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Instantaneous emissions models set in GIS for the TRANSIMS outputs
Transportation Research Part D 33 (2014) 155–165
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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Instantaneous emissions models set in GIS for the TRANSIMS outputs Adolfo Hernández-Moreno, Violeta Mugica-Álvarez ⇑ Universidad Autónoma Metropolitana-Azcapotzalco, Av. San Pablo 180 Col. Reynosa-Tamaulipas, Azcapotzalco, México, D.F. 02200, Mexico
a r t i c l e
i n f o
Keywords: Vehicle emissions GIS Instantaneous emission model TRANSIMS
a b s t r a c t A novel methodology that provides more detailed estimates of vehicular polluting emissions is offered, in order to contribute to the improvement and the precision of emission inventories of vehicle sources through the consideration of instantaneous speed changes or acceleration instead of average vehicular speeds. This paper presents the construction and application of an instantaneous emissions model designated hereunder as ‘‘Transims’s Snapshots-Based Emissions’’, which is set on a Geographic Information System that incorporates instantaneous fuel consumption factors and fuel-based emission factors to attain highest resolution of both, spatial and temporal distribution of vehicular polluting emissions based on traffic simulation through cellular automata with TRANSIMS. This work was applied to the road network of the Mexico City Metropolitan Area as case study. The development of this powerful tool led to obtaining 86,400 maps of the spatial and temporal distribution of vehicular emissions per vehicle circulating on the road network, including the following pollutants: carbon monoxide and carbon dioxide, nitrogen oxides, total hydrocarbons, sulfur oxides, polycyclic aromatic hydrocarbons, black carbon, particles PM10 and PM2.5. The said maps allowed identification with highest level of detail, of the emissions and Hot-spots of fuel consumption. Also, the model permitted to obtain the emissions’ longitudinal profiles of a given vehicle along its route. This study shows that the integration method of the polynomial regression models represents an opportunity for each city to develop more easily and openly its own regional emissions models without requiring deeper programming knowledge. Ó 2014 Elsevier Ltd. All rights reserved.
Introduction Polluting emissions from automotive vehicles comprise a set of pollutants generated through different processes of the vehicular activity, which includes criteria pollutants1 (i.e. carbon monoxide and particulate material), ozone precursors (i.e. hydrocarbons), toxic substances (i.e. polycyclic aromatic hydrocarbons) and short-lived pollutants (i.e. black carbon). During many years, the method applied to such emissions has been based on the use of emission factors rated at average speeds applied to generalized estimates of the vehicular activity. Recently, there has been an increased use of traffic
⇑ Corresponding author. Tel.: +52 (55) 5318 9577. E-mail address: [email protected] (V. Mugica-Álvarez). The United States Environmental Protection Agency (USEPA) calls six common pollutants ‘‘criteria air pollutants (also known as ‘‘criteria pollutants’’) because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards (EPA, 2007). 1
http://dx.doi.org/10.1016/j.trd.2014.06.002 1361-9209/Ó 2014 Elsevier Ltd. All rights reserved.
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simulators to determine such vehicular activity because they are considered the tools that best reproduce the vehicular traffic phenomenon (Chowdhury et al., 2000). To date, there are vehicular traffic simulators developed over different movement theories; hydrodynamic, kinetic, of coupled-map lattice, car-following and cellular automata. Some of the available simulators, whether commercial or open source, use instantaneous emission factors, as the case is for Synchro SimTrafficÒ (Trafficware Ltd., 2006). However, these display the emissions assessments over averages of vehicular activity for each link, which limits the spatial resolution level available to the researcher. This limitation has as origin the theoretical base of the traffic simulation based on continuous models, like the car-following, and on the strategy used for summarizing the enormous amount of information generated by traffic simulation. Among the open-source traffic simulators available, TRANSIMS (Transportation Analysis and Simulation System) is a simulator based on cellular automata that was developed by Los Alamos National Laboratory with financing from the U.S. Department of Transportation, from the Environmental Protection Agency and from the U.S. Department of Energy, as part of the Travel Model Improvement Program and is distributed under agreement of NASA Open Source Agreement Version 1.3. TRANSIMS comprises an integrated system of trips forecasting models designed with the aim of providing transport planners with exact, full information on the traffic impacts. TRANSIMS has its own emissions assessment modulus. However, as is common with other simulators, it is limited by the use of tables of emission factors and vehicular activity averages. An attractive feature of TRANSIMS is its capacity to generate instantaneous maps (also known as snapshots), which are geo-referenced maps in shape format (⁄.shp) with the geographic location of each vehicle that traverses the road network daily, second-by-second. This way it is possible to know the operating features of each vehicle including its instantaneous speed and acceleration changes at any instant of time. Some authors, like Eissfeldt and Schrader (2001), have concluded that to associate the old emission factors modeled at average speed with the traffic simulation does not allow obtaining the expected improvements to estimate the pollutant’s emissions because the use of such factors may entail an important underestimation. Consequently, it has been considered more adequate to estimate emissions based on the instantaneous emission models (IEM) that consider the transient behavior of vehicles’ acceleration-deceleration, apart from the speed changes (Ahn and Rakha, 2008). After, Ahn et al. (2002) presented the method to integrate an instantaneous emissions model (also called microscopic) that used polynomial regression models, subsequent models have been developed following the same strategy, as the case is with the VERSIT+ (Smit et al., 2007) and VT-Micro (Ahn and Rakha, 2008) models. In order to maximize the benefits of traffic simulation for determining the spatial and temporal distributions of vehicular emissions; this work presents the development of an instantaneous emissions model based on instant fuel consumption applicable to outcomes of the TRANSIMS applying it to the Mexico City case. This methodology works as a guide for those regions which have not proper models. They could develop one without deep programming knowledge and reduce the technology dependence. Theoretical development The new instantaneous emissions model, IEM, presented here is a product of the conjoint application of two kinds of factors. On the one hand, the utilization of instantaneous fuel consumption put forward by the Federal Highway Administration from Oak Ridge National Labs (FHWA, 1999). On the other, the application of fuel-based emission factors, FEF, obtained through previous studies. The regionalization of the model was carried out calibrating against the MOBILE6Mex (EGR, 2003). A similar treatment to that presented by Rakha et al. (2004, 2000), permits obtaining the polynomial regression models for the instantaneous fuel consumption, CCinst|aci, for each acceleration value, aci, as a function of the instantaneous speed, V (see Eq. (1)).
CC inst jaci ¼ b0 þ b1 V þ b2 V 2 þ þ bn V n
ð1Þ
where bi are the regression coefficients. The fuel consumption models are calibrated and regionalized against the results of the MOBILE6Mex model, approximating the models to the local data applying fitting factors, ki, which can affect each element of the polynomial regression model as shown by Eq. (2),
CC inst-local jaci ¼ k0 b0 þ k1 b1 V þ k2 b2 V 2 þ þ kn bn V n
ð2Þ
where CCinst-local is the regionalized fuel consumption. The polluting emissions can be determined applying fuel-based emission factors (Liao, 2013; Galvis et al., 2013; Fu et al., 2014), such that once the instantaneous fuel consumption had been estimated, the instantaneous polluting emissions can be determined multiplying by the fuel-based emission factors consumption, as shown by Eq.(3).
IEj jaci ¼ ðFEF j Þ CC inst-local jac i
ð3Þ
where IEj|aci are the instantaneous emissions of the pollutant j at a given acceleration aci and FEFj is the fuel-based emission factor of the pollutant j. The FEF of each contaminant can be taken from the literature or measured in the field as those obtained by Schifter et al. (2005).
