A wind-tunnel study on diffusion from urban major roads

J. Wind Eng. Ind. Aerodyn. 99 (2011) 1227–1242

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A wind-tunnel study on diffusion from urban major roads Isao Kanda, Yukio Yamao, Kiyoshi Uehara, Toshimasa Ohara n National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, Japan

a r t i c l e i n f o

abstract

Article history: Received 21 July 2009 Received in revised form 17 October 2011 Accepted 20 October 2011 Available online 9 November 2011

We evaluate the performance of reduced-scale wind-tunnel experiments that simulate vehicle-induced pollutant diffusion in urban roadside area. From Japanese urban areas, we selected four sites that cover a wide range of road structures, building density, and roadside features. At each site, four field stations were installed to monitor concentration of air pollutants including nitrogen oxides (NOx). In the windtunnel experiments, ethane was emitted from along the major roads, and its concentration was compared with the background-subtracted field values. For annual average of NOx in the year 2006, we found that an appropriately normalized concentration agreed fairly well between wind-tunnel and field measurements. The wind-tunnel concentration distribution measured at a high spatial resolution revealed that roadside features such as tall buildings, noise barriers, and trees have considerable effect on the concentration on the downwind or upwind side of the roads. The high-resolution results are expected to serve as a useful database for evaluating numerical air pollution models. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Air pollution Automobile Diffusion NOx Wind tunnel

1. Introduction In urban roadside areas, motor vehicles are a major source of air pollutants such as NOx or particulate matter, which are known to cause human respiratory problems (Nitta et al., 1993; Lighty et al., 2000; HEI, 2009). Even though emission regulations have been upgraded and the overall air quality is improving, immediate neighborhoods of major roads are still exposed to higher pollutant concentration than the environmental standards (at least, in Japan). Whether for taking countermeasures or for conducting epidemiological studies, it is essential to know the concentration distribution around major roads. Because field monitoring at sufficiently high spatial density is not feasible, models are often employed to estimate concentration. There are various ways of concentration estimation. Gaussian plume models (e.g., van den Hout and Baars, 1988; Benson, 1992; Japan Environment Agency, 2000) have been widely used for concentration estimation. Although they are simple, quick, and well-tuned for the real-world diffusion behavior, they cannot account for the effect of complex building configurations particularly variations in the road-parallel direction. To overcome this drawback, numerical models that resolve obstacle shapes in numerical grids and solve fluid dynamical equations iteratively have been developed and employed (e.g., Eichhorn, 1989; Yoshikawa et al., 2003a,b; Blocken et al., 2008). Hereinafter, these models are called obstacle-resolving numerical models. They are still costly, but as computational power increases, are likely to become principal tools

n

Corresponding author. Tel.: þ81 29 850 2718; fax: þ81 29 850 2580. E-mail addresses: [email protected] (I. Kanda), [email protected] (T. Ohara). 0167-6105/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jweia.2011.10.009

for concentration prediction. Finally, wind-tunnel diffusion experiments are often conducted at selected sites. They are generally more costly and time-consuming than numerical models, but produce reliable concentration distribution in complex building configurations under controlled wind conditions. This paper is concerned with the wind-tunnel method. Previous representative wind-tunnel studies on roadside air pollution over a few-hundred-meter range are Schatzmann and Leitl (2002), Arnold et al. (2004), and Uehara et al. (2007). Schatzmann and Leitl (2002) conducted a 1:200 scale wind-tunnel diffusion experiment for the area around Goettinger Strasse in Hanover, Germany, and compared concentration of NOx at a permanent field monitoring station near a curb. They found fairly good agreement between wind-tunnel and one-year averaged field measurements although relatively large disagreement occurred in the wind directions for which the normalized concentration had a maximum. They attributed this disagreement to the large concentration gradient near the monitoring station. They noted that an obstacle-resolving numerical model MISKAM performed very poorly against the field data (Ketzel et al., 1999). Arnold et al. (2004) reports a comprehensive campaign DAPPLE consisting of field monitoring, wind-tunnel experiments, and prediction model evaluation. The target site is a 500-m diameter area around a busy intersection in Westminster, London, UK. In the field, nine fixed stations monitored ambient NO for 27 or 19 days, and 10 mobile stations were deployed to sample concentration of released tracer gas. The authors give a brief description of a 1:200 scale wind-tunnel experiment, which is expected to be compared with field data. Uehara et al. (2007) conducted 1:300 scale wind-tunnel diffusion experiments of an area around Ikegamishincho intersection in

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Kawasaki, Japan. The concentration in the wind tunnel was compared with that monitored at a roadside permanent station. They found that the annual average concentration (not sorted into different wind directions as Schatzmann and Leitl did) was considerably smaller in the wind tunnel than in the field. We suppose that the large concentration gradient around the monitoring station for the prevailing wind directions, which were nearly parallel to the major roads, was the cause of the discrepancy. From the above review of wind-tunnel studies, we realize that availability of many long-term monitoring stations is crucial in demonstrating the validity of wind-tunnel experiments. The many stations in the DAPPLE project may have been installed for this reason, but the monitoring period might not be sufficient considering the significant scatter of individual hourly normalized concentration data over a year presented by Schatzmann and Leitl (2002). A large number of data is needed to obtain a reliable mean distribution. Also, as inferred from Uehara et al. (2007), the angle between the major roads and the prevailing wind directions is an important factor affecting the difficulty of the comparison between wind tunnel and field. If the prevailing wind is nearparallel to the road, in addition to the steep concentration gradient near the road, contribution from emission outside the wind-tunnel model, and vehicle-induced turbulence can make the comparison unnecessarily difficult. In order to mitigate the above problems, we investigate the vehicle-induced pollutant diffusion at four different urban sites at each of which four long-term monitoring stations were installed. The four stations were located at different distances from the target road with three on one side and one on the opposite side, thus representing various geometrical relations with respect to the road. The angle between the major road and the prevailing wind directions varied widely among the sites. Also, to reduce statistical uncertainty due to unknown variability in weather and traffic conditions, field monitoring data were averaged over a year. By this scheme, the wind-tunnel experiments conducted for the four sites could be put to a fairly robust test of reliability against field data. In our study, emission rate was estimated using traffic survey results. However, road traffic is highly variable, and many researchers often opted for tracer-gas release experiments with densely distributed monitoring posts even if feasible experiment periods were rather short. Emission-controlled short-term spatially dense monitoring and emission-uncontrolled long-term spatially sparse monitoring (our study) have both advantages and disadvantages. For our purpose of comparison with windtunnel experiments, however, the large ensemble of weather conditions particularly of the wind direction in the long-term monitoring was important. By tracer-gas experiments, wind direction realizations are only a few, and the effect of roadside structures cannot be evaluated comprehensively. As will be shown in Section 4.4, the sampling period of a year resulted in

so many samples in most wind directions that 95% confidence intervals of the annual mean became quite narrow. Section 2 describes the target sites and the emission estimation method. Section 3 explains the wind-tunnel experiment method. The wind-tunnel and field monitoring data are compared in Section 4. Conclusion is drawn in Section 5.

