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The influence of car speed on dispersion of exhaust gases
Amosphwic
Enuironmenr
Vol. 22, No. 2, pp. 273-281.
ooo4-6981/88
1988. 0
Printed in Great Britain.
s3.00+0.00
1988 Per&amon Journals Ltd.
THE INFLUENCE OF CAR SPEED ON DISPERSION OF EXHAUST GASES K. E.
GRG~NSKEI
Norwegian Institute for Air Research, P.O. Box 64, N-2001 Liliestr#m, Norway (First received 14 October
1986 and received
for
publication 31 July 1987)
Abstract-Results of seven dual tracer experiments (SF, and CBrFJ) show that the vertical diffusion of exhaust gases tends to be larger from cars driving with high speed than from cars driving with lower speed. The experiments were carried out over a fiat snow covered plain and the tracer gases were emitted from two moving cars. The vertical tracer distributions were determined at l&30 and 70 m from the road using S-10 m masts. Gaussian distributions were found to fit the vertical profiles of tracer concentrations. The results encourage further use of dual tracer experiments to study dispersion effects of the interaction of car wakes with the atmospheric surface layer. When the atmospheric wind was perpendicular to the road (five experiments), the vertical dispersion parameter for tracer material (a,) was linearly dependent on an estimated scale of the wake in each experiment. The coefficients of linearity varied with the state of the atmospheric surface Iayer as indicated by wind m~urements and stability &s&cation. The empirical results are applicable only when the scales of turbulence in the atmospheric surface layer are smaller than the scale of the wake. Houses and trees located close to the road will influence the dispersion of the automobile exhaust and the conditions for interaction are changed. Key
word
index:
Tracer experiments, diffusion of car exhaust, wake effects on diffusion.
1. INTRDDU~ION In Norway as in other colrntries automobile exhaust is an important air pollution problem, particularly for persons living or working closer than 100 m from roads with high traffic loads. Therefore, use of dispersion models to describe the extent of the polluted area adjacent to roads in different regions is important in connection with urban and traffic planning. The highest pollution loadings occur particularly in the winter when the atmospheric turbulence is small and the car emissions are large. The following models have been used in Norway to provide information on pollutant concentrations close to roads with high traffic:
EPA HIWAY-model (Zimmerman and Thompson, 1975) Model based on a General Motors (GM) study (Chock, 1977) The Nordic model for concentration calculations in street canyons (Larssen, 1984). The EPA HIWAY-model employs Pasquill-Giffords dispersion parameters for the atmospheric surface layer to calculate pollution concentration close to highways. The im~rtan~ of vehicle wakes for the dispersion of pollution close to a road is well documented (Chock, 1977), based upon tracer experiments and on theoretical evaluation (Eskridge et al., 1979). The diffusion can be viewed as the result of the interaction between the development of the vehicle wake and the dispersive state of the atmosphere. The development of the vehicle wake is dependent on the 273
vehicle speed and the size and shape of the vehicle. The dispersive state of the atmosphere is dependent on the size and structure of the roughness elements, the terrain, the wind speed and the surface-layer heat balance. The interaction further depends on the wind direction to the roadway, the wind shear and on the thermal stratifi~tion of the atmosphere. The EPA HIWAY- model (Petersen, 1980) includes the results of General Motors tracer experiments and suggestions for improvements formulated in a paper by Rao and Keenan (1980). To better understand the interaction between the car wakes and the atmospheric surface layer, tracer experiments were carried out with different car velocities. Two cars emitting different tracer components (SF6 and CBrF,) were driven one after the other with different speeds. The samples of the tracer gases were taken downwind of the road using plastic syringes for the two components. The instruments and equipment used for tracer sampling are described by Heggen and Sivertsen (1983). This emission and sampling procedure was selected in order to minimize the difference in dispersion resulting from variations in the ambient wind and turbulence condition. By changing the driving procedure it was possible to study dispersion effect of parameters describing the wake development, i.e. the speed of the cars.
2. TRACER EXPERIMENTS
Seven dual tracer studies were conducted in March and April 1982 by the road Rv-22 in Skedsmo,
274
K. E.
GRONSKE~
Norway. The course of the tracer-emitting cars and the sampling stations are shown in Fig. I. Apart from a IO--IS m deep river canyon and a small forest the test grounds were flat. At the time of the experiment. the area was snow-covered. The locations of tracer samplers attached to three masts are shown. The masts are located 10.30and 70 m from the edge of the road. One sampler is Iocated about 1 m from the edge ofthe road and 1 m above the ground level. Measurements of wind speed and wind direction were carried out on top of the 10 m mast, 30 m from the edge of the road using a Woelfle wind recorder. Wind speed was further recorded 5 and 1 m above the ground over the sampling periods by using ‘Fuess small wind recorders’. The temperatures 5 and 1 m above the ground were determined by using one ventilated thermometer first at the 5 m level and then at the 1 m level before and after each experiment. The tracer concentrations were determined as 15min average values as a result of a time-interrupted line source. Emission procedure Two cars were driven along the 650 m road segment emitting tracer gases from tubes attached to the
exhaust pipes of the cars. The height of the cars emitting tracer gases was t 3 m. The emission intensity was recorded by a calibrated gas flow meter. The accuracy of the emission intensity is estimated to be about f lo:;, considering control of the Rowmeter setting in the cars and the flowmeter calibration curve. To avoid influence of systematic errors in the concentration determination of the two tracer gases, the tracers were emitted alternatively from the car driven at high speed and from the car driven at low speed. The fast moving car was followed by the slow moving car. The distance between the cars was 20-30 m when they started and about 150 m when they passed the sampling line, and the wakes behind the cars did not interfere with each other. The driving procedure was repeated and the contribution of tracer gases after six-eight traverses of each car was collected in each sampler. The first traverse of the two cars started simultaneously with the samplers. The last traverse of the pair of cars ended before the samplers stopped. In low wind conditions the tracer material emitted from the last traverse may not have passed the mast 70 m from the road. This source of error may influence the mass consistency in some experiments, the error is less important for the determination of rr:-
i IOm 30m
(b)
I
c mm
c
II
Fig. 1. (a) A map of the test field. (b) A cross-section of the sampling network.
275
The influence of car speed on dispersion of exhaust gases Table 1. Date and time of experiments, meteorological measurements
u Test
Date
Time
(msl-O’)
u*
u5 (ms-‘)
u
rpl0
(ms-‘)
(ms-‘)
(“)
Remarks*
Stab.?
cl 10 U5 : Wind speed 10, 5 and 1 m above the ground. (f,. q,,: Wind direction 10 m above the ground. AT: Temperature difference between 5 and 1 m. Two numbers refer to m~urements before and after the tracer experiment. U: Wind speed perpendicular to the road. The orientation of the road is 95-275” (see Fig. 1). *Remarks: The experiments are classified according to observation of wind direction in relation to orientation of the road. (Parallel, perpendicular or wind shift.) t Stab.: The experiments are classified according to observation of wind and temperature stratification. S: Stable atmospheric surface layer. N: Neutral atmospheric surface layer.
values. Time lapse films of tests show that an average of
three-four cars per minute passed the test section. These cars could interfere with the dispersion behind the slow moving car and seldom with the dispersion behind the fast moving car. However, this is a typical traffic sit~tion on small roads, representing noise in data when the development of wakes under different atmospheric conditions is studied.
