Introduction
Air pollutants from gases to particulates in megacities are associated with
a mixture of various sources, including primary/secondary and
natural/anthropogenic sources, and air pollution has become a major human health concern (An et al., 2013). Emissions from human activities and
natural processes can react with ozone and light to form secondary
pollutants, which are more difficult to analyse. Resulting from the
complexities of local to regional emissions, the term “complex atmospheric
pollution” has emerged in the last decade (Chan and Yao, 2008; Fang et al.,
2009). Driven by rapid industrialization and urbanization, Beijing, the
capital city of China, has received extensive global attention regarding its
contribution to the atmospheric environment. Numerical model simulation is a
very effective tool for proportionally estimating contributions to air
pollution from various sources under certain atmospheric conditions (Cheng
et al., 2007; Wang and Xie, 2009). The accuracy of emission source inventory
is the key to air quality numerical simulation. In recent years,
transportation emissions have become the most significant emission source in
Chinese megacities (e.g. Beijing) (He et al., 2002). There are differing
opinions in quantitative research regarding the pollution contribution of
vehicle emissions (Song et al., 2006; Cheng et al., 2013; Wu et al., 2014).
Numerical model simulation is an effective method of quantifying a portion
of on-road vehicle emissions accounting for air pollution, particularly in
different periods and regions. However, numerical model simulation relies
heavily on the accuracy of mesoscale meteorological models and emission
inventories, which have shown significant improvements in the past two
decades due to the development of new physical parameterization and
data assimilation techniques. Although plenty of research exists on the
climate characteristics of Beijing (An et al., 2007; Wu et al., 2014), no
integrated emission inventory model reflects simultaneously the factors of
traffic volume, speed and fleet composition at a particular road segment.
Therefore, the accuracy of emission source inventory in an air quality
numerical simulation has become a challenge.
The establishment of vehicle emission inventory requires a large amount of
data, such as emission factors, traffic activity, fleet composition and the
combined situation of these factors, which is strongly influenced by the
local driving circle, road information, traffic characteristics, etc. Until
recently, most of the emission inventories in Chinese cities have been
developed by utilizing the MOBILE model from the US Environmental
Protection Agency (EPA) or similar macro-scale models (Hao et al., 2000; Fu
et al., 2001; Cai and Xie, 2007; Guo et al., 2007), in which the inventory
approach is defined as a top-down method. For this method, the emission
factors are uniform for the same vehicle category in the entire study
region, combined with the number of kilometres travelled (VKT) for each
vehicle fleet, to estimate the average emissions on a large geographic
scale. Then, emissions are allocated as required by the air quality model to
hourly or daily emissions by the local time-varying characteristic and
allocated to grid cells by the local population and/or road density.
However, there are some limitations in the top-down methodology. For
example, the same emissions factors under average speed circumstances cannot
reflect the influences of velocity changes at different road segments at
different times; the spatial and temporal distribution method cannot reflect
the dramatic difference of traffic flow characteristics on various road
segments (Reynolds, 2000). Thus, the macro-scale emission inventories may
not reflect the real emission conditions for on-road vehicles in the city,
and the low spatial and temporal resolutions are also limited in the
application of air quality models. Additionally, because the strategies are
converted to individual vehicles (e.g. requiring stricter emission limits
for new vehicles, strengthening the management of in-use vehicles,
eliminating high-emitting vehicles) and transportation management (e.g.
developing public transportation, improving travel conditions, adopting
traffic control measures), the top-down inventories are not able to assess
the effects of air quality improvement from the implemented strategies
because of the limited reflection of spatial and temporal variation in
complex urban traffic conditions. Therefore, more accurate and higher-resolution vehicle emission inventories are currently needed in Beijing.
There are two obstacles in the establishment of a vehicle emission inventory: reliable vehicular emission factors based on the local vehicle
emission conditions and comprehensive traffic data (e.g. traffic volume,
speed, fleet composition) displaying the traffic flow characteristics of
each road (Wang et al., 2008). With the increase of research, some
higher-resolution vehicle emission inventories in Chinese cities were established
based on bottom-up methodology (Wang et al., 2008; Huo et al., 2009;
Wang and Xie, 2009; Zhou et
al., 2015). However, most of those inventories had some limitations regarding
reflection on real-time variation of vehicle emissions on each road due to
the lack of collection methodology of real-time traffic data.