2. Field monitoring and emission estimation In the following text, subscript w is used to denote the windtunnel variables and f the field ones except for the cases where the distinction is obvious from the context. A long-term monitoring campaign was conducted as a part of an epidemiological study (SORA project standing for Study On Respiratory disease and Automobile exhaust) by the Japanese Ministry of Environment. In Japanese urban areas, 10 sites along major roads with estimated high diesel exhaust particle emission were selected. At each site, four monitoring stations were installed; three stations on one side of the main road at approximately 5, 35 and 70 m from the curb, and another on the other side at about 150 m from the opposite curb. The stations are called Stns.1, 2, 3, and 4 in the order of increasing distance from the main road. At about 1.5 m from the ground, traffic-related air pollutants including NOx, PM2:5 , and black carbon were measured continuously from 2005 to 2009. NOx was measured by the chemiluminescence method. Hourly averaged concentrations were recorded in data loggers and collected for analysis. More details of field measurements can be found in Naser et al. (2009). The field observation had two purposes: (1) to obtain actual concentration and capture the long-term trend, which was essential for the epidemiological aspect of the project, and (2) to provide validation data for wind-tunnel or numerical simulation, which had to be employed to estimate concentration at more than 10,000 health-survey object locations. If the sole purpose were the latter, controlled emission experiments with receptor points at much higher spatial density (Yoshikawa et al., 2003a,b; Arnold et al., 2004) would be a viable option. Such experiments, however, could feasibly be conducted only for a short duration, at most several runs of hour-long measurements, and the weather condition cannot be chosen arbitrarily. In long-term observations, on the other hand, it is extremely difficult to increase the monitoring points because ideal siting inevitably falls on private residences where regular maintenance of monitoring equipment is almost impossible. Even our spatial density, 3–4 in 600-m diameter, is probably an unprecedented one for long-term monitoring. In the wind-tunnel study, four sites with nearly straight roads and without nearby major intersections were chosen. These requirements were necessary for accurate vehicle emission estimation described later. Table 1 lists the geometrical features of

Table 1 Features of the studied sites. See Section 4.1 for the explanation of z0,f and df . Site

Hachimanyama

Itaka

Kadoma

Ashiya

Prefecture Latitudea Longitudea Surface road Express way Noise barrier

Tokyo 351390 53:900 1391360 49:200 Loop 8 – – 0.24 10.0

Aichi 351100 38:300 137100 7:400 Route 302 Higashimeihan (underground)

Hyougo 341430 47:400 1351180 54:800 Route 43 Hanshin (elevated)

0.22 8.9

Osaka 341440 33:600 1351340 55:900 Osaka central loop Kinki (elevated) – 0.44 7.7

0.74 7.4

0.87 3.6

0.40 5.4

0.83 5.7

lp H f (m) z0,f (m) df (m) a

Values at Stn.1.

J

J

0.35 9.2

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Fig. 1. Plan view of the studied sites: Hachimanyama (a), Itaka (b), Kadoma (c), and Ashiya (d). Top is north. Gray tone indicates the building height in stories larger than 3. The thick lines near the center lines are the locations of the emission source pipes. The intermittent lines along the roadside in Itaka (b) and Ashiya (d) are noise barriers. Filled circles represent the field monitoring stations. Circled numbers indicate the location of velocity profile measurement.

Fig. 2. Vertical sections of the studied sites. The panels correspond to those in Fig. 1. The sections are normal to the main roads and through Stn.1 (crossed boxes). The horizontal flame width is 70 m. Structures not coinciding with the section plane (e.g., columns of elevated roads) are drawn by dotted lines. Trees or bushes are drawn only when they extend over a substantial distance along the roadside.

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Fig. 3. Wind direction frequency in percentage at nearby air-monitoring stations. The dashed lines indicate the approximate direction of the major roads in front of Stn.1. The panels correspond to those in Fig. 1.

the four sites: Hachimanyama, Itaka, Kadoma, and Ashiya. The plan area ratio

lp ¼

ðbuilding plan areaÞ ðdomain areaÞ

ð1Þ

had typical urban values from 0.22 to 0.44 (cf. Grimmond and Oke, 1999). The average height H f of the buildings (weighed by the base area) was 7.7–10.0, i.e., about three-story high. Hachimanyama, a Tokyo residential area, had relatively tall apartments on the roadside; Itaka, located in the outskirts of central Nagoya, had many unused small lots; Kadoma, with many residences of workers at nearby factories, had densely packed one-story houses; Ashiya, a residential area between Kobe and Osaka, had small parks on the roadside. Fig. 1 shows the plan views of the region modeled in the windtunnel study. The black circles represent the field monitoring stations. For Itaka, Stn.4 could not be included in the wind-tunnel model because its inclusion would have also included a region where a highway junction in the south of the model area had a significant effect on the concentration field. Building heights (numbers of stories) are shown by gray tone. For buildings taller than four stories, the numbers of stories are indicated. Fig. 2 shows the vertical sections through Stn.1 (crossed boxes). The figure panels are oriented such that Stn.1 is on the right of the road. Notable features are as follows. Hachimanyama (a) had considerable stretch of dense roadside trees. Itaka (b) and Ashiya (d) had 5-m-tall ground-based noise barriers along the curb. In Fig. 2, the noise barriers are indicated as ‘NB’ and in Fig. 1,