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The models of the CCinst|aci and those of the IEj|aci integrate jointly the IEM, which are incorporated in one GIS tool, where the knowledge of the geographic position of the vehicles, together with all their information of instant speed and acceleration allows simultaneous determination of the instantaneous pollutant emissions and their spatial distribution. As study case, the vehicular traffic was simulated in the road network (residential, principal and highway streets) of the MCMA using TRANSIMS. The snapshots or instant activity maps of TRANSIMS over those where the GIS tool had been applied are generated by running the ArcSnapshot modulus of the simulator. The latter converts the Snapshot file (generated by the Micro simulator modulus of TRANISMS) into maps of the shape (⁄.shp) sort for ArcGisÒ (TRANSIMS Open-Source, 2007). The maps contain the location of the vehicles per second per day and the information on their operating conditions like speed and instant acceleration. Results Three sets of fuel consumption models were developed; passenger cars, trucks and busses using the FHWA data. Table 1 presents the polynomial regression models obtained where the best fittings were achieved with fourth order polynomials with respect to speed for passenger cars and trucks. Although for the case of busses most were fitted to third and fourth orders. In addition, Table 1 shows the statistics concerning the error of each model. For the three types of vehicles, note that only one model served to describe the fuel consumption for negative accelerations (be it decelerations). In general, the models display goodness of fit to observations. The uncertainties are displayed in Table 2 to complete the statistical analyst of the models. At the same time, the emission factors at average speed were modeled for the whole speed range allowed by MOBILE6Mex (EGR, 2003). Note that this model was calibrated for México’s conditions, thus it considers the fuel quality regional features, meteorology, altitude and characterization of the vehicular fleets. For modeling purposes, the vehicular fleets estimated during 2010 were used. The fuel yields corresponding to cero (0.0 ms2) acceleration, which is the equivalent to constant speed, were extracted from the results of MOBILE6Mex and compared with those obtained through the models
Table 1 Coefficients and error statistics of fuel consumption polynomial regression models (see Eq. (1)). b1
b2
b3
b4
R2
Standard error
4.88E04 5.39E04 5.48E04 6.83E04 9.35E04 9.34E04 1.58E03 1.55E03 1.49E03 1.48E03 1.56E03
7.95E05 1.37E05 1.84E03 1.95E04 2.01E04 2.63E04 2.65E04 1.32E04 7.60E05 2.94E04 4.16E04
1.03E05 5.33E06 1.15E05 2.25E07 9.60E06 2.11E05 1.43E04 1.40E04 1.19E04 8.45E05 6.23E05
4.90E07 2.67E07 6.23E07 1.43E07 2.03E07 9.24E07 8.23E06 8.73E06 8.06E06 6.38E06 5.19E06
7.27E09 4.25E09 1.04E08 4.82E09 – 9.28E09 1.36E07 1.51E07 1.45E07 1.18E07 9.87E08
0.874 0.974 0.984 0.997 0.990 0.995 0.981 0.989 0.994 0.991 0.987
0.00002 0.00006 0.00010 0.00008 0.00022 0.00019 0.00041 0.00031 0.00023 0.00026 0.00031
Trucks <0 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
0.00065 9.683E4 +1.215E3 0.0209 0.0367 0.0143 0.033 0.022 9.731E3 0.001 0.001
– 5.244E4 6.456E4 0.012 0.0308 0.004 0.0445 0.033 0.021 0.057 0.0689
– 7.108E5 5.779E5 0.007 0.0073 6.394E4 0.0148 0.0138 0.0117 3.435E3 5.818E3
– 4.886E6 3.799E6 5.773E4 3.260E4 2.737E4 1.034E3 0.001 9.181E4 2.053E5 1.817E4
– 9.810E8 6.842E8 1.594E5 3.781E6 1.004E5 2.233E5 2.256E5 2.116E5 2.017E6 1.495E6
– 0.979 0.990 0.732 0.868 0.874 0.878 0.878 0.862 0.823 0.826
– 0.00024 0.00029 0.02349 0.03608 0.03487 0.03330 0.03243 0.03358 0.03252 0.03120
Busses <0 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
3.4E4 7.546E4 1.0E3 3.75E2 4.372E2 4.665E2 1.31E2 3.0E2 5.0E2 6.0E2 9.0E2
– 4.909E4 7.655E4 2.508E2 3.202E2 5.565E2 3.018E2 4.744E2 6.086E2 6.205E2 3.534E2
– 1.356E4 3.038E5 3.100E3 4.451E3 1.295E2 1.226E2 2.649E3 4.455E3 5.245E3 3.217E3
– 8.671E6 8.096E7 6.984E5 1.212E4 7.996E4 9.531E4 4.572E5 1.024E4 1.340E4 8.618E5
– 1.852E7 – – – 1.547E5 2.191E5 – – – –
– 0.943 0.964 0.871 0.835 0.869 0.864 0.801 0.817 0.774 0.457
– 0.00045 0.00054 0.03968 0.03869 0.03499 0.03292 0.03705 0.03207 0.02903 0.02866
Acceleration (m s2) Passenger cars <0 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
b0
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Table 2 Uncertainty from polynomial regression models. Acceleration (m s2)
Sb0
Sb1
Sb2
Sb3
Sb4
Passenger cars <0 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
1.044E05 2.715E05 4.867E05 3.815E05 8.745E05 9.191E05 2.034E04 1.548E04 1.134E04 1.276E04 1.505E04
5.423E06 1.411E05 2.529E05 1.982E05 2.699E05 4.776E05 1.057E04 8.042E05 5.891E05 6.632E05 7.820E05
8.109E07 2.110E06 3.782E06 2.964E06 2.214E06 7.142E06 1.580E05 1.203E05 8.809E06 9.917E06 1.169E05
4.351E08 1.132E07 2.029E07 1.590E07 5.131E08 3.832E07 8.479E07 6.452E07 4.726E07 5.321E07 6.274E07
7.423E10 1.931E09 3.462E09 2.713E09 – 6.537E09 1.446E08 1.101E08 8.063E09 9.077E09 1.070E08
Trucks 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
1.275E04 1.559E04 1.277E02 1.961E02 1.895E02 1.810E02 1.762E02 1.825E02 1.767E02 1.695E02
8.454E05 1.033E04 8.463E03 1.300E02 1.256E02 1.199E02 1.168E02 1.210E02 1.171E02 1.124E02
1.634E05 1.997E05 1.636E03 2.513E03 2.428E03 2.318E03 2.258E03 2.338E03 2.264E03 2.172E03
1.158E06 1.415E06 1.159E04 1.780E04 1.720E04 1.643E04 1.600E04 1.656E04 1.604E04 1.539E04
2.697E08 3.297E08 2.700E06 4.147E06 4.008E06 3.827E06 3.727E06 3.859E06 3.737E06 3.585E06
Busses 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
2.419E04 2.434E04 1.781E02 1.737E02 1.901E02 1.789E02 1.663E02 1.440E02 1.303E02 1.286E02
1.603E04 9.976E05 7.299E03 7.117E03 1.260E02 1.186E02 6.816E03 5.900E03 5.339E03 5.272E03
3.099E05 1.094E05 8.003E04 7.803E04 2.436E03 2.292E03 7.473E04 6.468E04 5.854E04 5.780E04
2.196E06 3.374E07 2.469E05 2.408E05 1.726E04 1.624E04 2.306E05 1.996E05 1.806E05 1.783E05
5.116E08 – – 4.021E06 3.783E06 – – – –
in Table 1, (see Fig. 1). From the comparison, it is observed that the data of the model for passenger cars are displayed above those of the MOBILE6Mex model for speeds smaller than 10 ms1 and below those for speeds greater than 10 ms1. The differences can be interpreted as follows: on the one hand, the FHWA data are not regional, because they do not show the altitude effect of the MCMA that induces efficiency losses of internal combustion vehicles. Consequently, the fitting model based on FHWA data underestimates the emissions for speeds greater than 10 ms1. Therefore, the regression model was fitted to agree with the MOBILE6Mex at speeds greater than 10 ms1. On the other, the model MOBILE6Mex uses a linear model that converges at zero emissions at zero speed, which implies that the vehicle does not consume fuel when it is operating at idle
Fig. 1. Comparison among fuel consumption models of private cars.