3. RESULTS
Details of the tracer concent~tions for each experiment will be provided in a separate report on request (Grdnskei, 1986). The meteorological conditions (Table 1)and the emission data (Table 2) are shown for each experiment. The a,-values shown in Table 3 were determined by the least-squares approximation of the observed vertical con~ntration profile to a Gaussian distribution, In addition to the a,-value, the first moment of the
vertical concentration
distribution oc czdz I Z=&---.
1 2 3 4 5 6 I QSF, vSF*
QSF, (10-3mlm-‘s-‘) 1.2 2.4 12 12 9.4 ::t:
(1)
cdz s0 In obliquewind situations (parallel), the vertical extent of the tracers was large, and the profile observations in S-10 m high masts were not adequate for estimating the first moment. In most cases the least-squares fit to a Gaussian curve was used to determine the standard deviation of the observed vertical tracer profile. In some cases, this method gave unreasonable a,-values, as when the top of the tracer cloud was not determined by measurements. Rao and Keenan (1980) suggested the use of two single measurements to determine the a,-values by Equation (2). z:-z:
2 *’
=
2 In (C, /C,)
Table 2. Emission data
Test
was estimated.
PSF, (kmh-‘)
QCB~F, (10-3mlm-1s-L)
80 40 -5 -5 -5 80 40
Emission rate of SF,. 1 Speed of the car emitting SF+
QCB~F, Emission rate of CBrF, . vCarF, i Speed of the car emitting CBrF,.
33 16 22 43 27 33 22
PC&F, (kmh-‘) 40 80 80 40 80 40 80
(2)
216
K. E. GRONSKE-I
C’ Test 1
2 3 4 5
Tracer
_.._. _ .I. - . _ i! 0:
(kmh-‘)
(m)
(m)
80
4.65
--
40 40 80 -5 80 -5 40 -5 80
4.25 2.95 2.45 5.35 4.05 3.55 4.45 2.05 2.25 2.15
SF6
CBrF, SF6 CBrF, SF, CBrF, SF, CBrF, SF, CBrF,
6
SF,
80
7
CBrF, SF, CBrF,
40 40 80
1.95 1.48 2.20
(2,
(12.48) .--. 12.10 2.12 4.77 2.04 8.91 4.59 6.21 7.90 --. 6.70 1.57 (2.81) 1.87 4.85 1.17 2.19 1.68 2.43 1.79 2.19 1.88 3.52
z
z
Im)
$,
_..
_.
3.37 3.55
1.70 9.70 9.41 14.54
..-.
-. 8.84 (4.80) 4.05 2.97 3.57 3.99
5.15 3.87 3.20 2.49 3.75 3.75
(ml _^---_
,4(P,” (‘)
Stab
0.8
85
s
1.1
5
N
I .4
85
N
0.3
~
N
5.6
15
N
1.3
25
S
2.7
to
S
1’I 0 tms
‘I
._.-.
~~ 1182 3.59 2.49 2.10 2.18 2.96
V is the car speed, Y - 5 km h 1means walking. The met~roiogi~l conditions are ~har~terized by the wind speed at 10 m (U,,), the angle (Aq,,) between the perpendicuiar line to the road and wind direction and the stability classification.
CI and C2 are the measured concentrations at the levels Z, and Zz. respectively. To reduce the effect of errors in single measurements the average value of the two lowest and the two highest measurements were used to estimate C, and C, in Equation (2). The 6, values obtained using individual observations are shown in a separate report (Gr#nskei, 1986). The values calculated using Equation (2) with average values for C, and C2 are given in parentheses in Table 3, indicating that a different method of calculation is used. When the vertical profile exhibits a Gaussian distribution with a maximum concentration at ground level, the following relationship should exist between 2 and a,:
z=
5
J
-CT,.
x
The average value of Z/u, for the numbers given in Table - 3 is 0.78 which is a good approximation of J2ln. The tabled results suggest that when the driving speed is high, the o,-values become large. In an oblique wind situation (tests I,3 and 4) the observed a,-values become large and the accuracy is probably poor since the masts are not high enough to cover the profiles. Increasing the driving speed to 80 km h- 1 gives l&20% higher a,-values close to the road. It was a tendency for the difference in a,-values for different driving speeds to increase with distance from the road. For the consideration of uncertainty the following observations are made: (1) Different methods of a,-calculation from profile observations give different a,-estimates. The standard deviation of the difference between the estimates is
0.4 m (14 y0 of the average o,-value). The absolute value of the difference decreases with decreasing g,values. (2) The linear regression between the c, and Zvalues given in Table 3 yields:
In a Gaussian distribution, the ratio between the first moment and the standard deviation is 0.8 (5/a, = 0.8). Results of the Student t-test show that the present data support this interrelation at the 95 “/, significance level. (3) Increased vertical standard deviation with increased driving speed is observed at the 95 y0 significance level according to the results of the Student f-test. Based on these observations it was concluded that the vertical dispersion increased as a result of increased driving speed. Using the I-IIWAY-2 model the a,-values at different distances from the road were calculated for the experiments 2,5,6 and 7. The calculated a,-values (a,) are compared with the observed values (Q,,) in Fig. 2. This figure shows that for tests 6 and 7 the calculated values represent overestimates. For tests 2 and 5 the correspondence between calculated and observed values is acceptable. For each calculated value in Fig. 2, two observed values are shown, one for low car speed and one for high car speed. In 10 out of 12 cases depicted, higher driving speed yields larger a,-values in comparison to the lower driving speed results. The concentration measurements 1 m from the edge of the road and 1 m above the ground were used to estimate the u,-values close to the road. The corresponding values were calculated using HIWAYand the results of calculations are shown in Table 4.
277
The influence of car speed on dispersion of exhaust gases
changes in dispersion parameters. However, the observed variation in vertical dispersion with variation in the driving speed of the cars (Fig. 2) indicate that an improved description could be obtained by considering the development of the wake behind the cars and the interaction of the wakes with the turbulent atmospheric surface layer. 4. DISCUSSION
OF RESULTS
4.1. Estimates of emissions 9
012345676
The simultaneous measurements of wind and concentration profiles were used to estimate the flux of tracer gases perpendicular to the road at different distances. Equation (4) was applied to estimate the fluxes
IO
co (ml
Fig. 2. Scatter diagram between observed (a) and calculated o, values (a,). The calculated values refer to the HIWAY- model. Two observed values connected by a horizontal line represent two values
H
for different driving speeds. The marked points represent the u,,-value for thecar with a high driving
F= f0
speed.