by intermittent gray lines beside the roads. The road structure ranges from single surface (Hachimanyama), to surface and elevated (Kadoma and Ashiya) to surface and underground (Itaka). For Itaka, the underground road (Higashimeihan Expressway) is covered by a semi-open structure called ‘louver’. Meteorological conditions were obtained from nearby localgovernment air monitoring stations and weather observatories. Wind direction and wind speed were measured as 10-min averages prior to the hour, and we use these averages as that of the entire hour including the 10-min period. Typical wind speed was 2–3 m s  1 for all the sites. Wind direction frequency in the year 2006 is shown in Fig. 3. The atmospheric stability was classified by the Pasquill–Gifford system. We adopted the classification method of the Nuclear Safety Commission of Japan, which is based on insolation in the daytime, cloud cover at night, and wind speed at 10 m from the ground (Japan Environment Agency, 2000). Because the wind-tunnel experiments were conducted in isothermal air, only field data under neutral (class D)1 atmospheric stability were used

1 The atmospheric stability is classified as D if the wind speed U (m s  1) at height 10 m from the ground, the solar insolation I (kW m  2), and the cloud coverage S (parts per 10) satisfy one of the following conditions: (daytime) U o 3 and 0:01 r I o 0:3 or 3 r U o 4 and 0:04 r I o 0:3 or 4r U o 6 and I o 0:3 or U Z 6 and I o 0:6; (nighttime) U Z 4 and 0 r S r 4 or U Z 3 and 5 r S r 10 or 8r S r 10 for any U. Nighttime definition is an abridged one. See Japan Environment Agency (2000) for the detailed definition.

I. Kanda et al. / J. Wind Eng. Ind. Aerodyn. 99 (2011) 1227–1242

for analysis. For all the sites, class D was the most frequent ð  50%Þ stability class. Emission rate from the main roads was estimated as follows. It was assumed that the emission rate along the roads inside the model area is uniform and the emission from other small roads is negligible. The emission rate Q L,f of NOx per road length was calculated by X Ei ðVÞNi , ð2Þ Q L,f ¼ i

where i denotes the type of vehicles (sedan, bus, small truck, and normal truck), Ni is the vehicle flow rate, and Ei ðVÞ is the emission factor as a function of the vehicle speed V. Flow rate N i was obtained by on-site 24-h traffic counting on a weekday and a weekend. Table 2 P lists the total flow rate i N i and large-vehicle ratio at each site. For the emission factor Ei ðVÞ, we adopted the values determined in Naser et al. (2009) for the year 2006 in Japan. Table 3 lists the coefficients for the functional form of Ei ðVÞ. The vehicle speed V was calculated by 8 V ðg r gf Þ, > > > f > < V p V f ð3Þ V ¼ g g ðggf Þ þV f ðgf o g o gp Þ, > > p f > > : Vp ðg Z gp Þ,

Table 2 Traffic volume and truck ratio on the studied roads. Site

Road

P

Hachimanyama Itaka

Loop 8 Route 302 Higashimeihan Osaka central loop Kinki Route 43 Hanshin

78 33 40 102 70 66 94

Kadoma Ashiya

a b

i Ni

a

1231

where g is the degree of congestion (traffic flow rate divided by the road capacity), and p and f stand for ‘peak’ (most congested) and ‘free’ (not congested). The ‘peak’ values V p and gp were taken from the traffic census in 2005 administered by the Japanese government. For ‘free’ values, we assumed gf ¼ 0:5 and V f ¼ ðspeed limitÞ. Note that because the model areas included one or two signals where idling and acceleration enhance emission rate considerably, the above uniform Q L and V f as the speed limit are not accurate. It was, however, decided that the above crude definition is sufficient because non-uniform Q L is difficult to be simulated in the wind-tunnel experiment and because the immediate neighborhoods of the monitoring stations were away from the signals.

3. Wind-tunnel experiments Diffusion experiments were conducted in the Atmospheric Diffusion Wind Tunnel (closed-circuit type) at National Institute for Environmental Studies of Japan (Ogawa et al., 1981). The dimension of the test section is 2.6 m wide, 2 m high, and 24 m long. Fig. 4 shows the side view of the test section. City models of 1:300 scale and 2 m diameter were placed on a turn-table whose center was 13.5 m from the test section entrance. The model ground was elevated by 11–12 mm from the wind-tunnel floor. The approach flow turbulence was generated by a castellated fence at the test-section entrance, five Counihan-type spires (Counihan, 1969), and 50 mm-wide, 20 mm-high and 1 mm-thick

Large-vehicleb ratio (%) 23 11 19 20 19 25 36

Number of all types of vehicles in thousands per day on a weekday. Normal trucks, buses and special-purpose vehicles.

Table 3 Coefficients for the emission factor Ei ðV Þ ¼ a þ bV þ cV 2 þ d=V. Units of Ei and V are 1

m2 s1 and km h

, respectively.

Vehicle type

a

b

c

d

Sedan Bus Small truck Normal truck

9.2252E  12 5.8619E  10 5.9889E  11 6.2938E  10

 5.1698E  14  5.6332E  12  3.5842E  13  5.9992E  12

1.3575E  15 4.2972E  14 3.0073E  15 4.6196E  14

1.8932E  10 4.2737E  09 7.0584E  10 4.6180E  09

Fig. 5. Vertical velocity profile of the approach flow. The thick curve is the log-law profile with z^ w,0 ¼ 2 mm and d^ w ¼ 3 mm.

Fig. 4. Side view of the wind-tunnel experiment setup. The aspect ratio is not to scale.

