Assessment of vehicular emission dispersion models applied in street canyons in Guangzhou, PRC

Ewiro-t

Pergamon

Intematicml, VoL21, No. 1. pp. 3946.1995 Copyright 01995 Elscvicr Sciwcc Ltd Printedin the USA. All righu xwmved 0160-4120/95$9.5040

0160-4120(94)00038-7

ASSESSMENT OF VEHICULAR EMISSION DISPERSION MODELS APPLIED IN STREET CANYONS IN GUANGZHOU, PRC

L.Y. Chan and W.T. Hung Department of Civil and Structural Engineering, Hong Kong Polytechnic, Hong Kong

Y. Qin institute of Environmental Science, Zhongshan University, Guangzhou, 510275, P. R. China

El 9405-165

M (Received

15 May 1994; accepted

20 November

1994)

The applicability of four simple dispersion mathematical models, namely APRAC, GZE, CALINE4, and PWILG were assessed by comparing the predicted CO and NO. concentrations with the measured values in street canyons in Guangzhou. These simple models were comparatively accurate in predicting maximum ground concentration. The accuracy of CO prediction was much influenced by the assumption of vehicular composition. The uncertainty of emission sources other than vehicle emissions was an important error source in predicting NO, concentrations.

of the transport wind is one of the major error sources. This error is amplified with increasing time and distance from the source (Clarke et al. 1983). Air dispersion in street canyons is different from that in an open flat region or a complex terrain region. Restricted by the buildings located on two sides of the street, prevailing wind near the canyon bottom is usually along the street, although instantaneously vertical vortices may exist. When the wind at rooflevel is weak, the impact of vehicular movements on the air flow becomes significant. The vertical and horizontal turbulence intensities have similar values in the street canyon and are much weaker than those in open flat land (Qin and Kot 1993). The scale of turbulence influencing concentration fluctuation is limited in street canyons (Csanady 1973).

INTRODUCTION

Air dispersion modelling has become an important technique in air pollution control and management. It is important to know the accuracy and limitations of these models. Weil et al. (1992) reviewed methods of model evaluation and suggested three major sources of error in these models: 1) errors in the model physics, that is, in the mathematical idealization of dispersion; 2) uncertainties or errors in the model input variables; and 3) errors in concentration measurements which concurred with Fox (1984) and Venkatram (1982). An assessment of various dispersion models (Gaussian plume models, urban dispersion models, complex terrain models, and the long distance transport model) demonstrated that the uncertainty 39

L.Y. Chan et al.

40

NOTATIONS G.

K Q U

x,2 H B

pollutant concentration on leeward side of buildings in street canyon, gm-3 empirical non-dimensional constant average rate of emission along the street, gm-l s-l wind speed, rns’l horizontal and vertical distances of receptor relative to the center of the traffic, m average building height, m street width, m

Vehicular emission is generally considered as a line source in air dispersion models. Simple models have been developed to simulate dispersion of vehicular emissions in street canyons in the past two decades, for example, empirical models (Johnson et al. 1973; Simmon 1981), box models (Nicholson 1975) and Gaussian dispersion models (Benson 1989). Vehicular emission, however, is not a stable and continual source. Controlled by traffic lights, traffic flow in urban street exhibits platoon movements. Vehicle emissions vary with traffic and ambient conditions, which these simple models have not considered. More complicated models (AP-42, EPA 1975; EMFAC, California Air Resources Board 1986; COPERT, Eggleston et al. 1989; MOBILE4 Trinity Consultants 1991) were developed taking into account more parameters such as ambient temperature, vehicle speed, mileage accrual rates, calendar year, and operating cycle. This study compared and evaluated some simple and popular air dispersion models for street canyons using data obtained in Guangzhou, P.R. China, in July 1991. Applicability of these models was identified. DESCRIPTION

OF MODELS

Four simple models for simulating dispersion of vehicular emissions in the street canyon were evaluated using the measured data. They were the empirical model used in APRAC (Johnson et al, 1973). Guangzhou empirical model (GZE) (Qin and Kot 1993), CALINE4 (Benson 1989), and the parallel wind and infinite line source Gaussian model (PWILG) (Qin and Kot 1993). These models are briefly described below.

