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Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information
Journal of Environmental Management xxx (2016) 1e11
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Research article
Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information Long Pan a, Enjian Yao a, b, *, Yang Yang a a
School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Haidian District, Beijing 100044, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 January 2016 Received in revised form 29 August 2016 Accepted 1 September 2016 Available online xxx
With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Traffic-related air pollution Motor vehicle emissions Dispersion Air quality modeling Population exposure
1. Introduction Currently, many Chinese cities have serious air pollution. In 2014, Beijing residents suffered 45 heavily polluted days that air quality scored higher than 200 on the Air Quality Index (AQI) (Beijing Municipal People's Government, 2015). Motor vehicles are commonly viewed as one of the major sources causing serious air pollution in urban area. For example, 31.1% of the PM 2.5 in Beijing area is contributed by motor vehicles, which further become the leading polluting source (Beijing Municipal Bureau of
* Corresponding author. School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China. E-mail addresses: [email protected] (L. Pan), [email protected] (E. Yao), [email protected] (Y. Yang).
Environmental Protection, 2014). High concentration of trafficrelated air pollution may cause adverse health effects, such as exacerbating asthma, impairing lung function, increasing cardiovascular morbidity and mortality, aggravating adverse birth outcomes, and declining cognitive ability, etc. (Batterman et al., 2014; Tsai et al., 2010). For this reason, traffic-related air pollution becomes a major concern for the public and the authorities, evaluating its impact has become more necessary. However, detecting the concentration of traffic-related air pollution is difficult, since the monitoring stations usually can only detect the concentration of ambient air pollution caused by various factors as a whole. Source apportionment method can obtain the contribution rate of traffic, but it often costs much time and could not provide the real-time results. For example, Beijing Municipal Bureau of Environmental Protection released results of source apportionment after a year
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Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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L. Pan et al. / Journal of Environmental Management xxx (2016) 1e11
and half of monitoring and analyzing (Beijing Municipal Bureau of Environmental Protection, 2014). Luckily, the difficulty of extracting the traffic-related air pollution from the ambient air pollution can be avoided by considering the dispersion process from traffic pollution sources to traffic-related air pollution. With real-time traffic information, the input of the dispersion process, namely, the intensity of traffic pollution sources, can be obtained by establishing the relationship between traffic information and traffic emissions. Then, concentration of the traffic-related air pollution could be obtained by modeling the dispersion process with meteorological information. Being a major source of pollution in urban area, vehicle emissions are usually estimated by using traffic data from simulations or measurements as inputs for emission models. Existing research on simulative traffic data often use PARAMICS to obtain traffic status data and second-by-second vehicular operation data (Amirjamshidi et al., 2013; Misra et al., 2013). Although this method could obtain the input of the microscopic emission model, which describes the emissions of vehicles meticulously, simulative traffic data may not fully reflect real condition of vehicular operation. On the other hand, advances in traffic detection techniques make it possible to obtain relative sufficient traffic status data. Among these techniques, classical traffic detectors provide accurate information about the traffic status but those information are limited to specific locations. Also, installation and maintenance cost is too high. In comparison, Floating Car Data (FCD), which contains positional information of moving vehicles, is more suitable to monitor traffic conditions in urban networks (Tulic et al., 2014). Currently, some big cities in China (e.g., Beijing, Shanghai, and Wuhan) have the configuration of FCD, which assures obtaining massive traffic status data. For example, using FCD of taxis, Liu et al. (2013) successfully estimated the vehicle emissions and the air quality in Shanghai, China. Many studies have used emission models, which take traffic information (fleet composition, vehicle speed or link-based speed, etc.) as inputs, to estimate traffic-related emissions that cause air pollution. Based on driving parameters, emission models can be divided into three types: microscopic models, mesoscopic models, and macroscopic models (Yao and Song, 2013). Microscopic driving parameters reflect the driving characteristics of vehicles in the context of each point of time, such as instantaneous speed and instantaneous acceleration. Mesoscopic traffic parameters reflect the driving characteristics of vehicles in the context of a period of time, such as average speed and proportion of acceleration or deceleration. The CMEM model and VT-Micro model (Ahn, 1998; Ahn et al., 2002) are typical microscopic models for estimating the emissions accurately. CMEM model utilizes vehicle operational variables (e.g., speed, acceleration, and road grade) and modelcalibrated parameters (e.g., cold-start coefficients and enginefriction factor) as input data (Barth, 2010). Also, VT-Micro model requires substantial amounts of input data such as second-bysecond vehicle velocity profiles. However, obtaining these input data in practice is difficult, especially in most cities of China. As a result, microscopic models may not be applicable for estimating vehicle emissions in the urban scale. Macroscopic models are usually used for large-scale analysis, such as estimation of comprehensive emission factors or total emission amounts in an area (Liu et al., 2013). The most popular macroscopic models are MOBILE, COPERT, and IVE, which are applied by researchers in many cities in China. However, there are two problems when using macroscopic models to estimate the real-time traffic emissions of traffic networks in China. First, these models are often intended to predict emission inventories for large regional areas, but they may not be well suited for evaluating traffic operational improvements that are more “microscopic” (Barth et al., 2001). Thus, these models
do not fully consider the impact of vehicles’ operating mode on emissions, resulting in being unable to reflect the characteristics of vehicle emissions under different traffic conditions. Second, the emission inventories of these models are based on tests of American or European vehicles, while vehicles in China usually have different emission performance. To get more accurate input data of air dispersion model, this study adopts mesoscopic models (linkbased emission factor models) considering transient vehicle behaviors. For example, link-based emission factor model established by Yao and Song (2013) fully analyzed the influence of the vehicle speed, acceleration, and other driving conditions on the vehicle emissions, which improves both the applicability and accuracy for estimating vehicle emissions in urban area. Air dispersion models simulate the movement of air pollution by replicating physical atmospheric phenomena to estimate the spatial distribution of emission concentration (Misra et al., 2013), and vehicle emissions estimated by emission models and meteorological information are often used as input data. Among the common air dispersion models, box models are based on the principle of conservation of mass within a box, the inside of which is not defined and the air mass is treated as if it is well mixed and homogenously distributed (Holmes and Morawska, 2006). Eulerian and Lagrangian models are typically used for large domains, ranging from urban to global scales (Holmes and Morawska, 2006). Computational fluid dynamics (CFD) models have been introduced in environmental modeling for cases with complex geometries, such as street canyons. However, as CFD models are often used around complex geometry, a fine grid resolution is required to explicitly calculate turbulence up to a very small scale, which re} ssy et al., 2014). sults in extremely high computational costs (Leelo Compared to aforementioned dispersion models, Gaussian models, assuming a homogeneous, steady state flow and a steady-state point source, could response extremely fast because they only calculate a single formula for each receptor point instead of solving differential equations. The state-of-the-art Gaussian models include CALINE 4 (Benson, 1984), AERMOD (Cimorelli et al., 2005), and ADMS (Carruthers et al., 1994), which have been used to investigate local air quality in Detroit, Michigan, the U.S. (Batterman et al., 2014), in Shanghai, China (Liu et al., 2013), in Beijing, China (Cai and Xie, 2011), in the Greater Toronto Area, Canada (Amirjamshidi et al., 2013; Hatzopoulou and Miller, 2010), etc. However, these models need more detailed meteorological information, which is relatively difficult to obtain in many cities in China, especially the AERMOD (Chen et al., 2009). Moreover, most Gaussian dispersion models are designed for conditions with a significant wind speed, which is not suitable to calculate air pollution concentrations under calm wind conditions (Zhang and Batterman, 2010). To sum up, aiming to estimating and evaluating real-time impact of traffic-related air pollution with both real-time traffic information (link-based average traffic speed, traffic volume, vehicular fleet composition, etc.) and basic meteorological information, an integrated framework is proposed. First, this study calculates traffic emissions and concentration of traffic-related air pollution. Then, the impact of traffic-related air pollution and the population exposure are evaluated. The rest of the paper is organized as follows. In Section 2, two main parts of the integrated framework, the vehicle emissions estimation and the dispersion calculation, are introduced. An application example of the integrated framework is given in Section 3, in which the distribution of the concentration of the traffic-related air pollution and the population exposure are estimated and evaluated. Finally, some conclusions and the directions for the future work are provided in Section 4.
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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2. The integrated framework for estimating the concentration of traffic-related air pollution To calculate the concentration of traffic-related air pollution, an integrated concentration estimating framework incorporating the emission estimation and the dispersion calculation is proposed. First, considering the impact of the traffic status on the traffic emissions, the link-based traffic emission factor models are applied to obtain the real-time traffic emissions for each link in the road network with the hourly traffic status data. Then, regarding the link-based traffic emissions as the pollution sources and considering the different dispersion modes, corresponding dispersion models are applied to describe the dispersion process of traffic emissions based on the basic meteorological information.
3
affecting areas is introduced. It is notable that CO, one of the common air pollution, is chosen as an example of traffic-related air pollution in the rest of the paper, and the proposed framework is applicable to other air pollution which are stable while dispersing. 2.2.1. Gaussian line-source model The Gaussian line-source model is applied to calculate the distribution of pollutant concentration under windy condition (Arya, 1999), which is shown in Eqs. (2)e(3), and the emission rate from the source in Gaussian line-source model could be calculated by Eq. (4).
