Determination of traffic emissions—intercomparison of different calculation methods

The Science of the Total Environment 1891190 (1996) 187-196

Determination

of traffic emissions - intercomparison of different calculation methods

Abstrnct

This paper offers an overview of methodswhich may realistically be used, dependingon the specificneedsand accuracyrequired, to describethe emissionbehaviour of road transport. Theseincludeemissioncomputation methods based on real driving behaviour, methods based on road type classi&ation to facilitate emissianinventory devefopment,and miIeage-or kilometragerefated emissionbalances.No singlecomputationalmethod sufficesto meet ah suchr~uirements. Exampfe ~ompu~tions are presentedto reveal the major areasof application for eachmethod and to show how small changesin input parameterseffect the resultsof the calculations. It is a&o shown that considerableerrors of interpretation can arisewhen simplemethodsare apphedin the more complex problem areas. Keywords:

1. pro&m

Traffic emissions;Calculation methods;Gradient influence;Altitude influence

outtine

Air pollution has become a serious probiem both for the population at large and for the environment. Urban regions with high population densities are particularly exposed in this respect. While in former times the major saurces of poor air quality were to be found in industrial activities and domestic heating, in present times, as a result of the rapid increase in mobility, more attention must be given to nitrogen oxide and related summer ozone pollution. Motorised traffic is responsi-

ble for a considerable * Corresponding author.

part of nitrogen

oxide

emissions and emissions of ozone precursors, In order to be able to quantify such a share, it must be possible to establish realistic estimates of traffic induced pollution. As well as using the appropriate model, it is important to make sure that the input data is of the required quality. In the present paper therefore, various computational methods for different application ranges are discussed.

2. CalcuIation

methods

The demands placed upon emission computation vary according to the intended field of application and according to the degree of accuracy

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Table 1 Basic input data for different methodologies Driving significant

behaviour

Emission

for each road

factors

Source

to be defined for each driving pattern

emission

maps

tmis’sion calculation based on actual driving b.ehaviour to be grouped into streets with the same driving characteristics (fine classification)

predefined functions for different street categories

emission functions

emission calculation based on specific road categories predefined factors for to be grouped in some main street classes (coarse each street class classification) emission calculation based on vehicle kilometres travelled I

required. It should thus be possible to quantify changes in emission levels as a result of variations in local driving behaviour (e.g. new speed limit regulations in a particular area, etc.), to carry out computations at the level of emission inventory creation, or to offer conclusions at a more global level, e.g. concerning impact on greenhouse gases such as CO,. No single computational approach is capable of meeting all these requirements simultaneously. ” The level of detail of the emission census depends on the nature of the problem at hand. It is futile to expect emission calculations to achieve results which are superior in accuracy to that of the original survey data. The right model has to be chosen to meet the task at hand. It is not always necessary, sometimes not even possible, to work with the most detailed model. On the other

hand, using a simple model in a complex environment can easily lead to spurious conclusions. Table 1 shows the principle differences in methodology and basic values needed. The methodologies can also be classified from ‘bottom-up’ to ‘topdown’.

3. Calculations

on a local scale (micro scale)

Policies launched to induce alterations in driving behaviour can’affect emission levels. To identify and predict such potential emission changes micro-scale calculations pertaining to a specific locality are required. Examples of such policy actions are new speed limit regulations or changes in rights of way. It is, therefore, essential that the method of calculation used should be capable of

0 0

500

1000

1600

2000

2500

3000

s Iml

Fig. 1. Recorded driving behaviour on ‘speed limit-30’ and ‘speed limit-50’ stretches; v, vehicle speed (km/h); s, distance travelled (m).

taking into account changes in driving behaviour. Suitable computational models have been developed at a number of institutes [I -31 to help predict emission changes and to refine emission factors. Extensive use has been made of such a program to assess the impact of a reduction of speed limits on the secondary road network in Graz, Austria [4,5]. Results have already been described su~ciently in the literature named. The present paper therefore focuses on the applicabil,