In tests 2, 6 and 7, characterized by wind perpendicular to the road, the calculated a,-values (HIWAY-2) appear to be larger than the observed values. The reason for the discrepancy is probably that HIWAY- is developed for the fast running traffic at a highway with two lanes for each direction. Rv-22 in Norway is smaller and the adjacent ground is flat and snowcovered. The numbers of observations are smail and the present tracer data base is not sufficient to propose
ucdz ‘2: i k=l
(tests 1, 3 and 4) the flux estimates may differ considerably from the emission estimates. Considering experiments with wind perpendicular to the road, high discrepancies between flux estimates and emission intensities are found 30 and 70 m from the road in test 5. These o-values should be used with caution. For test 2 an excellent augment between Aux estimates and emission intensity is found for SF6. For CBrF, the
Test CBrFa SFe CBrF, SF6 CBrFs SF6 CBrF, SF6 CBrFa SF, CBrF, SF6 CBrFJ SF6
26,8S3 755 7403 1355 13,290 7100 7396 3097 2432 2405 17,550 556 7460 1364
33 1.2 16 2.4 22 12 43 12 27 9.4 33 1.4 22 3.3
1.8 1.8 0.8 0.8 -0.5 -0.5
4.0 4.0 1.0 1.0 1.2 1.2
0.5 0.7 2.2 1.8 2.6 2.7 2.2 0.8 1.5 2.0 2.0 1.6
40 80 80 40 80 -5 40 -5 80 _5 40 80 80 40
3.0
1.5 2.9 2.2
The values are compared with the corresponding u,-values calculated by HIWAY-2.
=o=~
Q J
(4)
Interpolations were made between observed C-values and U-values. The flux estimates are compared with the emission estimates in Table 5. In oblique wind situations
Table 4. o,-estimates at the edge of the road based on concentration observation and emission data
1 1 2 2 3 3 4 4 5 5 6 6 7 I
t&AZ,.
2 ;*
Q: Emission of tracer gaaea on the road. C,,: Tracer concentration 1 m above ground level by the edge of the road. U (1 m): Wind speed at the height of 1 m. VW: Speed of the car. uc: The standard deviation calculated by using the HIWAYmodel.
Table 5. Line source emission intensity and fluxes of tracer gases calculated for each experiment Flux
Test 1 SF, 2 3 4 5
C&F, SF, CBrF, SF, CBrFj SF,, CBrF, SF, CBrF3
6 SF, CBrF, 7 SF,, CBrF,
Emission
IO m
1.8
1.2 33 2.4 16 12 22 12 43 9.4 27 1.4 33 3.3 22
52 2.5 14.5 26 3.2 19 8.0 29 t.7 37 4.8 21
Unit = 10m3mlm-‘se
30 m 2.0 47 2.5 20 II 23 7.0 31 4.9 38 1.6 37 3.7 25
70 m (I.81 (36) 2.5 1x 5.5 27 2.4 3.4
I-J.7 1.3 39 5.5 17.9
’
average value of fluxes overestimates
the emission intensity by 80/,. At 30 m the high flux estimate indicates that the a,-value for the fast running car may be overestimated. For test 6 the average value of fluxes overestimates the emission intensity by 2 “/, for SF, and by 14 “/, for CBrF,. For test 7 the average value of fluxes overestimates the emission intensity by 41 “/ for SF6 and by 3 7: CBrFj. The fluctuation in fluxes indicates that the a,-value for the slow moving car may be overestimated and that the a,-value for the fast car may be underestimated. When the flux estimates are consistently different from the emission intensity, errors may be found in the emission system or in the measurements of wind (speed
and direction).
The estimation of a,-values from profile measurements is probably more accurate than indicated from the emission/flux relationships. The determination of the nz-values is not dependent on the absolute tracer concentration value. In that way correct n,-values may be obtained regardless of errors in the emission system or in the measurements of wind. Plume meandering coutd cause discrepancies between
the emission intensity and the flux estimates. 4.1. Purumeters
describing
the wake
The turbulent
wake behind the car often contains two vortices with opposite vorticity. This wake interacts with the background atmosphere to produce the observed dispersion of tracer gases. The wakes containing the tracer and the exhaust gases are transported with the wind velocity. A description of this situation is shown in Fig. 3.
The dependence of a,-values on the car speed and the development of the wake has been described by Eskridge and Hunt (1979). In their description the vehicle was considered as a point source of momentum loss. Effects due to differences in pressure and vorticity were ignored. According to this theory the scale of the wake (1) increases with distance behind the car.
s: distance h: height
behind the car of the car
i. and A: factors of proportionality.
According to Equation (5) and the simple geometrical relationships shown in Fig. 3, the following parameter can be shown to be related to the scale of the wake at
The dlstonce from the car to an observation potnt Distance from the roe.4 to an observation point !?a:,. Wmd spe-ed perpendicular to the rood. L/ Car speed along the road. 5
Fig. 3. The wake behind the car is moved perpendicular to the road by the wind.
The influence of car speed on dispersion different
Assuming the diffusion effect of the car generated turbulence (a,) and of the atmospheric turbulence (a,) to be additive the following approximation is proposed.
distances from the road
when x = h then I’ = 0. For each experiment with wind perpendicular to the road the observed a,-values are plotted against I’ in Fig. 4. For each of the experiments (tests 2, 56 and 7) the a,-values indicate a linear dependence on the scale of the wake. The following equation applies for each experiment CT,=al’+b.
(7)
Lines are drawn for the different tests in Fig. 4. It seems reasonable that the value of b is < 1 m considering a wake behind a 1.3 m high car. The coefficient of linearity (a) is larger in tests 2 and 5 compared to tests 6 and 7, the atmospheric surface layer and the finite disturbances behind the cars. According to the meteorological measurements shown in Table 1, the surface layer is classified as neutral in tests 2 and 5, stable in tests 6 and 7. The coefficient (a) is expected to be proportional to the height of the cars (h) and to decrease with increasing thermal stability. In the atmospheric surface layer the wind shear increases when the thermal stability increases. Increased wind shear is expected to increase the dispersion effect of the wake as more kinetic energy becomes available for the generation of turbulence when the air is mixed by the wake behind the cars. A linear relationship between I’ and u, seems reasonable close to the road, when turbulent intensity behind the car is larger than the turbulent intensity in the atmosphere. At larger distances the atmospheric turbulence will determine the diffusion.
9-
B-
7-
z
b”
6-
u2z = u:+u2 w
(8)
u,: diffusion by the atmospheric turbulence, to be selected for the area under consideration. u~=~.h{l+4C(~)O”[(X)O”-I]IDI.
(9)
Following Eskridge et al. (1979): C = 0.046. The background for Equations (8) and (9) is discussed by Gronskei (1982). Empirical results [Equation (7)] and simplified theoretical considerations [Equations (8) and (9)] indicate that the scale of car wakes as defined by Eskridge and Hunt (1979) may be used to improve description of dispersion of exhaust gases close to roads. The final functional form of correction remains to be found, and more tracer data are needed. A summary of theoretical and experimental determination of velocity and turbulence characteristics of wakes given by Eskridge and Thompson (1982) indicates that different length scales should be considered for the best characterization of wake-effects in other dispersion situations. The importance of the scale of eddies at the origin of the wake is pointed out by Bevilaqua and Lykoudis (1978) and effects of free stream turbulence on the development of wakes are reviewed by Bearman and Morel (1983). 4.3. Application of tracer data results Data for turbulence intensity and scale of the turbulence in the wake may be used to describe possible interaction between the surface layer of the ambient atmosphere and the wakes behind the cars. The observed dispersion of the pollution emitted in the wakes is the sum result of this interaction. With the notation of Eskridge et al. (1979), we refer to Fig. 5 for classification purposes.
