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2 Environ Model Assess (28) 13: DOI 1.17/s Flow and Pollutant Dispersion in Street Canyons using and ADMS-Urban S. Di Sabatino R. Buccolieri B. Pulvirenti R. E. Britter Received: 7 January 26 / Accepted: 5 May 27 / Published online: 7 July 27 Springer Science + Business Media B.V. 27 Abstract This paper is devoted to the study of flow within a small building arrangement and pollutant dispersion in street canyons starting from the simplest case of dispersion from a simple traffic source. Flow results from the commercial computational fluid dynamics (CFD) code are validated against wind tunnel data (CEDVAL). Dispersion results from are analysed using the well-validated atmospheric dispersion model ADMS-Urban. The k ɛ turbulence model and the advection-diffusion (AD) method are used for the CFD simulations. Sensitivity of dispersion results to wind direction within street canyons of aspect ratio equal to 1 is investigated. The analysis shows that the CFD model well reproduces the wind tunnel flow measurements and compares adequately with ADMS-Urban dispersion predictions for a simple traffic source by using a slightly modified k ɛ model. It is found that a Schmidt number of.4 is the most appropriate number for the simulation of a simple traffic source and in street canyons except for the case when the wind direction is perpendicular to the street canyon axis. For this last case a Schmidt number equal S. Di Sabatino (B) R. Buccolieri Dipartimento di Scienza dei Materiali, University of Lecce, Lecce, Italy silvana.disabatino@unile.it B. Pulvirenti Dipartimento di Ingegneria Energetica, Nucleare e del Controllo Ambientale, University of Bologna, Bologna, Italy R. E. Britter Department of Engineering, University of Cambridge, Cambridge, UK to.4 gives the best agreement with ADMS-Urban. Overall the modified k ɛ turbulence model may be accurate for the simulation of pollutant dispersion in street canyons provided that an appropriate choice for coefficients in the turbulence model and the Schmidt number in the diffusion model are made. Keywords Street canyons Dispersion Modelling ADMS-Urban 1 Introduction Air quality in urban areas is of great importance given its direct implications for the health of people living in those areas. Urban building arrangements, in particular the width of streets, their orientation, spacing and the presence of intersections are major factors in the determination of pollution dispersion at street level. Irregular shape buildings contribute to enhance turbulence and vertical mixing in the atmosphere, while W/H ratios (with W the width and H the height) of urban canyons affect street ventilation. More generally, buildings modify the flow field, influencing air exchanges and the dispersion of pollutants. In urban areas, in particular, pollution may be the result of emissions from traffic, domestic heating and cooling systems, industry or toxic agents accidentally and/or deliberately released into the atmosphere. Traditionally, information about concentrations is obtained using wind-tunnel experiments, which provide an opportunity to examine the effects of various parameters individually or in a controlled combination. For example, Park et al. [18] characterized the dispersion of vehicle emissions by conducting wind tunnel tests and

3 37 S. Di Sabatino et al. applying tracer gas techniques. They concluded that the dispersion of vehicle emission in street canyons is primarily affected by microscale climatic factors such as the W/H ratio. Recently, computational fluid dynamics (CFD) has become an attractive tool to predict concentration fields near buildings. Many works can be found in the literature reporting on the use of computational fluid dynamics (CFD) techniques to model flow and pollutant dispersion around isolated buildings or groups of buildings [7]. However, their use to calculate gas dispersion in urban areas is still limited to isolated cases and to a limited number of sources. The Reynolds averaged Navier Stokes equation (RANS) methods are amongst the most preferable procedures used to model urban dispersion problems, mainly due to their relatively inexpensive computational costs. Comparisons of the RANS results with wind tunnel data show that significant errors could occur in the prediction of concentrations. These errors could come from any inappropriate choice of the turbulence model, numerical schemes, grid resolution and so on [24]. Recently, Xie et al. [25] investigated the influence of building geometry on pollutant dispersion comparing different k ɛ models with wind tunnel measured data for the optimization of turbulence models. Their comparison showed that the standard k ɛ model [15] was the most optimum choice. On the other hand, integral flow and dispersion models are still the most used tools for the study of pollutant dispersion as they are able to account for a large number of emission sources at the same time; a result particularly useful to assess air quality in urban areas. The objective of this study is to compare CFD numerical simulations of flow and pollution concentration fields with predictions from an integral Gaussiantype dispersion model. This work is also an attempt to highlight advantages and disadvantages of two different types of modelling for pollutant concentration predictions in a real urban environment. We chose two commercial codes the CFD code, [9] and the quasi-gaussian atmospheric dispersion modelling system (ADMS-Urban) [5]. This choice is justified by their extensive use in Europe and in the world. In particular, is the world s most widely used commercial CFD code for a wide range of industrial flow applications. The number of industries that have benefited from using it continues to expand. has been used to successfully simulate the atmospheric boundary layer [21] but, at present there are not sufficient validation studies concerning its use for the predictions of pollutants in urban areas [11]. ADMS-Urban is being widely used for calculating gas dispersion into the atmosphere over scales of up to about 5 km from releases from various sources. The model is able to account for a large number and type of emission sources typically found in urban areas. Several validation studies (see for instance [12] and[3]) have shown that, especially over flat terrain, the predictions are consistent with experimental data. The study proposed here builds on and extends previous works based on and ADMS-Urban model verification and analysis. In particular, a comparison between and ADMS3 (the industrial version of ADMS-Urban) for atmospheric dispersion modelling from a single point source was analysed by Riddle et al. [21], who found a good agreement using the turbulence Reynolds stress model (RSM) [14] and the Lagrangian particle (LP) method. Their study remarks that CFD simulations are not an appropriate alternative to a model such as ADMS for routine atmospheric dispersion studies because of the large run times and the complexity of the setup. More recently, Di Sabatino et al. [8] extended the study to the comparison between ADMS- Urban and in an isolated street canyon. They obtained similar results using a slightly modified k ɛ turbulence model and the advection diffusion (AD) method which, together, are computationally less expensive than other available models. In this paper we use the three dimensional computational fluid dynamics model with both the standard and a modified k ɛ turbulence scheme to examine if the use of a general purpose CFD code can be a practical tool for studying air quality in urban areas. 