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Table 3 Fuel consumption fitted models. The fitting factor k2 is shown in parenthesis. Passenger cars and pickups CCinst|ac < 0 = 4.879E4 + 7.949E5*V (1.608)*1.0296E5*V2 + 4.896E7*V3–7.27157E9*V4 CCinst|ac = 0 = 5.390E4 + 1.374E5*V + (0.830)*5.328E6*V2–2.674E7*V3 + 4.247E9*V4 CCinst|ac = 0.3 = 5.483E4 + 1.843E3*V (0.150)*1.1450E5*V2 + 6.229E7*V3–1.042E8*V4 CCinst|ac = 0.6 = 6.826E4 + 1.949E4*V + (0.888)*2.250E7*V2 + 1.428E7*V3–4.819E9*V4 CCinst|ac = 0.9 = 9.348E4 + 2.013E4*V + (0.888)*9.599E6*V2–2.030E7*V3 CCinst|ac = 1.2 = 9.340E4 + 2.633E4*V + (0.888)*2.112E5*V2–9.237E7*V3 + 9.279E9*V4 CCinst|ac = 1.5 = 1.575E3–2.653E4*V + (0.950)*1.425E4*V2–8.229E6*V3 + 1.362E7*V4 CCinst|ac = 1.8 = 1.553E3–1.320E4*V + (0.932)*1.397E4*V2–8.730E6*V3 + 1.510E7*V4 CCinst|ac = 2.1 = 1.491E3 + 7.602E5*V + (0.911)*1.185E4*V2–8.064E6*V3 + 1.448E7*V4 CCinst|ac = 2.4 = 1.480E3 + 2.936E4*V + (0.862)*8.450E5*V2–6.378E6*V3 + 1.184E7*V4 CCinst|ac = 2.7 = 1.555E3 + 4.164E4*V + (0.816)*6.229E5*V2–5.187E6*V3 + 9.873E8*V4 Busses CCinst|ac = 0 = 7.546E4 (0.815)*4.909E4*V + 1.356E4*V2 8.671E6*V3 + 1.852E7*V4 CCinst|ac = 0.3 = 1E3 + (1.02)*7.655E4*V 3.038E5*V2 + 8.096E7*V3 CCinst|ac = 0.6 = 3.75E2 (0.98)*2.508E2*V + 3.100E3*V2 6.984E5*V3 CCinst|ac = 0.9 = 4.372E2 (0.98)*3.202E2*V + 4.451E3*V22 1.212E4*V3 CCinst|ac = 1.2 = 4.655E2 (0.98)*5.565E2*V + 1.295E2*V2 7.996E4*V3 + 1.547E5*V4 CCinst|ac = 1.5 = 1.31E2 (0.98)*3.018E2*V + 1.226E2*V2 9.531E4*V3 + 2.191E5*V4 CCinst|ac = 1.8 = 3.0E2 + (0.98)*4.744E2*V 2.649E3*V2 + 4.572E5*V3 CCinst|ac = 2.1 = 5.0E2 + (0.98)*6.086E2*V 4.455E3*V2 + 1.024E4*V3 CCinst|ac = 2.4 = 6.0E2 + (0.98)*6.205E2*V 5.245E3*V2 + 1.340E4*V3 CCinst|ac = 2.7 = 9.0E2 + (0.98)3.534E2*V 3.217E3*V2 + 8.618E5*V3
speed, thus introducing a significant error into the model because it underestimates the emissions at speeds smaller than 10 ms1. Therefore, the resulting plot for speeds smaller than that stated were not fitted. This fitting was achieved adding a factor to the quadratic term in the models, that is identified in Eq. (2) as k2, the values of which are shown inside parentheses in Table 3. As can be observed in Fig. 2A, the FHWA data and the MOBILE6Mex model have similar behavior for trucks, thus it was not required to fit the polynomial regression model for this case, though, the fitting was required for the busses case (see Fig. 2B). The fitted instantaneous fuel consumption models are also presented in Table 3. Once the fitted instantaneous fuel consumption models were obtained, then the fuel-based emission factors were calibrated for each pollutant, identified as FEFj in Eq. (3). Figs. 3–5 show the fittings for carbon monoxide (CO), nitrogen oxides (NOx) and hydrocarbons (HC) emissions, respectively. Therefore, as can be observed in Table 4, the calibrated factors were similar to those of other references. The others FEF incorporated in the GIS application were taken from other studies that were referenced in Table 5, some of which correspond to measurements carried out in the MCMA. The models of CCinst|aci and the IEj|aci were integrated in a GIS tool in ArcMapÒ, which we called ‘‘Transims’s Snapshot-Based Emissions’’. Fig. 6 shows the flow diagram of the design of the instantaneous emissions model configured in the GIS tool. This integrated novel tool allows calculation of the instantaneous emissions of the following pollutants: CO, NOx, HC, polycyclic aromatic hydrocarbons (PAHs), black carbon (BC), sulfur oxides (SOx), and carbon dioxide (CO2), particulate matter smaller than 10 lm (PM10) and smaller than 2.5 lm (PM2.5) from passenger cars, trucks and busses.
Fig. 2. Comparison among fuel consumption models of trucks and busses.
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Determining fuel-based emission factor of CO Mobile6Mex
Fied Model
0.350 CO emissions (g s-1)
0.300 0.250 0.200 0.150 0.100 0.050
EICO|ac0 = (117 g/l) * CCinst-local|ac0 *
0.000 0
5
10 15 Speed (m s-1)
20
25
Fig. 3. Fuel-based emission factor for CO fitted with MOBILE6Mex for passenger cars and pickups.
Determining fuel-based emission factor of NOx Mobile6Mex
Fitted Model
NOx emissions (g s-1)
0.016 0.014 0.012 0.01 0.008 0.006 0.004
EINOx|ac0 = (6.3 g/l) * CCinst-local|ac0
0.002 0 0
5
10
15
20
25
Speed (m s-1) Fig. 4. Fuel-based emission factor for NOx fitted with MOBILE6Mex for passenger cars and pickups.
Determining fuel-based emission factor of HC Mobile6Mex
Fitted Model
0.008 0.007 HC emissions (g s-1)
160
0.006 0.005 0.004 0.003 0.002
EIHC|ac0 = (3.6 g/l)* CCinst-local|ac0
0.001 0.000 0
5
10
15
20
25
Speed (m s-1) Fig. 5. Fuel-based emission factor for HC fitted with MOBILE6Mex for passenger cars and pickups.
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A. Hernández-Moreno, V. Mugica-Álvarez / Transportation Research Part D 33 (2014) 155–165 Table 4 Fuel-based emission factors of this study and from other references. Pollutant CO CO CO CO NOx NOx NOx NOx
Factor, FEF/g kg1
142
8.4 12.4
HC HC HC a b
Factor, FEF/g L1
Source
117 113.5 ± 13 114 ± 64a 104.9 ± 20%
This study Schifter et al. (2005) Zavala et al. (2009) Singer and Harley (1996)
6.3 9.84 ± 2.3 6.72 ± 1.6a 9.9 ± 4.8a
This study Schifter et al. (2005)b Zavala et al. (2009) Zavala et al. (2009)b
3.6 3.2 4.25
This study MOBILE6Mex Trafficware Ltd. (2006)
Conversion using the gasoline specific weight = 0.8. Vehicles without emissions control.
Table 5 Other applied fuel-based emission factors. Pollutant Gasoline CO NOx HC BC PAH CO2 SOx PM10 PM2.5 Diesel CO NOx HC BC PAH CO2 SOx PM10 PM2.5 a b c
Factor, FEF/g kg1
Factor, FEF/g L1
Source
0.27 ± 0.59 0.009 ± 0.009 3180
117 6.3 3.6 0.216a ± 0.47 0.0072a ± 0.0072 2544a 0.41 0.0359 0.0341
This model This model This model Jiang et al. (2005) Jiang et al. (2005) IPCC (2006)b MOBILE6Mex MOBILE6Mex MOBILE6Mex
23c (0–32) 38c (30–94) 1.5 1.17a (1.09–1.34) 0.005a,b 2658 0.409a 0.35 0.31c (0.13–1.34)
Thornhill et al. (2010) Thornhill et al. (2010) MOBILE6Mex Thornhill et al. (2010) Jiang et al. (2005) EPA (2005) MOBILE6Mex MOBILE6Mex Thornhill et al. (2010)
1.4 0.007 0.49 0.37
Converted using one specific weight of gasoline = 0.8 and diesel = 0.835. Taken from mentioned references in cited studies. Calculated using the factors estimated by Thornhill et al. (2010). Values in parentheses are 95% confidence intervals.
The tool ‘‘Transims’s Snapshots-Based Emissions’’ was used on the Snapshots obtained after executing the ArcSnapshot modulus of the simulator, to enable computing of the pollutants emissions. The GIS tool executes the emissions estimates by lots of up to 3600 snapshots at a rate of 40 files per minute, approximately. The size of the lots was limited by the buffer’s memory maximum capacity used by the GIS (ArcMapÒ). With the new GIS tool it was possible to obtain the longitudinal profiles along every link in the road network. The new longitudinal distribution of pollutant emissions were compared with those emissions estimated using the average speed factors model (see Fig. 7). Then, is possible to note that when the route includes short links and frequent intersections, the average speed pollutant emissions are underestimated. In opposition, when the route includes long links without intersections, the average speed pollutant emissions are overestimated significantly. Fig. 8 shows the comparison between the outputs using average emission factors and instantaneous factors. The A frame, shows clearly that the CO emissions are not the same in the lanes, and that it is possible to distinguish a differentiated emissions distribution throughout the area. Whereas the CO estimated emissions using average emission factors at C, show that there are no differences in the distinctive links of the roads. The B and D frames are the corresponding zooms of A and C. The D zoom shows again a homogeneous behavior of emissions, although applying instantaneous emissions models (B) it becomes possible to observe fully the differences among the links, e.g., the CO emissions at both sides of the roads are not the same; the vehicles that move away from the intersection display greater emissions than those going into the intersection, thereby indicating a difference between acceleration and deceleration processes. The right road after the intersection shows lower emissions because it is less used. The detail with which it is possible to see the spatial distribution of emissions
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START
Read specifications of vehicles j and their operation conditions in each instant of time i (Velocity Vij and acceleration acij)
second-bysecond Transims’ snapshots
Fuel?