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Fig. 6. Normalized velocity fluctuation spectra of the approach flow: alongwind (a) and crosswind (b) components. The dashed curves represent the Kaimal formulas (5) and (6).

roughness elements separated by 50 mm spanwise and 200 mm windward and arranged in a staggered formation on the windtunnel floor. The vertical profile of the approach flow was measured by a laser Doppler anemometer (Dantec Dynamics). The sampling frequency was 100–1000 Hz. Fig. 5 shows the result for the entrance wind speed U 1 ¼ 1:5 m s1 used in all the cases. The boundary layer thickness was 0.7–0.8 m. In Fig. 5, the origin of the vertical coordinate z is on the wind-tunnel floor. The thick line in Fig. 5 is an optimal fit to the log-law profile U¼

u^ n

k

ln

zd^ w , z^ 0,w

ð4Þ

where k ¼ 0:4 is the von Karman constant, the roughness length z^ 0,w is 2 mm, the zero-plane displacement d^ w is 3 mm, and the friction velocity u^ n is 0.106 m s  1. The values of z^ 0,w , d^ w , and u^ n were determined by the Nelder–Mead simplex method using the data in 20 rzðmmÞ r 300. The Reynolds number ReH based on the mean building height H w ¼ H f =300 and the wind speed at z ¼ H w was rather low  1300. However, the roughness Reynolds number Ren ¼ u^ n z^ w,0 =n ¼ 14, where n is the kinematic viscosity of air, satisfies the criterion Ren 4125 (Robins, 2003) for fully rough turbulent boundary layer. A further discussion will be given in Section 4.4 on the choice of the relatively low U 1 . Fig. 6 shows the windward and crosswind spectra of the velocity fluctuations of the approach flow in the boundary layer ðz ¼ 90 mmÞ. The horizontal axis represents the scaled frequency f ¼ nz=U where n (s  1) is the dimensional frequency. The dashed curves are the Kaimal spectra (Kaimal and Finnigan, 1994) fSu ðf Þ 2 u^ n

fSv ðf Þ 2 u^ n

¼

¼

102f ð1 þ 33f Þ5=3

,

17f ð1 þ 9:5f Þ5=3

ð5Þ

:

ð6Þ

The windward component Su agrees well with Eq. (5), whereas the crosswind component Sv is smaller than Eq. (6) in the low-frequency range. The smaller low-frequency Sv in the wind tunnel is due to the lack of crosswind fluctuations at scales larger than the width of the test section. A cut-off frequency f c ¼ ðz=UÞðU=2:6Þ ¼ 0:035 appropriate for the wind-tunnel width 2.6 m agrees well with the frequency where the measured spectrum deviates from Eq. (6). In a field dimension, say z ¼ 10 m and U¼2 m s  1, the cut-off frequency nc becomes 0.007 s  1. Therefore, as has been stated by previous authors (e.g., Schatzmann and Leitl, 2002), the wind-tunnel results represent relatively short-term average of 2–3 min in the field.

For diffusion experiments, tracer gas (ethane diluted by nitrogen) was emitted from small holes on aluminum source pipes placed along the roads. The concentration field C w was measured by a 12-channel flame ionization detector (FID; Kimoto Electric Co., Ltd.) calibrated by standard gas. The air at sampling points was sucked through stainless and Teflon tubings (about 10 m long) to the FID channels. The concentration of ethane detected as electric current of carbon ion was sampled every second until the accumulated mean value became almost constant. Typical averaging time was 3–4 min. Note that, due to mixing of air inside the tubings, the sampled concentration is different from the instantaneous value at the sampling points. Hence, the measured concentration is the result of turbulent diffusion by the velocity fluctuations of virtually the whole spectral range in the wind tunnel. As discussed in the previous paragraph, the wind-tunnel experiments averaged over such sufficiently long times correspond to 2–3 min average in the field. Therefore, the wind-tunnel results can be regarded as due to wind without meandering, whose spectral power resides mostly in the tens-of-minute range. As will be explained in Section 4.4, the real-scale hourly average concentration is estimated by considering wind-direction meandering observed in the field. The measurement was done in point and grid modes. In both cases, the background concentration was measured upwind of the model and was subtracted from the concentration in the model. In the point mode, sampling tubes were drawn to 3–4 points on the model where field monitoring stations were located. In the grid mode, an 11-channel sampling rake made of 3 mm O.D. and 470 mm length stainless pipes at 120 mm horizontal interval was used. The sampling rake was held parallel to the main road and was traversed in the normal direction to the road. At z¼ 6 mm (hereinafter, the origin of z is on the model ground), the concentration was measured at six different distances on each side of the main road. The measured points are shown in Fig. 12. Additionally at 300 and 600 mm from the main road, the concentration was measured at z ¼(40,80) and (80,100) mm, respectively. It was inevitable that the rake pipe interfered with the buildings on the model for the grid-mode measurements. In such cases, holes were drilled into the buildings. In the result figures (Fig. 12), the holed points are indicated by cross markers. Wind direction was varied by rotating the turn-table. For the point mode, 16-point compass directions (NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, NNW, N) were tested. For the grid mode, every other 16-point compass directions avoiding the directions in which the sampling rake span became nearly parallel to the wind were chosen because in such directions the air flow near the rake tended to follow the rake span direction, deviating from the given upwind direction.

I. Kanda et al. / J. Wind Eng. Ind. Aerodyn. 99 (2011) 1227–1242

Separate diffusion experiments were conducted for surface roads and elevated/underground highways. For Hachimanyama, source pipes were placed along the center line of Loop 8. For the surface-road experiments for other sites, the source pipes were placed along the center of each direction roads, i.e., two source lines for each site, because the different direction roads were separated by pillars (Kadoma, Ashiya) or louvers (Itaka) of the highways. Same amount of tracer gas was emitted from the two source lines because the estimated emission rates from the road vehicles on the surface roads had similar magnitudes with similar diurnal variations for the different directions. For the highway experiments of Itaka, the source pipes were placed on the ground along the center of the road because NOx, the target species in this study, from the underground highway was expected to pass through the louver without loss of mass. Two types of source pipes were used: one (type A) had dualnesting structure with both the inner and the outer pipes having holes of diameter 0.5 mm at 10 mm interval, whose openings were adjusted by pasting aluminum tapes such that the emission was uniform along the pipe, and another (type B) was made of simple pipes with 0.2 mm diameter holes at 10 mm interval. The outer diameter of the type A and B pipes were 8 and 6 mm, respectively. The pipe diameters (1.8–2.4 m at real scale) equals roughly to the average vehicle height. The type A pipes of length 1 m each were used for straight roads, and the type B for curved roads by combining 200 mm-long straight segments. For a 2-m road spanning the model diameter, two 1 m pipes were placed with the closed ends head-on at the model center, and the tracer gas was introduced from the two open ends at the model perimeter. The gas flow rate was 400 cm3 min  1 for each pipe, and the ethane dilution rate was chosen between 12% and 20% such that the concentration fell in the dynamic range of the detector. The pipes were elevated by 1 mm from the model ground and the holes were directed downward. The 2-mm diameter difference between type A and B pipes was presumed to have negligible effect on initial diffusion behavior. Fig. 7 shows the crosswind concentration profile at z ¼5 mm and downwind distance 100 mm from a 1-m long type B pipe laid perpendicular to the wind direction and supplied with 12% ethane gas. The wind condition was U 1 ¼ 1:5 m s1 , and the tripping fence, the spires and the roughness elements were removed to minimize vertical and horizontal diffusion. Otherwise, any nonuniformity of emission would have been smoothed out by diffusion. The downwind distance 100 mm was chosen as an appropriate one where the positioning errors of the apparatus (the sampling probes, the emission pipes, and the wind-tunnel floor) were sufficiently smaller than the vertical plume width ð  5 mmÞ, and also the crosswind plume width ð  10 mmÞ of a point-source