,$

-

“-l.l

.-_-

ity of such models for estimating emission levels in urban areas. Driving patterns were recorded in Graz over 3000 km. Fig. 1 shows a typical pattern for both a 30 km/h zone and a 50 km/h zone. The journeys depicted were recorded for the same road stretch but at different times of day. The driving behaviour during different test drives was qualitatively consistent on some parts of the routes, but varied considerably on other parts. Road sections were defined as between the centre points of two crossings and for each of these sections emission quantities were calculated. An analysis of the primary road network shows that the emission quantity may be depicted as a function of average driving speed. This is true for CO (Fig. 2) and with less accuracy also for NO, (Fig. 3, dots and dashed line). The regression coefficient r’ has a value of 0.99 for CO main streets and 0.92 for CO secondary streets, while for NO, the coefficient decreases to 0.22 for main streets and 0.1 for secondary streets. On looking at the data for the secondary road network, however, a different picture emerges. Fig. 2 also shows CO emission quantities on secondary roads. The dependence on average driving speed is still clearly observable. However,

“.”

-!

4

A

12

secondary main

-secondary - main

IO

streets 1

streets streets streets

/

I

I

0

10

20

15 mean

velocity

25

30

[km/h]

Fig. 2. CO-emission quantities on main streets and secondary streets.

35

40

190

P.J. Sturm et al. / The Science of’ the Total Environment 189/190 (1996) 187-196 1.8

1.6 1.4 1.2

0.6 A

secondary streets main streets -secondary streets

0.4

I

0.2 0 0

5

10

15

20 mean velocity

Fig. 3. NO,-emission

for a regional scale

Such emission calculations

30

35

40

quantities on main streets and secondary streets.

for nitrogen oxides (Fig. 3) a correspondence between emission levels and average speed is, at least on secondary roads, no longer present. This means then that in order to calculate urban emission levels, emission functions based on average driving speed can be employed with respect to the main road network, but for the secondary road network, at least as far as nitrogen oxides are concerned, computational methods based on driving patterns or behaviour are necessary. In Graz, 90% of the total mileage (kilometrage) volume occurs on the primary road network, which itself represents 25% of total network capacity. This means that sufficient accuracy can be obtained for emission inventory calculations using a simple computational model based on driving speed. To arrive at emission predictions and estimates for the secondary network it is absolutely essential to use calculations based on driving patterns.

4. Calculations

25 [km/h]

are mostly used to

estimate pollutant levels on individual road stretches or for the compilation of emission inventories. Mean driving speed is often used as a decisive parameter in determining the emission activity of an individual vehicle. As mean driving speed is related to specific driving behaviour and the same mean driving speed can occur with different road types (e.g. highway or extra urban roads), it is essential to take into account the road type too. Recent investigations are now tending to consider driving patterns which reflect real world driving in different road categories [2]. The updating of emission data has been a focal point for international investigations in recent years [2,6,7]. This has involved analysis concerning emission behaviour of new vehicle type and, above all, has aimed at a more accurate mapping of the dynamics of road traffic behaviour. Completely new emission factors have been derived as a result. Fig. 4 presents exemplary data for nitrogen oxide emissions for passenger cars as a function of mean driving speed. The functions shown are subdivided into the three road categories, urban rural and highway. The graphs are based on the

P.J. Sturm et al. / The Science of the Total Environment 189/190 (1996) 187-196

.

..I..

191

CI.

diesel

mean velocity

[km/h1

Fig. 4. Specific NO, emissions of passenger cars for Austria 1991, [9].

emission data derived in [Z] and the fleet composition for Austria for the year 1991 (45% gasoline cars conventional, 36% gasoline cars with closed loop cat, 19% diesel [9]).

both methods. Differences are shown in Table 2. While figures for fuel consumption, SO, emissions and CO emissions remained close, a reduction of 28% for hydrocarbons and 35% for nitrogen oxides was revealed. The reduction in NO, emission values is largely attributable to highway journeys, for both private as well as goods traffic. A different situation arises with hydrocarbons where for goods traffic a reduction was estimated in the low speed range but for private traffic lower emission values were calculated for higher speeds. Differences of a similar order of magnitude were also revealed on adapting the basic emission data for the computational model used by the Californian Environmental Protection Agency (CAR@ [lo].