Tests2and5
IO-
279
of exhaust gases
Test 7
5
Turbulence intensity in the wake: i,, = (W’2)o.5/U Scale of the wake: I, Turbulence intensity in the atmospheric surface layer: i,, = a $
4
Scale of the atmospheric turbulence:
3
2 I
0 I
2
3
4
1’
Fig. 4. The dependence of standard deviation of the vertical concentration profile on the scale of the car wake.
I,
(w’z )“.5: root mean square of the vertical velocity fluctuations U: horizontal wind speed a: factor of proportionality U,: friction velocity of the atmospheric surface layer. When the scale of the wake is larger than the scale of atmospheric turbulence (/,/I, > 1) atmospheric gradients in the wind and temperature may be in-
280
K. E.
L ‘1, Urtm
oreos
GR~NNSKEI
Camtrysick wtth small fcughnes5 ord mmrsicm
Fig. 5. Classification scheme of results from tracer experiments and of the applicability of line source dispersion models.
fluenced by wakes. The gradients should be considered for the further development and dispersion of pollution emitted in the wake. The region is indicated by the two hatched areas in the right part of Fig. 5. The area in the upper part of the figure represents the conditions close to the road when the turbulence intensity caused by the vehicles dominates over the intensity in the atmospheric surface layer. In an inversion situation wake mixing of thermally stratified air causes buoyant forces that may increase vertical mixing. On the other hand thermal stratification outside the wake restricts vertical movements. Wake parameters may be important for dispersion as a result of the development of two finite vortices introduced into the atmospheric surface layer. When the scale of the wake is approximately equal to the scale of atmospheric turbulence (I,//,1) atmospheric gradients are probably not important for the development of the wake. This situation is described by the theory of Eskridge et al. (1979). For these cases atmospheric and wake turbulence determine the diffusion. Close to the road the increased turbulence intensity will increase dispersion. At some distance from the road the atmospheric dispersion parameters may be used to describe the growth of the pollution cloud. Moving along the horizontal axis in Fig. 5 this region is indicated by an open area between the hatched regions. Close to the ground, wakes from other roughness elements (houses and trees) may interfere with the dispersion. Car wake parameters may not be important for the dispersion parameters except in the immediate neighbourhood of the cars when the turbulence in the wake is greater than in the atmospheric surface layer. The region is indicated by the two hatched areas to the left in Fig. 5. Far from the road the pollution concentration will have a stochastic variability that is not described by wake parameters. In these situations the car wake development is not restricting the dispersion process. The dual-tracer experiments are carried out in
situations when I, 3 1, over a flat snowcovered area. Parameters describing the car wake did also show up in the description of the dispersion. In the analyses with winds perpendicular to the road, HIWAYoverestimated the dispersion for the stable atmospheric cases. For the cases with neutral stability, HIWAYseemed to perform adequately. Tracer experiments in the urban area of Sarpsborg resulted in six err values between 26 and 73 m (average value: 48 m) at the distance of I50 m from a road (Gronskei, 1984). HIWAYunderestimated the vertical diffusion in these experiments, possibly resulting from not adequately accounting for the effects of larger roughness elements (houses and trees) in the residential area. More empirical data are needed on the diffusion of car traffic emissions in urban areas, particularly at distances of l(rlO0 m from the road. Parameters characterizing the wakes behind the houses and the trees are expected to be important for the description of vertical diffusion.
5. CONCLUSIONS
Results of two component tracer experiments show increased vertical dispersion as a result of increased driving speed of the cars (from 40 to 80 km h _ ‘). The car speed influences the development of the wake, which in turn affects the diffusion. The faster the car speed the faster the growth of the wake. The experiments were carried out over a flat snowcovered plain. A Gaussian distribution was found to fit the vertical distribution of tracer concentration. The standard deviation of the vertical tracer distribution may reveal a linear dependence on a parameter describing the vertical scale of the wake. When the wind shear is small and car speed is small the effect of the car wake is minimized over a flat surface in a stable surface layer. The tracer experiments indicate in some cases that the HIWAYmodel underestimates the pollution concentration by the roadway. The empirical result may not be reproducible when the vertical scale of atmospheric turbulence is larger than the scale of the wake. When houses and trees are present close to the road, the wakes of the cars are not believed to be important except in the immediate neighbourhood of the road. HIWAYmay overestimate pollution concentration downwind of the street canyon since the wake effects of the buildings are not taken into account in the dispersion parameters. Statistical inference based on few empirical data only represents a preliminary assessment of vehicle wake effects on dispersion. Since the present accuracy of dispersion calculation is low and since wake effects are important, the interaction between wakes and the turbulent atmospheric surface layer should be further studied.
The influence of car speed on dispersion of exhaust gases Acknowledgements-The author gratefully acknowledges the work of Reidar Heggen and Joseph Pacyna to accomplish the tracer experiments and the useful comments from John Irwin during the preparation of this paper.
REFERENCES
Bearman P. W. and Morel T. (1983) Effect of free stream turbulence on the flow around bluff bodies. Prog. Aerospace Sci. 20,97-123. Bevilaaua P. M. and Lvkoudis P. S. (1978) Turbutence memory in self-preser&rg wakes. J.‘ Flak Me& 89, 589-606. Chock D. P. (1977) General Motors sulfate dispersion experiment: assessment of the EPA HIWAY model. J. Air Pollut. Control Ass. 21, 394. Eskridge R. E., Binkowski F. S., Hunt J. C. R., Clark T. L. and Demerjian K. L. (1979) Highway modelling-II. Advection and diffusion of SF, tracer gas. J. appl.Met. 18,401-412. Eskridge R. E. and Hunt J. C. R. (1979) Highway modellingI. Prediction of velocity and turbulence fields in the wake of vehicles. J. appt. Met. 18, 387-400. Eskridge R. E. and Thompson R. S. (1982) Experimental and theoretical study of the wake of a block-shaped vehicle in a
281
shear-free boundary Row. Atmospheric Enoironment 16, 2821-2836. Gr#nskei K. E. (1982) Simphfied tr~tment of vertical diff~ion close to highways. LilI~tr~m, Norwegian Institute for Air Research (NILU F 14/82). Gr$nskei K. E. (1984) Registration of dispersion by tracer gas in Sarpsborg. LilIestr#m, Norwegian Institute for Air Research (NILU OR 24/84). Gronskei K. E. (1986) The influence of car speed on dispersion of exhaust gases. Lillestr@m, Norwegian Institute for Air Research lNILU TR 6/86). Heggen R. and Sivertsen B. (I983) Tracer gas techniques at NILU. Lillestr~m, Norwegian Institute for Air Research (NILU TR 8/83). Larssen S. (1984) Nordic method for calculation of automotive pollutant concentrations near streets. Lillestr$m, Norwegian Institute for Air Research (NILU OR 56/84). Petersen W. B. (1980) User’s guide for HIWAY-2: a highway air pollution model. Research Triangle Park, NC U.S. Environmental Protection Agency (EPA-@O/8-80-018). Rao S. T. and Keenan M. T. (1980) Suggestions for improvement ofthe EPA-HIWAY modei. .l. 2% Potlut. Con&f Ass. 30,247-256. Zimme~an I. R. and Thompson R. S. (1975) User’s guide for HIWAY: a highway air pollution model. Research Triangle Park, U.S. Environmental Protection Agency (EPA-650/474-008).