Diesel
Gasoline Fuel consumption models of gasoline (with new coefficients k y β)
Calculation of instantaneous fuel consumption (FCinst-local, j) with Vij and acij
Calculation of instantaneous fuel consumption (FCinst-local, j) with Vij and acij
Fuel consumption models of diesel (with new coefficients k y β)
Fuel-based emission factors of each pollutant k of gasoline vehicles (FEFk )
Calculation of instantaneous emissions (IEijk|aci) of each k pollutant
Calculation of instantaneous emissions (IEijk|aci) of each k pollutant
Fuel-based emission factors of each pollutant k of diesel vehicles (FEFk )
second-by-second Transims’ Snapshot with pollutant emissions
Spatial and temporal pollutant emissions distribution
END Fig. 6. Diagram of the tool ‘‘Transims Snapshot-Based Emissions’’.
is not only an advantage for the emission inventories but also for the design of traffic strategies, the air quality modeling and transport and road infrastructure policies. Fig. 9 shows the comparison carried out on a macro scale level where the vehicle emissions of CO are compared using a grid with cells of 5 km 5 km. The accumulated emissions by cell using the instantaneous emissions model had more capacity to describe the greatest spatial emissions in comparison with the speed average emission estimations where the spatial distribution were uniform and less detailed. The accuracy of the models developed with this methodology depends on the representativeness and the validity of fuel consumption data and fuel-based emission factors obtained for each city. In this case, the fuel consumption data were obtained from studies conducted by the FHWA where eight vehicles were selected based on their weight, engine size, and availability. On-road measurements are considered as more realistic than dynamometer measurements by several researchers. Although the sample was small, the light duty vehicle class included different types such as compact vehicles, light duty trucks (pickups), sport utility vehicles (SUV), and minivans. Samples included a wide range of model years and measurements of fuel consumption were carried out on public roads with minimum slope. The vehicles were tested at
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Fig. 7. Comparison between longitudinal profiles of CO2 emissions along one random link. Outputs of average speed emission model (red line) are uniform along the link. Outputs of instantaneous emissions model (dark line) displays the transitory behavior of emissions. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 8. Vehicular CO emissions distribution along the road network obtained applying traffic emissions coppled with TRANSIMS outputs (A and B) and the comparison with average speed based emission factors estimations (C and D). The emissions scale are the same.
steady speeds from 32 to 105 km h1, in nominal increases of 4 km h1. In addition vehicles were tested on a 1.6 km airport runway; this was used for very low- and very high-speed runs, and acceleration and deceleration runs that could not be safely conducted on public roads (FHWA, 1999). An assessment of this study reported that while an eight vehicle model cannot be expected to predict accurately, notwithstanding it is a reference to estimate fuel consumption for non-steady-state speed conditions under urban or highway traffic regimes (FHWA, 2012). As was mentioned above, the fuel-based emission factors were taken from previous studies carried out in México City by other authors. The fuel-based emission factors measured by Thornhill et al. (2010), in Mexico City using an Aerodyne Mobile Laboratory (AML) were determined on driving conditions for carbon dioxide, carbon monoxide, nitrogen oxides, fine particulate mass (PM2.5), and some volatile organic compounds. Jiang et al. (2005), obtained fuel-based emission factors of BC and PAH with a mobile laboratory driven through Mexico City and equipped with an aethalometer (to measure BC) and a photoionization aerosol sensor (to measure PAH). The study included approximately 75 h of on-road sampling where around 30,000 exhaust measurement points were identified, representing many vehicle types and driving conditions. MOBILE6Mex factors were calibrated for Mexican cities since 2004, to estimate emission factors for on-road vehicles. MOBILE6-Mexico can predict emissions of principal pollutants for several types of vehicles. Also, it was up-dated with assumptions about how quickly vehicle emission control systems deteriorate and about how much lower the emissions
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Fig. 9. Macro scale comparison between vehicle CO emissions inventory using instantaneous models (A) and average speed emissions model (B). The capacity of differentiation among links with high emissions is better using instantaneous emission models.
levels of future vehicles will be when compared to current vehicles. It is the most popular model in Mexico and has the official validation to estimate the average speed-based emissions. The technical difficulties involved in the validation process for this type of outputs have been explained by Smit et al. (2008) which established that ‘‘It is, however, nor possible to validate traffic emissions models in a strict scientific sense, since true road traffic emission values are unknown and cannot practically be determinate by measurement’’ (sic). However it is reasonable to accept that observations made in the last Figs. 6–8 exhibit logical behavior as far as reality is concerned. Conclusions This study presented the development of an instantaneous emissions model set in GIS that allowed obtaining the spatial and temporal distribution of pollutants’ emissions associated to the results of traffic simulation through cellular automata of TRANSIMS. The spatial and temporal resolutions of the said distribution of vehicular pollutants emissions were doubtlessly improved through traffic simulation using an instantaneous emissions model, set in a GIS environment. The IEM’s tool allowed translation of the benefits provided by the traffic simulation and augmented the resolution level during estimation and description of the vehicular pollutants emissions. The IEM was regionalized through the application of fitting factors on the polynomial correlation coefficients of the fuel consumption models. This estimation method avoids the use of emission factors tables rated at average speed, which do not allow obtaining a spatial description of the emissions along a link. The polynomial regression method applied in the instantaneous fuel consumption models allowed building instantaneous emissions’ models in an relatively easy manner, accessible to any city of the world, starting from local measurements such as fuel consumption factors and fuel based emission factors, without requiring deeper programming knowledge; this also avoids the necessity to import foreign emission factors models that demand qualified personnel to operate them. It is recommended the development of supplementary GIS tools to improve the graphical representation of the results and the analytic identification of the critical pollution points. References Ahn, K., Rakha, H., 2008. The effects of route choice decisions on vehicle energy consumption and emissions. Transp. Res. Part D 13, 151–167. 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), 182–190. Chowdhury, D., Santen, L., Schadschneider, A., 2000. Statistical physical of vehicular traffic and some related systems. Phys. Rep. 329 (4), 199–329. EGR, 2003. MOBILE6 Mexico. Prepared for the Western Governor’s Association by Eastern Research Group Inc., Texas. Eissfeldt, N., Schrader, R., 2001. Calculation of street traffic emissions with a queuing model. J. Comp. Technol. 7. EPA, 2005. Average Carbon Dioxide Emissions Resulting from Gasoline and Diesel Fuel. Environmental Protection Agency, U.S.A.. EPA, 2007. The Plain English Guide to the Clean Air Act. Environmental Protection Agency, United States. FHWA, 1999. Development and Validation of Light Duty Vehicle Modal Emissions and Fuel Consumption Values for Traffic Models. Federal Highway Administration, US Department of Transportation, US Department of Commerce.
