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ARTICLE IN PRESS

2

SCIENCE OF THE TOTAL ENVIRONMENT XX (2008) XXXXXX

detailed, spatially distributed emission rates at the local level, thus providing better estimates of the distributions of pollutants concentration in a city (Kinnee et al., 2004; Cohen

et al., 2005).

Beijing, the capital and also the center of policy and culture

in China, has an urban area of about 1000 km2 and a population more than 14 million. The road system in Beijing can be described as a network with ring roads and radial roads as its arteries. The road around the Forbidden City is called the first ring, and the ring roads beyond are the 2nd, 3rd, 4th, 5th and 6th ring roads in order of the radial distance from center of the city. In this study, we focus on the vehicle emissions within the 6th ring road which is described as the Beijing urban area. The total vehicle population of Beijing has almost tripled during the last decade reaching more than 2 million by the end of 2005 (BSB, 2006). Mobile sources have also been identified as the most important air pollution contributor in this metropolis (Hao and Wang, 2005).

of changes in land use, roadway network, and transportation

policy on emission estimates.

MOBILE5B, developed by the US Environmental Protection Agency (1994), uses average speed, vehicle fleet characteris- tics, ambient condition, and other parameters to estimate emission factors. In this study, grid vehicular speeds are used to obtain emission factors for each vehicle type in each grid. Grid VKT is multiplied by the corresponding grid emission factors and summed over all grids to obtain total emissions (Eq. (1)). This approach is physically representative of realistic traffic conditions in each grid cell of the city. Nevertheless ,

current practice favors applying the macro-scale approach (Eq. (2)) to develop the vehicle emission inventory for the cities in China.

m

n

QP =

XX

E F P i ; j V M T i ; j

ð1Þ

i=1 j=1

In this study, the grid-based vehicle emission inventory for Beijing urban area will be established using a bottom-up approach. The vehicle activity data from travel demand modeling were used in conjunction with emission factors from the MOBILE5B-China model to develop the grid-based emission inventory. The results were then compared with the vehicular emission inventory developed by the macro-scale method to analyze their uncertainties.

m

QP =

X

EF

P i;average

  • VMTi

ð2Þ

i=1

Where,

QP

is

the

total

emissions

for

pollutant

P,

g;

E F P i,j

is

the emission factor of pollutant P for vehicle type i in grid cell

j, g/km; EF

P i,average

is the average emission factor of pollutant P

for vehicle type i, g/km; VKTi,j is the vehicle kilometers traveled for vehicle type i in grid cell j, km; and VKTi is the total vehicle kilometers traveled for vehicle type i, km.

  • 2.

    Methodologies

    • 2.1.

      Modeling approach

The modeling procedure in this study consists of three consecutive steps that are shown in Fig. 1.

TransCAD (Caliper Corporation, 2006), one of the most popular and capable transportation planning software, is applied here to estimate the traffic flow and speed in each grid for the Beijing urban area. It may also be interfaced with pollutant emissions/dispersion models to simulate the effect

2.2.

Vehicle emission factors

Vehicle emission factors depend on many considerations, which can be grouped as vehicle emission control level; vehicle type and fuel type; utilization parameters such as age ,

accumulated mileage, inspection and maintenance; operating modes like average speed, fraction of cold/hot starts, air conditioning; and ambient parameters such as temperature and humidity. In this research, modified MOBILE5B (MOBI-

LE5B-China) was used to estimate vehicle emission factors under different speeds. Researchers at Tsinghua University have adopted MOBILE5B-China model to estimate vehicle emissions in their studies over the past 10 years (Fu et al., 1997; Hao et al., 2000, 2001, 2002, 2006; He et al., 1998). Information and internal functions specific to Beijing were substituted for many of the functions in MOBILE5B-China in recent years. Furthermore, with the help of the on-road vehicle

emissions measurement using PEMS in Beijing (Hu et al., 2004), speed-emission factor coefficients of Beijing are updated in MOBILE5B-China. This allows it to reflect the effect of driving patterns in Beijing on the emission factors. A detailed description of the study modifying and applying MOBILE5B- China to estimate vehicle emission factors in Beijing can be found in the literature (He, 1999; Hao et al., 2000).

Fig. 1 Modeling approach.

In this study, the vehicles driving in the urban area of Beijing were summarized into 6 classes: passenger car (PC), shuttle bus (SB), taxi, heavy duty truck (HDT), light duty truck (LDT) and bus. The variation of emission factors of HC, CO and NOx with speed (up to 80 km/h) for each vehicle class are calculated with MOBILE5B-China and shown in Fig. 2. It shows that HC and CO emission factors have a strong relationship with speed and generally decrease with increasing speeds,

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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