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emission was sufficiently small compared with the interval between the emission holes. Here, these vertical and crosswind plume widths (conventional Gaussian definition) were determined by a separate diffusion experiment from a point source with the same upwind condition. Fig. 7 indicates that uniformity of the emission is fairly good except near the edges. Uniformity tests for the type A pipes were conducted in a less stringent upwind condition (same as in the city-model diffusion experiments), and the uniformity was found as good as in Fig. 7. Note that, because emission characteristics of the pipes change with time due to rust or ware of adhesive, emission uniformity tests were conducted immediately before the city-model diffusion experiments. The experiments (Hachimanyama and Ashiya) using the type A pipes were conducted several months before those (Itaka and Kadoma) using the type B pipes, and the more stringent test adopted for the type B pipes could not be repeated for the type A pipes under the same pipe condition when the corresponding city-model experiments were conducted.

4. Results 4.1. Vertical velocity profiles The vertical velocity profiles over the city models were measured by the laser Doppler anemometer at the three points indicated by circled numbers in Fig. 1. The wind directions were chosen such that the roads were approximately perpendicular to the wind: W for Hachimanyama, E for Itaka, WSW for Kadoma, and NNW for Ashiya. Fig. 8 shows the results. The thick lines in Fig. 8 indicate the log-law profiles with the roughness length z0,w ¼ z0,f =300 and the zero-plane displacement dw ¼ df =300, where z0,f and df were calculated by a morphometric method using the average building density and height (MacDonald et al., 1998) (see Table 1 for the calculated field values z0,f and df ). Note that the model-scale zero-plane displacement dw is substantially larger than the value d^ w ¼ 3 mm of the approach flow. This issue will be discussed in the next subsection. The friction velocity un was determined from the measured velocity at z ¼ 200 mm assuming the log-law profile U ¼ ðun =kÞ lnððzdw Þ=z0,w Þ. In Fig. 8, the log-law profiles are drawn in the range 2H w rz r 0:25dw recommended by Bottema (1997), where H w ¼ H f =300 and dw ¼ 0:8 m is the approximate thickness of the boundary layer. We observe that the log-law profiles based on the parameters determined solely by the geometrical information agree well with the measured profiles. Below z  2H w , there are considerable variations of U depending on the configuration of the buildings. A notable one is the velocity defect downwind of the tall buildings at the third point (Fig. 8(b) square markers) for Itaka. 4.2. Comparison with roughness-only cases

Fig. 7. Crosswind concentration profile downwind of a source pipe. Five 200 mm type-B pipe segments are connected to form an 1-m emission source line.

We examine the appropriateness of the approach flow. As mentioned in Section 4.1, the zero-plane displacement of the approach flow is much smaller than those over the city models. Also, the vertical offset 11–12 mm of the model ground from the wind-tunnel floor is of concern. These discontinuities give rise to a suspicion that there is a substantial change in the boundarylayer properties at the upwind edge of the city models and hence the diffusion occurs in an internal boundary layer capped by a considerably different outer layer. Here, we shall regard the approach flow appropriate if the concentration fields in the presence of the city models are not much different from those when the models are replaced by the roughness elements of the same type as in the upwind region.

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Fig. 8. Vertical velocity profiles at three selected points on the city models. Correspondence between the point numbers and the figure markers are 1,circle; 2,triangle; and 3,square. The panels correspond to those in Fig. 1. The thick curves represent log-law profiles with zf ,0 =300 and df =300 determined by the morphometric method of MacDonald et al. (1998) (see Table 1). The dash-dot lines indicate the mean building height H w .

Fig. 9. Concentration fields in the vertical plane at 600 mm downwind of the model center line. (a) Ashiya (Fig. 1(d)) with SE wind direction, (b) prediction from the pointsource diffusion experiment with 20 mm high roughness elements (H2), and (c) same with 40 mm high roughness elements (H4).

For this examination, a separate diffusion experiment was conducted without the city models and with the roughness elements laid in place. Tracer gas was emitted from a point source on the wind-tunnel floor. The concentration field C 0 due

to the point source was fitted by a Gaussian form ! aðxÞ y2 z2 exp  C0 ¼  , sy ðxÞsz ðxÞ 2sy ðxÞ2 2sz ðxÞ2

ð7Þ

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where x is the downwind distance from the source, y the crosswind coordinate with the origin at the source point, sy ðxÞ and sz ðxÞ the crosswind and vertical plume widths, respectively, and aðxÞ is a parameter with the dimension of emission rate divided by velocity. The concentration cw from an infinitesimal line source segment of length dl on the ground, which can be regarded as a point source, was estimated by cw ¼