AC U :; N1 C, C, GM

pollutant concentrations from local street at windward sides of buildings along a street canyon, gm-3 pollutant concentrations in wide street canyon, gm-3 wind speed at roof level, ms” vertical dispersion coefficient, m composite emission factor, g/km traffic volume, veh/h predicted concentrations, pL/L observed concentrations, pL/L geometric mean

APRAC

The empirical model derived by Johnson et al. (1973) uses different formula to predict vehicular emission pollutant concentration on the leeward and windward side of the street canyon. AcL-

KQ

(u+O.5)

[(x2+22)1'2+21

(1)

KQ(H-2) Acw- (u+O.5)HB

(2)

where K is an empirical constant. K is taken as 7 for canyons having a height to width ratio H/B = 1. GZE An empirical model was constructed to simulate the dispersion of vehicular emission in the narrow street canyons in Guangzhou (Qin and Kot 1993). Equation 1 was used to predict concentration on both leeward and windward sides, because the difference of concentration between the two sides caused by vortex can be neglected in the previous studies. When the wind direction wasp less than 90’ or greater than 270°, the concentration measured was considered as on the leeward side of the road. The concentration measured was considered as windward side concentration when the wind direction was between 90” and 270”. The value of 6 for K was estimated in Guangzhou. CAL/NE4

CALINE4 (Benson 1989) is a dispersion model for predicting air pollution concentrations near roadways.

41

Vehicular emission dispersion models

The model is based on the Gaussian diffusion equation and employs a mixing zone concept to characterize pollutant dispersion over the roadway. It provides the option for street canyons. Multiple reflections are calculated in simulating dispersion of vehicular emissions in the canyon. Prediction results of CALINE4 depend on the cumulating link iength. The length of the monitoring street section between two intersections was used in the model performance evaluation (180 m for Jiefang Road Middle and 206 m for Changti Road). P WILG

a Jiefang

Road

Middel

W

4.7

13.81

7.5

13.91

4.5

lo

The parallel wind and infinite line source Gaussian model performed well in predicting pollution concentration in wide street canyons in Guangzhou (Qin and Kot 1993): (3) Q Ac-

Changti

Road

(2x)1’wuz(r)

where cr, is the vertical dispersion coefficient. r = [x2+(z/b)2]1/2, b = cr,/o,. Upward flow exists in the street canyons. A reflection of the pollutant plumes on the facades of the buildings at both sides of the canyon was assumed.

S

THE SURVEY

e

Two typical street canyons in the central business area of Guangzhou, Jiefang Road Middle and Changti Road, were selected for monitoring vehicular emission dispersion. Jiefang Road Middle is a major north-south oneway traffic throughway across the city. The average daily traffic volume is more than 29 000. The buildings on both sides are three to four stories (approximately 14 m high). The road section at the monitoring site is 15.2 m wide. The motor way and the bicycle ways are divided by an iron fence. The pedestrian pavements are covered by canopies. Three automatic air sampling points and one automatic wind measuring point at roof level were set up in Jiefang Road Middle (Fig. la). Monitoring was conducted for nine consecutive days. Changti Road is a east-west one-way traffic street. The buildings on two sides of the street are high but not uniform. The daily traffic volume is more than 33 000 vehicles. The road section at the monitoring points is 15.3 m wide. The buildings on the south side of the street are about 30 m high while the buildings on the north side are about 20 m high. Three air sampling points and one wind monitoring point were set up in Changti Road. The air sampling points were

Automatic Sampling Point

Anenone~~

Probe

Fig. 1. Configurations of streets and locations of monitoring points (Unit: m).

located on the same side of the street (Fig. lb). Monitoring was conducted for seven consecutive days. The concentration of CO, NO, and NO,, the wind speed and direction at roof-level, and the traffic volume and speed were measured. CO was sampled at all sampling points. NO and NO, were sampled only at sampling points 1 and 4. Data measured were stored in a data logger in one minute intervals continually. The traffic volume and speed were measured manually. THE POLLUTANT

CONCENTRATION

The l-h average concentrations (C) of CO and NO, and the fluctuation intensities (SC/C) are shown in Table 1. CO and NO, concentrations were high in Jiefang Road Middle. The maximum values of CO and NO, concentrations were 40.2 and 0.37 @L/L).

L.Y. Chan et al.

42

Table l.Monitoring

results

@L/L).

of CO and NO,concentrations. C: l-hpollutant concentration

Changti Road ___________-___-_ co NOx

Jiefang Road Middle ____________________ NOx co

____L________________n1413_____~~~~~____-___~~~~~_____~~~~~5.9 0.16 Avg. 9.2 18.0 0.37 Max. 40.2 C 0.1 0.02 Min. 0.5 -__-__--__-__--__-__~-~~~~~~-~~-~~~-~~-~~--~--~~~-~~--~--~~0.46 0.43 Avg. 0.47 2.00 2.67 Max. 2.45 0,/C 0.06 0.13 Min. 0.17

0.09 0.33 0.01 0.48 1.50 0.11

that the values of the concentration fluctuation intensity ranged from 0.2 to 4 near the centre of the plume, with increasing value at the edges of the plume. They also found a considerable correlation between the turbulent intensity and the concentration fluctuation intensity although the correlation was not strong. No significant correlation between the concentration fluctuation intensity measured in the street canyon and the wind direction deviation measured at the roof-top level could be discerned in this study (Fig. 2). The traffic volume fluctuation intensity measured in the Jiefang Road middle was about 0.4 which is of similar magnitude to the concentration fluctuation intensity.