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FHWA, 2012. Federal Highway Administration. Retrieved 03.21.14, from Revised Monograph on Traffic Flow Theory, Cap. 7.:. Fu, M.L., Ding, Y., Yin, H., Ji, Z., Yu, L.X., Liang, B., et al, 2014. Study on fuel-based emission factors from construction equipment. Beijing Ligong Daxue Xuebao/Trans. Beijing Inst. Technol. 34 (2), 138–142. Galvis, B., Bergin, M., Russell, A., 2013. Fuel-based fine particulate and black carbon emission factors from a railyard area in Atlanta. J. Air Waste Manag. Assoc. 63 (6), 648–658. IPCC, 2006. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories-Emissions: Energy, Road Transport. (J. Penman, D. Kruger, I. Galbally, T. Hirais, L. Buendia, R. Hoppaus, et al., Eds.) IPCC National Greenhouse Gas Inventories Programme. Jiang, M., Marr, L., Dunlea, E., Herndon, S., Jayne, J., Kolb, C., et al, 2005. Vehicle fleet emissions of black carbon, polycyclic aromatic hydrocarbons, and other pollutants measured by a mobile laboratory in Mexico City. Atmos. Chem. Phys. 5, 3377–3387. Liao, T.Y., 2013. Fuel-based signal optimization model. Transp. Res. Part D 23, 1–8. Rakha, H., Van Aerde, M., Ahn, K., Trani, A., 2000. Requirements for evaluating traffic signal control impacts on energy and emissions based on instantaneous speed and acceleration measurements. Transp. Res. Rec. 1738, 56–67. Rakha, H., Ahn, K., Trani, A., 2004. Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions. Transp. Res. Part D 9, 49–74. Schifter, I., Díaz, L., Mugica, V., López-Salinas, E., 2005. Fuel-based motor emission inventory for the metropolitan area of Mexico city. Atmos. Environ. 39 (5), 931–940. Singer, B., Harley, R., 1996. Fuel-based motor vehicle emission inventory. J. Air Waste Manag. Assoc. 46, 581–593. Smit, R., Smokers, R., Rabé, E., 2007. A new modelling approach for road traffic emissions: VERSIT+. Transp. Res. Part D 12 (6), 414–422. Smit, R., Brown, A., Chan, Y., 2008. Do air pollution emissions and fuel consumption models for roadways include the effects of congestion in the roadway traffic flow? Environ. Modell. Softw. 23 (10), 1262–1270. Thornhill, D., Williams, A., Onasch, T., Wood, E., Herndon, S., Kolb, C., et al, 2010. Application of positive matrix factorization to on-road measurements for source apportionment of diesel- and gasoline-powered vehicle emissions in Mexico City. Atmos. Chem. Phys. 10, 3629–3644. Trafficware Ltd., 2006. Synchro Studio 7 User Guide. U.S.A. TRANSIMS Open-Source, 2007. TRANSIMS Open-Source. Retrieved April 2013, from Transims an open source transportation modeling and simulation toolbox: https://code.google.com/p/transims/. Zavala, M., Herndon, S., Wood, E., Onasch, T., Knighton, W., Kolb, C., et al, 2009. Evaluation of mobile emissions contributions to Mexico City’s emissions inventory using on-road and cross-road emission measurements and ambient data. Atmos. Chem. Phys. Discuss. 9, 6363–6395.
Contents lists available at ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Instantaneous emissions models set in GIS for the TRANSIMS outputs Adolfo Hernández-Moreno, Violeta Mugica-Álvarez ⇑ Universidad Autónoma Metropolitana-Azcapotzalco, Av. San Pablo 180 Col. Reynosa-Tamaulipas, Azcapotzalco, México, D.F. 02200, Mexico
a r t i c l e
i n f o
Keywords: Vehicle emissions GIS Instantaneous emission model TRANSIMS
a b s t r a c t A novel methodology that provides more detailed estimates of vehicular polluting emissions is offered, in order to contribute to the improvement and the precision of emission inventories of vehicle sources through the consideration of instantaneous speed changes or acceleration instead of average vehicular speeds. This paper presents the construction and application of an instantaneous emissions model designated hereunder as ‘‘Transims’s Snapshots-Based Emissions’’, which is set on a Geographic Information System that incorporates instantaneous fuel consumption factors and fuel-based emission factors to attain highest resolution of both, spatial and temporal distribution of vehicular polluting emissions based on traffic simulation through cellular automata with TRANSIMS. This work was applied to the road network of the Mexico City Metropolitan Area as case study. The development of this powerful tool led to obtaining 86,400 maps of the spatial and temporal distribution of vehicular emissions per vehicle circulating on the road network, including the following pollutants: carbon monoxide and carbon dioxide, nitrogen oxides, total hydrocarbons, sulfur oxides, polycyclic aromatic hydrocarbons, black carbon, particles PM10 and PM2.5. The said maps allowed identification with highest level of detail, of the emissions and Hot-spots of fuel consumption. Also, the model permitted to obtain the emissions’ longitudinal profiles of a given vehicle along its route. This study shows that the integration method of the polynomial regression models represents an opportunity for each city to develop more easily and openly its own regional emissions models without requiring deeper programming knowledge. Ó 2014 Elsevier Ltd. All rights reserved.
Introduction Polluting emissions from automotive vehicles comprise a set of pollutants generated through different processes of the vehicular activity, which includes criteria pollutants1 (i.e. carbon monoxide and particulate material), ozone precursors (i.e. hydrocarbons), toxic substances (i.e. polycyclic aromatic hydrocarbons) and short-lived pollutants (i.e. black carbon). During many years, the method applied to such emissions has been based on the use of emission factors rated at average speeds applied to generalized estimates of the vehicular activity. Recently, there has been an increased use of traffic
⇑ Corresponding author. Tel.: +52 (55) 5318 9577. E-mail address: [email protected] (V. Mugica-Álvarez). The United States Environmental Protection Agency (USEPA) calls six common pollutants ‘‘criteria air pollutants (also known as ‘‘criteria pollutants’’) because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The set of limits based on human health is called primary standards (EPA, 2007). 1
http://dx.doi.org/10.1016/j.trd.2014.06.002 1361-9209/Ó 2014 Elsevier Ltd. All rights reserved.
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simulators to determine such vehicular activity because they are considered the tools that best reproduce the vehicular traffic phenomenon (Chowdhury et al., 2000). To date, there are vehicular traffic simulators developed over different movement theories; hydrodynamic, kinetic, of coupled-map lattice, car-following and cellular automata. Some of the available simulators, whether commercial or open source, use instantaneous emission factors, as the case is for Synchro SimTrafficÒ (Trafficware Ltd., 2006). However, these display the emissions assessments over averages of vehicular activity for each link, which limits the spatial resolution level available to the researcher. This limitation has as origin the theoretical base of the traffic simulation based on continuous models, like the car-following, and on the strategy used for summarizing the enormous amount of information generated by traffic simulation. Among the open-source traffic simulators available, TRANSIMS (Transportation Analysis and Simulation System) is a simulator based on cellular automata that was developed by Los Alamos National Laboratory with financing from the U.S. Department of Transportation, from the Environmental Protection Agency and from the U.S. Department of Energy, as part of the Travel Model Improvement Program and is distributed under agreement of NASA Open Source Agreement Version 1.3. TRANSIMS comprises an integrated system of trips forecasting models designed with the aim of providing transport planners with exact, full information on the traffic impacts. TRANSIMS has its own emissions assessment modulus. However, as is common with other simulators, it is limited by the use of tables of emission factors and vehicular activity averages. An attractive feature of TRANSIMS is its capacity to generate instantaneous maps (also known as snapshots), which are geo-referenced maps in shape format (⁄.shp) with the geographic location of each vehicle that traverses the road network daily, second-by-second. This way it is possible to know the operating features of each vehicle including its instantaneous speed and acceleration changes at any instant of time. Some authors, like Eissfeldt and Schrader (2001), have concluded that to associate the old emission factors modeled at average speed with the traffic simulation does not allow obtaining the expected improvements to estimate the pollutant’s emissions because the use of such factors may entail an important underestimation. Consequently, it has been considered more adequate to estimate emissions based on the instantaneous emission models (IEM) that consider the transient behavior of vehicles’ acceleration-deceleration, apart from the speed changes (Ahn and Rakha, 2008). After, Ahn et al. (2002) presented the method to integrate an instantaneous emissions model (also called microscopic) that used polynomial regression models, subsequent models have been developed following the same strategy, as the case is with the VERSIT+ (Smit et al., 2007) and VT-Micro (Ahn and Rakha, 2008) models. In order to maximize the benefits of traffic simulation for determining the spatial and temporal distributions of vehicular emissions; this work presents the development of an instantaneous emissions model based on instant fuel consumption applicable to outcomes of the TRANSIMS applying it to the Mexico City case. This methodology works as a guide for those regions which have not proper models. They could develop one without deep programming knowledge and reduce the technology dependence. Theoretical development The new instantaneous emissions model, IEM, presented here is a product of the conjoint application of two kinds of factors. On the one hand, the utilization of instantaneous fuel consumption put forward by the Federal Highway Administration from Oak Ridge National Labs (FHWA, 1999). On the other, the application of fuel-based emission factors, FEF, obtained through previous studies. The regionalization of the model was carried out calibrating against the MOBILE6Mex (EGR, 2003). A similar treatment to that presented by Rakha et al. (2004, 2000), permits obtaining the polynomial regression models for the instantaneous fuel consumption, CCinst|aci, for each acceleration value, aci, as a function of the instantaneous speed, V (see Eq. (1)).
CC inst jaci ¼ b0 þ b1 V þ b2 V 2 þ þ bn V n
ð1Þ
where bi are the regression coefficients. The fuel consumption models are calibrated and regionalized against the results of the MOBILE6Mex model, approximating the models to the local data applying fitting factors, ki, which can affect each element of the polynomial regression model as shown by Eq. (2),
CC inst-local jaci ¼ k0 b0 þ k1 b1 V þ k2 b2 V 2 þ þ kn bn V n
ð2Þ
where CCinst-local is the regionalized fuel consumption. The polluting emissions can be determined applying fuel-based emission factors (Liao, 2013; Galvis et al., 2013; Fu et al., 2014), such that once the instantaneous fuel consumption had been estimated, the instantaneous polluting emissions can be determined multiplying by the fuel-based emission factors consumption, as shown by Eq.(3).