Cji
Qi ¼ pffiffiffiffiffiffi l exp 2pui sizj
0 2 0 1 13 ! Li i Li i B 2 yj C7 6 B 2 þ yj C i 4erf @pffiffiffi A þ erf @pffiffiffi A5 2szj 2siyj 2siyj z2j
2.1. Emission estimation
(2)
Ei ¼ Ri
n X
EFk Vki
(1)
k¼1
where Ei is the emission rate of road link i, g/h; EFk is the emission factor of vehicle type k, g/km, which is calculated by link-based average traffic speed (more details in Yao and Song (2013)); n is the total number of vehicle types; Vki is the vehicle volume of vehicle type k in road link i, veh/h. The vehicle volume could be obtained by fixed traffic detectors, such as remote traffic microwave sensor (RTMS) and vehicle loop detectors. Ri is the length of road link i, km. 2.2. Dispersion calculation Air dispersion models simulate the movement of air pollution by replicating physical atmospheric phenomena to estimate the spatial distribution of emission concentration with vehicle emissions, which are estimated by emission models based on meteorological information. In this paper, Gaussian dispersion models, which are based on basic meteorological information and have a relative fast response time, are integrated into the estimating framework. However, Gaussian dispersion models are sensitive to the wind speed, resulting in being applicable to a certain range of wind speed. Therefore, the Gaussian line-source dispersion model and the puff integration model are both integrated according to the meteorological conditions. First, the Gaussian line-source dispersion model under the windy condition (when the wind speed is more than or equal to 2 m/s) and the puff integration model under calm wind condition (when the wind speed is less than 2 m/s) are presented. Second, to meet the demand of aforementioned dispersion models, the obtainment of the pollutant sources and the
2 erf ðlÞ ¼ pffiffiffi
p
Qli ¼ Ei
Zl
2
et dt
(3)
0
. 3:6 106 $Ri
(4)
where Cji is the concentration of CO at receptor j caused by source i, g/m3; Qli is the emission rate of CO from source i in the Gaussian line-source model, g/m∙s; ui is the wind speed of pollutant source i, m/s; zj is the height above ground of receptor j, m; Li is the crosswind length of line-source i, m; yij is the crosswind distance between i and j, m; siyj and sizj are horizontal dispersion parameter and vertical dispersion parameter respectively, m; erf ðlÞ is the Gauss error function. 2.2.2. Puff integration model The puff integration model (Jiekuan, 1991) is a supplement to the aforementioned Gaussian line-source model under calm wind condition, in which the pollutant sources are considered as point sources. The equations are shown in Eqs. (5)e(8). Moreover, the emission rate from the source in the puff integration model could be calculated by Eq. (9).
2
3 i 2 nh u 6 7 Cji ¼ exp4 2 5 1 2 3 2 i i i2 i ð2pÞ syj szj Aj 2 syj pffiffiffi io p ffiffiffi 2 Dij þ 2Dij p exp Di2 j $f Qpi
=
Traffic emission factor models estimate traffic emissions on the road links of a road network based on the attributes such as traffic status information (link-based average traffic speed, traffic volume or vehicular speed profile) and vehicular fleet composition. Although microscopic emission models that account for variations in instantaneous speed provide more accurate results than average speed models, the input data of microscopic emission models is difficult to be obtained, especially in the urban scale. Therefore, the emission factor models established by Yao and Song (2013), whose database contains hundreds of typical car types and different car ages by a portable emission measurement system (PEMS) under actual driving conditions in Beijing, are applied to evaluate average hourly traffic-related emission by using the average speed and the vehicular fleet composition of the road link. The emission of a road link is calculated by Eq. (1).
Dij ¼ xij ui
2 2Aij siyj
2 2 2 2 2 2 siyj þ z2j 2 sizj xij þ yij Aij ¼
1 fðlÞ ¼ pffiffiffiffiffiffi 2p
Zl ∞
Qpi ¼ Ei =3600
2 t dt exp 2
(5)
(6)
(7)
(8)
(9)
where fðlÞ is the standard normal distribution function; Qpi is the emission rate of CO from source i in the puff integration model, g/s; xij is the downwind distance between i and j, m; Dij and Aij are the
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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intermediate coefficients; other coefficients have the same meaning in Eqs. (2)e(3). 2.2.3. Calculation of receptor's concentration In the subsection, to calculate the concentration of a receptor, the processing method of the pollution source is introduced first. Then, the calculating method of the concentration of the receptor is presented. When the pollutant sources are processed, a windy condition (u > 2m=s) and a calm wind condition (u 2m=s) are both considered, and the Gaussian line-source model and the puff integration model are applied correspondingly. To meet the demand of the dispersion models, the pollutant sources are considered as finite line sources perpendicular to the direction of the wind or as a point source correspondingly, as shown in Fig. 1, in which a represents the angle between the wind direction and the north, b represents the angle between the link direction and the north, and Point O is the middle point of the link. Moreover, the brown solid line is the link, which is a finite line source, and the red solid line, which is perpendicular to the wind direction, is the processed finite line source. Since a receptor is usually affected by some pollutant sources together, the concentration of traffic-related air pollution at a given receptor is estimated by the summation of the concentration produced by the pollutant sources which could affect the receptor (within the affecting area), as shown in Eq. (10).
Cj ¼
X
Cji
(12)
where T is the time interval, s; uwind is the wind speed, m/s. In this paper, T is assigned as 3600s (1 h) because the traffic and meteorological information are both obtained hourly, and when the wind speed is lower or equal to 0.5 m/s, uwind is assigned as 0.5 m/s, meaning that air pollutants spread the farthest distance with speed 0.5 m/s; when the wind speed is larger than 0.5 m/s, uwind is assigned as the wind speed. When the wind speed is larger than 0.5 m/s, the dispersion of the emissions is affected by the wind direction. Therefore, an upwind circular sector (see Fig. 2(b)) is determined to describe the affecting area of a receptor, of which the borders are calculated by Eq. (11) and Eq. (13).
y ¼ ±4sy10 x 10
(13)
where sy10 is the horizontal dispersion parameter at a downwind distance of 10 m. Fig. 2 shows the affecting area (the area filled with slant lines including the edge) calculated based on Eqs. (11)e(13), as well as the wind direction (the x-axis), the receptor (the black solid circle in the center of Fig. 2), and some pollutant sources (the black solid triangles). In Fig. 2(a), three pollutant sources which could affect the receptor are in the affecting area. Fig. 2(b) illustrates that three pollutant sources are in the affecting area, while other two pollutant sources are outside the region.
(10)
i2ARj
3. Traffic-related air pollution impact analysis
where Cj is the CO concentration at receptor j; Cji is the CO concentration at receptor j caused by source i; ARj is the set of pollutant sources within the affecting area of receptor j. The affecting area of a receptor is determined by different methods according to the different conditions of wind speed. Air pollutants are often distributed over the downwind side of pollutant sources when the wind speed is more than 0.5 m/s; however, when the wind speed is less than or equal to 0.5 m/s, the wind direction is usually unstable, and the spread of the emissions covers 360 , leading the affecting area to be regarded as a circle (Fig. 2(a)), which is calculated by Eq. (11).
x2 þ y 2 ¼ R 2
R ¼ T$uwind
(11)
where the positive direction of x-axis is the wind direction; the positive direction of y-axis is perpendicular to the x-axis and form a 90 angle counterclockwise to the positive direction of x-axis; R is the radius of the affecting region, which is calculated by Eq. (12).
3.1. Data The integrated estimating framework is applied to the main area of Beijing, China, as shown in Fig. 3. The main area of Beijing covers about 130*190 km2, in which the modeled road network consisted of 36,853 links representing 10,853 km of roads, including expressways (4733 links), arterial roads (18,582 links), and secondary roads (13,538 links). Considering the computational cost and the calculated accuracy of the pollutant concentration, the study area is divided into thousands of zones by the grids with the side of 1 km, and the road links are cut into short links by these grids. Moreover, for fully analyzing the real-time impact of traffic-related air pollution, the hourly traffic information (link-based average traffic speed, traffic volume, etc.) is obtained by roadside traffic detectors (e.g. RTMS and vehicle loop detectors) and Floating Car Data (FCD), and hourly basic meteorological information (wind speed, wind direction, cloud cover, etc.) is obtained by meteorological monitoring stations, on Feb 9 and Feb 20, 2014, which are typical days under a calm wind condition and a windy condition, respectively. In addition, because the hourly vehicular fleet composition is hard to obtain, the constant vehicular fleet composition is used, which was composed by 95.4% of light-duty vehicles, 1.1% of mid-duty vehicles, and 3.5% of heavy-duty vehicles. To evaluate the population exposure to CO, the static demographic data which include the population of each grid is achieved. Air Pollutant Index (API), an index of CO concentration, is proposed to display the impacts of CO concentration in different grids (see Table 1), and the API of CO is calculated referring to Air Quality Index (AQI), as shown in Eq. (14). The CO concentration is divided into five grades to represent the API of CO (see Table 2).
API ¼
APIL API S C LC S þ APIS LC L LC S
(14)
Fig. 1. Relationship between the link and the wind direction.
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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Fig. 2. Illustration of affecting area under different wind speeds.
Fig. 3. Study area and road network in Beijing.
approximately 54.5.