They exceed the Chinese ambient air quality third class standard (16 and 0.15 ttL/L, UDC 1982) by about 2.5 times. The average concentrations of CO and NO, were 9.2 and 0.13 pL/L. The pollutant concentrations measured in Changti Road were lower than those in Jiefang Road Middle, because the scale of the street canyon was relatively large. The maximum values of CO and NO, concentration were 18.5 and 0.33 pL/L. The average concentrations were 5.9 pL/L for CO and 0.09 pL/L for NO,. The concentration fluctuation intensities measured in the street canyons ranging from 0.06 to 2.67 pL/L were much weaker than those observed in the atmospheric plumes. Lewellen and Sykes (1986) conducted observations of atmospheric plumes and found

3

2 -

b-

*o

a0

1

IO

I

0

20

30 Wind

40

50

Direction oco

Fig. 2. Concentration

fluctuation

60

Deviation ANO intensity

70

80

(degree) X

vs. wind direction

deviation.

90

Vehicular

emission

ASSESSMENT Composite

dispersion

models

43

OF MODELS

emission

are calculated. The results are shown in Table 2. The ratio of predicted to observed concentrations are compared.

factors

The composite emission factors of vehicles output by MOBILE4.1 (Trinity Consultants 1991) for the calendar year 1981 were adopted in this study. These factors have been shown to be appropriate to be used for Guangzhou (Qin and Chan 1993). The motor vehicles were classified into five types: LDGV (light-duty gasoline vehicle), LDGTl (light-duty gasoline truck), LDGT2, HDGV (heavy-duty gasoline vehicle) and MC (motorcycle). Vehicular emissions, Q, may be calculated by the formula:

Mean bias

All four models underpredict concentrations for both CO and NO,. The accuracy of these simple application models can be, however, compared to the accuracy of the plume dispersion model in predicting maximum concentration as demonstrated by Hanna and Paine (1989). The mean bias statistic indicates that PWILG underpredicted the average concentration for CO by 68% and by 64% for NO, in this narrow street canyon situation, although PWILG was shown to perform well in predicting vehicular emissions concentration in wide streets with high but not continual buildings in Guangzhou (Qin and Kot 1993). CALINE4 underpredicted by 20% for CO and by 14% for NO,. The predicting results of CALINE4 depend on the cumulating link length. The cumulating link lengths used in this evaluation exercise were short. APRAC and GZE are both empirical models. There was no significant difference between the two models for both CO and NO, in the mean bias statistic. APRAC underpredicted by 51% the average concentration for CO and by 43% for NO,, while GZE underpredicted by 48% for CO and by 36% for NO,.

(4) where Ej is the composite emission factor for vehicle type j, Nj the traffic volume for the vehicle type j. Employed

statistics

Three statistics of the observed concentration and predicted concentration paired in time and space were used to evaluate the model performance: MeanBias-q-r0

Correlation r-D/acpacO

(6)

where an overbar indicates an average, and subscripts p and o represent predicted and observed values respectively; and

CP=

+

2c,

Correlation

The correlation coefficients, r in Table 2, showed similar low values for the same pollutant in all four models. The correlation statistic was slightly higher for NO, than for CO. No conclusive remark can be made because of the .low values of the correlation coefficients.

(7)

In addition to the maximum overall concentration, the average of the top 25 concentration, C(Top 25).

Table

2. Evaluation

results

of the models

coefficient

(C in units pL/L). C.: observed concentration; C,: predicted relation; %FAC 2: percentage factor of two.

cc

g-c;

r

concentration;

r: coefficient

%FAC

2

Cc (maX)

(2~)

(T0pc.25)

C (TcyP

25)

-----_--------__-----~~-~~-~~~~~~--~-~~~~~~~~_____~_~~____-_~-____APRAC 8.8 -4.5 0.24 41 GZE -4.2 0.27 51 CALINE4 -1.8 0.27 55 PWILG -6.0 0.27 23 -----_-_-----____----~-------~~_~~~~-~_~_______-__~~~---~---~~_--_-

40.2

co n=372

NC, n-160

APRAC GZE

CALINE4 PWILG

0.14

-0.06 -0.05

0.36 0.33

36 55

-0.02 -0.09

0.31 0.33

64 30

0.36

26.6 22.8 38.0 11.3 0.35 0.30

0.32 0.14

16.5

0.24

9.6 9.1 15.8 5.7 0.16 0.17

0.24 0.10

of car,

L.Y. Chan et al.