IEj jaci ¼ ðFEF j Þ CC inst-local jac i
ð3Þ
where IEj|aci are the instantaneous emissions of the pollutant j at a given acceleration aci and FEFj is the fuel-based emission factor of the pollutant j. The FEF of each contaminant can be taken from the literature or measured in the field as those obtained by Schifter et al. (2005).
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The models of the CCinst|aci and those of the IEj|aci integrate jointly the IEM, which are incorporated in one GIS tool, where the knowledge of the geographic position of the vehicles, together with all their information of instant speed and acceleration allows simultaneous determination of the instantaneous pollutant emissions and their spatial distribution. As study case, the vehicular traffic was simulated in the road network (residential, principal and highway streets) of the MCMA using TRANSIMS. The snapshots or instant activity maps of TRANSIMS over those where the GIS tool had been applied are generated by running the ArcSnapshot modulus of the simulator. The latter converts the Snapshot file (generated by the Micro simulator modulus of TRANISMS) into maps of the shape (⁄.shp) sort for ArcGisÒ (TRANSIMS Open-Source, 2007). The maps contain the location of the vehicles per second per day and the information on their operating conditions like speed and instant acceleration. Results Three sets of fuel consumption models were developed; passenger cars, trucks and busses using the FHWA data. Table 1 presents the polynomial regression models obtained where the best fittings were achieved with fourth order polynomials with respect to speed for passenger cars and trucks. Although for the case of busses most were fitted to third and fourth orders. In addition, Table 1 shows the statistics concerning the error of each model. For the three types of vehicles, note that only one model served to describe the fuel consumption for negative accelerations (be it decelerations). In general, the models display goodness of fit to observations. The uncertainties are displayed in Table 2 to complete the statistical analyst of the models. At the same time, the emission factors at average speed were modeled for the whole speed range allowed by MOBILE6Mex (EGR, 2003). Note that this model was calibrated for México’s conditions, thus it considers the fuel quality regional features, meteorology, altitude and characterization of the vehicular fleets. For modeling purposes, the vehicular fleets estimated during 2010 were used. The fuel yields corresponding to cero (0.0 ms2) acceleration, which is the equivalent to constant speed, were extracted from the results of MOBILE6Mex and compared with those obtained through the models
Table 1 Coefficients and error statistics of fuel consumption polynomial regression models (see Eq. (1)). b1
b2
b3
b4
R2
Standard error
4.88E04 5.39E04 5.48E04 6.83E04 9.35E04 9.34E04 1.58E03 1.55E03 1.49E03 1.48E03 1.56E03
7.95E05 1.37E05 1.84E03 1.95E04 2.01E04 2.63E04 2.65E04 1.32E04 7.60E05 2.94E04 4.16E04
1.03E05 5.33E06 1.15E05 2.25E07 9.60E06 2.11E05 1.43E04 1.40E04 1.19E04 8.45E05 6.23E05
4.90E07 2.67E07 6.23E07 1.43E07 2.03E07 9.24E07 8.23E06 8.73E06 8.06E06 6.38E06 5.19E06
7.27E09 4.25E09 1.04E08 4.82E09 – 9.28E09 1.36E07 1.51E07 1.45E07 1.18E07 9.87E08
0.874 0.974 0.984 0.997 0.990 0.995 0.981 0.989 0.994 0.991 0.987
0.00002 0.00006 0.00010 0.00008 0.00022 0.00019 0.00041 0.00031 0.00023 0.00026 0.00031
Trucks <0 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
0.00065 9.683E4 +1.215E3 0.0209 0.0367 0.0143 0.033 0.022 9.731E3 0.001 0.001
– 5.244E4 6.456E4 0.012 0.0308 0.004 0.0445 0.033 0.021 0.057 0.0689
– 7.108E5 5.779E5 0.007 0.0073 6.394E4 0.0148 0.0138 0.0117 3.435E3 5.818E3
– 4.886E6 3.799E6 5.773E4 3.260E4 2.737E4 1.034E3 0.001 9.181E4 2.053E5 1.817E4
– 9.810E8 6.842E8 1.594E5 3.781E6 1.004E5 2.233E5 2.256E5 2.116E5 2.017E6 1.495E6
– 0.979 0.990 0.732 0.868 0.874 0.878 0.878 0.862 0.823 0.826
– 0.00024 0.00029 0.02349 0.03608 0.03487 0.03330 0.03243 0.03358 0.03252 0.03120
Busses <0 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
3.4E4 7.546E4 1.0E3 3.75E2 4.372E2 4.665E2 1.31E2 3.0E2 5.0E2 6.0E2 9.0E2
– 4.909E4 7.655E4 2.508E2 3.202E2 5.565E2 3.018E2 4.744E2 6.086E2 6.205E2 3.534E2
– 1.356E4 3.038E5 3.100E3 4.451E3 1.295E2 1.226E2 2.649E3 4.455E3 5.245E3 3.217E3
– 8.671E6 8.096E7 6.984E5 1.212E4 7.996E4 9.531E4 4.572E5 1.024E4 1.340E4 8.618E5
– 1.852E7 – – – 1.547E5 2.191E5 – – – –
– 0.943 0.964 0.871 0.835 0.869 0.864 0.801 0.817 0.774 0.457
– 0.00045 0.00054 0.03968 0.03869 0.03499 0.03292 0.03705 0.03207 0.02903 0.02866
Acceleration (m s2) Passenger cars <0 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
b0
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Table 2 Uncertainty from polynomial regression models. Acceleration (m s2)
Sb0
Sb1
Sb2
Sb3
Sb4
Passenger cars <0 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
1.044E05 2.715E05 4.867E05 3.815E05 8.745E05 9.191E05 2.034E04 1.548E04 1.134E04 1.276E04 1.505E04
5.423E06 1.411E05 2.529E05 1.982E05 2.699E05 4.776E05 1.057E04 8.042E05 5.891E05 6.632E05 7.820E05
8.109E07 2.110E06 3.782E06 2.964E06 2.214E06 7.142E06 1.580E05 1.203E05 8.809E06 9.917E06 1.169E05
4.351E08 1.132E07 2.029E07 1.590E07 5.131E08 3.832E07 8.479E07 6.452E07 4.726E07 5.321E07 6.274E07
7.423E10 1.931E09 3.462E09 2.713E09 – 6.537E09 1.446E08 1.101E08 8.063E09 9.077E09 1.070E08
Trucks 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
1.275E04 1.559E04 1.277E02 1.961E02 1.895E02 1.810E02 1.762E02 1.825E02 1.767E02 1.695E02
8.454E05 1.033E04 8.463E03 1.300E02 1.256E02 1.199E02 1.168E02 1.210E02 1.171E02 1.124E02
1.634E05 1.997E05 1.636E03 2.513E03 2.428E03 2.318E03 2.258E03 2.338E03 2.264E03 2.172E03
1.158E06 1.415E06 1.159E04 1.780E04 1.720E04 1.643E04 1.600E04 1.656E04 1.604E04 1.539E04
2.697E08 3.297E08 2.700E06 4.147E06 4.008E06 3.827E06 3.727E06 3.859E06 3.737E06 3.585E06
Busses 0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7
2.419E04 2.434E04 1.781E02 1.737E02 1.901E02 1.789E02 1.663E02 1.440E02 1.303E02 1.286E02
1.603E04 9.976E05 7.299E03 7.117E03 1.260E02 1.186E02 6.816E03 5.900E03 5.339E03 5.272E03
3.099E05 1.094E05 8.003E04 7.803E04 2.436E03 2.292E03 7.473E04 6.468E04 5.854E04 5.780E04
2.196E06 3.374E07 2.469E05 2.408E05 1.726E04 1.624E04 2.306E05 1.996E05 1.806E05 1.783E05
5.116E08 – – 4.021E06 3.783E06 – – – –
in Table 1, (see Fig. 1). From the comparison, it is observed that the data of the model for passenger cars are displayed above those of the MOBILE6Mex model for speeds smaller than 10 ms1 and below those for speeds greater than 10 ms1. The differences can be interpreted as follows: on the one hand, the FHWA data are not regional, because they do not show the altitude effect of the MCMA that induces efficiency losses of internal combustion vehicles. Consequently, the fitting model based on FHWA data underestimates the emissions for speeds greater than 10 ms1. Therefore, the regression model was fitted to agree with the MOBILE6Mex at speeds greater than 10 ms1. On the other, the model MOBILE6Mex uses a linear model that converges at zero emissions at zero speed, which implies that the vehicle does not consume fuel when it is operating at idle
Fig. 1. Comparison among fuel consumption models of private cars.