Q L,w C 0 dl, Q0

ð8Þ

where Q 0 is the emission rate in the point-source experiments and Q L,w the emission rate per length in the city model experiments. By integrating Eq. (8) along the length of the sources using the point-source experiment result (7), the concentration C w by the line sources could be obtained. Note that integration had to be done numerically because some ‘line’ sources were curved as shown in Fig. 1. Fig. 9 shows the normalized concentration field (C w =Q L,w ) at 600 mm (farthest grid measurement line) downwind of the model center line for the surface-road experiments of Ashiya with the SE wind direction. The horizontal axis ym is the coordinate along the road with the origin at the model center. In addition to the concentration with the 20-mm-high roughness elements (H2), concentration field was predicted based on a point-source experiment with 40-mm-high roughness elements (H4). The concentration for the H2 roughness-only case (Fig. 9(b)) is much closer to that with the city model (Fig. 9(a)) than that with the H4 roughness-only case (Fig. 9(c)). Similar results were obtained for other wind directions and city models. Although the vertical diffusion by the H2 roughness is a little more efficient than the city models, the difference may be regarded insignificant. Hence, we adopted the H2 roughness. Here, we comment on the vertical diffusion by the approach flow. Fig. 10 shows the real-scale sz ðxÞ for the H2 roughness elements. Plotted together are the Briggs (1973) formula for urban roughness under Pasquill–Gifford stability class D, and the Pasquill–Gifford–Smith values (Smith and Singer, 1965) for z0,f ¼ 0:3 and 1 m under stability class C. The class C values rather than the class D of the Pasquill–Gifford–Smith are plotted merely because the class C values are closer to the Briggs’ class D values. We observe that wind-tunnel experiments correspond

Fig. 10. Growth of the vertical plume width sz with downwind distance x. Circle marks are the experimental results with the roughness elements of height 20 mm. Dotted curve indicates the Briggs formula for urban area under the stability class D. Solid and dashed curves are the Pasquill–Gifford–Smith values for zf ,0 ¼ 0:3 and 1 m, respectively, for the stability class C.

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approximately to classes C–D. Hence, the approach flow is suited for neutral stability condition. 4.3. Concentration field at z¼6 mm We first examine the sensitivity to vertical position. Highresolution vertical profiles were measured at the immediate downwind roadside for the east wind direction of Kadoma. Fig. 11 shows the result. We observe that the concentration is insensitive to z near the ground up to about half the mean building height. The near-ground vertical gradient of concentration becomes smaller further downwind as the tracer gas is diffused vertically. This is caused by vigorous mixing in the urban-canopy layer and wake turbulence downwind of source pipes. Because z ¼6 mm, corresponding to 1.8 m in the real scale, is much smaller than 0:5H w (see Table 1), the following results are little affected by errors in vertical positioning of the sampling tubes. Fig. 12 shows the normalized concentration contours (C w =Q L,w ) at z ¼6 mm in the surface-road experiments for wind directions for which the field monitoring stations Stns.1, 2, and 3 were on the downwind side of the road. Notable features are described below. In the following, terms upwind and downwind are used in reference to the wind direction of the approach flow regardless of the local wind direction. The concentration contours exhibit fairly complex structures compared to the simple road-parallel contours produced by conventional Gaussian-plume prediction models. Particularly noteworthy is that the high or low concentration near the roads affects the region further downwind of the roads. Roadside tall buildings, which are also wide, have considerable effect on the concentration field. When tall buildings are located upwind of the source, the recirculation wake flow on the downwind side of the buildings advects the tracer gas in the upwind direction. Contour bulges on the upwind side of the roads for Itaka and Kadoma are such examples. When tall buildings are located downwind of the source, the recirculation flow on the upwind side of the buildings sweeps the tracer gas in the upwind direction. The low concentration upwind of the 15-story apartment in Hachimanyama is a typical example. When there are no obstacles for this upwind sweep flow, the tracer gas is advected to the upwind side of the roads. The contour

Fig. 11. Vertical profiles of concentration at the immediate downwind roadside for the east wind direction of Kadoma. To avoid congestion, profiles at every other sampling points are shown.

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Fig. 12. Contours of C w =Q L,w at z ¼ 6 mm. The contours are drawn at the powers of 2. The dots are the grid measurement points; the cross markers indicate that the points are inside the holes drilled into buildings. The arrows on the perimeter of the models indicate the wind direction. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

bulge on the upwind side for Hachimanyama is created primarily by this effect. Tall buildings downwind of the roads have blocking effects. Because tracer gas is diluted in the highly turbulent vicinity of such buildings, there are low-concentration region on the downwind side. The green areas on the downwind side of tall buildings for Hachimanyama and Itaka are typical examples. Noise barriers are also effective in blocking the tracer gas. When the wind is from the road to noise barriers, the upward deflection of air flow on the windward side raises the effective emission height and the wake turbulence on the leeward side enhances mixing. In a field monitoring along a highway, Bowker et al. (2007) found decrease in the concentration of ultra-fine particulate matter as the receptor point moved from open to barrier-blocked roadside. Our results indicate that noise barriers are effective only when they are sufficiently continuous. For Itaka where a noise barrier is continuous for more than 300 m on the west side of the roads, the concentration near the roads is distinctly lower than that for other city models. In contrast, for Ashiya where the noise barriers are intermittent, the roadside concentration is comparable to the city models without noise barriers. The loss of the blocking effect is caused by intrusion of the tracer gas through the openings and entrainment into the barrier wake. 4.4. Comparison with field monitoring data The wind-tunnel and the field measurement results are compared in terms of a normalized concentration defined as follows.

Denoting the road type by j (e.g., j ¼ ‘surface’ or ‘elevated’ for Ashiya), the wind-tunnel results can be normalized as C n,w,j ¼

C w,j H w U r,w , Q L,w,j

ð9Þ

where the subscript w denotes wind tunnel values, and U r,w is the wind speed at z¼50 mm (15 m in the real scale). This height was chosen because the wind speed is relatively insensitive to the building configuration at this height (see Fig. 8) and because 15 m is a typical height of anemometers on air-monitoring stations. The field concentration C~f predicted from the wind-tunnel experiments becomes C~f ¼

1 X C n,w,j Q L,f,j , H f U r,f j

ð10Þ

where the subscript f denotes field values, and the field wind speed U r,f was calculated from the field monitoring value assuming the log-law profile with zf,0 and df in Table 1. Note that C~f is the contribution from the main roads. Dividing both the sides of P Eq. (10) by the total emission rate j Q L,f,j , we obtain P C~f H f U r,f j C n,w,j Q L,f,j P P ¼ , ð11Þ j Q L,f,j j Q L,f,j which has an appropriate form for a normalized concentration. Replacing C~f by the field measurement data C f minus the background concentration C b measured at nearby local-government air monitoring stations sufficiently away from busy roads, the left-hand side of Eq. (11) becomes the field normalized