Table 1 Index of CO concentration. Index CO Concentration (mg/m3)
0 0
50 0.002
100 0.09
150 0.4
200 1.5
300 3.0
400 5.0
500 7.0
Table 2 Grades of API of CO. API Grade
0 0
1e125 1
126e250 2
250e375 3
376e500 4
where API is the API value of CO concentration; C is the concentration of CO; LCL is the larger value, which are related to the index, closest to C; LCS is the smaller value between the concentration limit values close to C; APIL is the traffic-related air pollution index corresponding to the LCL; APIS is the traffic-related air pollution index corresponding to the LCS. For example, if the CO concentration is 0.01 mg/m3, then APIL and APIS are 100 and 50, respectively, and LCL and LCS are 0.09 mg/m3 and 0.002 mg/m3, respectively; therefore, the API could be calculated by Eq. (14) and is
3.2. Impact analysis and discussion 3.2.1. CO concentration Based on the proposed framework, the traffic emissions of each short link, which is considered as the intensity of pollutant source, is calculated. In addition, meteorological conditions are considered to be constant in each hour to meet the demand of the dispersion models, and the pollutant concentration of the central point of each grid is regarded as the concentration of each grid. Taking the data on Feb 9th, 2014 as an example, the distributions of the API of CO concentration at 7:00e8:00 (morning peak hour), 12:00e13:00 (one of flat peak hours), and 18:00e19:00 (evening peak hour) are estimated and shown in Fig. 4, in which the five grades of CO API (Grade 0 to Grade 4) are represented as white, light grey, grey, dark grey, and black, respectively. The result shows that the CO concentration is much higher in the central area of Beijing, especially near the main roads with higher traffic emissions. In contrast, the CO concentration of the peripheral area is much lower than that of the central area because
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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L. Pan et al. / Journal of Environmental Management xxx (2016) 1e11
Fig. 4. Distribution of CO concentration.
of less traffic emissions. Moreover, it shows a temporal characteristic similar to the traffic volume that the CO concentration in the study area is higher at 7:00e8:00 and 18:00e19:00, which is much higher than that at 12:00e13:00. Because of being related to the traffic emissions which could affect the distribution of the CO concentration significantly, the traffic status (traffic volume and traffic speed) may have a correlation with the CO concentration directly. However, describing the traffic volume and the traffic speed of a grid is difficult because there may be many road links under different traffic status in a grid; consequently, traffic intensity and traffic speed of a grid are defined by Eqs. (15) and (16).
TIj ¼
X
Vi Ri
(15)
i2Nj
TSj ¼
X
subsection 2.2.3). For example, suppose the wind speeds are 0.2 m/ s and 1 m/s, respectively, the affecting grid collections are shown in Fig. 5. When the wind speed is less than 0.5 m/s or more than 0.5 m/s, the affecting area is calculated by Eqs. (11)e(13), and when more than 50% area of a grid is in the affecting area, this grid is defined to be in the affecting grid collection. To be clear, the target grid is always included in the affecting grid collection. Based on this definition, the affecting grid collections under different wind speeds are shown in Fig. 5(a) and (b) as the yellow grids and the red grid (the target grid). Then, the traffic intensity and the traffic speed of an affecting grid collection are calculated by Eqs. (17) and (18).
TIacollection
¼
X
, TIi
Na
(17)
i2Ca
si Ri
(16)
i2Nj
where TIj is the traffic intensity of grid j, pcu*km; TSj is the traffic speed of grid j, km/h; Vi is the traffic volume of road link i in grid j, pcu; Ri is the length of road link i in grid j, km; Nj is the set of the road links in grid j; si is the traffic speed of road link i, km/h. Generally, the CO concentration of a grid is affected not only by the traffic intensity of the grid itself, but also by those of the adjacent grids. Facing this situation, the concept of the affecting area in subsection 2.2.3 is used to determine the affecting grid collection of the target grid, and the relationship between traffic status of the affecting grid collection and the CO concentration is evaluated. The affecting area, which determines the affecting grid collection, is classified based on the wind direction (more details in
TScollection a
¼
X
, TSi
Na
(18)
i2Ca
where TIacollection is the traffic intensity of the affecting grid collection of grid a; TScollection is the traffic speed of the affecting grid a collection of grid a; Ca is the collection of grids in the affecting grid collection of grid a; Na is the number of grids in the affecting grid collection of grid a. The data of two grids (see Fig. 6) on Feb 9th are chosen to display the relationship between CO concentration and traffic status (traffic intensity and traffic speed). The CO concentrations in these grids as well as the traffic status (the traffic intensity and the traffic speed) of the grids within the
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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Fig. 5. Examples of the affecting grid collection.
Fig. 6. Locations of the typical grids.
affecting area of S1 or S2 are presented in Fig. 7. In Fig. 7(a) and (b), the trend of the CO concentration corresponds to that of the traffic intensity, and it could be inferred that the CO concentration may be correlative with the traffic intensity. Similarly, in Fig. 7(c) and (d), the CO concentration is relatively higher while the traffic speed is relatively lower, especially at 7:00e8:00, 8:00e9:00, and 17:00e18:00 of S2 on Feb 9th. Based on the data in S1 and S2, the CO concentration has a certain correlation with the traffic intensity (the correlation coefficient is 0.77) or with the traffic speed (the correlation coefficient is 0.81). Therefore, to analyze the multi-factor influence of the CO concentration, one of the multiple regression models, Multiple Linear Regression (MLR) method, is applied. The results of analysis of variance (ANOVA) and the regression model are shown in Table 3 and Table 4, respectively. According to Table 3, the two parameters that influence CO concentration were statistically significant (p-value < 0.01). In Table 4, the traffic intensity and the traffic speed were also statistically significant (p-value < 0.05), and the adjusted R2 of the
regression model is 0.617, which shows that the model is capable of explaining at least 61.7% of the input data. The result indicates that the methods to determine the dynamic affecting areas are reasonable, and the traffic intensity and the traffic speed of an affecting grid collection have a certain relationship with the CO concentration of the corresponding target grid. In the regression model, the coefficients of the traffic speed and the traffic intensity are negative and positive, respectively, indicating that the traffic speed has a negative effect on the CO concentration, while the traffic intensity has a positive effect. It should be noted that the traffic speed is mainly below 60 km/h (see Fig. 7(c) and (d)), and at this range the traffic speed is viewed having a negative relationship with the traffic emissions per unit length (Yao and Song, 2013); however, if the traffic speed is mainly above 60 km/h, it may have an opposite effect on the CO concentration. In addition, for studying the influence of the meteorological information on the CO concentration, the relationship between the wind speed of the affecting grid collection and the CO
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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L. Pan et al. / Journal of Environmental Management xxx (2016) 1e11
Fig. 7. Traffic intensity indexes and traffic speed as well as CO concentrations in S1 and S2.
Table 3 Result of ANOVA. Model
Sum of square
df
Mean square
F-value
p-value
Regression Residual Total
17.641 10.212 27.853
2 45 47
8.821 0.227
38.869
0.000
models by comparing the predicted concentrations to the observed concentrations (Chang and Hanna, 2005). Therefore, Pearson Correlation Coefficient (PCC) and FAC2 are chosen to quantify the model performance. The performance is more satisfying if the value of PCC or FAC2 is closer to one.
Table 4 Result of the regression model. Model
Unstandardized coefficients
Standardized coefficients
B
Std. error
Beta
Constant Traffic speed Traffic intensity
1.152 0.030 0.041
0.770 0.012 0.006
0.238 0.676
concentration of the target grid is analyzed. The result shows that the correlation coefficients are 0.17(S1) and 0.11(S2), indicating that the wind speed near the target grid have a slight positive effect on dissipating the CO concentration of the target grid. The reason why there is not a strong effect may be because the concentration is not only influenced by the wind speed but also by other factors of meteorological condition (e.g., wind direction, humidity, and cloudage). To verify the result of the integrated framework, the observed ambient CO concentration is obtained to compare with the estimating result. For mainly analyzing the relationship between the traffic information and the CO concentration, the monitoring station in Xizhimen, where the traffic is much heavier, is chosen to be compared with ambient CO concentration predicted at the same location. Furthermore, the fraction of model estimates within a factor of two of observations (FAC2, see Eq. (19)) is a common approach usually used to evaluate the performance of dispersion
t
p-value
R2
Adjusted R2
1.496 2.447 6.957
0.142 0.018 0.000
0.633
0.617
Cp FAC2 ¼ n 0:5 2:0 N Co
(19)
where FAC2 is the value of FAC2; Co and Cp are observed concentrations and predicted concentrations, respectively; N is the number of observed concentrations; n(.) is the number of predicted concentrations that satisfy the condition. Although the ambient CO concentration in Beijing cannot be calculated by the proposed framework (only the traffic-related CO concentration could be calculated), background CO concentration, which could be obtained from five monitoring stations away from central area of Beijing, is used to estimate the ambient CO concentration, as shown in Eq. (20). traffic
CT ¼ CT
þ CTback
(20)
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L. Pan et al. / Journal of Environmental Management xxx (2016) 1e11
9
Fig. 8. Observed ambient CO concentration and predicted ambient CO concentration by MA at the monitoring station in Xizhimen.
where CT is the predicted ambient CO concentration at time T, mg/ m3; CTtraffic is the traffic-related CO concentration calculated by our approach at time T, mg/m3; CTback is the background CO concentration at time T, mg/m3. Moreover, based on the predicted ambient CO concentration, Moving Average (MA) method is used to reflect the trend of the CO concentration curve, as shown in Eq. (21).