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Table 3 Fuel consumption fitted models. The fitting factor k2 is shown in parenthesis. Passenger cars and pickups CCinst|ac < 0 = 4.879E4 + 7.949E5*V (1.608)*1.0296E5*V2 + 4.896E7*V3–7.27157E9*V4 CCinst|ac = 0 = 5.390E4 + 1.374E5*V + (0.830)*5.328E6*V2–2.674E7*V3 + 4.247E9*V4 CCinst|ac = 0.3 = 5.483E4 + 1.843E3*V (0.150)*1.1450E5*V2 + 6.229E7*V3–1.042E8*V4 CCinst|ac = 0.6 = 6.826E4 + 1.949E4*V + (0.888)*2.250E7*V2 + 1.428E7*V3–4.819E9*V4 CCinst|ac = 0.9 = 9.348E4 + 2.013E4*V + (0.888)*9.599E6*V2–2.030E7*V3 CCinst|ac = 1.2 = 9.340E4 + 2.633E4*V + (0.888)*2.112E5*V2–9.237E7*V3 + 9.279E9*V4 CCinst|ac = 1.5 = 1.575E3–2.653E4*V + (0.950)*1.425E4*V2–8.229E6*V3 + 1.362E7*V4 CCinst|ac = 1.8 = 1.553E3–1.320E4*V + (0.932)*1.397E4*V2–8.730E6*V3 + 1.510E7*V4 CCinst|ac = 2.1 = 1.491E3 + 7.602E5*V + (0.911)*1.185E4*V2–8.064E6*V3 + 1.448E7*V4 CCinst|ac = 2.4 = 1.480E3 + 2.936E4*V + (0.862)*8.450E5*V2–6.378E6*V3 + 1.184E7*V4 CCinst|ac = 2.7 = 1.555E3 + 4.164E4*V + (0.816)*6.229E5*V2–5.187E6*V3 + 9.873E8*V4 Busses CCinst|ac = 0 = 7.546E4 (0.815)*4.909E4*V + 1.356E4*V2 8.671E6*V3 + 1.852E7*V4 CCinst|ac = 0.3 = 1E3 + (1.02)*7.655E4*V 3.038E5*V2 + 8.096E7*V3 CCinst|ac = 0.6 = 3.75E2 (0.98)*2.508E2*V + 3.100E3*V2 6.984E5*V3 CCinst|ac = 0.9 = 4.372E2 (0.98)*3.202E2*V + 4.451E3*V22 1.212E4*V3 CCinst|ac = 1.2 = 4.655E2 (0.98)*5.565E2*V + 1.295E2*V2 7.996E4*V3 + 1.547E5*V4 CCinst|ac = 1.5 = 1.31E2 (0.98)*3.018E2*V + 1.226E2*V2 9.531E4*V3 + 2.191E5*V4 CCinst|ac = 1.8 = 3.0E2 + (0.98)*4.744E2*V 2.649E3*V2 + 4.572E5*V3 CCinst|ac = 2.1 = 5.0E2 + (0.98)*6.086E2*V 4.455E3*V2 + 1.024E4*V3 CCinst|ac = 2.4 = 6.0E2 + (0.98)*6.205E2*V 5.245E3*V2 + 1.340E4*V3 CCinst|ac = 2.7 = 9.0E2 + (0.98)3.534E2*V 3.217E3*V2 + 8.618E5*V3
speed, thus introducing a significant error into the model because it underestimates the emissions at speeds smaller than 10 ms1. Therefore, the resulting plot for speeds smaller than that stated were not fitted. This fitting was achieved adding a factor to the quadratic term in the models, that is identified in Eq. (2) as k2, the values of which are shown inside parentheses in Table 3. As can be observed in Fig. 2A, the FHWA data and the MOBILE6Mex model have similar behavior for trucks, thus it was not required to fit the polynomial regression model for this case, though, the fitting was required for the busses case (see Fig. 2B). The fitted instantaneous fuel consumption models are also presented in Table 3. Once the fitted instantaneous fuel consumption models were obtained, then the fuel-based emission factors were calibrated for each pollutant, identified as FEFj in Eq. (3). Figs. 3–5 show the fittings for carbon monoxide (CO), nitrogen oxides (NOx) and hydrocarbons (HC) emissions, respectively. Therefore, as can be observed in Table 4, the calibrated factors were similar to those of other references. The others FEF incorporated in the GIS application were taken from other studies that were referenced in Table 5, some of which correspond to measurements carried out in the MCMA. The models of CCinst|aci and the IEj|aci were integrated in a GIS tool in ArcMapÒ, which we called ‘‘Transims’s Snapshot-Based Emissions’’. Fig. 6 shows the flow diagram of the design of the instantaneous emissions model configured in the GIS tool. This integrated novel tool allows calculation of the instantaneous emissions of the following pollutants: CO, NOx, HC, polycyclic aromatic hydrocarbons (PAHs), black carbon (BC), sulfur oxides (SOx), and carbon dioxide (CO2), particulate matter smaller than 10 lm (PM10) and smaller than 2.5 lm (PM2.5) from passenger cars, trucks and busses.
Fig. 2. Comparison among fuel consumption models of trucks and busses.
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Determining fuel-based emission factor of CO Mobile6Mex
Fied Model
0.350 CO emissions (g s-1)
0.300 0.250 0.200 0.150 0.100 0.050
EICO|ac0 = (117 g/l) * CCinst-local|ac0 *
0.000 0
5
10 15 Speed (m s-1)
20
25
Fig. 3. Fuel-based emission factor for CO fitted with MOBILE6Mex for passenger cars and pickups.
Determining fuel-based emission factor of NOx Mobile6Mex
Fitted Model
NOx emissions (g s-1)
0.016 0.014 0.012 0.01 0.008 0.006 0.004
EINOx|ac0 = (6.3 g/l) * CCinst-local|ac0
0.002 0 0
5
10
15
20
25
Speed (m s-1) Fig. 4. Fuel-based emission factor for NOx fitted with MOBILE6Mex for passenger cars and pickups.
Determining fuel-based emission factor of HC Mobile6Mex
Fitted Model
0.008 0.007 HC emissions (g s-1)
160
0.006 0.005 0.004 0.003 0.002
EIHC|ac0 = (3.6 g/l)* CCinst-local|ac0
0.001 0.000 0
5
10
15
20
25
Speed (m s-1) Fig. 5. Fuel-based emission factor for HC fitted with MOBILE6Mex for passenger cars and pickups.
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Factor, FEF/g kg1
142
8.4 12.4
HC HC HC a b
Factor, FEF/g L1
Source
117 113.5 ± 13 114 ± 64a 104.9 ± 20%
This study Schifter et al. (2005) Zavala et al. (2009) Singer and Harley (1996)
6.3 9.84 ± 2.3 6.72 ± 1.6a 9.9 ± 4.8a
This study Schifter et al. (2005)b Zavala et al. (2009) Zavala et al. (2009)b
3.6 3.2 4.25
This study MOBILE6Mex Trafficware Ltd. (2006)
Conversion using the gasoline specific weight = 0.8. Vehicles without emissions control.
Table 5 Other applied fuel-based emission factors. Pollutant Gasoline CO NOx HC BC PAH CO2 SOx PM10 PM2.5 Diesel CO NOx HC BC PAH CO2 SOx PM10 PM2.5 a b c
Factor, FEF/g kg1
Factor, FEF/g L1
Source
0.27 ± 0.59 0.009 ± 0.009 3180
117 6.3 3.6 0.216a ± 0.47 0.0072a ± 0.0072 2544a 0.41 0.0359 0.0341
This model This model This model Jiang et al. (2005) Jiang et al. (2005) IPCC (2006)b MOBILE6Mex MOBILE6Mex MOBILE6Mex
23c (0–32) 38c (30–94) 1.5 1.17a (1.09–1.34) 0.005a,b 2658 0.409a 0.35 0.31c (0.13–1.34)
Thornhill et al. (2010) Thornhill et al. (2010) MOBILE6Mex Thornhill et al. (2010) Jiang et al. (2005) EPA (2005) MOBILE6Mex MOBILE6Mex Thornhill et al. (2010)
1.4 0.007 0.49 0.37
Converted using one specific weight of gasoline = 0.8 and diesel = 0.835. Taken from mentioned references in cited studies. Calculated using the factors estimated by Thornhill et al. (2010). Values in parentheses are 95% confidence intervals.