Driven by the development of traffic data observation technology, the
conventional loop coil detector and video detector are gradually being replaced by a higher cost–benefit sensor system. This system now makes the acquisition of
mass fine traffic data feasible. Meanwhile, the rapid development of
geographic information system (GIS) and Global Positioning System (GPS)
technology makes a strong connection between traffic activity data and road
information. Infrastructure sensors and floating cars are believed to be the
main sources for the current traffic data collection. The infrastructure
sensors consist of fixed-point detectors installed in roads, and floating
cars are mobile probe vehicles (e.g. buses and taxis) with GPS
positioning devices. It is difficult to cover the entire road network of the city
with the information collected by the infrastructure sensors from a static point on a road, which is lacking space coherence (Naranjo et al.,
2012).
Floating cars collect information from the vehicles that travelled on the
road segments, data which are then utilized to estimate the average speeds,
traffic intensity and other relevant conditions (e.g. congestion status).
However, the temporal and spatial resolutions of current traffic data are too
low to establish hour-scale and road-scale vehicle emission inventory. It
needs near-real-time (NRT) traffic data on the entire network, which can be
collected by integrating the floating car data, radio frequency
identification data and video identification data.
The purpose of this paper is to develop a high temporal–spatial resolution
vehicle emission (HTSVE) inventory for Beijing based on local emission factors and
NRT traffic data using a bottom-up methodology. The road system of Beijing,
the capital of China, consists of urban freeways, artery roads, collector
roads and local roads. The scope of this research is the area within the
sixth ring road and the surrounding area, which is the main activity area
for people in Beijing. This project is divided into two parts: Part 1
elaborates on the development of a high temporal–spatial resolution vehicle
emission inventory in Beijing, and Part 2 analyses the effect of vehicle
emissions on urban air quality.
Methodology and data
In this study, a vehicle emission inventory model based on bottom-up
methodology was used to develop an inventory for vehicular emissions. The
model simulated the emissions for each road segment during each hour,
depending on the traffic volume and the emission rates of these vehicles on
the road segment during the following period:
Qi,jp=∑cEFc,vp×VTc,i,j×Li,
where Qi,jp is the emission of
pollutant p on road segment i at moment j (g h-1); EFc,vp is the emission factor of pollutant p for vehicle category
c at speed v (g km-1); VTc,i,j is the traffic
volume of category c on road i at moment j (veh h-1); Li
is the length of road i (km). The total urban emissions is the sum of the
vehicle emissions on all roads.
There are three necessary elements for the model: emission factors, vehicle
activity and road segment information. Emission factors are based on the
mass of the laboratory measurement and the on-road measurement data. The
vehicle activity included traffic volume, average speed and fleet
composition on the entire road segment. Road information consists of road
length, line number and road type (including freeway, artery road, collector
road and local road) of each road segment. In terms of the traffic speed on
this segment, the road has been divided into fine segmentations and was
grouped as urban freeway, artery road or local road (a local road consists
of collector roads and residential roads because of the negligible
differences between them in Beijing).
Emission factors
It is widely known that vehicle emission rates are largely related to
vehicle characteristics, including vehicle classification, utilization
parameters, operating conditions and environmental conditions. The vehicle
characteristics comprise of vehicle category, fuel type and vehicle emission
control level; the utilization parameters involve vehicle age, accumulated
mileage, inspection and maintenance; the operating conditions include cold
or hot starts, average vehicle speed and the influence of driver behaviour;
the environmental conditions include ambient temperature, humidity and
altitude.