CTMA ¼
1 ð2CT þ CTþ1 Þ 3
(21)
where CTMA is the CO concentration calculated by MA, mg/m3; CT and CTþ1 are the CO concentrations at time T and (Tþ1) respectively, mg/m3. Fig. 8 shows the observed ambient CO concentration and the predicted ambient CO concentration by MA at the monitoring station in Xizhimen in Feb 9th, 2014 and Feb 20th, 2014, respectively. Overall, although there are some errors between the predicted concentration by MA and the observed concentration, the predicted value resembles the trend of observed ambient CO concentration in both two days, with the PCCs being 0.67 (Feb 9th) and 0.79 (Feb 20th), and the FAC2s being 0.67 (Feb 9th) and 1 (Feb
Fig. 9. Distribution of population density and population exposure to CO within the central area of Beijing.
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L. Pan et al. / Journal of Environmental Management xxx (2016) 1e11
20th). Chang and Hanna (2005) suggest that the model with a FAC2 greater than 0.5 is viewed to have an acceptable performance, indicating that the predicting performance of the proposed framework is satisfying, and the selection and the application of the dispersion models considering different meteorological conditions are reasonable. 3.2.2. Population exposure to CO Population exposure to CO was estimated as a multiplication of the population dwelling in each grid and the CO concentration in the grid (Amirjamshidi et al., 2013), as shown in Eq. (22).
PET ¼ P$CT
(22)
where PET is the population exposure to CO, person*mg/m3; P is the population, person. Fig. 9(a) shows the distribution of the population density in central area of Beijing, and Fig. 9(b) and (c) show the distributions of population exposure to CO at 19:00e20:00 on Feb 9th (under a calm wind condition) and Feb 20th (under a windy condition), respectively. The time interval of 19:00e20:00 is chosen as the research time is because the demographic data is based on the residences, and the residents are usually at home during this time interval. In Fig. 9(a), the population densities of the grids sharing the similar distance from the center are nearly the same, and with the distance increasing, the population density decreases. Remarkably, the population density of the central point of the area is much lower because the Forbidden City is located there, and there are almost no dwellings. In contrast with Fig. 9(a), population exposure to CO shows a rather different pattern from the distribution of population density on Feb 9th, as shown in Fig. 9(b). On Feb 9th (under a calm wind condition), the impact of the population exposure is more significant in the east because the grids in the east with a higher population density also have a higher CO concentration. Moreover, within the Fourth Ring Road, the areas averagely have a higher population density and a higher CO concentration under calm wind condition, meaning that the traffic-related air pollution has a heavier impact on public health in these areas. However, on Feb 20th (under a windy condition), the impact of the population exposure seems slightly stronger in the northwest, the southwest, and the northeast parts of the central area of Beijing. 4. Conclusions In this paper, an integrated framework incorporating the emission estimation and the dispersion calculation is proposed to estimate the concentration of traffic-related air pollution in urban area with real-time traffic data and basic meteorological data, and then the impact of traffic-related air pollution is further evaluated. First, traffic emissions are calculated by vehicle emission factor models sensitive to the traffic status based on real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on different meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Next, the concentration of traffic-related air pollution is calculated by Gaussian dispersion model and puff integration model for corresponding conditions. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that the traffic-related CO concentration is higher in morning and evening peak hours, and the areas polluted most are within the scope of Fourth Ring Road,
especially near the main roads and at the downwind direction. Moreover, the relationship between traffic indicators and trafficrelated CO concentration is quantified by Multiple Linear Regression (MLR). Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area. Gaussian dispersion models are applied based on the assumption that the traffic emission intensity should keep relative steady between several hours (i.e. hourly traffic flow and speed do not vary dramatically), and the results calculated by the proposed framework may not be well suitable to the cases in which the traffic status and the meteorological condition fluctuate dramatically. In further study, the dispersion model being more sensitive to or other supplementary methods to handle the fluctuation of the traffic emissions and the meteorological condition could be considered and applied. Moreover, after finishing calculating the traffic-related air pollution concentrations for a whole year with demanding input data, more interesting characteristics of traffic-related air pollution in Beijing may be obtained. In addition, based on more data, we could have a fully sensitivity analysis of the model from the aspects of the traffic and the meteorological conditions. Acknowledgements This work was supported by Research Fund for the Doctoral Program of Higher Education of China [grant number 20130009110002]; and the Fundamental Research Funds for the Central Universities [grant number 2016YJS077]. References Ahn, K., 1998. Microscopic Fuel Consumption and Emission Modeling. Virginia Polytechnic Institute and State University. Ahn, K., Rakha, H., Trani, A., Van Aerde, M., 2002. Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. J. Transp. Eng. 128 (2), 182e190. Amirjamshidi, G., Mostafa, T.S., Misra, A., Roorda, M.J., 2013. Integrated model for microsimulating vehicle emissions, pollutant dispersion and population exposure. Transp. Res. Part D Transp. Environ. 18 (0), 16e24. http://dx.doi.org/ 10.1016/j.trd.2012.08.003. Arya, S.P., 1999. Air Pollution Meteorology and Dispersion. Oxford University Press, New York. Barth, M., 2010. The comprehensive modal emission model (CMEM) for predicting light-duty vehicle emissions. In: Paper Presented at the Transportation Planning and Air Quality IV: Persistent Problems and Promising Solutions. Barth, M., Malcolm, C., Younglove, T., Hill, N., 2001. Recent validation efforts for a comprehensive modal emissions model. Transp. Res. Rec. J. Transp. Res. Board 1750, 13e23. Batterman, S., Ganguly, R., Isakov, V., Burke, J., Arunachalam, S., Snyder, M., Robins, T., Lewis, T., 2014. Dispersion modeling of traffic-related air pollutant exposures and health effects among children with asthma in Detroit, Michigan. Transp. Res. Rec. J. Transp. Res. Board 2452. Beijing Municipal Bureau of Environmental Protection, 2014. Beijing Environmental Statement 2014. Retrieved from. http://www.bjepb.gov.cn/bjepb/resource/cms/ 2015/04/2015041609380279715.pdf. Beijing Municipal People's Government, 2015. Implementation Status Report of Beijing's Air Pollution Prevention and Control Regulation. China. Retrieved from. http://www.bjrd.gov.cn/zdgz/zyfb/bg/201505/t20150508_149012.html. Benson, P.E., 1984. CALINE4-A Dispersion Model for Predicting Air Pollutant Concentrations Near Roadways. Office of Transportation Laboratory. California Department of Transportation, Sacramento, USA. Cai, H., Xie, S., 2011. Traffic-related air pollution modeling during the 2008 Beijing Olympic Games: the effects of an odd-even day traffic restriction scheme. Sci. Total Environ. 409 (10), 1935e1948. http://dx.doi.org/10.1016/ j.scitotenv.2011.01.025. Carruthers, D.J., Holroyd, R.J., Hunt, J.C.R., Weng, W.S., Robins, A.G., Apsley, D.D., Thompson, D.J., Smith, F.B., 1994. UK-ADMS: a new approach to modelling dispersion in the earth's atmospheric boundary layer. J. Wind Eng. Industrial Aerodynamics 52 (0), 139e153. http://dx.doi.org/10.1016/0167-6105(94)90044-2. Chang, J.C., Hanna, S.R., 2005. Technical Descriptions and User's Guide for the BOOT
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L. Pan et al. / Journal of Environmental Management xxx (2016) 1e11 Statistical Model Evaluation Software Package, Version 2.0. Chen, H., Bai, S., Eisinger, D.S., Niemeier, D., Claggett, M., 2009. Predicting near-road PM2.5 concentrations: comparative assessment of CALINE4, CAL3QHC, and AERMOD. Transp. Res. Rec. J. Transp. Res. Board 12 (2123), 26e37. Cimorelli, A.J., Perry, S.G., Venkatram, A., Weil, J.C., Paine, R., Wilson, R.B., Lee, R.F., Peters, W.D., Brode, R.W., 2005. AERMOD: a dispersion model for industrial source applications. Part I: general model formulation and boundary layer characterization. J. Appl. Meteorology 44 (5), 682e693. Hatzopoulou, M., Miller, E.J., 2010. Linking an activity-based travel demand model with traffic emission and dispersion models: transport's contribution to air pollution in Toronto. Transp. Res. Part D Transp. Environ. 15 (6), 315e325. http://dx.doi.org/10.1016/j.trd.2010.03.007. Holmes, N.S., Morawska, L., 2006. A review of dispersion modelling and its application to the dispersion of particles: an overview of different dispersion models available. Atmos. Environ. 40 (30), 5902e5928. http://dx.doi.org/10.1016/ j.atmosenv.2006.06.003. Lagzi, I., Me }ssy Jr., A.F.M., k, F., Havasi, A., sz Leelo Izsa aros, R., 2014. Dispersion modeling of air pollutants in the atmosphere: a review. Central Eur. J. Geosciences 6 (3), 257e278. Jiekuan, S., 1991. An analytical dispersion model and practical calculation of low wind speed puff diffusion. Shanghai Environ. Sci. (07), 40e42.