The tool ‘‘Transims’s Snapshots-Based Emissions’’ was used on the Snapshots obtained after executing the ArcSnapshot modulus of the simulator, to enable computing of the pollutants emissions. The GIS tool executes the emissions estimates by lots of up to 3600 snapshots at a rate of 40 files per minute, approximately. The size of the lots was limited by the buffer’s memory maximum capacity used by the GIS (ArcMapÒ). With the new GIS tool it was possible to obtain the longitudinal profiles along every link in the road network. The new longitudinal distribution of pollutant emissions were compared with those emissions estimated using the average speed factors model (see Fig. 7). Then, is possible to note that when the route includes short links and frequent intersections, the average speed pollutant emissions are underestimated. In opposition, when the route includes long links without intersections, the average speed pollutant emissions are overestimated significantly. Fig. 8 shows the comparison between the outputs using average emission factors and instantaneous factors. The A frame, shows clearly that the CO emissions are not the same in the lanes, and that it is possible to distinguish a differentiated emissions distribution throughout the area. Whereas the CO estimated emissions using average emission factors at C, show that there are no differences in the distinctive links of the roads. The B and D frames are the corresponding zooms of A and C. The D zoom shows again a homogeneous behavior of emissions, although applying instantaneous emissions models (B) it becomes possible to observe fully the differences among the links, e.g., the CO emissions at both sides of the roads are not the same; the vehicles that move away from the intersection display greater emissions than those going into the intersection, thereby indicating a difference between acceleration and deceleration processes. The right road after the intersection shows lower emissions because it is less used. The detail with which it is possible to see the spatial distribution of emissions
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Read specifications of vehicles j and their operation conditions in each instant of time i (Velocity Vij and acceleration acij)
second-bysecond Transims’ snapshots
Fuel?
Diesel
Gasoline Fuel consumption models of gasoline (with new coefficients k y β)
Calculation of instantaneous fuel consumption (FCinst-local, j) with Vij and acij
Calculation of instantaneous fuel consumption (FCinst-local, j) with Vij and acij
Fuel consumption models of diesel (with new coefficients k y β)
Fuel-based emission factors of each pollutant k of gasoline vehicles (FEFk )
Calculation of instantaneous emissions (IEijk|aci) of each k pollutant
Calculation of instantaneous emissions (IEijk|aci) of each k pollutant
Fuel-based emission factors of each pollutant k of diesel vehicles (FEFk )
second-by-second Transims’ Snapshot with pollutant emissions
Spatial and temporal pollutant emissions distribution
END Fig. 6. Diagram of the tool ‘‘Transims Snapshot-Based Emissions’’.
is not only an advantage for the emission inventories but also for the design of traffic strategies, the air quality modeling and transport and road infrastructure policies. Fig. 9 shows the comparison carried out on a macro scale level where the vehicle emissions of CO are compared using a grid with cells of 5 km 5 km. The accumulated emissions by cell using the instantaneous emissions model had more capacity to describe the greatest spatial emissions in comparison with the speed average emission estimations where the spatial distribution were uniform and less detailed. The accuracy of the models developed with this methodology depends on the representativeness and the validity of fuel consumption data and fuel-based emission factors obtained for each city. In this case, the fuel consumption data were obtained from studies conducted by the FHWA where eight vehicles were selected based on their weight, engine size, and availability. On-road measurements are considered as more realistic than dynamometer measurements by several researchers. Although the sample was small, the light duty vehicle class included different types such as compact vehicles, light duty trucks (pickups), sport utility vehicles (SUV), and minivans. Samples included a wide range of model years and measurements of fuel consumption were carried out on public roads with minimum slope. The vehicles were tested at
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Fig. 7. Comparison between longitudinal profiles of CO2 emissions along one random link. Outputs of average speed emission model (red line) are uniform along the link. Outputs of instantaneous emissions model (dark line) displays the transitory behavior of emissions. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 8. Vehicular CO emissions distribution along the road network obtained applying traffic emissions coppled with TRANSIMS outputs (A and B) and the comparison with average speed based emission factors estimations (C and D). The emissions scale are the same.
steady speeds from 32 to 105 km h1, in nominal increases of 4 km h1. In addition vehicles were tested on a 1.6 km airport runway; this was used for very low- and very high-speed runs, and acceleration and deceleration runs that could not be safely conducted on public roads (FHWA, 1999). An assessment of this study reported that while an eight vehicle model cannot be expected to predict accurately, notwithstanding it is a reference to estimate fuel consumption for non-steady-state speed conditions under urban or highway traffic regimes (FHWA, 2012). As was mentioned above, the fuel-based emission factors were taken from previous studies carried out in México City by other authors. The fuel-based emission factors measured by Thornhill et al. (2010), in Mexico City using an Aerodyne Mobile Laboratory (AML) were determined on driving conditions for carbon dioxide, carbon monoxide, nitrogen oxides, fine particulate mass (PM2.5), and some volatile organic compounds. Jiang et al. (2005), obtained fuel-based emission factors of BC and PAH with a mobile laboratory driven through Mexico City and equipped with an aethalometer (to measure BC) and a photoionization aerosol sensor (to measure PAH). The study included approximately 75 h of on-road sampling where around 30,000 exhaust measurement points were identified, representing many vehicle types and driving conditions. MOBILE6Mex factors were calibrated for Mexican cities since 2004, to estimate emission factors for on-road vehicles. MOBILE6-Mexico can predict emissions of principal pollutants for several types of vehicles. Also, it was up-dated with assumptions about how quickly vehicle emission control systems deteriorate and about how much lower the emissions
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Fig. 9. Macro scale comparison between vehicle CO emissions inventory using instantaneous models (A) and average speed emissions model (B). The capacity of differentiation among links with high emissions is better using instantaneous emission models.
levels of future vehicles will be when compared to current vehicles. It is the most popular model in Mexico and has the official validation to estimate the average speed-based emissions. The technical difficulties involved in the validation process for this type of outputs have been explained by Smit et al. (2008) which established that ‘‘It is, however, nor possible to validate traffic emissions models in a strict scientific sense, since true road traffic emission values are unknown and cannot practically be determinate by measurement’’ (sic). However it is reasonable to accept that observations made in the last Figs. 6–8 exhibit logical behavior as far as reality is concerned. Conclusions This study presented the development of an instantaneous emissions model set in GIS that allowed obtaining the spatial and temporal distribution of pollutants’ emissions associated to the results of traffic simulation through cellular automata of TRANSIMS. The spatial and temporal resolutions of the said distribution of vehicular pollutants emissions were doubtlessly improved through traffic simulation using an instantaneous emissions model, set in a GIS environment. The IEM’s tool allowed translation of the benefits provided by the traffic simulation and augmented the resolution level during estimation and description of the vehicular pollutants emissions. The IEM was regionalized through the application of fitting factors on the polynomial correlation coefficients of the fuel consumption models. This estimation method avoids the use of emission factors tables rated at average speed, which do not allow obtaining a spatial description of the emissions along a link. The polynomial regression method applied in the instantaneous fuel consumption models allowed building instantaneous emissions’ models in an relatively easy manner, accessible to any city of the world, starting from local measurements such as fuel consumption factors and fuel based emission factors, without requiring deeper programming knowledge; this also avoids the necessity to import foreign emission factors models that demand qualified personnel to operate them. It is recommended the development of supplementary GIS tools to improve the graphical representation of the results and the analytic identification of the critical pollution points. References Ahn, K., Rakha, H., 2008. The effects of route choice decisions on vehicle energy consumption and emissions. Transp. Res. Part D 13, 151–167. 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), 182–190. Chowdhury, D., Santen, L., Schadschneider, A., 2000. Statistical physical of vehicular traffic and some related systems. Phys. Rep. 329 (4), 199–329. EGR, 2003. MOBILE6 Mexico. Prepared for the Western Governor’s Association by Eastern Research Group Inc., Texas. Eissfeldt, N., Schrader, R., 2001. Calculation of street traffic emissions with a queuing model. J. Comp. Technol. 7. EPA, 2005. Average Carbon Dioxide Emissions Resulting from Gasoline and Diesel Fuel. Environmental Protection Agency, U.S.A.. EPA, 2007. The Plain English Guide to the Clean Air Act. Environmental Protection Agency, United States. FHWA, 1999. Development and Validation of Light Duty Vehicle Modal Emissions and Fuel Consumption Values for Traffic Models. Federal Highway Administration, US Department of Transportation, US Department of Commerce.
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FHWA, 2012. Federal Highway Administration. Retrieved 03.21.14, from Revised Monograph on Traffic Flow Theory, Cap. 7.: