) recording, and personal interview following the IVE method as described in IVE field data collection activities document (http://www.issrc.org/ive/). The obtained data included the registered MC population, engine technology, fuel characteristics, road length, meteorology, and on-road vehicle activities
Motorcycle fleet and driving activities in Hanoi
The questionnaire survey results show that the majority of the MC fleet in Hanoi was equipped with four-stroke engines (99%) and carburetor fuel systems (67%). Most of MC in Hanoi (99%) had small (<100 cc) or medium (100–300 cc) engine sizes. They were also of low usage (69% with odometer readings <25,000 km). The average fleet age was 3.6 years and most of the fleet were quite new, 67% were of below 3 years old. Our results show that 47% of MC in Hanoi complied with EURO2 and 18% with EURO3.
Conclusions
As of 2008, the MC fleet in Hanoi composed mostly of 4-stroke engine and relatively new vehicles with low odometer readings, but was largely not equipped with exhaust control devices. Over 47% of the fleet complied with EURO2 and 18% complied with EURO3. Most of the MC driving in Hanoi belonged to low engine stress modes. The driving on arterials (highest share, 49%) and residential streets (17%) was characterized with frequent stops and idling conditions.
Emission factors, driving activity and
Acknowledgments
The authors would like to thank Dr. Hoang Duong Tung and the staff of the Centre for Environmental, Monitoring, Data and Information (CEMDI) of MONRE, Vietnam for the technical assistance and partial financial support for the data collection in Hanoi. Further acknowledgment is extended to students and staff of the air quality research group at the Asian Institute of Technology and at CEMDI for their assistance in the field survey and the data analysis process. Other partners are thanked for
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