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Liu, H., Chen, X., Wang, Y., Han, S., 2013. Vehicle emission and near-road air quality modeling for Shanghai, China: based on global positioning system data from taxis and revised MOVES emission inventory. Transp. Res. Rec. J. Transp. Res. Board 19 (2340), 38e48. Misra, A., Roorda, M.J., MacLean, H.L., 2013. An integrated modelling approach to estimate urban traffic emissions. Atmos. Environ. 73 (0), 81e91. http:// dx.doi.org/10.1016/j.atmosenv.2013.03.013. Tsai, D.H., Wang, J.L., Chuang, K.J., Chan, C.C., 2010. Traffic-related air pollution and cardiovascular mortality in central Taiwan. Sci. Total Environ. 408 (8), 1818e1823. http://dx.doi.org/10.1016/j.scitotenv.2010.01.044. Tulic, M., Bauer, D., Scherrer, W., 2014. Link and route travel time prediction including the corresponding reliability in an urban network based on taxi floating car data. Transp. Res. Rec. J. Transp. Res. Board (2442), 140e149, 10.3141/2442-15. Yao, E., Song, Y., 2013. Study on eco-route planning algorithm and environmental impact assessment. J. Intelligent Transp. Syst. Technol. Plan. Operations 17 (1), 42e53. Zhang, K., Batterman, S., 2010. Near-road air pollutant concentrations of CO and PM2.5: a comparison of MOBILE6.2/CALINE4 and generalized additive models. Atmos. Environ. 44 (14), 1740e1748. http://dx.doi.org/10.1016/j.atmosenv.2010.02.008.
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Research article
Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information Long Pan a, Enjian Yao a, b, *, Yang Yang a a
School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Haidian District, Beijing 100044, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 January 2016 Received in revised form 29 August 2016 Accepted 1 September 2016 Available online xxx
With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Traffic-related air pollution Motor vehicle emissions Dispersion Air quality modeling Population exposure
1. Introduction Currently, many Chinese cities have serious air pollution. In 2014, Beijing residents suffered 45 heavily polluted days that air quality scored higher than 200 on the Air Quality Index (AQI) (Beijing Municipal People's Government, 2015). Motor vehicles are commonly viewed as one of the major sources causing serious air pollution in urban area. For example, 31.1% of the PM 2.5 in Beijing area is contributed by motor vehicles, which further become the leading polluting source (Beijing Municipal Bureau of
* Corresponding author. School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China. E-mail addresses: [email protected] (L. Pan), [email protected] (E. Yao), [email protected] (Y. Yang).
Environmental Protection, 2014). High concentration of trafficrelated air pollution may cause adverse health effects, such as exacerbating asthma, impairing lung function, increasing cardiovascular morbidity and mortality, aggravating adverse birth outcomes, and declining cognitive ability, etc. (Batterman et al., 2014; Tsai et al., 2010). For this reason, traffic-related air pollution becomes a major concern for the public and the authorities, evaluating its impact has become more necessary. However, detecting the concentration of traffic-related air pollution is difficult, since the monitoring stations usually can only detect the concentration of ambient air pollution caused by various factors as a whole. Source apportionment method can obtain the contribution rate of traffic, but it often costs much time and could not provide the real-time results. For example, Beijing Municipal Bureau of Environmental Protection released results of source apportionment after a year
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L. Pan et al. / Journal of Environmental Management xxx (2016) 1e11
and half of monitoring and analyzing (Beijing Municipal Bureau of Environmental Protection, 2014). Luckily, the difficulty of extracting the traffic-related air pollution from the ambient air pollution can be avoided by considering the dispersion process from traffic pollution sources to traffic-related air pollution. With real-time traffic information, the input of the dispersion process, namely, the intensity of traffic pollution sources, can be obtained by establishing the relationship between traffic information and traffic emissions. Then, concentration of the traffic-related air pollution could be obtained by modeling the dispersion process with meteorological information. Being a major source of pollution in urban area, vehicle emissions are usually estimated by using traffic data from simulations or measurements as inputs for emission models. Existing research on simulative traffic data often use PARAMICS to obtain traffic status data and second-by-second vehicular operation data (Amirjamshidi et al., 2013; Misra et al., 2013). Although this method could obtain the input of the microscopic emission model, which describes the emissions of vehicles meticulously, simulative traffic data may not fully reflect real condition of vehicular operation. On the other hand, advances in traffic detection techniques make it possible to obtain relative sufficient traffic status data. Among these techniques, classical traffic detectors provide accurate information about the traffic status but those information are limited to specific locations. Also, installation and maintenance cost is too high. In comparison, Floating Car Data (FCD), which contains positional information of moving vehicles, is more suitable to monitor traffic conditions in urban networks (Tulic et al., 2014). Currently, some big cities in China (e.g., Beijing, Shanghai, and Wuhan) have the configuration of FCD, which assures obtaining massive traffic status data. For example, using FCD of taxis, Liu et al. (2013) successfully estimated the vehicle emissions and the air quality in Shanghai, China. Many studies have used emission models, which take traffic information (fleet composition, vehicle speed or link-based speed, etc.) as inputs, to estimate traffic-related emissions that cause air pollution. Based on driving parameters, emission models can be divided into three types: microscopic models, mesoscopic models, and macroscopic models (Yao and Song, 2013). Microscopic driving parameters reflect the driving characteristics of vehicles in the context of each point of time, such as instantaneous speed and instantaneous acceleration. Mesoscopic traffic parameters reflect the driving characteristics of vehicles in the context of a period of time, such as average speed and proportion of acceleration or deceleration. The CMEM model and VT-Micro model (Ahn, 1998; Ahn et al., 2002) are typical microscopic models for estimating the emissions accurately. CMEM model utilizes vehicle operational variables (e.g., speed, acceleration, and road grade) and modelcalibrated parameters (e.g., cold-start coefficients and enginefriction factor) as input data (Barth, 2010). Also, VT-Micro model requires substantial amounts of input data such as second-bysecond vehicle velocity profiles. However, obtaining these input data in practice is difficult, especially in most cities of China. As a result, microscopic models may not be applicable for estimating vehicle emissions in the urban scale. Macroscopic models are usually used for large-scale analysis, such as estimation of comprehensive emission factors or total emission amounts in an area (Liu et al., 2013). The most popular macroscopic models are MOBILE, COPERT, and IVE, which are applied by researchers in many cities in China. However, there are two problems when using macroscopic models to estimate the real-time traffic emissions of traffic networks in China. First, these models are often intended to predict emission inventories for large regional areas, but they may not be well suited for evaluating traffic operational improvements that are more “microscopic” (Barth et al., 2001). Thus, these models
do not fully consider the impact of vehicles’ operating mode on emissions, resulting in being unable to reflect the characteristics of vehicle emissions under different traffic conditions. Second, the emission inventories of these models are based on tests of American or European vehicles, while vehicles in China usually have different emission performance. To get more accurate input data of air dispersion model, this study adopts mesoscopic models (linkbased emission factor models) considering transient vehicle behaviors. For example, link-based emission factor model established by Yao and Song (2013) fully analyzed the influence of the vehicle speed, acceleration, and other driving conditions on the vehicle emissions, which improves both the applicability and accuracy for estimating vehicle emissions in urban area. Air dispersion models simulate the movement of air pollution by replicating physical atmospheric phenomena to estimate the spatial distribution of emission concentration (Misra et al., 2013), and vehicle emissions estimated by emission models and meteorological information are often used as input data. Among the common air dispersion models, box models are based on the principle of conservation of mass within a box, the inside of which is not defined and the air mass is treated as if it is well mixed and homogenously distributed (Holmes and Morawska, 2006). Eulerian and Lagrangian models are typically used for large domains, ranging from urban to global scales (Holmes and Morawska, 2006). Computational fluid dynamics (CFD) models have been introduced in environmental modeling for cases with complex geometries, such as street canyons. However, as CFD models are often used around complex geometry, a fine grid resolution is required to explicitly calculate turbulence up to a very small scale, which re} ssy et al., 2014). sults in extremely high computational costs (Leelo Compared to aforementioned dispersion models, Gaussian models, assuming a homogeneous, steady state flow and a steady-state point source, could response extremely fast because they only calculate a single formula for each receptor point instead of solving differential equations. The state-of-the-art Gaussian models include CALINE 4 (Benson, 1984), AERMOD (Cimorelli et al., 2005), and ADMS (Carruthers et al., 1994), which have been used to investigate local air quality in Detroit, Michigan, the U.S. (Batterman et al., 2014), in Shanghai, China (Liu et al., 2013), in Beijing, China (Cai and Xie, 2011), in the Greater Toronto Area, Canada (Amirjamshidi et al., 2013; Hatzopoulou and Miller, 2010), etc. However, these models need more detailed meteorological information, which is relatively difficult to obtain in many cities in China, especially the AERMOD (Chen et al., 2009). Moreover, most Gaussian dispersion models are designed for conditions with a significant wind speed, which is not suitable to calculate air pollution concentrations under calm wind conditions (Zhang and Batterman, 2010). To sum up, aiming to estimating and evaluating real-time impact of traffic-related air pollution with both real-time traffic information (link-based average traffic speed, traffic volume, vehicular fleet composition, etc.) and basic meteorological information, an integrated framework is proposed. First, this study calculates traffic emissions and concentration of traffic-related air pollution. Then, the impact of traffic-related air pollution and the population exposure are evaluated. The rest of the paper is organized as follows. In Section 2, two main parts of the integrated framework, the vehicle emissions estimation and the dispersion calculation, are introduced. An application example of the integrated framework is given in Section 3, in which the distribution of the concentration of the traffic-related air pollution and the population exposure are estimated and evaluated. Finally, some conclusions and the directions for the future work are provided in Section 4.