Enuironmenr
Vol. 22, No. 2, pp. 273-281.
ooo4-6981/88
1988. 0
Printed in Great Britain.
s3.00+0.00
1988 Per&amon Journals Ltd.
THE INFLUENCE OF CAR SPEED ON DISPERSION OF EXHAUST GASES K. E.
GRG~NSKEI
Norwegian Institute for Air Research, P.O. Box 64, N-2001 Liliestr#m, Norway (First received 14 October
1986 and received
for
publication 31 July 1987)
Abstract-Results of seven dual tracer experiments (SF, and CBrFJ) show that the vertical diffusion of exhaust gases tends to be larger from cars driving with high speed than from cars driving with lower speed. The experiments were carried out over a fiat snow covered plain and the tracer gases were emitted from two moving cars. The vertical tracer distributions were determined at l&30 and 70 m from the road using S-10 m masts. Gaussian distributions were found to fit the vertical profiles of tracer concentrations. The results encourage further use of dual tracer experiments to study dispersion effects of the interaction of car wakes with the atmospheric surface layer. When the atmospheric wind was perpendicular to the road (five experiments), the vertical dispersion parameter for tracer material (a,) was linearly dependent on an estimated scale of the wake in each experiment. The coefficients of linearity varied with the state of the atmospheric surface Iayer as indicated by wind m~urements and stability &s&cation. The empirical results are applicable only when the scales of turbulence in the atmospheric surface layer are smaller than the scale of the wake. Houses and trees located close to the road will influence the dispersion of the automobile exhaust and the conditions for interaction are changed. Key
word
index:
Tracer experiments, diffusion of car exhaust, wake effects on diffusion.
1. INTRDDU~ION In Norway as in other colrntries automobile exhaust is an important air pollution problem, particularly for persons living or working closer than 100 m from roads with high traffic loads. Therefore, use of dispersion models to describe the extent of the polluted area adjacent to roads in different regions is important in connection with urban and traffic planning. The highest pollution loadings occur particularly in the winter when the atmospheric turbulence is small and the car emissions are large. The following models have been used in Norway to provide information on pollutant concentrations close to roads with high traffic:
EPA HIWAY-model (Zimmerman and Thompson, 1975) Model based on a General Motors (GM) study (Chock, 1977) The Nordic model for concentration calculations in street canyons (Larssen, 1984). The EPA HIWAY-model employs Pasquill-Giffords dispersion parameters for the atmospheric surface layer to calculate pollution concentration close to highways. The im~rtan~ of vehicle wakes for the dispersion of pollution close to a road is well documented (Chock, 1977), based upon tracer experiments and on theoretical evaluation (Eskridge et al., 1979). The diffusion can be viewed as the result of the interaction between the development of the vehicle wake and the dispersive state of the atmosphere. The development of the vehicle wake is dependent on the 273
vehicle speed and the size and shape of the vehicle. The dispersive state of the atmosphere is dependent on the size and structure of the roughness elements, the terrain, the wind speed and the surface-layer heat balance. The interaction further depends on the wind direction to the roadway, the wind shear and on the thermal stratifi~tion of the atmosphere. The EPA HIWAY- model (Petersen, 1980) includes the results of General Motors tracer experiments and suggestions for improvements formulated in a paper by Rao and Keenan (1980). To better understand the interaction between the car wakes and the atmospheric surface layer, tracer experiments were carried out with different car velocities. Two cars emitting different tracer components (SF6 and CBrF,) were driven one after the other with different speeds. The samples of the tracer gases were taken downwind of the road using plastic syringes for the two components. The instruments and equipment used for tracer sampling are described by Heggen and Sivertsen (1983). This emission and sampling procedure was selected in order to minimize the difference in dispersion resulting from variations in the ambient wind and turbulence condition. By changing the driving procedure it was possible to study dispersion effect of parameters describing the wake development, i.e. the speed of the cars.
2. TRACER EXPERIMENTS
Seven dual tracer studies were conducted in March and April 1982 by the road Rv-22 in Skedsmo,
274
K. E.
GRONSKE~
Norway. The course of the tracer-emitting cars and the sampling stations are shown in Fig. I. Apart from a IO--IS m deep river canyon and a small forest the test grounds were flat. At the time of the experiment. the area was snow-covered. The locations of tracer samplers attached to three masts are shown. The masts are located 10.30and 70 m from the edge of the road. One sampler is Iocated about 1 m from the edge ofthe road and 1 m above the ground level. Measurements of wind speed and wind direction were carried out on top of the 10 m mast, 30 m from the edge of the road using a Woelfle wind recorder. Wind speed was further recorded 5 and 1 m above the ground over the sampling periods by using ‘Fuess small wind recorders’. The temperatures 5 and 1 m above the ground were determined by using one ventilated thermometer first at the 5 m level and then at the 1 m level before and after each experiment. The tracer concentrations were determined as 15min average values as a result of a time-interrupted line source. Emission procedure Two cars were driven along the 650 m road segment emitting tracer gases from tubes attached to the
exhaust pipes of the cars. The height of the cars emitting tracer gases was t 3 m. The emission intensity was recorded by a calibrated gas flow meter. The accuracy of the emission intensity is estimated to be about f lo:;, considering control of the Rowmeter setting in the cars and the flowmeter calibration curve. To avoid influence of systematic errors in the concentration determination of the two tracer gases, the tracers were emitted alternatively from the car driven at high speed and from the car driven at low speed. The fast moving car was followed by the slow moving car. The distance between the cars was 20-30 m when they started and about 150 m when they passed the sampling line, and the wakes behind the cars did not interfere with each other. The driving procedure was repeated and the contribution of tracer gases after six-eight traverses of each car was collected in each sampler. The first traverse of the two cars started simultaneously with the samplers. The last traverse of the pair of cars ended before the samplers stopped. In low wind conditions the tracer material emitted from the last traverse may not have passed the mast 70 m from the road. This source of error may influence the mass consistency in some experiments, the error is less important for the determination of rr:-
i IOm 30m
(b)
I
c mm
c
II
Fig. 1. (a) A map of the test field. (b) A cross-section of the sampling network.
