Evaluation of GRAL for the Pollutant Dispersion under Low Wind Speed Conditions from Two Road

Tunnel Portals in Nanjing

MIAO Shujiang, FU Dafang Municipal Engineering Department, School of Civil Engineering

Southeast University Nanjing, Jiangsu Province, China

miaosj@seu.edu.cn

Abstract—A Lagrangian atmospheric dispersion model GRAL dealing with the dispersion of pollutants from a roadway tunnel portal has been introduced, with its application in Nanjing. The results show the dispersion characteristics under local meteorological conditions, especially in low wind speed conditions. A comparison with a Gaussian point source model and a CFD model (FLUENT), has been made. Under the low wind speeds and parallel-to-the-road wind conditions, GRAL has advantages over the Gaussian and CFD models, while the latter two overestimate concentrations as a result of neglecting the influence of the ambient wind fluctuations on position of the jet flow from the portal. The cases here might be useful in someway for pollution controlling, environment impact assessment and project design.

Keywords-tunnel portal; dispersion; GRAL; FLUENT; low wind speed

I. INTRODUCTION Pollution dispersion from tunnel portals becomes a serious

environmental problem, with a strong need for air quality models dealing with it. While the models should be used for environmental impact assessment studies, they must require a minimum of computation time on conventional personal computers. From this point of view, Eulerian microscale models (RANS-models, Gaussian models) are not a suitable choice.

A Lagrangian model GRAL [1] has been introduced here. This model accounts pollutant jet stream from tunnel portals for functions of the ambient wind field, which is strongly evident in low wind situations. Nanjing is under low wind speed all through the year, where the pollutant jet bending effect is evident. However the Gaussian models could not accurately describe the dispersion process for low wind speed conditions and when the wind direction is almost parallel to the road, with a tendency to overestimate concentrations [2]. Inhomogenities in wind- and turbulence fields due to

topographical effects cannot be accounted for in the Gaussian models either. Considering the meteorological conditions in Nanjing, GRAL was used to simulate air pollution from Jiuhuashan tunnel portal and Mofan Road portal.

II. MODELING APPROACHES GRAL (Graz Lagrange Model) is developed by Austrian

researchers Oettl et al [1]. It is based on the idea of a bending jet flow as a function of the ambient wind field. The bending jet flow is modeled by the tracks of numerous particles released from a portal. It is assumed that:

The jet flow changes its direction according to two forces: friction forces, which cause the jet flow to slow down, and pressure forces perpendicular to the jet flow, due to the ambient wind field, which finally change the direction of the tunnel jet flow.

• The diffusion due to the jet stream changing position according to the ambient wind direction and speed has typical length scales in the order of tens of meters while eddies evolving due to the shear stresses along the jet stream or due to the traffic are of the order of a few meters. Thus the fluctuating ambient wind direction should be taken into account as a main process influencing pollutant dispersion from road tunnel portals, which is ADAPT, Additional diffusion by the influence of a fluctuating ambient wind field on the position of the jet flow.

• The buoyancy caused by the temperature difference between the ambient and the tunnel air might lead to vertical diffusion of pollutant. GRAL uses a modified version of Van Dop’s model for plume rise stacks [3] as the buoyancy part of itself.

• NO2 has been chosen as indicator since it is health-related vehicle pollutants.

• The chemical processes in the diffusion are not included in GRAL.

Supported by Natural Science Foundation of China (80108012).