Due to the significant differences among different vehicle classification,
the emission factors were classified by the vehicle classification and
modified by the utilization parameters, operating conditions and
environmental conditions in Beijing. With the existing classification method
of the Ministry of Environmental Protection and the Ministry of Transport in
China, vehicles have been classified as follows: (1) vehicle category was
classed as a light duty vehicle (LDV), middle duty vehicle (MDV), heavy duty
vehicle (HDV), light duty truck (LDT), middle duty truck (MDT), heavy duty
truck (HDT), bus or taxi; (2) fuel type was classified as gasoline, diesel or
other (e.g. liquefied natural gas or compressed natural gas); (3) vehicle
emission control levels were classified as Pre-China I, China I, China II,
China III, China IV and China V, which were respectively equivalent to
Pre-Euro, Euro I, Euro II, Euro III, Euro IV and Euro V.
The emission factors were corrected by the widely used emission factors of
the
Computer Programme to Calculate Emissions from Road Transport (COPERT) model
on the basis of local emission factors. The local emission factors were
collected from a mass of measuring data from the Tsinghua University and
China Automotive Technology & Research Center, such as bench testing and
on-road vehicle emissions measurements in Beijing (Huo et al., 2009; Hu et al.,
2012; Wu et al., 2012; Wang et al., 2013). Meanwhile, the fuel consumption
factors for vehicles were measured and included in this model. The
emission factors of NOx, HC, CO, PM and the fuel consumption factors of
gasoline and diesel are shown in Fig. 1.
Emission factors of NOx, HC, CO, PM and fuel consumption factors of
gasoline and diesel (Huo et al., 2009; Hu et al., 2012; Wu et al., 2012; Wang et
al., 2013): (a) emission factors of NOx, (b) emission factors of HC, (c) emission factors of CO, (d) emission factors of PM, (e) fuel consumption of
gasoline and (f) fuel consumption factors of diesel.
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Vehicle activity
The model based on the NRT traffic volume and speed of the road segment,
which were collected by the NRT floating car data and video identification
data in 2013, was utilized to simulate the emission inventory. The fleet
composition was collected by traffic survey data and vehicle registration
information in Beijing.
According to the GPS data from on-road vehicles, the floating car data
covered information within 2 weeks for the entire city. The video cameras
were installed on typical roads to gather video identification data. The
data collection points are shown in Fig. 2. The traffic survey data were
collected from a video field survey of more than 300 min on typical
roads.
Point location of video cameras.
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Average running speed based on floating car data
Floating car data technology is recently believed to be an advanced
technological method to collect traffic information in intelligent transport
systems (ITSs). Based on GPS devices, floating cars periodically record
information such as time, speed, latitude and longitude while moving and
send those data back to an information centre via on-board wireless
transmission equipment. In this research, the floating car data were
processed to calculate the average speed following the steps below: (1) the
unqualified data of each transfer interval longer than 150 s at speeds
over 120 km h-1 were filtered; (2) the position data from floating car within
the road segment were fixed by the map algorithm by matching and route
estimation; (3) the single vehicle speed was calculated by the travel length
divided by the travel time; (4) the single vehicle speed on the same road
segment within an hour were averaged to find the average running speed
value. Therefore, the hourly average running speed on each road segment is
obtained from the floating car data collection and processing. The average
vehicle running speed is one of the important parameters of traffic data and is utilized to estimate traffic flow.
Traffic flow from speed
The traffic volume was estimated by the average speed based on the
relationship between the traffic speed and volume. The relationship between
the traffic speed and volume and the same speed-flow model was established
using models such as the Greenshield model, the Greenberg model and the
Underwood model (Wang et al., 2013; Hooper et al., 2014).
According to the traffic volume observed by the video identification data
and traffic speed estimated by floating car data, the speed-flow model for
the traffic in Beijing was designed on every road segment and was grouped
into three road types including the urban freeway, artery roads and local
roads. In this study, the Greenshield, Greenberg and Underwood models were
fitted for three road types. The Underwood model was used
because it had the best goodness of fit (R2) among these models.
V=kmulnufu,
where V is the traffic volume at speed u (veh h-1); u is the
traffic speed (km h-1); km is the best fitting traffic density (veh km-1);
uf is the free speed (km h-1). km and uf were determined by
fitting the Underwood model based on the video identification data and the
floating car data from the different road types.
Vehicle fleet composition
Hourly traffic average speed on different road types in Beijing.