275
The influence of car speed on dispersion of exhaust gases Table 1. Date and time of experiments, meteorological measurements
u Test
Date
Time
(msl-O’)
u*
u5 (ms-‘)
u
rpl0
(ms-‘)
(ms-‘)
(“)
Remarks*
Stab.?
cl 10 U5 : Wind speed 10, 5 and 1 m above the ground. (f,. q,,: Wind direction 10 m above the ground. AT: Temperature difference between 5 and 1 m. Two numbers refer to m~urements before and after the tracer experiment. U: Wind speed perpendicular to the road. The orientation of the road is 95-275” (see Fig. 1). *Remarks: The experiments are classified according to observation of wind direction in relation to orientation of the road. (Parallel, perpendicular or wind shift.) t Stab.: The experiments are classified according to observation of wind and temperature stratification. S: Stable atmospheric surface layer. N: Neutral atmospheric surface layer.
values. Time lapse films of tests show that an average of
three-four cars per minute passed the test section. These cars could interfere with the dispersion behind the slow moving car and seldom with the dispersion behind the fast moving car. However, this is a typical traffic sit~tion on small roads, representing noise in data when the development of wakes under different atmospheric conditions is studied.
3. RESULTS
Details of the tracer concent~tions for each experiment will be provided in a separate report on request (Grdnskei, 1986). The meteorological conditions (Table 1)and the emission data (Table 2) are shown for each experiment. The a,-values shown in Table 3 were determined by the least-squares approximation of the observed vertical con~ntration profile to a Gaussian distribution, In addition to the a,-value, the first moment of the
vertical concentration
distribution oc czdz I Z=&---.
1 2 3 4 5 6 I QSF, vSF*
QSF, (10-3mlm-‘s-‘) 1.2 2.4 12 12 9.4 ::t:
(1)
cdz s0 In obliquewind situations (parallel), the vertical extent of the tracers was large, and the profile observations in S-10 m high masts were not adequate for estimating the first moment. In most cases the least-squares fit to a Gaussian curve was used to determine the standard deviation of the observed vertical tracer profile. In some cases, this method gave unreasonable a,-values, as when the top of the tracer cloud was not determined by measurements. Rao and Keenan (1980) suggested the use of two single measurements to determine the a,-values by Equation (2). z:-z:
2 *’
=
2 In (C, /C,)
Table 2. Emission data
Test
was estimated.
PSF, (kmh-‘)
QCB~F, (10-3mlm-1s-L)
80 40 -5 -5 -5 80 40
Emission rate of SF,. 1 Speed of the car emitting SF+
QCB~F, Emission rate of CBrF, . vCarF, i Speed of the car emitting CBrF,.
33 16 22 43 27 33 22
PC&F, (kmh-‘) 40 80 80 40 80 40 80
(2)
216
K. E. GRONSKE-I
C’ Test 1
2 3 4 5
Tracer
_.._. _ .I. - . _ i! 0:
(kmh-‘)
(m)
(m)
80
4.65
--
40 40 80 -5 80 -5 40 -5 80
4.25 2.95 2.45 5.35 4.05 3.55 4.45 2.05 2.25 2.15
SF6
CBrF, SF6 CBrF, SF, CBrF, SF, CBrF, SF, CBrF,
6
SF,
80
7
CBrF, SF, CBrF,
40 40 80
1.95 1.48 2.20
(2,
(12.48) .--. 12.10 2.12 4.77 2.04 8.91 4.59 6.21 7.90 --. 6.70 1.57 (2.81) 1.87 4.85 1.17 2.19 1.68 2.43 1.79 2.19 1.88 3.52
z
z
Im)
$,
_..
_.
3.37 3.55
1.70 9.70 9.41 14.54
..-.
-. 8.84 (4.80) 4.05 2.97 3.57 3.99
5.15 3.87 3.20 2.49 3.75 3.75
(ml _^---_
,4(P,” (‘)
Stab
0.8
85
s
1.1
5
N
I .4
85
N
0.3
~
N
5.6
15
N
1.3
25
S
2.7
to
S
1’I 0 tms
‘I
._.-.
~~ 1182 3.59 2.49 2.10 2.18 2.96
V is the car speed, Y - 5 km h 1means walking. The met~roiogi~l conditions are ~har~terized by the wind speed at 10 m (U,,), the angle (Aq,,) between the perpendicuiar line to the road and wind direction and the stability classification.
CI and C2 are the measured concentrations at the levels Z, and Zz. respectively. To reduce the effect of errors in single measurements the average value of the two lowest and the two highest measurements were used to estimate C, and C, in Equation (2). The 6, values obtained using individual observations are shown in a separate report (Gr#nskei, 1986). The values calculated using Equation (2) with average values for C, and C2 are given in parentheses in Table 3, indicating that a different method of calculation is used. When the vertical profile exhibits a Gaussian distribution with a maximum concentration at ground level, the following relationship should exist between 2 and a,:
z=
5
J
-CT,.
x
The average value of Z/u, for the numbers given in Table - 3 is 0.78 which is a good approximation of J2ln. The tabled results suggest that when the driving speed is high, the o,-values become large. In an oblique wind situation (tests I,3 and 4) the observed a,-values become large and the accuracy is probably poor since the masts are not high enough to cover the profiles. Increasing the driving speed to 80 km h- 1 gives l&20% higher a,-values close to the road. It was a tendency for the difference in a,-values for different driving speeds to increase with distance from the road. For the consideration of uncertainty the following observations are made: (1) Different methods of a,-calculation from profile observations give different a,-estimates. The standard deviation of the difference between the estimates is
0.4 m (14 y0 of the average o,-value). The absolute value of the difference decreases with decreasing g,values. (2) The linear regression between the c, and Zvalues given in Table 3 yields:
In a Gaussian distribution, the ratio between the first moment and the standard deviation is 0.8 (5/a, = 0.8). Results of the Student t-test show that the present data support this interrelation at the 95 “/, significance level. (3) Increased vertical standard deviation with increased driving speed is observed at the 95 y0 significance level according to the results of the Student f-test. Based on these observations it was concluded that the vertical dispersion increased as a result of increased driving speed. Using the I-IIWAY-2 model the a,-values at different distances from the road were calculated for the experiments 2,5,6 and 7. The calculated a,-values (a,) are compared with the observed values (Q,,) in Fig. 2. This figure shows that for tests 6 and 7 the calculated values represent overestimates. For tests 2 and 5 the correspondence between calculated and observed values is acceptable. For each calculated value in Fig. 2, two observed values are shown, one for low car speed and one for high car speed. In 10 out of 12 cases depicted, higher driving speed yields larger a,-values in comparison to the lower driving speed results. The concentration measurements 1 m from the edge of the road and 1 m above the ground were used to estimate the u,-values close to the road. The corresponding values were calculated using HIWAYand the results of calculations are shown in Table 4.
277
The influence of car speed on dispersion of exhaust gases
changes in dispersion parameters. However, the observed variation in vertical dispersion with variation in the driving speed of the cars (Fig. 2) indicate that an improved description could be obtained by considering the development of the wake behind the cars and the interaction of the wakes with the turbulent atmospheric surface layer. 4. DISCUSSION
OF RESULTS
4.1. Estimates of emissions 9
012345676
The simultaneous measurements of wind and concentration profiles were used to estimate the flux of tracer gases perpendicular to the road at different distances. Equation (4) was applied to estimate the fluxes
IO
co (ml
Fig. 2. Scatter diagram between observed (a) and calculated o, values (a,). The calculated values refer to the HIWAY- model. Two observed values connected by a horizontal line represent two values
H
for different driving speeds. The marked points represent the u,,-value for thecar with a high driving
F= f0
speed.