978-1-4244-4639-1/09/$25.00 ©2009 IEEE

Then, the fundamental equations are:

2

2p pdU U

Kdt y

∂= −

∂ . (1)

( )2

2 2pS pAP

U UUy b

−∂ ≈∂ . (2)

(1 )K tα= + . (3)

21

2 nA

nsdU Udt

β=. (4)

(1 )tβ γ= + . (5)

ydy dbb

=. (6)

where Up (m·s-1) is the flow speed along the jet stream (defined as x-axis); K (m2·s-1) is the turbulence exchange coefficient; t (s) is dispersion time; UpS (m·s-1) is the wind speed of the jet stream along the x-axis; UpA (m·s-1) is the ambient wind speed in x-direction; b (m) is the width of the jet stream (for t = 0; b = the width of the tunnel portal); α (m2·s-2) is an empirical constant, which describes the effect of turbulent friction; β (m-

1) is an empirical constant, which mainly reflects the area upon which the pressure acts; γ (m-1·s-1) is an empirical constant.

III. MODEL SET-UP Jiuhuashan tunnel lies through the bottom of Xuanwu Lake,

with a length of 2796 m and two bores for each direction with six lanes in total. Each portal has a width of 13 m and a height of 4.9 m. The pollution dispersion was simulated at the south portal. Mofan Road tunnel is located under Nanrui Road, Sanpailou Street, and Zhongshan North road, having a length of 1444 m and two bores for each direction. Each portal has a width of 12.5 m and a height of 4.9 m. The pollution dispersion was simulated at the west portal. In calculation, the two tunnels were both orientated into a northwest-southeast direction.

Nanjing is of northern subtropical monsoon climate. East-northeast and east-southeast wind prevail, the average wind speed is 2.7 m·s-1, and the frequency of calm condition (wind speed <1.5 m·s-1) is 29.1%. It implicates that Nanjing belongs to low wind speed state, which would act upon the pollution diffusion. According to the meteorological data of year 1994, 1995, 1996 and 2002, under Pasquill stability classes, class D has the highest frequency of 40.03%.

This study used the Nanjing meteorological data of year 1999, and the most unfavorable meteorological conditions (i.e. typical meteorological conditions with 98% percentile) to simulate air dispersion.

The parameters α and γ in GRAL are chosen according to empirical experiences. They are associated with traffic pollution situation (vehicle volume, vehicle speed, discharge speed, pollutant factor etc.), ambient wind direction and speed, portal topography and portal construction. In order to make the choice easier, a so-called stiffness parameter has been introduced by Oettl et al. 2005 [5]. It could be understood as the degree for the jet stream keeping its quality, varying between 0 and 100%. It decreases while α, γ increase [5]. The stiffness was taken to be 90% here, with α and γ around 0.01 correspondingly.

The calculating domain for GRAL was 250 m×250 m, including the portals. The concentrations of pollutants of 2.5 m above the ground were calculated, in order to take into account traffic-induced turbulence. The grid size used here was taken to be 5 m×5 m. The roughness length 0.66 m, in accordance with [6], was estimated from the references providing the metrological data of Nanjing [7]. The NO2 source strength was predicted by the traffic volume and emission factors, which were 0.77 kg·h-1 for Jiuhuashan tunnel, and 0.21 kg·h-1 for Mofan road tunnel. The data were recorded on an hourly basis, so the dispersion time was chosen as 1 h.

Figure 1. 98% percentile simulated concentrations for NO2 through a year in μg·m-3(while a for Jiuhuashan Tunnel southern portal, b for Mofan Road

Tunnel western portal).

Figure 2. Maximum simulated NO2 concentrations through a year in μg·m-3

(while a for Jiuhuashan Tunnel southern portal, b for Mofan Road Tunnel western portal).

11

4069

99

6950 7000 7050 7100 7150 7200342300

342350

342400

342450

342500

342550

Y/m

X/m

3.212

20

28

6950 7000 7050 7100 7150 7200342300

342350

342400

342450

342500

342550

Y/m

X/m (a) (b)

2677

1.3E2

6950 7000 7050 7100 7150 7200342300

342350

342400

342450

342500

342550

Y/m

X/m

7.4 22

36

6950 7000 7050 7100 7150 7200342300

342350

342400

342450

342500

342550

Y/m

X/m (a) (b)

4.316

27

6950 7000 7050 7100 7150 7200342300

342350

342400

342450

342500

342550

Y/m

X/m

1.24.5

7.7

6950 7000 7050 7100 7150 7200342300

342350

342400

342450

342500

342550

Y/m

X/m (a) (b)

Figure 3. Mean simulated NO2 concentrations through a year in μg·m-3(while a for Jiuhuashan Tunnel southern portal, b for Mofan Road Tunnel western

portal).

