This paper presents a bottom-up methodology based on the local emission
factors, complemented with the widely used emission factors of Computer
Programme to Calculate Emissions from Road Transport (COPERT) model and near-real-time traffic data on road segments to develop a vehicle emission
inventory with high temporal–spatial resolution (HTSVE) for the Beijing
urban area. To simulate real-world vehicle emissions accurately, the road
has been divided into segments according to the driving cycle (traffic
speed) on this road segment. The results show that the vehicle emissions of
NO
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.
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:
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).
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 NO
Emission factors of NO
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.
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
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 (
Hourly traffic average speed on different road types in Beijing.
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.
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
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.
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.
Daily vehicle emission on different road types of Beijing (unit: Mg day
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 NO
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
Grid-based vehicle emission inventory of NO
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.
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 NO
Hourly variation of vehicle emissions by road type on weekdays and weekends.
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: 10
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 NO
The vehicle emission contribution of different vehicle types.
The vehicle emission contribution of different emission control level.
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
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.
Annual vehicle emissions in different reports (unit: 10
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 NO
Spatial distributions of vehicle emissions in Beijing urban core
area:
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.
Both a bottom-up methodology using local emission factors and NRT traffic
data are applied to estimate the emissions of on-road vehicles in the Beijing
urban core area. The total vehicle emissions of NO
HTSVE established in this study can be extended in various ways. For example, it can be used to evaluate the impact of urban land plans on traffic emissions and the effect of traffic management measures on vehicle emissions reduction. Meanwhile, HTSVE can be transformed into an arbitrary scale grid according to the demands of the researcher. It can also be used as an accurate vehicle emission source inventory for air quality numerical simulation. In Part 2 of this project, the result shows that the accuracy of air quality simulation has been improved by using HTSVE.
This work was supported by the National Science and Technology Infrastructure Program (2014BAC16B03), China's National 973 Program (2011CB503801) and the National 863 Program (2012AA063303).Edited by: X.-Y. Zhang