44

0:

APRAC

I c: WINE4

10.0 I

I d: PWILG

1

10

100

0.01

0.10

1 .oo

NOx

co

Observed ConcentrationN(mL/L) Fig. 3. Ratio of predicted-to-observed

A factor of

concentration

two

The percentages of a factor of 2 (i.e., the predicted concentrations that falls within flOO% of the values of the observed concentrations) as shown in Table 2

as a function

of observed

concentration.

indicate that CALINE4 and GZE performed better than the others. CALINE4 performed well with 55% for CO, 64% for NO, and GZE, 5 1% for CO, and 55% for NO,.

Vehicular emission

Maximum

dispersion

models

4s

and top concentrations

mon in Guangzhou.

Similar results were obtained from a study on the emission rates from vehicles in tunnels (Pierson et al. 1990) and another study on remote sensing measurement of CO emission from on-road vehicles (Stephens and Cadle 1991; Lawson et al. 1990). These studies revealed that the measured vehicle emissions of HC and CO were significantly higher than those predicted by the models (Cadle et al. 1991). One of the major reasons was that the vehicle emission models cannot properly estimate the high emission rate of a few on-road vehicles in poor maintenance conditions. Another study showed that nearly half of the CO emissions were emitted from a small fraction of these poorly maintained vehicles (Rueff 1992). In the case of NO,, all four models overpredicted the NO, concentration when the observed concentration was extremely low (C,cO.O5 pL/L). The GM of C /C did not show significant difference when th$ obOserved concentration was between 0.05 and 0.20 pL/L. The GM of Cp/Co decreased when the observed concentration was above 0.20 pL/L. In Guangzhou, the average 67% of NO, in the street canyon was found to come from vehicular emissions and the contribution of other sources was significant (Qin and Chan 1993). The variation trend of the GM of Ct,/Co for the NO, could be attributed to the models underprediction of NO, concentration in the street canyon because the contributions from other sources were ignored. Further examination of the C /Co ratio given by the two models APRAC and GZfigives interesting

The results of the maximum concentration and the top 25 concentrations in Table 2 indicate that CALINE4 performed significantly well for both CO and NO, (accuracy of about +ll%). APRAC and GZE showed similar performance although the accuracy of APRAC for maximum concentration was slightly higher than that of GZE. PWILG performed poorly. Ratio of predicted

to observed

concentration

Figures 3a to 3d show the geometric mean (GM) of C,/C, and its uncertainty, denoted by the vertical bars, of four models. The uncertainty is estimated at the 95% confidence level. Each figure is composed of 46 points for CO and 20 points for NO, in each of the eight monitoring intervals. For the same pollutant, the variation trends of C,/C, were similar for all models. In the case of CO, the GM of C Co was nearly constant at the observed concentration 9/elow 11.5 p.L/L. The predicted average concentrations in CALINE4 were close to the observed values. The predicted average concentrations in APRAC, GZE, and PWILG were about 24%, 20%, and 58% lower than the observed values, respectively. The GM of C,/C, decreased when the observed concentration CO was above 11.5 FL/L. Remarks

The variation trend of the GM of C,/C, for CO could be attributed to the high CO emissions from some poorly maintained vehicles which are com10.0

r

CO (n=137)

0.1

0

90

180

NOx (n=68)

270

360

0

Wind Direction oAPRAC Fig. 4. Ratio of predicted/observed

concentration

90

180

270

360

(degree)

?? GZE

as a function

of wind direction in Changti Road.

L.Y. Chan et al.

46

results. APRAC and GZE are both empirical models. APRAC uses different formulas to predict emission concentrations at windward and leeward side of the road section, while GZE uses the same formula for both situations. Figure 4 shows the geometric means of C,/C, and their uncertainty, denoted by the vertical bars, for APRAC and GZE. The uncertainty was estimated at the 95% confidence level. There was no significant difference for APRAC and GZE in predicting leeward-side pollutant concentrations. APRAC underpredicted windward-side pollutant concentration significantly, while GZE performed well in predicting windward pollution level. CONCLUSIONS

Using the data measured in a field study in Guangzhou, four simple models, APRAC, GZE, CALINE4, and PWILG, were evaluated. CALINE4 and PWILG are both Gaussian models. CALINE4 overpredicted vehicular emission pollution in narrow street canyons if long cumulating link lengths were used. PWILG underpredicted vehicular emission pollution in narrow street canyons if only one reflection on each side was calculated. APRAC and GZE are both empirical models. GZE performed significantly better than APRAC in predicting windward side concentration in narrow street canyons probably because the concentration difference between the two sides was small. The uncertain contribution from sources other than vehicle emissions and the excess emissions produced from poorly maintained vehicles are major causes of errors in predicting emission concentrations in street canyons. This research Kong Croucher Foundation.

Acknowledgment

was sponsored by the Hong

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