IV. RESULTS AND DISCUSSION

A. Results from GRAL for long time concentration prediction Fig.1, 2, 3 provide the NO2 concentrations modeled with

GRAL3.5 (i.e. last major update of GRAL was in May, 2003) all through the year, which are 98% percentile contour (the distribution with the highest probability), maximum and mean value contour, where x and y are the respective geographical coordinates of the computing domain, with x in the east direction. All concentrations in contours are given in μg·m-3.

Modeled NO2 with GRAL3.5 give 0.0043-0.11 mg·m-3 for Jiuhuashan tunnel, and for Mofan road tunnel, 0.0012-0.031 mg·m-3 in fig. 3. It could be drawn that the area around portal has a steep pollutant concentration gradient, where the air quality exceeds the national standards, which is consistent with that the portal is treated as pollution source. Thus the air quality of neighborhood around portal needs to be controlled, such as using clean energy motor vehicles, building block walls etc.

Fig.1, 3 indicate the pollutants basically decreases along the tunnel axis, while from fig.2, the crosswind (the angle between the ambient wind and the jet stream is 45°, 90° or 135°) makes the diffusion along the normal direction of portal, having a larger range than axial diffusion. The modeled distributions agree well with the actual wind directions in Nanjing, where dominate winds are ENE and ESE.

Fig.2 shows that high concentration contaminates have a large influence on the lateral areas of the portal, which would make the air quality of sensitive areas (e.g. residential areas) around the portal exceeds the national standards. It should take measures to control air quality, like building block wall around portal, and planting green belt outside the wall [8].

Figure 4. The relative positions of receptors for Jiuhuashan tunnel (a) and Mofan Road tunnel (b).

B. Results from GRAL and Gaussian Models for hourly averaged concentrations Several receptors (fig.4) have been chosen to illustrate the

differences between GRAL and the Gaussian model. When simulating diffusion from tunnel portal, the Gaussian virtual point source model is often be used, which treats each area source as an effective point source.

The background concentrations around tunnels were obtained from [9, 10]. Table 1, 2 give the hourly-averaged ground level concentrations (g.l.c) predicted by GRAL3.5 and the Gaussian model. All the data were obtained from adding the contributed values calculated by models to the background concentrations.

The ventilation system was not in consideration here. Whether the ventilation system is longitudinal, transverse or semi-transverse, it would decrease the pollution level around the portal. The simulation here could be seen as in the worst condition (nature ventilation).

It is worth mention that the stiffness was set equal to 90%, which means the possibility that the jet stream could maintain its quality was over 90%, and the ambient wind field had little impact on the jet stream. This assumption is in consistent with the low wind conditions, which is favorable for the jet stream to keep its quality. The quite low mean wind speeds causes the ambient wind to meander, and ADAPT effect would not be negligible. The horizontal diffusion has been strengthened while the vertical turbulence is weak in such conditions. Also the advection is small when compared with the one in wind. The pollutant is well mixed in the whole computing domain just after streaming out of the portal, which makes the dispersion close to molecular diffusion. In general, low wind speeds have an impact on pollutant dispersion, however, the Gaussian models could not simulate the dispersion in low wind conditions well [11], while GRAL including the ambient wind influence could more accurately simulate the pollutant dispersion in such situations.

C. Comparison with Gaussian Model The below tables also give comparison between different

models for hourly averaged values. Obviously Gaussian model has a clear tendency to overestimate when compared with GRAL. This is because the Gaussian model does not make the influence of the ambient wind field on the jet stream into its framework, which is especially important in low wind speed and parallel-to-the-road conditions.

