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ava i l a b l e a t w w w. s c i e n c e d i r e c t . c o m

w w w. e l s ev i e r. c o m / l o c a t e / s c i t o t e n v

A bottom-up methodology to estimate vehicle emissions for the Beijing urban area

Haikun Wanga, Lixin Fua,

  • , Xin Lina, Yu Zhoua, Jinchuan Chenb

aDepartment of Environmental Science and Engineering, Tsinghua University, Beijing 100084, PR China bBeijing Transportation Research Center, Beijing 100055, PR China



Article history: Received 10 July 2008 Received in revised form 23 October 2008 Accepted 9 November 2008

Vehicle exhaust emissions have posed a serious threat in recent years to the urban air quality of Beijing. It is necessary to accurately estimate the magnitude and distribution of these emissions in order to reduce the uncertainty of local scale air quality modeling assessment. This paper provides a bottom-up approach by combining vehicle emission factors and vehicle activity data from a travel demand model estimated at the grid level to generate vehicle emissions data for the Beijing urban area in 2005. In that year, vehicular

Keywords: Vehicle missions Air quality Grid-based Traffic data Beijing

emissions of HC, CO and NOx were respectively 13.33×104, 100.02×104 and 7.55×104 tons. The grid-based emissions were also compared with the vehicular emission inventory developed by macro-scale approach. It shows this bottom-up approach can result in better estimates of the levels and spatial distribution of vehicle emissions than the macro-scale method that relies on more average and aggregated information.

© 2008 Elsevier B.V. All rights reserved.



Vehicle emission inventory for regional or national scale are usually developed using a macro-scale (or top-down) a p p r o a c h i n C h i n a . F o r t h e s e a p p l i c a t i o n s , e m i s s i o n f a c t o r s

are assumed to represent long-term vehicle population averages for a given vehicle class, and are often based on

default or average inputs. Average vehicle activity data, such

as Vehicle Kilometers Traveled (VKT), are estimated by investigation and/or a statistical method for each fleet. The emissions inventory is estimated as the product of emission factors and vehicle activity. Then, some spatial surrogates, such as population and road density, are used to allocate macro-scale emissions to grid cells as required by the air quality model (Cook et al., 2006; Hao et al., 2000; He, 1999).

However, there exist some limitations of this approach. The same emission factors for each vehicle fleet under average speed, or emissions allocated using the spatial

surrogates from a larger geographic scale, may not reflect the real vehicle emission conditions at local scales. Thus, top-

down inventories, which are practical for national scale applications, may mischaracterize emissions at the individual county or sub-county. A study conducted in MinneapolisSt. Paul, Minnesota, which used spatial surrogates to allocate mobile source emissions to census tracts, found that the dispersion model tended to over predict at low monitored concentrations, and under predict at levels of high monitored concentrations (Pratt et al., 2004). Cook et al. (2006) also found that estimation for individual road links to develop an emission inventory results in a much different spatial distribution of emissions than applying top-down methodology.

More accurate vehicle emission inventory can be devel- oped using a bottom-up approach that relies on using more detailed emission factors and vehicle activity data from a travel demand model (TDM). TDM used for transportation planning can provide more detailed information on the spatial distribution of roadway types, vehicle activity, and speeds along those roads. These data may be used with the emission factors from the vehicle emission model to create more

  • Corresponding author. Tel./fax: +86 10 62771465.

E-mail address: fuchen@mail.tsinghua.edu.cn (L. Fu).

0048-9697/$ see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2008.11.008

Please cite this article as: Wang H, et al, A bottom-up methodology to estimate vehicle emissions for the Beijing urban area, Sci Total Environ (2008), doi:10.1016/j.scitotenv.2008.11.008

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