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2. The integrated framework for estimating the concentration of traffic-related air pollution To calculate the concentration of traffic-related air pollution, an integrated concentration estimating framework incorporating the emission estimation and the dispersion calculation is proposed. First, considering the impact of the traffic status on the traffic emissions, the link-based traffic emission factor models are applied to obtain the real-time traffic emissions for each link in the road network with the hourly traffic status data. Then, regarding the link-based traffic emissions as the pollution sources and considering the different dispersion modes, corresponding dispersion models are applied to describe the dispersion process of traffic emissions based on the basic meteorological information.
3
affecting areas is introduced. It is notable that CO, one of the common air pollution, is chosen as an example of traffic-related air pollution in the rest of the paper, and the proposed framework is applicable to other air pollution which are stable while dispersing. 2.2.1. Gaussian line-source model The Gaussian line-source model is applied to calculate the distribution of pollutant concentration under windy condition (Arya, 1999), which is shown in Eqs. (2)e(3), and the emission rate from the source in Gaussian line-source model could be calculated by Eq. (4).
Cji
Qi ¼ pffiffiffiffiffiffi l exp 2pui sizj
0 2 0 1 13 ! Li i Li i B 2 yj C7 6 B 2 þ yj C i 4erf @pffiffiffi A þ erf @pffiffiffi A5 2szj 2siyj 2siyj z2j
2.1. Emission estimation
(2)
Ei ¼ Ri
n X
EFk Vki
(1)
k¼1
where Ei is the emission rate of road link i, g/h; EFk is the emission factor of vehicle type k, g/km, which is calculated by link-based average traffic speed (more details in Yao and Song (2013)); n is the total number of vehicle types; Vki is the vehicle volume of vehicle type k in road link i, veh/h. The vehicle volume could be obtained by fixed traffic detectors, such as remote traffic microwave sensor (RTMS) and vehicle loop detectors. Ri is the length of road link i, km. 2.2. Dispersion calculation Air dispersion models simulate the movement of air pollution by replicating physical atmospheric phenomena to estimate the spatial distribution of emission concentration with vehicle emissions, which are estimated by emission models based on meteorological information. In this paper, Gaussian dispersion models, which are based on basic meteorological information and have a relative fast response time, are integrated into the estimating framework. However, Gaussian dispersion models are sensitive to the wind speed, resulting in being applicable to a certain range of wind speed. Therefore, the Gaussian line-source dispersion model and the puff integration model are both integrated according to the meteorological conditions. First, the Gaussian line-source dispersion model under the windy condition (when the wind speed is more than or equal to 2 m/s) and the puff integration model under calm wind condition (when the wind speed is less than 2 m/s) are presented. Second, to meet the demand of aforementioned dispersion models, the obtainment of the pollutant sources and the
2 erf ðlÞ ¼ pffiffiffi
p
Qli ¼ Ei
Zl
2
et dt
(3)
0
. 3:6 106 $Ri
(4)
where Cji is the concentration of CO at receptor j caused by source i, g/m3; Qli is the emission rate of CO from source i in the Gaussian line-source model, g/m∙s; ui is the wind speed of pollutant source i, m/s; zj is the height above ground of receptor j, m; Li is the crosswind length of line-source i, m; yij is the crosswind distance between i and j, m; siyj and sizj are horizontal dispersion parameter and vertical dispersion parameter respectively, m; erf ðlÞ is the Gauss error function. 2.2.2. Puff integration model The puff integration model (Jiekuan, 1991) is a supplement to the aforementioned Gaussian line-source model under calm wind condition, in which the pollutant sources are considered as point sources. The equations are shown in Eqs. (5)e(8). Moreover, the emission rate from the source in the puff integration model could be calculated by Eq. (9).
2
3 i 2 nh u 6 7 Cji ¼ exp4 2 5 1 2 3 2 i i i2 i ð2pÞ syj szj Aj 2 syj pffiffiffi io p ffiffiffi 2 Dij þ 2Dij p exp Di2 j $f Qpi
=
Traffic emission factor models estimate traffic emissions on the road links of a road network based on the attributes such as traffic status information (link-based average traffic speed, traffic volume or vehicular speed profile) and vehicular fleet composition. Although microscopic emission models that account for variations in instantaneous speed provide more accurate results than average speed models, the input data of microscopic emission models is difficult to be obtained, especially in the urban scale. Therefore, the emission factor models established by Yao and Song (2013), whose database contains hundreds of typical car types and different car ages by a portable emission measurement system (PEMS) under actual driving conditions in Beijing, are applied to evaluate average hourly traffic-related emission by using the average speed and the vehicular fleet composition of the road link. The emission of a road link is calculated by Eq. (1).
Dij ¼ xij ui
2 2Aij siyj
2 2 2 2 2 2 siyj þ z2j 2 sizj xij þ yij Aij ¼
1 fðlÞ ¼ pffiffiffiffiffiffi 2p
Zl ∞
Qpi ¼ Ei =3600
2 t dt exp 2
(5)
(6)
(7)
(8)
(9)
where fðlÞ is the standard normal distribution function; Qpi is the emission rate of CO from source i in the puff integration model, g/s; xij is the downwind distance between i and j, m; Dij and Aij are the
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intermediate coefficients; other coefficients have the same meaning in Eqs. (2)e(3). 2.2.3. Calculation of receptor's concentration In the subsection, to calculate the concentration of a receptor, the processing method of the pollution source is introduced first. Then, the calculating method of the concentration of the receptor is presented. When the pollutant sources are processed, a windy condition (u > 2m=s) and a calm wind condition (u 2m=s) are both considered, and the Gaussian line-source model and the puff integration model are applied correspondingly. To meet the demand of the dispersion models, the pollutant sources are considered as finite line sources perpendicular to the direction of the wind or as a point source correspondingly, as shown in Fig. 1, in which a represents the angle between the wind direction and the north, b represents the angle between the link direction and the north, and Point O is the middle point of the link. Moreover, the brown solid line is the link, which is a finite line source, and the red solid line, which is perpendicular to the wind direction, is the processed finite line source. Since a receptor is usually affected by some pollutant sources together, the concentration of traffic-related air pollution at a given receptor is estimated by the summation of the concentration produced by the pollutant sources which could affect the receptor (within the affecting area), as shown in Eq. (10).
Cj ¼
X
Cji
(12)
where T is the time interval, s; uwind is the wind speed, m/s. In this paper, T is assigned as 3600s (1 h) because the traffic and meteorological information are both obtained hourly, and when the wind speed is lower or equal to 0.5 m/s, uwind is assigned as 0.5 m/s, meaning that air pollutants spread the farthest distance with speed 0.5 m/s; when the wind speed is larger than 0.5 m/s, uwind is assigned as the wind speed. When the wind speed is larger than 0.5 m/s, the dispersion of the emissions is affected by the wind direction. Therefore, an upwind circular sector (see Fig. 2(b)) is determined to describe the affecting area of a receptor, of which the borders are calculated by Eq. (11) and Eq. (13).
y ¼ ±4sy10 x 10
(13)
where sy10 is the horizontal dispersion parameter at a downwind distance of 10 m. Fig. 2 shows the affecting area (the area filled with slant lines including the edge) calculated based on Eqs. (11)e(13), as well as the wind direction (the x-axis), the receptor (the black solid circle in the center of Fig. 2), and some pollutant sources (the black solid triangles). In Fig. 2(a), three pollutant sources which could affect the receptor are in the affecting area. Fig. 2(b) illustrates that three pollutant sources are in the affecting area, while other two pollutant sources are outside the region.
(10)
i2ARj
3. Traffic-related air pollution impact analysis
where Cj is the CO concentration at receptor j; Cji is the CO concentration at receptor j caused by source i; ARj is the set of pollutant sources within the affecting area of receptor j. The affecting area of a receptor is determined by different methods according to the different conditions of wind speed. Air pollutants are often distributed over the downwind side of pollutant sources when the wind speed is more than 0.5 m/s; however, when the wind speed is less than or equal to 0.5 m/s, the wind direction is usually unstable, and the spread of the emissions covers 360 , leading the affecting area to be regarded as a circle (Fig. 2(a)), which is calculated by Eq. (11).
x2 þ y 2 ¼ R 2
R ¼ T$uwind
(11)
where the positive direction of x-axis is the wind direction; the positive direction of y-axis is perpendicular to the x-axis and form a 90 angle counterclockwise to the positive direction of x-axis; R is the radius of the affecting region, which is calculated by Eq. (12).