In tests 2, 6 and 7, characterized by wind perpendicular to the road, the calculated a,-values (HIWAY-2) appear to be larger than the observed values. The reason for the discrepancy is probably that HIWAY- is developed for the fast running traffic at a highway with two lanes for each direction. Rv-22 in Norway is smaller and the adjacent ground is flat and snowcovered. The numbers of observations are smail and the present tracer data base is not sufficient to propose
ucdz ‘2: i k=l
(tests 1, 3 and 4) the flux estimates may differ considerably from the emission estimates. Considering experiments with wind perpendicular to the road, high discrepancies between flux estimates and emission intensities are found 30 and 70 m from the road in test 5. These o-values should be used with caution. For test 2 an excellent augment between Aux estimates and emission intensity is found for SF6. For CBrF, the
Test CBrFa SFe CBrF, SF6 CBrFs SF6 CBrF, SF6 CBrFa SF, CBrF, SF6 CBrFJ SF6
26,8S3 755 7403 1355 13,290 7100 7396 3097 2432 2405 17,550 556 7460 1364
33 1.2 16 2.4 22 12 43 12 27 9.4 33 1.4 22 3.3
1.8 1.8 0.8 0.8 -0.5 -0.5
4.0 4.0 1.0 1.0 1.2 1.2
0.5 0.7 2.2 1.8 2.6 2.7 2.2 0.8 1.5 2.0 2.0 1.6
40 80 80 40 80 -5 40 -5 80 _5 40 80 80 40
3.0
1.5 2.9 2.2
The values are compared with the corresponding u,-values calculated by HIWAY-2.
=o=~
Q J
(4)
Interpolations were made between observed C-values and U-values. The flux estimates are compared with the emission estimates in Table 5. In oblique wind situations
Table 4. o,-estimates at the edge of the road based on concentration observation and emission data
1 1 2 2 3 3 4 4 5 5 6 6 7 I
t&AZ,.
2 ;*
Q: Emission of tracer gaaea on the road. C,,: Tracer concentration 1 m above ground level by the edge of the road. U (1 m): Wind speed at the height of 1 m. VW: Speed of the car. uc: The standard deviation calculated by using the HIWAYmodel.
Table 5. Line source emission intensity and fluxes of tracer gases calculated for each experiment Flux
Test 1 SF, 2 3 4 5
C&F, SF, CBrF, SF, CBrFj SF,, CBrF, SF, CBrF3
6 SF, CBrF, 7 SF,, CBrF,
Emission
IO m
1.8
1.2 33 2.4 16 12 22 12 43 9.4 27 1.4 33 3.3 22
52 2.5 14.5 26 3.2 19 8.0 29 t.7 37 4.8 21
Unit = 10m3mlm-‘se
30 m 2.0 47 2.5 20 II 23 7.0 31 4.9 38 1.6 37 3.7 25
70 m (I.81 (36) 2.5 1x 5.5 27 2.4 3.4
I-J.7 1.3 39 5.5 17.9
’
average value of fluxes overestimates
the emission intensity by 80/,. At 30 m the high flux estimate indicates that the a,-value for the fast running car may be overestimated. For test 6 the average value of fluxes overestimates the emission intensity by 2 “/, for SF, and by 14 “/, for CBrF,. For test 7 the average value of fluxes overestimates the emission intensity by 41 “/ for SF6 and by 3 7: CBrFj. The fluctuation in fluxes indicates that the a,-value for the slow moving car may be overestimated and that the a,-value for the fast car may be underestimated. When the flux estimates are consistently different from the emission intensity, errors may be found in the emission system or in the measurements of wind (speed
and direction).
The estimation of a,-values from profile measurements is probably more accurate than indicated from the emission/flux relationships. The determination of the nz-values is not dependent on the absolute tracer concentration value. In that way correct n,-values may be obtained regardless of errors in the emission system or in the measurements of wind. Plume meandering coutd cause discrepancies between
the emission intensity and the flux estimates. 4.1. Purumeters
describing
the wake
The turbulent
wake behind the car often contains two vortices with opposite vorticity. This wake interacts with the background atmosphere to produce the observed dispersion of tracer gases. The wakes containing the tracer and the exhaust gases are transported with the wind velocity. A description of this situation is shown in Fig. 3.
The dependence of a,-values on the car speed and the development of the wake has been described by Eskridge and Hunt (1979). In their description the vehicle was considered as a point source of momentum loss. Effects due to differences in pressure and vorticity were ignored. According to this theory the scale of the wake (1) increases with distance behind the car.
s: distance h: height
behind the car of the car
i. and A: factors of proportionality.
According to Equation (5) and the simple geometrical relationships shown in Fig. 3, the following parameter can be shown to be related to the scale of the wake at
The dlstonce from the car to an observation potnt Distance from the roe.4 to an observation point !?a:,. Wmd spe-ed perpendicular to the rood. L/ Car speed along the road. 5
Fig. 3. The wake behind the car is moved perpendicular to the road by the wind.
The influence of car speed on dispersion different
Assuming the diffusion effect of the car generated turbulence (a,) and of the atmospheric turbulence (a,) to be additive the following approximation is proposed.
distances from the road
when x = h then I’ = 0. For each experiment with wind perpendicular to the road the observed a,-values are plotted against I’ in Fig. 4. For each of the experiments (tests 2, 56 and 7) the a,-values indicate a linear dependence on the scale of the wake. The following equation applies for each experiment CT,=al’+b.
(7)
Lines are drawn for the different tests in Fig. 4. It seems reasonable that the value of b is < 1 m considering a wake behind a 1.3 m high car. The coefficient of linearity (a) is larger in tests 2 and 5 compared to tests 6 and 7, the atmospheric surface layer and the finite disturbances behind the cars. According to the meteorological measurements shown in Table 1, the surface layer is classified as neutral in tests 2 and 5, stable in tests 6 and 7. The coefficient (a) is expected to be proportional to the height of the cars (h) and to decrease with increasing thermal stability. In the atmospheric surface layer the wind shear increases when the thermal stability increases. Increased wind shear is expected to increase the dispersion effect of the wake as more kinetic energy becomes available for the generation of turbulence when the air is mixed by the wake behind the cars. A linear relationship between I’ and u, seems reasonable close to the road, when turbulent intensity behind the car is larger than the turbulent intensity in the atmosphere. At larger distances the atmospheric turbulence will determine the diffusion.
9-
B-
7-
z
b”
6-
u2z = u:+u2 w
(8)
u,: diffusion by the atmospheric turbulence, to be selected for the area under consideration. u~=~.h{l+4C(~)O”[(X)O”-I]IDI.