And GRAL is more sensitive near portals than places far from portals. The receptors around Jiuhuashan tunnel portal is of 1.5-2.5 km, and 15-50 m for Mofan road tunnel portal. For Jiuhuashan tunnel, the peak ratio of predicted value with GRAL to ones using Gaussian model for NO2 is 0.0054. For Mofan road tunnel, the maximum ratio is 1.

These two conclusions are both because the Gaussian model does not make the influence of the ambient wind field on the jet stream into its framework, which is especially important in low wind speed and parallel-to-the-road conditions.

TABLE I. HOURLY-AVERAGED GROUND LEVEL CONCENTRATIONS PREDICTED FOR JIUHUASHAN TUNNEL

Type Receptor NO2

Gaussian model

(mg·m-3)

GRAL3.5 (mg·m-3)

GRAL3.5/Gaussian model

Predicted value

1 0.012 0.00001 0.0008 2 0.01 0.0001 0.01

5000 6000 7000 8000 9000340000

341000

342000

343000

344000

345000 receptor

Y/m

X /m

portal

2

5

14

3

7000 7010 7020 7030 7040 7050 7060342435

342440

342445

342450

342455

342460 recptor

Y/m

X/m

24

portal

1

3

(a) (b)

3 0.056 0.0003 0.0054 4 0.093 0.0001 0.0011 5 0.081 0.00001 0.0001

Added value

1 0.056 0.0440 0.7857 2 0.089 0.0791 0.8888 3 0.144 0.0883 0.6132 4 0.165 0.0721 0.4370 5 0.167 0.0860 0.5150

TABLE II. HOURLY-AVERAGED GROUND LEVEL CONCENTRATIONS PREDICTED FOR MOFAN ROAD TUNNEL

Type Receptor

NO2 Gaussian

model (mg·m-3)

GRAL3.5 (mg·m-3)

GRAL3.5/Gaussian model

Predicted value

1 0.04 0.02 0.5 2 0.05 0.03 0.6 3 0.05 0.04 0.8 4 0.05 0.05 1

Added value

1 0.15 0.13 0.8667 2 0.14 0.12 0.8571 3 0.13 0.12 0.9231 4 0.15 0.15 1

D. Comparison with FLUENT A comparison with a popular commercial steady-state

computational fluid dynamics (CFD) model (FLUENT). And it shows that GRAL has advantages over the CFD models when simulating the dispersion from tunnel portals, since the latter suffer from high CPU-requirements, complexity of handle, and tendency to overestimate concentrations.

V. CONCLUSIONS The model introduced here is in a Lagrangian framework,

with simple algorithm, low demand for CPU and preprocessing time, no resolution or artificial diffusion errors, the ability to calculate statistical concentration values (percentiles, annual mean) and the applicability to adjacent street networks or various sources (point, line, area).

From the contours, it could be concluded that the air quality near the tunnel portal should be improved to meet the national standards, measures including recommending clean energy motor vehicles, building block walls, and planting green belt etc.

Low wind speeds conditions lead to slow dispersion, and the horizontal diffusion has been strengthened while the vertical turbulence is weak. The ADAPT effect is evident. And there is no adequate model available for the deal for dispersion of a jet stream in the ambient low wind speeds. E.g. Eulerian microscale models could deal neither with plume meandering, nor with the influence of the wind direction fluctuations on the position of the jet stream centre-line. GRAL makes the bending jet flow become a function of the ambient wind field, which includes the ADAPT effect. Therefore, GRAL shows a better performance better than the Gaussian and CFD models in the parallel-to-the-road wind and low wind speed conditions.

ACKNOWLEDGMENT Great thanks to Dr. Dietmar Oettl and Prof. Peter Johann

Sturm for their selfless help.