3.1. Data The integrated estimating framework is applied to the main area of Beijing, China, as shown in Fig. 3. The main area of Beijing covers about 130*190 km2, in which the modeled road network consisted of 36,853 links representing 10,853 km of roads, including expressways (4733 links), arterial roads (18,582 links), and secondary roads (13,538 links). Considering the computational cost and the calculated accuracy of the pollutant concentration, the study area is divided into thousands of zones by the grids with the side of 1 km, and the road links are cut into short links by these grids. Moreover, for fully analyzing the real-time impact of traffic-related air pollution, the hourly traffic information (link-based average traffic speed, traffic volume, etc.) is obtained by roadside traffic detectors (e.g. RTMS and vehicle loop detectors) and Floating Car Data (FCD), and hourly basic meteorological information (wind speed, wind direction, cloud cover, etc.) is obtained by meteorological monitoring stations, on Feb 9 and Feb 20, 2014, which are typical days under a calm wind condition and a windy condition, respectively. In addition, because the hourly vehicular fleet composition is hard to obtain, the constant vehicular fleet composition is used, which was composed by 95.4% of light-duty vehicles, 1.1% of mid-duty vehicles, and 3.5% of heavy-duty vehicles. To evaluate the population exposure to CO, the static demographic data which include the population of each grid is achieved. Air Pollutant Index (API), an index of CO concentration, is proposed to display the impacts of CO concentration in different grids (see Table 1), and the API of CO is calculated referring to Air Quality Index (AQI), as shown in Eq. (14). The CO concentration is divided into five grades to represent the API of CO (see Table 2).
API ¼
APIL API S C LC S þ APIS LC L LC S
(14)
Fig. 1. Relationship between the link and the wind direction.
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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Fig. 2. Illustration of affecting area under different wind speeds.
Fig. 3. Study area and road network in Beijing.
approximately 54.5.
Table 1 Index of CO concentration. Index CO Concentration (mg/m3)
0 0
50 0.002
100 0.09
150 0.4
200 1.5
300 3.0
400 5.0
500 7.0
Table 2 Grades of API of CO. API Grade
0 0
1e125 1
126e250 2
250e375 3
376e500 4
where API is the API value of CO concentration; C is the concentration of CO; LCL is the larger value, which are related to the index, closest to C; LCS is the smaller value between the concentration limit values close to C; APIL is the traffic-related air pollution index corresponding to the LCL; APIS is the traffic-related air pollution index corresponding to the LCS. For example, if the CO concentration is 0.01 mg/m3, then APIL and APIS are 100 and 50, respectively, and LCL and LCS are 0.09 mg/m3 and 0.002 mg/m3, respectively; therefore, the API could be calculated by Eq. (14) and is
3.2. Impact analysis and discussion 3.2.1. CO concentration Based on the proposed framework, the traffic emissions of each short link, which is considered as the intensity of pollutant source, is calculated. In addition, meteorological conditions are considered to be constant in each hour to meet the demand of the dispersion models, and the pollutant concentration of the central point of each grid is regarded as the concentration of each grid. Taking the data on Feb 9th, 2014 as an example, the distributions of the API of CO concentration at 7:00e8:00 (morning peak hour), 12:00e13:00 (one of flat peak hours), and 18:00e19:00 (evening peak hour) are estimated and shown in Fig. 4, in which the five grades of CO API (Grade 0 to Grade 4) are represented as white, light grey, grey, dark grey, and black, respectively. The result shows that the CO concentration is much higher in the central area of Beijing, especially near the main roads with higher traffic emissions. In contrast, the CO concentration of the peripheral area is much lower than that of the central area because
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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L. Pan et al. / Journal of Environmental Management xxx (2016) 1e11
Fig. 4. Distribution of CO concentration.
of less traffic emissions. Moreover, it shows a temporal characteristic similar to the traffic volume that the CO concentration in the study area is higher at 7:00e8:00 and 18:00e19:00, which is much higher than that at 12:00e13:00. Because of being related to the traffic emissions which could affect the distribution of the CO concentration significantly, the traffic status (traffic volume and traffic speed) may have a correlation with the CO concentration directly. However, describing the traffic volume and the traffic speed of a grid is difficult because there may be many road links under different traffic status in a grid; consequently, traffic intensity and traffic speed of a grid are defined by Eqs. (15) and (16).
TIj ¼
X
Vi Ri
(15)
i2Nj
TSj ¼
X
subsection 2.2.3). For example, suppose the wind speeds are 0.2 m/ s and 1 m/s, respectively, the affecting grid collections are shown in Fig. 5. When the wind speed is less than 0.5 m/s or more than 0.5 m/s, the affecting area is calculated by Eqs. (11)e(13), and when more than 50% area of a grid is in the affecting area, this grid is defined to be in the affecting grid collection. To be clear, the target grid is always included in the affecting grid collection. Based on this definition, the affecting grid collections under different wind speeds are shown in Fig. 5(a) and (b) as the yellow grids and the red grid (the target grid). Then, the traffic intensity and the traffic speed of an affecting grid collection are calculated by Eqs. (17) and (18).
TIacollection
¼
X
, TIi
Na
(17)
i2Ca
si Ri
(16)
i2Nj
where TIj is the traffic intensity of grid j, pcu*km; TSj is the traffic speed of grid j, km/h; Vi is the traffic volume of road link i in grid j, pcu; Ri is the length of road link i in grid j, km; Nj is the set of the road links in grid j; si is the traffic speed of road link i, km/h. Generally, the CO concentration of a grid is affected not only by the traffic intensity of the grid itself, but also by those of the adjacent grids. Facing this situation, the concept of the affecting area in subsection 2.2.3 is used to determine the affecting grid collection of the target grid, and the relationship between traffic status of the affecting grid collection and the CO concentration is evaluated. The affecting area, which determines the affecting grid collection, is classified based on the wind direction (more details in
TScollection a
¼
X
, TSi
Na
(18)
i2Ca
where TIacollection is the traffic intensity of the affecting grid collection of grid a; TScollection is the traffic speed of the affecting grid a collection of grid a; Ca is the collection of grids in the affecting grid collection of grid a; Na is the number of grids in the affecting grid collection of grid a. The data of two grids (see Fig. 6) on Feb 9th are chosen to display the relationship between CO concentration and traffic status (traffic intensity and traffic speed). The CO concentrations in these grids as well as the traffic status (the traffic intensity and the traffic speed) of the grids within the
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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Fig. 5. Examples of the affecting grid collection.
Fig. 6. Locations of the typical grids.
affecting area of S1 or S2 are presented in Fig. 7. In Fig. 7(a) and (b), the trend of the CO concentration corresponds to that of the traffic intensity, and it could be inferred that the CO concentration may be correlative with the traffic intensity. Similarly, in Fig. 7(c) and (d), the CO concentration is relatively higher while the traffic speed is relatively lower, especially at 7:00e8:00, 8:00e9:00, and 17:00e18:00 of S2 on Feb 9th. Based on the data in S1 and S2, the CO concentration has a certain correlation with the traffic intensity (the correlation coefficient is 0.77) or with the traffic speed (the correlation coefficient is 0.81). Therefore, to analyze the multi-factor influence of the CO concentration, one of the multiple regression models, Multiple Linear Regression (MLR) method, is applied. The results of analysis of variance (ANOVA) and the regression model are shown in Table 3 and Table 4, respectively. According to Table 3, the two parameters that influence CO concentration were statistically significant (p-value < 0.01). In Table 4, the traffic intensity and the traffic speed were also statistically significant (p-value < 0.05), and the adjusted R2 of the
regression model is 0.617, which shows that the model is capable of explaining at least 61.7% of the input data. The result indicates that the methods to determine the dynamic affecting areas are reasonable, and the traffic intensity and the traffic speed of an affecting grid collection have a certain relationship with the CO concentration of the corresponding target grid. In the regression model, the coefficients of the traffic speed and the traffic intensity are negative and positive, respectively, indicating that the traffic speed has a negative effect on the CO concentration, while the traffic intensity has a positive effect. It should be noted that the traffic speed is mainly below 60 km/h (see Fig. 7(c) and (d)), and at this range the traffic speed is viewed having a negative relationship with the traffic emissions per unit length (Yao and Song, 2013); however, if the traffic speed is mainly above 60 km/h, it may have an opposite effect on the CO concentration. In addition, for studying the influence of the meteorological information on the CO concentration, the relationship between the wind speed of the affecting grid collection and the CO
Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010
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Fig. 7. Traffic intensity indexes and traffic speed as well as CO concentrations in S1 and S2.
Table 3 Result of ANOVA. Model
Sum of square
df
Mean square
F-value
p-value
Regression Residual Total
17.641 10.212 27.853
2 45 47
8.821 0.227
38.869
0.000
models by comparing the predicted concentrations to the observed concentrations (Chang and Hanna, 2005). Therefore, Pearson Correlation Coefficient (PCC) and FAC2 are chosen to quantify the model performance. The performance is more satisfying if the value of PCC or FAC2 is closer to one.