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concentration C n,f ¼

ðC f C b ÞH f U r,f P : j Q L,f,j

ð12Þ

Using Eq. (9), the right-hand side of (11) becomes the windtunnel normalized concentration Q L,f,j H w U r,w X C n,w ¼ P C w,j : Q Q L,w,j j L,f,j j

ð13Þ

As mentioned in Section 3, wind-tunnel results correspond to 2–3-min averages in the field. To convert to 1-h averages measured in the field, we need to account for wind-direction fluctuations sy . Davies and Thomson (1999) determined from their and other authors’ observations that, for 1-h average, vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 12 u0 u  2 u B C u s B 0:8 C þ 0:5 , ð14Þ sy  v ¼ u t@ z A Uf Uf ln z0,f where sv is the crosswind velocity fluctuation. Note that Eq. (14) implies that sv tends to a constant 0.5 m s  1 as the wind speed U f becomes small. Inserting our typical values z0,f ¼ 0:7 m, z ¼ 15 m and U f ¼ 2 m s1 , we find sy ¼ 211, i.e., approximately the interval between adjacent 16-point compass directions. Assuming a Gaussian distribution of the wind-direction fluctuation, 1-h average concentration can be obtained approximately by the following weighted average: C^ n,w,k ¼ 14ðC n,w,k1 þ2C n,w,k þC n,w,k þ 1 Þ,

ð15Þ

where k denotes a 16-point compass direction. The emission rate Q f,j was estimated by the method explained in Section 2 for weekdays and weekends. Fig. 13 shows the weekday emission rates for NOx. At all the studied sites, the

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daytime Q f,j was considerably larger than the night-time value. We also observe asynchronous behaviors of surface-road and highway emissions, which necessitates separate wind-tunnel experiments for different roads. Normalized concentrations of NOx were calculated for the hourly periods in the year 2006 under neutral atmospheric stability (Pasquill–Gifford stability class D) and 1r U r,f ðm s1 Þ r 10. Lowwind-speed conditions U r,f o1 m s1 were excluded because the magnitude of velocity fluctuations sv relative to U r,f increases rapidly as U r,f becomes small (see Eq. (14)) and diffusion is more enhanced than under stronger wind conditions where sv =U r,f is almost constant, a phenomenon our wind-tunnel experiments did not simulate. High-wind-speed conditions U r,f 4 10 m s1 were excluded because the harsh weather could affect driving behavior. In this analysis, choice of the monitoring stations for the background concentration C b is important. In Eq. (12), it is assumed that C f C b is the contribution only from the target roads modeled in the wind-tunnel experiments. In many previous studies, it was often assumed that any station distant from major emission sources including the target roads but as close as possible to the concerned roadside qualifies for a background station. However, validity of this assumption has not been examined quantitatively because there was no way of knowing the true background concentration at the roadside sites. In our study, by the arrangement of the roadside stations, this assumption could be examined partially though not completely, and the best station for C b could be chosen objectively from multiple candidates (there are about five non-roadside monitoring stations 2 per 100 km in the neighborhood of the studies sites). If the wind is nearly perpendicular to the target road and is from Stn.4 to the other stations, then the concentration at Stn.4 can be regarded as the background value at the site. On the contrary, if the roadnormal wind is from Stns.1–3 to Stn.4, the concentration at Stn.3 is the most appropriate background value because Stns.1 and

Fig. 13. Diurnal variations of the emission rate per length of NOx on weekdays.

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Fig. 14. Correlation of NOx concentration values between the chosen background stations and Stn.4 when the wind was nearly normal to the target roads and from Stn.4 to the roads. The names inside the parentheses after C b are those of the background stations.

2 could be in the region where eddies on the windward walls of large buildings could transport pollutant in the upwind direction (see Section 4.3). The best background stations were chosen as the ones that had the highest correlated concentration with Stn.3 or 4 when the wind direction was nearly perpendicular to the target roads. Fig. 14 shows comparison of concentration values at the chosen background stations and at Stn.4 when the wind was nearly normal to the target roads and from Stn.4 to the roads. Similarly good correlation was obtained with Stn.3 for the opposite wind directions. Large deviations from the perfect correlation are considered to be caused by sporadic high emissions due, for example, to passages of ill-maintained vehicles, which are infrequent in Japan but do occur. Figs. 15–18 compare the annual averages of C^ n,w and C n,f for each 16-point compass direction at the field monitoring stations inside the wind-tunnel model areas. The annual averages were calculated from the hourly values described above. The field value C n,f is subject to uncertainties in weather and traffic conditions. The error bars in Figs. 15–18 represent 95% confidence intervals of the mean values. We observe that the large number of samples in the one-year monitoring period results in fairly narrow confidence intervals. Although large discrepancies exist in some cases, overall agreement between C^ n,w and C n,f is fairly good. Some aspects are discussed below. For Hachimanyama, accurate reproduction of the roadside bushes was critical in achieving good agreement at Stns.2 and 3. In a preliminary experiment where the model bushes were much

sparser than the real ones, the resulting C^ n,w was about double the values in Fig. 15(b) and (c). The poor comparison at Stn.1 for ESE–S are also considered to be caused by inaccurate reproduction of roadside plants. As found for the noise barriers of Itaka in Section 4.3, detailed geometries of roadside features had surprisingly large effects on the concentration field. A recent paper by Gromke and Ruck (2009) also report considerable effect of tree porosity in street canyons. The overall agreement between wind-tunnel and field results indicate that the initial mixing by moving traffic was well simulated by the source pipes of diameter 6–8 mm with the emission holes directed downward. The success is attributable to the fact that the pipe diameter is about the mean vehicle height at the real scale, and that the downward emission assured entrainment of the tracer gas into the turbulent flow field around the pipes, reproducing vigorous mixing by moving vehicles. However, considering the continuing efforts to understand mixing by vehicles (Kastner-Klein et al., 2000; Kanda et al., 2006; Dong and Chan, 2006) and the above-mentioned sensitivity to roadside features, our method may not be valid when applied to model scales other than 1:300 because the nature of the turbulence around the pipes and the jet flow from the pipe holes is likely to be different. Possible causes for the large discrepancies in some cases of Figs. 15–18 are (1) errors in emission estimation along the main roads, (2) unaccounted emission sources other than the main roads, (3) non-negligible emission sources near the background

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Fig. 15. Normalized concentration averaged over the year 2006 in Hachimanyama: Stn.1 (a), Stn.2 (b), Stn.3 (c), and Stn.4 (d). Wind-tunnel predictions (C^ n,w , triangles) and field monitoring values (C n,f , circles). Numbers above the top border of panel (a) show the numbers of samples. The error bars on C n,f indicate 95% confidence intervals.