4.1. influence of different emission databases As stated above, recent research has tended to concentrate more on real driving behaviour. The older computational models (e.g. [8]) were based on existing data derived from simplified driving cycles. In order to investigate the extent to which the improved database affects the results of emission computations, emission calculations were carried out for a defined area. The region chosen was the federal province of Styria, since relevant data from existing emission inventory calculations was readily available [9]. Taking the old caiculation as a reference point (lo%), the calculation based on new data revealed considerable differences in emission estimates. Vehicle fleet composition and input data were for the year 1991 for

4.2. hjhence

of gradient and altitude

Road gradient has a definite influence on the emission level since it affects engine load and driving resistance. While additional emissions, as

Table 2 Percentage differences in emission results using old and new emission data; road network and vehicle Aeet Styria 1991 [9]

Old data set N’ew data set Passenger cars Heavy duty vehicles

co (%f

NO, (%)

HC (%)

so, (%(I)

Fuel consumption (%)

100 i-6 +5 +6

100 -35 -33 -36

100 -28 -28 -27

100 -3 -1 -1

100 -6 -9 -I

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--+-

6%

-4% -A-2% -

0%

*-2%

mean

velocity

-a-

-4%

-

-6 %

[km/h]

Fig. 5. CO-gradient factors for passenger cars with closed loop cat [2].

a result of inclines, are often accompanied by emission decreases on declines, it should not be assumed that a full compensation takes place. Typical data are provided in Fig. 5 for the CO gradient factors for a car with closed loop catalyst, taken as a function of mean driving speed and gradient [2]. Concerning Fig. 5 it should be noted that when looking at the same mean velocity for different gradients a different driving behaviour has been taken into account. The effect of altitude is considerable. The quantity of air used in combustion has a direct influence on the level of pollutant emissions. This problem was dealt with in considerable detail in earlier investigations [l 11. More recent work [2] has shown that unexpected increases in emission

levels for cars fitted with catalysts may arise. Gradient and altitude need to be considered carefully in emission calculations in mountainous countries such as Austria. Table 3 shows specific altitude factors [2]. The following calculation is designed to illustrate how large the errors may be when such factors are not taken into consideration. Once again the starting point is emission calculation undertaken for the federal province of Styria (see Section 4.1). The differences between the two calculations are shown in Table 4. The calculation including altitude and inclined gradients serves as reference point (100%). Where the influence of gradient and altitude is not considered CO values can deviate by a margin of 40%, and those of NO, (goods traffic) by 15%.

Table 3 Factor to take into account altitude [2] Engine concept Passenger car closed loop cat

1000 m/O m/O 1000 m/O 2000 m/O 1000 m/O 2000 m/O 1000 m/O 2000 m/O 2000

Passenger car gasoline convention Passenger car diesel Heavy duty vehicle diesel

m m m m m m m m

HC

co

NO,

Fuel consumption

2.4 10.25 1.22 1.37

2.6 11.42 1.78 2.48

0.67

0.99

1.67

1.07

2.30 1.09 1.24

1.26 1.35 2.73

Particulates

1.01

1.02

0.74 0.54

1.02 1.02

-

0.92 0.84

0.99 0.97

0.77 0.81

1.01 1.01

1.oo 1.03

1.12 1.69

P.J. Sturm et al. / The Science of the Total Environment 189/190 (1996) 187-196

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Table 4 Percentage differences between calculations including and excluding gradient and altitude; road network and vehicle fleet: Styria 1991 [9]

Road traffic Road traffic Passenger cars Heavy duty vehicles Max”

Gradient altitude

CO

NO,

HC

SO2

Particulates

Fuel consumption

With Without Without Without Without

100 -39 -40 -19 -15

100 +6 fl5 -4 -54

100 -18 -21 -8 -65

100 -3 -2 -4 -45

100 -6 +6 -9 -47

100 -3 -2 -4 -45

* Maximum observed on one specific stretch.