(9)
Following Eskridge et al. (1979): C = 0.046. The background for Equations (8) and (9) is discussed by Gronskei (1982). Empirical results [Equation (7)] and simplified theoretical considerations [Equations (8) and (9)] indicate that the scale of car wakes as defined by Eskridge and Hunt (1979) may be used to improve description of dispersion of exhaust gases close to roads. The final functional form of correction remains to be found, and more tracer data are needed. A summary of theoretical and experimental determination of velocity and turbulence characteristics of wakes given by Eskridge and Thompson (1982) indicates that different length scales should be considered for the best characterization of wake-effects in other dispersion situations. The importance of the scale of eddies at the origin of the wake is pointed out by Bevilaqua and Lykoudis (1978) and effects of free stream turbulence on the development of wakes are reviewed by Bearman and Morel (1983). 4.3. Application of tracer data results Data for turbulence intensity and scale of the turbulence in the wake may be used to describe possible interaction between the surface layer of the ambient atmosphere and the wakes behind the cars. The observed dispersion of the pollution emitted in the wakes is the sum result of this interaction. With the notation of Eskridge et al. (1979), we refer to Fig. 5 for classification purposes.
Tests2and5
IO-
279
of exhaust gases
Test 7
5
Turbulence intensity in the wake: i,, = (W’2)o.5/U Scale of the wake: I, Turbulence intensity in the atmospheric surface layer: i,, = a $
4
Scale of the atmospheric turbulence:
3
2 I
0 I
2
3
4
1’
Fig. 4. The dependence of standard deviation of the vertical concentration profile on the scale of the car wake.
I,
(w’z )“.5: root mean square of the vertical velocity fluctuations U: horizontal wind speed a: factor of proportionality U,: friction velocity of the atmospheric surface layer. When the scale of the wake is larger than the scale of atmospheric turbulence (/,/I, > 1) atmospheric gradients in the wind and temperature may be in-
280
K. E.
L ‘1, Urtm
oreos
GR~NNSKEI
Camtrysick wtth small fcughnes5 ord mmrsicm
Fig. 5. Classification scheme of results from tracer experiments and of the applicability of line source dispersion models.
fluenced by wakes. The gradients should be considered for the further development and dispersion of pollution emitted in the wake. The region is indicated by the two hatched areas in the right part of Fig. 5. The area in the upper part of the figure represents the conditions close to the road when the turbulence intensity caused by the vehicles dominates over the intensity in the atmospheric surface layer. In an inversion situation wake mixing of thermally stratified air causes buoyant forces that may increase vertical mixing. On the other hand thermal stratification outside the wake restricts vertical movements. Wake parameters may be important for dispersion as a result of the development of two finite vortices introduced into the atmospheric surface layer. When the scale of the wake is approximately equal to the scale of atmospheric turbulence (I,//,1) atmospheric gradients are probably not important for the development of the wake. This situation is described by the theory of Eskridge et al. (1979). For these cases atmospheric and wake turbulence determine the diffusion. Close to the road the increased turbulence intensity will increase dispersion. At some distance from the road the atmospheric dispersion parameters may be used to describe the growth of the pollution cloud. Moving along the horizontal axis in Fig. 5 this region is indicated by an open area between the hatched regions. Close to the ground, wakes from other roughness elements (houses and trees) may interfere with the dispersion. Car wake parameters may not be important for the dispersion parameters except in the immediate neighbourhood of the cars when the turbulence in the wake is greater than in the atmospheric surface layer. The region is indicated by the two hatched areas to the left in Fig. 5. Far from the road the pollution concentration will have a stochastic variability that is not described by wake parameters. In these situations the car wake development is not restricting the dispersion process. The dual-tracer experiments are carried out in
situations when I, 3 1, over a flat snowcovered area. Parameters describing the car wake did also show up in the description of the dispersion. In the analyses with winds perpendicular to the road, HIWAYoverestimated the dispersion for the stable atmospheric cases. For the cases with neutral stability, HIWAYseemed to perform adequately. Tracer experiments in the urban area of Sarpsborg resulted in six err values between 26 and 73 m (average value: 48 m) at the distance of I50 m from a road (Gronskei, 1984). HIWAYunderestimated the vertical diffusion in these experiments, possibly resulting from not adequately accounting for the effects of larger roughness elements (houses and trees) in the residential area. More empirical data are needed on the diffusion of car traffic emissions in urban areas, particularly at distances of l(rlO0 m from the road. Parameters characterizing the wakes behind the houses and the trees are expected to be important for the description of vertical diffusion.
5. CONCLUSIONS
Results of two component tracer experiments show increased vertical dispersion as a result of increased driving speed of the cars (from 40 to 80 km h _ ‘). The car speed influences the development of the wake, which in turn affects the diffusion. The faster the car speed the faster the growth of the wake. The experiments were carried out over a flat snowcovered plain. A Gaussian distribution was found to fit the vertical distribution of tracer concentration. The standard deviation of the vertical tracer distribution may reveal a linear dependence on a parameter describing the vertical scale of the wake. When the wind shear is small and car speed is small the effect of the car wake is minimized over a flat surface in a stable surface layer. The tracer experiments indicate in some cases that the HIWAYmodel underestimates the pollution concentration by the roadway. The empirical result may not be reproducible when the vertical scale of atmospheric turbulence is larger than the scale of the wake. When houses and trees are present close to the road, the wakes of the cars are not believed to be important except in the immediate neighbourhood of the road. HIWAYmay overestimate pollution concentration downwind of the street canyon since the wake effects of the buildings are not taken into account in the dispersion parameters. Statistical inference based on few empirical data only represents a preliminary assessment of vehicle wake effects on dispersion. Since the present accuracy of dispersion calculation is low and since wake effects are important, the interaction between wakes and the turbulent atmospheric surface layer should be further studied.
The influence of car speed on dispersion of exhaust gases Acknowledgements-The author gratefully acknowledges the work of Reidar Heggen and Joseph Pacyna to accomplish the tracer experiments and the useful comments from John Irwin during the preparation of this paper.
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281
shear-free boundary Row. Atmospheric Enoironment 16, 2821-2836. Gr#nskei K. E. (1982) Simphfied tr~tment of vertical diff~ion close to highways. LilI~tr~m, Norwegian Institute for Air Research (NILU F 14/82). Gr$nskei K. E. (1984) Registration of dispersion by tracer gas in Sarpsborg. LilIestr#m, Norwegian Institute for Air Research (NILU OR 24/84). Gronskei K. E. (1986) The influence of car speed on dispersion of exhaust gases. Lillestr@m, Norwegian Institute for Air Research lNILU TR 6/86). Heggen R. and Sivertsen B. (I983) Tracer gas techniques at NILU. Lillestr~m, Norwegian Institute for Air Research (NILU TR 8/83). Larssen S. (1984) Nordic method for calculation of automotive pollutant concentrations near streets. Lillestr$m, Norwegian Institute for Air Research (NILU OR 56/84). Petersen W. B. (1980) User’s guide for HIWAY-2: a highway air pollution model. Research Triangle Park, NC U.S. Environmental Protection Agency (EPA-@O/8-80-018). Rao S. T. and Keenan M. T. (1980) Suggestions for improvement ofthe EPA-HIWAY modei. .l. 2% Potlut. Con&f Ass. 30,247-256. Zimme~an I. R. and Thompson R. S. (1975) User’s guide for HIWAY: a highway air pollution model. Research Triangle Park, U.S. Environmental Protection Agency (EPA-650/474-008).