REFERENCES [1] D. Oettl, P. J. Sturm, M. Bacher, G. Pretterhofer, and R. A. Almbauer,

“A simple model for the dispersion of pollutants from a road tunnel portal,” Atmospheric Environment, vol. 36, pp. 2943-2953, 2002.

[2] D. Oettl, J. Kukkonen, R. A. Almbauer, P. J. Sturm, M. Pohjola, and J. Härkönen, “Evaluation of a Gaussian and a Lagrangian model against a roadside dataset, with emphasis on low wind speed conditions,” Atmospheric Environment, vol. 35, pp. 2123-2132, 2001.

[3] H. Van Dop, “Buoyant plume rise in a Lagrangian framework,” Atmospheric Environment, vol. 26A, pp. 1335-1346, 1992.

[4] P. Zannetti, Air pollution modeling. Theories, Computational Methods and Available Software. Southampton, Boston: Computational Mechanics Publications, 1990, pp. 444.

[5] D. Oettl, P. J. Sturm, and R. A. Almbauer, “Evaluation of GRAL for the pollutant dispersion from a city street tunnel portal at depressed level,” Environmental Modeling and Software, vol. 20, pp. 499-504, 2005.

[6] H. S. Zhang, F. Y. Li, and J. Y. Chen, “Statistical Characteristics of Atmospheric Turbulence in Different Underlying Surface Conditions,” Plateau Meteorology, vol. 23(5), pp. 598-604, 2004 (in Chinese)

[7] J. L. Peng, Characteristics Analysis of land-Atmosphere Energy Transfer and Turbulence over Urban and Suburban Underlying Surfaces in Nanjing winter. Ms D Thesis. Nanjing: Nanjing University of Information Science and Technology, Apirl 2008, pp. 41-42 (in Chinese).

[8] J. H. Kuang, G. Q. Zhu, and B. Yu, “Study on the controlling method of exhaust gas diffusion from a tunnel portal,” Chinese Journal of Computational Mechanics, vol. 22(6), pp. 771-775, 2005 (in Chinese)

[9] Jiangsu Provincial Academy of Environmental science, The environmental impact assessment report of Nanjing fast inner circumference highway Xuanwu Lake Tunnel in south-north direction. Report. Nanjing: Jiangsu Provincial Academy of Environmental science, 2004 (in Chinese).

[10] Jiangsu Transportation Research Institute, The environmental impact assessment report of Nanjing fast inner circumference highway west section project. Report. Nanjing: Jiangsu Transportation Research Institute, 2007 (in Chinese).

[11] N. S. Holmes, and L. Morawska, “A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available,” Atmospheric Environment, vol. 40, pp. 5902-5928, 2006.

[12] J. Kukkonen, J. Härkönen, J. Walden, A. Karppinen, and K. Lusa, “Evaluation of the dispersion model CAR-FMI against data from a measurement campaign near a major road,” Atmospheric Environment, vol. 35, pp. 949-960, 2001.

[13] W. M. Jiang, H. B. Yu, G. L. Xie, J. Wu, H. G. Zhu, and H. N. Sun, “An experimental study on environmental impact of automobile exhaust from urban transportation tunnels,” ACTA Scientiae circumstantiae, vol. 18(2), pp. 188-193, 1998 (in Chinese).

[14] D. Oettl, P. J. Sturm, R. A. Almbauer, S. Okamoto, and K. Horiuchi, “Dispersion from road tunnel portals: comparison of two different modeling approaches,” Atmospheric Environment, vol. 37, pp. 5165-5175, 2003.

[15] D. Oettl, and P. J. Sturm, User-guide GRAL 3.5TM, private communications, 2007.

[16] S. Okamoto, K. Sakai, K. Matsumoto, K. Horiuchi, and K. Kobayashi, “Development and application of a three-dimensional Taylor-Galerkin numerical model for air quality simulation near roadway tunnel portals,” Journal of Applied Meteorology, vol. 37, pp. 1010-1025, 1998.