Fuel consumption tends to be stable since inclines and declines largely compensate for one another. These estimates are based on altitudes with a mean range of 400-600 m (maximum of 1500 m) as is typical for Styria. Therefore altitude is only of minor importance in explaining deviations in emission values. The major part of the deviation in the figures is attributable to the influence of uphill gradients. When looking on specific road stretches the error can be much higher. For assessment studies at a local scale it is therefore absolutely necessary to take into account these influences. 4.3. Comparison of calculations based on road types (bottom-up) and kilometrage (top-down)

Calculations based on kilometrage are employed when the available data is not suitable for calculations based on specific roads or when the latter would involve excessive effort. Kilometrage based models are primarily of use in the compilation of emission estimates on national or global level and trend forecasts. They should be used exclusively for such purposes. The necessary input data for vehicle kilometres travelled based calculations is derived from statistics and is, therefore, hardly likely to correspond with data collected in

local traffic surveys. Since traffic input data is classified at least as urban, rural and highway, starting data on emissions is framed accordingly. This clearly leads to oversimplification. The extent to which this can influence the accuracy of results is illustrated in an example [9]. A calculation based on specific roads and a kilometrage based calculation have been carried out and compared for a particular computational area, namely the federal province of Styria, in Austria. Such a comparison is only permissible when the traffic data is the same, or to put it concretely, when the kilometrage for the respective road categories is identical. For road traffic in the federal province of Styria, the following differences were ascertained between statistical data and traffic survey counts. As would be expected, the comparison in Table 5 shows that traffic surveys result in less kilometrage than is indicated in official statistics. Traffic counts do not cover total road traffic, while official statistics are based on a general procedure covering annual kilometrage. For a lot of secondary roads in urban areas traffic counts do not exist and results from traffic models are not always available. For that reason the input values used in the latter result in an underestimation, mainly in urban traffic. When kilometrage is

Table 5 Kilometrage differences between traffic counts and official statistics, Styria 1991, (1000 km/day). [9] Kilometrage

Highway

Rural

Urban

Total

Official statistics Traffic counts

6580.1 (27%) 4278.3 (21%)

10620.3 (43%) 13953.9 (69%)

7427.8 (30%) 1975.2 (10%)

24 628.2 20 207.4

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f&O90 (1996) 187-- 196

Table 6 Compared road and kilometrage based emission data, Styria 1991, [9] Calculation method based on:

CO f%)

NO, f%)

HC (‘%)

SO, (%)

Particulates (!A)

Fuel consumption (W!,

Specific roads Kilometrage

100 -36

100 +4

100 -14

100 -5

IO0 -6

100 -2

adjusted in terms of order of magnitude (quantity and share), and when emission calculations are adapted to fit Styrian topographic conditions, percentage difference figures can be arrived at (see Table 6). For the kilometrage based calculation an average factor for altitude and gradient were used. The differences in the results arrived at from the two computational methods are considerable. For total traffic they vary from - 36 to + 4%. For car traffic alone variations are much greater. Fuel cons~ption figures on the other hand, match surprisingly well. This clearly shows that a eorrespondence of fuel consumption figures with survey figures is no guarantee of the correctness of emission quantities. The decisive factors in this respect are consideration of local traffic volume, and consideration of local road and driving conditions (driving activity, road gradients, curvature, etc.). The resulting differences between the two computational methods reach a maximum of 36% which is not particularly high. It should be noted, however, that-in order to minimize the presumable differences-in both cases the basic emission data was derived from the same source. This was speed-related emission functions for the road type approach, and the three emission factors for the kilometrage-based, top-down approach. The latter are, however, weighted according to kilometrage and derived from the emission functions. Considerably higher differences can be expected between the two methods when different emission factor sources are used (e.g. COPERT factors for the top-down model). 5. Calculating Stricter

evaporative emissions

exhaust gas limits

are leading

to a

relative lowering of emission quantities produced during actual journeys. At the same time the relative share of cold start and evaporative emissions is increasing. It is, therefore, becoming more important to quantify such emissions with sufficient accuracy. Asce~aining the emissions produced during that phase when the engine, and particularly the catalyst have not reached normal operating temperature is at present the subject of international research. Evaporation loss c~culations are mostly estimations based on statistical data. Such emissions are categorised as hot or warm soak, diurnal losses and running losses, depending on the cause. In some cases resting losses are also considered (e.g. cw.

Running losses can clearly be ascribed to mobile traffic. However, the remaining evaporation emissions can only be collected on the basis of existing statistics covering factors such as parking duration, duration of journey, etc. In order to minimise evaporation losses for petrol driven cars, legislation was introduced making the installa~on of charcoal canisters in the fuel system compulsory. Two different approaches to calculating evaporation losses have proved useful for European conditions. One is based on CORINAIR [8] and the other on TirV [13]. Different input parameters are required for each method. The former relies on fuel vapor pressure and air temperature as input data, while the latter uses parking duration and air temperature. As vehicles fitted with charcoal canisters produce considerably Fewer evaporation emissions, the exact percentage of petrol driven cars thus equipped must be known before either of these calculations can be employed. In order to compare the two methods of calculation, computations of evaporation losses for car

Table “I Car evaporation losses in Austria, 1992, [14] CORINAIR year) Hot soak emissions Warm soak emissions Diurnal emissions Running losses Total

(t/

TUV (t/year)

14696.7 1667. I

12570.4 6603.6

8136.3 2149.5 26649.6

9639.4 28813.4

traffic in Austria were undertaken, using 1992 as base year [14]. Input data was as follows: 1.5 million cars (56%) were without canisters, while 1.2 million cars (44%) had canisters fitted. 3641.2 million stops per year, of which 53% were hot (i.e. the vehicle had been driven more than 5 km) and 47% were warm (i.e. the vehicle had been driven less than 5 km). The duration of 27.5% of all stops was longer than 6.5 h (highest emission factor), the remaining 72.5% were shorter, depending on the reason for the journey (work. shopping, leisure, etc.). Average annual kilometrage per car was 13 500 km. The necessary data on temperature was taken from available statistics and weighted according to the car registration distribution for the whole of Austria. Car evaporation losses for 1992 are shown in Table 7. The totals for both methods of calculation correspond quite closely. However, differences in the various categories are cleariy noticeable. The largest difference lies in the warm soak category, with the figure for CORINAIR calculations being only 25% of the TiiV figures. The TUV method does not include running losses at all. Diurnal losses are in total similar for both methods. However, when the data for specific months is considered, then it can be found that figures for CORINAIR are slightly higher in July and August, and remarkably lower for other months of the year.

6. Conclusions

Road traffic will continue to be a significant source of air pollution in urban areas in the

future. Quantification of emission levels requires that suitable computational methods and appropriate data are available. Methodological approaches are in fact known. For each vehicle an emission value corresponding to the actual engine load can be assigned. This approach provides adequate accuracy for many applications, particularly those at a simpler level. There is still a great need for further research however, since many gaps in the basic emission data and in traffic statistics remain, This is particularly true concerning the influence of altitude on pollutant emission levels. Investigations to date have confirmed the existence of a strong link between emission quantity and altitude for all engines and exhaust gas purification systems. The influence of cold start emissions is also considerable. Since a large proportion of journeys are very short the full advantages of car catalysts are never realised. Calculation methods have been developed to match certain requirements. Each of these methods has limitations and should be used only in those appli~tions for which it was designed. The above comparison of individual computational methods has shown that the quality of the input data, with respect to both emission factors and to the level of detail used to describe the road network, has a noticeable impact on emission results calculated. For roads in mountainous countries it is also necessary to consider altitude and road gradient. In addition it was also possible to show that the use of different methods in any application is likely to lead to noticeable differences in resulting emission estimates. References [I] R. Pischinger and J. Haghofer, Eine ~ethode zur Berechnung des KraFtstoffverbrauches und der Schadstoffemissionen von Kraftfahrzeugen aus dem Geschwindigkeitsverlauf. XX FISITA Congress, SAE 845114. 1984. [2] D. Hassel, P. Jost, F.J. Weber, F. Dursbeck. KS. Sonnborn and D. Piettau, Abgas-Enlissionsfaktorexl von Pkw in der Bundesrepublik Deutschland, Abgasemissionen von Fahrzeugen der Baujahre 1986 bis 1990, AbschluBbericht des Techischen Uberwachungs-Vereines Rheinland im Auftrag des Umweltbundesamtes, Berlin 1994.