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Considering the significant emission differences between different vehicles,
more attention should be paid to emission control technologies (Heeb et al.,
2003; Karlsson, 2004). The fleet composition of driving vehicles is
estimated to calculate emissions based on vehicle information and the video
data from typical roads in Beijing.
Results and discussions
Traffic characteristics in Beijing
Traffic speed, traffic volume and fleet composition show the main
characteristics of vehicle activities that quantify vehicle emissions in
Beijing. According to the floating GPS car data, the hourly average traffic
speed fluctuates at different times throughout the day but shows similarity
for the different road types. The daily average traffic speed on weekdays is
close to the weekend speeds, as illustrated in Fig. 3; however, the trends
of hourly traffic speed on the urban freeway and the artery roads changes
significantly from weekdays to weekends. There are two low-speed valleys on
weekdays during the early and afternoon peak hours at approximately 08:00
and 18:00 (GMT +8) respectively. On weekends, the early valley period
appears 2 h later, and the late valley period appears 1 h earlier
than on weekdays. The traffic speed is lower than that on weekdays during
the off-peak hours. For the local road, the variation of traffic speed is
similar between weekdays and weekends.
The traffic volume of vehicles contributes significantly to influence
pollutant emissions. As shown in Fig. 4, the average daily traffic volume on
weekdays is close to the traffic volume on weekends. However, the variation
tendencies display a different picture during different moments between
weekdays and weekends. The overall traffic volume changes dramatically at
different times during a day, and two obvious peaks of traffic volume appear
at 08:00 and 18:00. On weekends, the early peak period appears 2 h
later, and the late peak period appears 1 h early than on weekdays: the
variation extent around the traffic volume peak is significantly lower than
on weekdays.
Hourly traffic volume on different road types in Beijing.
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The contributions to emission vary significantly based on different types of
vehicles. Therefore, the fleet composition is a major factor affecting the
release of emissions, as shown in Table 1.
Fleet composition in Beijing.
Vehicle types
Pre-China 1
China 1
China 2
China 3
China 4
China 5
Total
LDV
2.32 %
2.34 %
8.71 %
11.10 %
46.39 %
3.72 %
74.58 %
MDV
1.44 %
0.33 %
0.36 %
0.29 %
0.20 %
0.00 %
2.63 %
HDV
0.23 %
0.09 %
0.40 %
0.93 %
0.56 %
0.00 %
2.21 %
LDT
0.40 %
0.36 %
0.44 %
0.71 %
2.61 %
0.00 %
4.53 %
MDT
0.18 %
0.07 %
0.09 %
0.42 %
0.65 %
0.00 %
1.40 %
HDT
0.21 %
0.28 %
0.14 %
1.15 %
1.76 %
0.00 %
3.54 %
Taxi
0.00 %
0.00 %
1.68 %
3.06 %
3.76 %
0.35 %
8.84 %
Bus
0.02 %
0.10 %
0.53 %
0.99 %
0.63 %
0.00 %
2.27 %
Daily vehicle emission on different road types of Beijing (unit: Mg day-1).
Date
Road type
Length (km)
NOx
CO
HC
PM
Weekdays
Urban freeway
2169.49
111.09
447.12
22.40
4.33
Artery road
3786.94
124.53
502.89
25.16
4.85
Local road
4586.06
56.49
228.36
11.42
2.20
Weekends
Urban freeway
2169.49
95.39
383.79
19.21
3.71
Artery road
3786.94
110.81
446.98
22.36
4.31
Local road
4586.06
74.04
299.36
14.97
2.88
Vehicle emission inventory
Emission inventory
Using the methodology described above, a high temporal–spatial resolution
vehicle emission inventory was established in this study. The total
daily emissions of each road, which is a sum of emissions during a 24 h
period, is shown in Table 2. The daily total emissions of the urban freeway,
artery roads and local roads are 288.71 Mg of NOx, 58.29 Mg of HC,
116.58 Mg of CO and 11.24 Mg of PM. It is clear that the
emissions of each pollutant display a descending order for the urban
freeway, the artery road and the local road. High emission intensity and the
long length of the artery road (approximately 33 % of total length of roads in
Beijing) contribute to the highest emissions. Although the urban freeway
length is 2169 km, accounting for 22 % of total length of roads in Beijing,
the emissions of the urban freeway account for more than 38 % of the total
emissions, which is a little lower than the artery roads for each type of
pollutant. The local road emissions are lower than those on the urban
freeways and artery roads, although the length of local roads is the
longest in Beijing (approximately 38 % of the total length of roads).
The spatial distributions of emissions among the night, off-peak hours,
morning and afternoon peak hours are illustrated in Fig. 5. With the
assistance of ArcGIS, vehicle emissions are estimated at a 1 km × 1 km
resolution for the Beijing urban area. The emission intensity of vehicles
decreases from the centre to the periphery of the city with a radiating
structure during the night, off-peak, morning and afternoon peak hours. The
reason for the high emissions at the city centre is mainly caused by the
high traffic volume and low vehicle speed. In the surrounding areas of
Beijing, the high emissions are mostly distributed in the areas with
the urban freeways and the major intersections.
Grid-based vehicle emission inventory of NOx in Beijing.
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As illustrated in Fig. 5, the northern areas have the highest emission
intensities, the southern areas have the lowest emission intensities, and
the emission intensities of eastern areas are slightly higher than the
western areas. The difference of emissions among the various areas is mainly
caused by the different degrees of prosperity. More business activities and
human activities occur in the northern areas than other areas, leading to
more intense traffic activities in the northern areas.
The emission intensity of 8:00 to 09:00 and 17:00 to 18:00 is much higher than
for the rest of day because of high traffic volume during those times. Due
to serious traffic congestion, vehicles emit more pollutants when they
operate at low speed with frequent accelerations, decelerations and in idle
mode.
Temporal variation in emissions
According to the emission factors and vehicle activities, the vehicle
emission inventory model mentioned above was used to calculate the
pollutant emissions rate. The emissions of NOx, HC, CO and PM show similar
trends within a day. For example, the emissions of NOx vary throughout the
day but reach agreement among the urban freeway, artery roads and local
roads, as shown in Fig. 6. However, there is an obvious difference in the
vehicle emissions scenario between emissions on weekdays and weekends. For
all road types, the temporal variations of vehicle emissions are much closer
to the traffic flow, occurring separately at two emission peaks in the
morning and afternoon. The daytime emissions account for approximately
70 % of the daily total emissions because most private and business
activities are conducted during the daytime.
Hourly variation of vehicle emissions by road type on weekdays and
weekends.
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Spatial variation in emissions
As a result of the vehicle emission inventory model, the spatial
distribution of emissions has a strong correlation with the location of
Beijing. Table 3 summarize the emission intensities in different areas of
Beijing on weekdays and weekends. For both weekdays and weekends, vehicle
emission intensities in the centre area of the city are higher than in the
outside areas. The area between the second and third ring has the strongest
emission intensity because of its intensive road system and intense traffic
activities (shown as higher volume and lower traffic speed). Although the
urban centre (within the second ring) has the highest traffic density and
the lowest traffic speed, the high density of freeways and artery roads in
the area between the second and third ring causes the highest vehicle
emission intensities, which is consistent with the forecast in 2004 that the
emission intensities in the areas between the second and fourth rings could
be as high as those in the urban centre, caused by rapid construction on the
outside of the city centre (Huo et al., 2009).
Daily vehicle emission intensities within different areas of
Beijing (unit: 104 Mg km-2).
Date
Ring
NOx
CO
HC
PM
Weekdays
Within the second ring
0.377
1.520
0.076
0.015
Between the second and third ring
0.411
1.656
0.083
0.016
Between the third and fourth ring
0.366
1.477
0.074
0.014
Between the fourth and fifth ring
0.201
0.810
0.041
0.008
Between the fifth and sixth ring
0.058
0.234
0.012
0.002
Weekends
Within the second ring
0.368
1.483
0.074
0.014
Between the second and third ring
0.389
1.567
0.078
0.015
Between the third and fourth ring
0.347
1.398
0.070
0.013
Between the fourth and fifth ring
0.186
0.749
0.037
0.007
Between the fifth and sixth ring
0.057
0.230
0.012
0.002
Contribution of vehicle classification
Each on-road vehicle is used for the estimation of the bottom-up vehicle
emissions. The contribution of different vehicle types is shown in Fig. 7.