Fig. 16. Normalized concentration averaged over the year 2006 in Itaka: Stn.1 (a), Stn.2 (b), and Stn.3 (c). See explanations in Fig. 15.

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Fig. 17. Normalized concentration averaged over the year 2006 in Kadoma: Stn.1 (a), Stn.2 (b), Stn.3 (c), and Stn.4 (d). See explanations in Fig. 15.

station, (4) inaccuracy in the model geometry, and (5) insufficient dynamical similarity between wind-tunnel experiments and the real-scale phenomena. These factors are discussed below. (1) Although the model areas are away from major intersections, the model areas spanning 600 m inevitably include traffic signals. Near traffic signals, especially in the region where vehicles accelerate from a stopped state, emission rate is enhanced significantly. An estimate based on chassis-dynamometer emission measurements and microscopic traffic simulations shows that, at a busy intersection, the emission rate of NOx is enhanced with a peak at the stop line of about 3–5 multiples of that in the off-signal region and a width of about 100 m (Yoshikawa et al., 2003b). The signals are at least 150 m away from Stn.1, but the enhanced emission around the signals may affect the concentration at the monitoring stations when the wind is near-parallel to the roads. (2) Emission sources other than the main roads include room heating by burning fossil fuels, burning of fallen leaves or vehicles on side streets. These emission sources contribute to the background concentration C b . Side-street traffic, for example, constitutes almost 40% of the total traffic in terms of travel distance (NIES, 2008). Our analysis assumes that these sources are uniformly distributed and the emission rate is much smaller than that from the target roads. However, at the spatial resolution of a few tens of meters treated in this study, these sources can have significant local effects. (3) Located in urban area, background stations are also close to roads although the traffic volume on such roads is much smaller than that on the trunk roads studied in this paper. Hence, at Stn.4 where the concentration contribution C f C b from main roads is at most 11% of the background concentration C b , even small

vehicle-originated emission near background stations can greatly affect the normalized concentration C n,f . The poor comparisons at Stn.4 in Figs. 15–18 are probably for this reason. (4) The city models were constructed with extreme care based on the maps and the photographs of all the individual buildings inside the target area. There were, however, still overlooked temporary elements such as deciduous trees, billboards or parked vehicles. Note that leaves on trees were simulated by sponge foam, and those on deciduous trees were made to have intermediate appearance between summer and winter. These elements were initially considered unimportant for diffusion behavior, but in view of the sensitivity to the roadside bush density in Hachimanyama mentioned above, they will have to be modeled properly if more prediction accuracy are required. (5) The wind speed U 1 ¼ 1:5 m s1 satisfies the roughness Reynolds number criterion suggested by Robins (2003), but it is rather small for the turbulent flow around bluff bodies to be independent of the Reynolds number ReH based on the obstacle height. Castro and Robins (1977) reported ReH 4 4000 is necessary for independence, later research found ReH Z ð23Þ  104 is more appropriate, and Lim et al. (2007) demonstrated that Reynolds-number dependence exists for some quantities even if ReH Zð23Þ  104 . Our experiments, however, has only ReH  1300. The low wind speed was chosen because we plan to extend our work to non-neutral stability conditions as was done by Uehara et al. (2007). Our wind tunnel can produce vertical temperature difference of up to 100 1C. To achieve dynamical similarity with the real-scale phenomena, the Froude number pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Fr ¼ U= Hg DT=T , where DT is the temperature difference, g the gravitational acceleration, and T a reference temperature, needs to be matched. However, for DT t 100 1C and with the size of our wind

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Fig. 18. Normalized concentration averaged over the year 2006 in Ashiya: Stn.1 (a), Stn.2 (b), Stn.3 (c), and Stn.4 (d). See explanations in Fig. 15.

tunnel, rather low wind speed is required to achieve Fr comparable to distinctly stable or unstable conditions in the real atmosphere. U 1 ¼ 1:5 m s1 is a compromise between the two opposing requirements of increasing the Reynolds number and decreasing the Froude number. Fortunately, it has been shown by many authors (e.g., Uehara et al., 2003) that flow separation from obstacle surfaces is suppressed if the roughness Reynolds number of the approach flow is sufficiently large ð \ 5:4Þ, and then ReH becomes less important than when the same obstacles are placed on a smooth open surface. This should be the case with our study treating built-up urban areas. In fact, in aseparate study (to be published) with a sparse uniform urban canopy ðlp ¼ 11% and H ¼ 48 mmÞ, we found that the normalized concentration at z ¼ 5 mm and about 6H downwind of a ground point source with U 1 ¼ 1:5 m s1 was about 10% lower than with U 1 ¼ 3:5 or 6.0 m s  1 (which resulted in almost the same concentration) although the difference decayed rapidly with height. This 10% difference is unlikely to be spatially uniform because dependence on U 1 should vary with locations relative to sources and obstacles. However, in view of potentially larger influences of other factors mentioned above, the Reynolds number dependence can be regarded a minor problem.

5. Conclusion Wind-tunnel simulations of diffusion of vehicle-originated pollutants from major roads were conducted for four Japanese urban sites. The measurements were done in point and grid modes. In the point mode, the measured concentration was compared with the field monitoring data at 3–4 stations at each site. For the annual average of NOx in the year 2006, the overall agreement was fairly good despite various sources of errors in

background concentration, emission estimation, and model geometry. Having established good correlation with field data in the point mode, the grid-mode results with accurately known emission rate, approach-flow condition, and model geometry provide useful database for evaluating obstacle-resolving numerical prediction models.

Acknowledgments The work was conducted as a part of the SORA project administered by the Japanese Ministry of Environment. The authors would like to thank Mr. S. Kagoshima for the construction of the city models.

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