Table 4 Result of the regression model. Model
Unstandardized coefficients
Standardized coefficients
B
Std. error
Beta
Constant Traffic speed Traffic intensity
1.152 0.030 0.041
0.770 0.012 0.006
0.238 0.676
concentration of the target grid is analyzed. The result shows that the correlation coefficients are 0.17(S1) and 0.11(S2), indicating that the wind speed near the target grid have a slight positive effect on dissipating the CO concentration of the target grid. The reason why there is not a strong effect may be because the concentration is not only influenced by the wind speed but also by other factors of meteorological condition (e.g., wind direction, humidity, and cloudage). To verify the result of the integrated framework, the observed ambient CO concentration is obtained to compare with the estimating result. For mainly analyzing the relationship between the traffic information and the CO concentration, the monitoring station in Xizhimen, where the traffic is much heavier, is chosen to be compared with ambient CO concentration predicted at the same location. Furthermore, the fraction of model estimates within a factor of two of observations (FAC2, see Eq. (19)) is a common approach usually used to evaluate the performance of dispersion
t
p-value
R2
Adjusted R2
1.496 2.447 6.957
0.142 0.018 0.000
0.633
0.617
Cp FAC2 ¼ n 0:5 2:0 N Co
(19)
where FAC2 is the value of FAC2; Co and Cp are observed concentrations and predicted concentrations, respectively; N is the number of observed concentrations; n(.) is the number of predicted concentrations that satisfy the condition. Although the ambient CO concentration in Beijing cannot be calculated by the proposed framework (only the traffic-related CO concentration could be calculated), background CO concentration, which could be obtained from five monitoring stations away from central area of Beijing, is used to estimate the ambient CO concentration, as shown in Eq. (20). traffic
CT ¼ CT
þ CTback
(20)
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Fig. 8. Observed ambient CO concentration and predicted ambient CO concentration by MA at the monitoring station in Xizhimen.
where CT is the predicted ambient CO concentration at time T, mg/ m3; CTtraffic is the traffic-related CO concentration calculated by our approach at time T, mg/m3; CTback is the background CO concentration at time T, mg/m3. Moreover, based on the predicted ambient CO concentration, Moving Average (MA) method is used to reflect the trend of the CO concentration curve, as shown in Eq. (21).
CTMA ¼
1 ð2CT þ CTþ1 Þ 3
(21)
where CTMA is the CO concentration calculated by MA, mg/m3; CT and CTþ1 are the CO concentrations at time T and (Tþ1) respectively, mg/m3. Fig. 8 shows the observed ambient CO concentration and the predicted ambient CO concentration by MA at the monitoring station in Xizhimen in Feb 9th, 2014 and Feb 20th, 2014, respectively. Overall, although there are some errors between the predicted concentration by MA and the observed concentration, the predicted value resembles the trend of observed ambient CO concentration in both two days, with the PCCs being 0.67 (Feb 9th) and 0.79 (Feb 20th), and the FAC2s being 0.67 (Feb 9th) and 1 (Feb
Fig. 9. Distribution of population density and population exposure to CO within the central area of Beijing.
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L. Pan et al. / Journal of Environmental Management xxx (2016) 1e11
20th). Chang and Hanna (2005) suggest that the model with a FAC2 greater than 0.5 is viewed to have an acceptable performance, indicating that the predicting performance of the proposed framework is satisfying, and the selection and the application of the dispersion models considering different meteorological conditions are reasonable. 3.2.2. Population exposure to CO Population exposure to CO was estimated as a multiplication of the population dwelling in each grid and the CO concentration in the grid (Amirjamshidi et al., 2013), as shown in Eq. (22).
PET ¼ P$CT
(22)
where PET is the population exposure to CO, person*mg/m3; P is the population, person. Fig. 9(a) shows the distribution of the population density in central area of Beijing, and Fig. 9(b) and (c) show the distributions of population exposure to CO at 19:00e20:00 on Feb 9th (under a calm wind condition) and Feb 20th (under a windy condition), respectively. The time interval of 19:00e20:00 is chosen as the research time is because the demographic data is based on the residences, and the residents are usually at home during this time interval. In Fig. 9(a), the population densities of the grids sharing the similar distance from the center are nearly the same, and with the distance increasing, the population density decreases. Remarkably, the population density of the central point of the area is much lower because the Forbidden City is located there, and there are almost no dwellings. In contrast with Fig. 9(a), population exposure to CO shows a rather different pattern from the distribution of population density on Feb 9th, as shown in Fig. 9(b). On Feb 9th (under a calm wind condition), the impact of the population exposure is more significant in the east because the grids in the east with a higher population density also have a higher CO concentration. Moreover, within the Fourth Ring Road, the areas averagely have a higher population density and a higher CO concentration under calm wind condition, meaning that the traffic-related air pollution has a heavier impact on public health in these areas. However, on Feb 20th (under a windy condition), the impact of the population exposure seems slightly stronger in the northwest, the southwest, and the northeast parts of the central area of Beijing. 4. Conclusions In this paper, an integrated framework incorporating the emission estimation and the dispersion calculation is proposed to estimate the concentration of traffic-related air pollution in urban area with real-time traffic data and basic meteorological data, and then the impact of traffic-related air pollution is further evaluated. First, traffic emissions are calculated by vehicle emission factor models sensitive to the traffic status based on real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on different meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Next, the concentration of traffic-related air pollution is calculated by Gaussian dispersion model and puff integration model for corresponding conditions. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that the traffic-related CO concentration is higher in morning and evening peak hours, and the areas polluted most are within the scope of Fourth Ring Road,
especially near the main roads and at the downwind direction. Moreover, the relationship between traffic indicators and trafficrelated CO concentration is quantified by Multiple Linear Regression (MLR). Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area. Gaussian dispersion models are applied based on the assumption that the traffic emission intensity should keep relative steady between several hours (i.e. hourly traffic flow and speed do not vary dramatically), and the results calculated by the proposed framework may not be well suitable to the cases in which the traffic status and the meteorological condition fluctuate dramatically. In further study, the dispersion model being more sensitive to or other supplementary methods to handle the fluctuation of the traffic emissions and the meteorological condition could be considered and applied. Moreover, after finishing calculating the traffic-related air pollution concentrations for a whole year with demanding input data, more interesting characteristics of traffic-related air pollution in Beijing may be obtained. In addition, based on more data, we could have a fully sensitivity analysis of the model from the aspects of the traffic and the meteorological conditions. Acknowledgements This work was supported by Research Fund for the Doctoral Program of Higher Education of China [grant number 20130009110002]; and the Fundamental Research Funds for the Central Universities [grant number 2016YJS077]. References Ahn, K., 1998. Microscopic Fuel Consumption and Emission Modeling. Virginia Polytechnic Institute and State University. Ahn, K., Rakha, H., Trani, A., Van Aerde, M., 2002. Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. J. Transp. Eng. 128 (2), 182e190. Amirjamshidi, G., Mostafa, T.S., Misra, A., Roorda, M.J., 2013. Integrated model for microsimulating vehicle emissions, pollutant dispersion and population exposure. Transp. Res. Part D Transp. Environ. 18 (0), 16e24. http://dx.doi.org/ 10.1016/j.trd.2012.08.003. Arya, S.P., 1999. Air Pollution Meteorology and Dispersion. Oxford University Press, New York. Barth, M., 2010. The comprehensive modal emission model (CMEM) for predicting light-duty vehicle emissions. In: Paper Presented at the Transportation Planning and Air Quality IV: Persistent Problems and Promising Solutions. Barth, M., Malcolm, C., Younglove, T., Hill, N., 2001. Recent validation efforts for a comprehensive modal emissions model. Transp. Res. Rec. J. Transp. Res. Board 1750, 13e23. Batterman, S., Ganguly, R., Isakov, V., Burke, J., Arunachalam, S., Snyder, M., Robins, T., Lewis, T., 2014. Dispersion modeling of traffic-related air pollutant exposures and health effects among children with asthma in Detroit, Michigan. Transp. Res. Rec. J. Transp. Res. Board 2452. Beijing Municipal Bureau of Environmental Protection, 2014. Beijing Environmental Statement 2014. Retrieved from. http://www.bjepb.gov.cn/bjepb/resource/cms/ 2015/04/2015041609380279715.pdf. Beijing Municipal People's Government, 2015. Implementation Status Report of Beijing's Air Pollution Prevention and Control Regulation. China. Retrieved from. http://www.bjrd.gov.cn/zdgz/zyfb/bg/201505/t20150508_149012.html. Benson, P.E., 1984. CALINE4-A Dispersion Model for Predicting Air Pollutant Concentrations Near Roadways. Office of Transportation Laboratory. California Department of Transportation, Sacramento, USA. Cai, H., Xie, S., 2011. Traffic-related air pollution modeling during the 2008 Beijing Olympic Games: the effects of an odd-even day traffic restriction scheme. Sci. Total Environ. 409 (10), 1935e1948. http://dx.doi.org/10.1016/ j.scitotenv.2011.01.025. Carruthers, D.J., Holroyd, R.J., Hunt, J.C.R., Weng, W.S., Robins, A.G., Apsley, D.D., Thompson, D.J., Smith, F.B., 1994. UK-ADMS: a new approach to modelling dispersion in the earth's atmospheric boundary layer. J. Wind Eng. Industrial Aerodynamics 52 (0), 139e153. http://dx.doi.org/10.1016/0167-6105(94)90044-2. Chang, J.C., Hanna, S.R., 2005. Technical Descriptions and User's Guide for the BOOT
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Please cite this article in press as: Pan, L., et al., Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information, Journal of Environmental Management (2016), http://dx.doi.org/10.1016/j.jenvman.2016.09.010