Although the number of LDVs is highest, their NOx and PM emission contributions
are lower than those of HDTs and buses; their PM emissions are even lower than those of HDVs. The HC and CO
emission contributions of LDVs and HDTs account for the largest proportion. As
shown in Fig. 8, the vehicles with lower emission control levels have the
higher emission contributions.
The vehicle emission contribution of different vehicle types.
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The vehicle emission contribution of different emission control level.
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Comparison between fuel consumption and fuel sales
Based on the fuel consumption factors and vehicle activities, the fuel
consumption of on-road vehicles was calculated by this model. The gasoline
and diesel consumption was 429.63 × 104 and 141.02 × 104 Mg respectively for the Beijing urban core area in 2013.
According to the data from the petroleum sale company, the retail sales of
gasoline and diesel fuel for the area in 2013 were 364 × 104
and 121 × 104 Mg respectively. However, the consumption of
gasoline and diesel was 18 and 16.5 % higher than the sales of
gasoline and diesel respectively because out-of-town vehicles driving into
Beijing commonly refuelled outside the city.
In order to estimate the effects of out-of-town vehicles on fuel consumption
calculation, the number of permits issued to out-of-town vehicles upon
entering Beijing, collected from the Beijing Vehicle Emission Management Centre, was investigated along with
their
travel distance and time. The statistical results shows that there were 80 million
out-of-town vehicles driving into Beijing, and each vehicle travelled 2
days in Beijing with a distance of 100 km per day. According to the above
statistics, the VKT of out-of-town vehicles accounts for 12.6 % of the
total VKT. When the fuel consumption of the out-of-town vehicles is added, the
total fuel consumption values are closer to the fuel sale values.
Comparison with other inventories
Annual vehicle emissions in different reports (unit: 104 Mg yr-1).
Species
Region
NOx
HC
CO
PM
HTSVE
Beijing
10.54
2.13
42.51
0.41
VECU (He et al., 2016)
Beijing
5.85
1.62
64.29
0.48
China Vehicle Emission Control Annual Report (2013)
Beijing
8.69
8.61
78.11
0.41
Table 4 illustrates some uncertainties that exist in HTSVE after a
comparison with the vehicle emission inventory known as the Chinese Unified Atmospheric
Chemistry Environment (hereafter refer to VECU) developed by the China
Meteorological Administration (He et al., 2016) and the inventory of the China
Vehicle Emission Control Annual Report (Ministry of Environmental Protection
of the People's Republic of China, 2013). By comparing vehicle emissions
between HTSVE and VECU, it is clear that NOx and HC vehicle emissions in
HTSVE are higher than in VECU, and CO and PM vehicle emissions in HTSVE are
slightly lower than in VECU, as shown in Table 4. The vehicle emissions of
NOx and PM in the China Vehicle Emission Control Annual Report are in an
agreement with the emissions in HTSVE, whereas the emissions of HC and CO
show significant differences. Moreover, the HC emissions in HTSVE are higher
than the HC emissions in VECU, but lower than the HC emissions in the China
Vehicle Emission Control Annual Report. The spatial distributions of vehicle
emissions for the Beijing urban area in HTSVE and VECU are shown in Fig. 9. This
figure shows that the high temporal–spatial resolution in HTSVE would be
helpful to accurately produce a numerical simulation of the air quality of
the city.
Spatial distributions of vehicle emissions in Beijing urban core
area: (a) NOx emissions in VECU, (b) NOx emissions in HTSVE, (c) PM
emissions in VECU and (d) PM emissions in HTSVE.
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In conclusion, HTSVE established in this paper was similar to to VECU and
inventory of China Vehicle Emission Control Annual Report on the order of
magnitude. However, HTSVE was indirectly evaluated by the comparison of fuel
consumption and fuel sale values. This showed that HTSVE could be closed with
the actual emissions of on-road vehicles. Meanwhile, HTSVE had an advantage over VECU
regarding
air quality numerical simulation (He et al., 2016), which
indicates that HTSVE can better depict vehicle emissions in temporal and spatial
trends.