P.J. Sturm et al. / The Science of’ the Total Environment 189/190 (1996) 187-196

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P.J. Sturm, K. Pucher and R. Almbauer, Determination of Motor Vehicle Emissions As a Function of the Driving Behavior, Int, Conf. The Emission Inventory, Pasadena, October 1993, Air and Waste Management Association, Pittsburg, 1993. von Tempo-30 auf die 141 R. Pischinger, et al., Aus~rkungen Kfz-Abg~emissionen in Graz, Heft Nr. 13, Sch~ftenreihe des Institutes fur Stral3enbat.t und Verkehrswesen der Technischen Universitat Graz, 199 1. PI P.J. Sturm, R.A. Almbauer and R. Kunz, Air quality study for the city of Graz, Austria, in H. Power, N. Moussiopoulos, C.A. Brebbia (Eds.), Urban Air Pollution, Vol. 1, Computational Mechanics Publications, [31

1994.

El S. Rodt, Forschungsprogramm

zur Ermittlung aktueller Emissionsfaktoren fiir Nu~ah~euge, in R. Pischinger (Ed.), Abg~missionen und Immissionen durch den StraBenverkehr, Heft 64, Mitteilungen des Institutes fur und Thermodynamik, Verbrennungskraftmashinen Technische Universitlt Graz, 1992, pp. 115- 13 1. [71 P. Jost, D. Hassel and K.S. Sonnbom, A new method to determine exhaust emission factors for heavy duty vehicles, 169 (1995) 213-219. VI H.S. Eggleston, et al., Corinair working group on emission factors for calculating 1990 emissions from road traffic, Vol. 1, Methodology and Emission Factors, Final Report to the CEC/DGXI, Study Contract No. B4-3045

(91) 10 PH, December 1991. P.J. Sturm, Abgasemissionen des Stragenverkehrs und ihre Ausbreitung in der Atmosph&e, VDI Fortschrittsberichte, Reihe 15, Nr. 139, VDI-Verlag Dusseldorf, 1995. V-Y F. Medina, H. Malono and E. Yotter, Calculation of OnRoad Motor Vehicle Emission Factors, Int. Conf. The Emission Inventory, Pasadena, Air and Waste Management Association, Pittsburgh, October 1993. I?11 R. Pischinger, H. Schweiger and K. Pucher, Auswirkungen der Abgasgesetzgebung auf die Tunnelliiftung, Schriftenreihe StraBenforschung, Heft 337, Bundesministerium fiir wirtschaftliche Angelegenheiten, Wien 1987. WI CARB, California Environmental Protection Agency, Air Resources Board: Methodology for Estimating Emissions from On-Road Motor Vehicles, Vol. I: EMFAC 7 F, Vol. II: WEIGHT, Vol. III: BURDEN 7 F; State of California, Air Resources Board, Technical Support Division, Sept. 1993. u31 P. Heine and A. Baretti, Emissionsfaktoren filr die Verdampfungsemissionen von Kraftfahrzeugen mit Ottomotoren, Forschungsbericht 104 05 149 UBA-FB 89-020, Rheinisch- Westfalischer Technischischer Uberwachungsverein e.V., 1989. Verdunstungsemissionsberechnung von 1141 W. Landler, Kohlenwasserstoffen des Pkw-Verkehrs, Diploma&&t, Technische Universit~t Graz, 1994 [91