2 Model Availability and General Description is a state-of-the-art computer program for modelling fluid flow and heat transfer in complex geometries. The code is available from national fluent vendors for both academic and public institutions by acquiring an annual renewable licence. It solves the governing conservation equations of fluid dynamics by a finite-volume formulation on a structured, nonorthogonal, curvilinear coordinate grid system using a collocated variable arrangement. Three different spatial discretization schemes may be used, that is powerlaw, second-order upwind, and QUICK, a bounded third-order accurate method. Temporal discretization is achieved by a second-order, implicit Euler scheme. Pressure/Velocity coupling is achieved by the SIM- PLE algorithm resulting in a set of algebraic equations which are solved using a line-by-line tridiagonal matrix algorithm, accelerated by an additive-correction type

4 Flow and pollutant dispersion in street canyons 371 of multigrid method and block-correction. Additional equation solvers are also available to the user. models turbulent flows with the standard k ɛ model, an RNG model, and a second-moment closure or Reynolds-stress model (RSM) [9]. ADMS-Urban is a PC based air quality management system of dispersion in the atmosphere of passive, buoyant or slightly dense, continuous or finite durations releases from single or multiple sources. The model is available on request directly from the main developer Cambridge Environmental Research Consultants or from national vendors. Both annual and permanent licences are available. The model uses an up-todate parameterisation of the boundary layer structure based on the Monin Obukhov length and the boundary layer height. Concentration distributions are Gaussian in stable and neutral conditions, but the vertical distributions is non-gaussian in convective conditions to take account of the skewed structure of the vertical component of the turbulence. A range of modules allow for the effects of plume rise, complex terrain, street canyons, noise barriers and buildings. The street canyon module is based on the largely used OSPM model [13] which considers a trapezoidal recirculation area whose dimensions depend upon the street canyon aspect ratio and wind speed above the street canyon top. Therefore the recirculation area is somehow prescribed in models such as ADMS-Urban and this aspect should be kept in mind when comparing results with a CFD code. The parameters for the wind field and the Gaussian plume model are calculated by a meteorological pre-processor. The OSPM model has been extensively tested against measurements at several street locations. Examples of comparison of model results with measurements are given in [2] and[13]. 3 Methodology In this work concentration predictions from a traffic source (modelled as a line source with cross-wind dimension) placed within a regular urban street canyon are investigated. It is recognised that the modelling of a traffic source is straightforward in operational models of the type of ADMS-Urban but it is not with general purpose engineering-type CFD models. In this study we document each step undertaken to achieve the final goal in with the aim of assessing the validity of results and giving recommendation for the use of commercial generic CFD codes for the prediction of inert pollutant concentrations in street canyons. Separate simulations are performed to study the flow before proceeding with the modelling of pollutant dispersion. At first we simulate the simplest atmospheric neutral boundary layer flow over a surface with a specified surface roughness length. After, we investigate the best model setup for the simulation of small building arrangements and compare flow results with available wind tunnel data (CEDVAL) [6] to obtain confidence in the CFD simulations. Finally, we study dispersion by modelling a finite traffic source in the atmospheric neutral boundary layer without buildings and after a finite traffic source in a single street canyon for various wind directions. ADMS-Urban is used to provide benchmark data for the study as it is routinely validated against monitoring data (see for instance [4]) and has undergone several validation studies with good datasets (see for instance [12]). 3.1 ADMS-Urban General Flow and Dispersion Setup Initially, ADMS-Urban is run to calculate the boundary layer velocity profiles to use them as the upstream boundary conditions in. The velocity (or wind) profile in a neutrally stratified atmosphere is calculated through the logarithmic law: U(z) = u κ ln z + z (1) z where U(z) is the average wind speed at the height z above the ground, z is the surface roughness, u is the friction velocity and κ is the von Karman s constant. As in [21] a neutral boundary layer of height 8 mis initially used, with a wind speed of 5 ms 1 (at a height of 1 m above the ground) and a surface roughness length of.1 m. The simulation of gas dispersion from a traffic source is done using a line source which is 1 m long and 1 m wide. As an example, a release of CO is chosen with an emission rate of 1 g/s. A similar emission rate is also used for the simulation of a finite traffic source in a street canyon with aspect ratio (W/H) equal to 1, with W = H = 2 m. In this case the traffic source has dimensions of 1 2 m. Street canyon aspect ratios from 1 to.5 correspond to those for which a skimming flow regime [16] develops. This is the most common flow regime found in most European city centres and American cities [19]. 3.2 General Flow Setup The computational domain used to simulate the neutral atmospheric boundary layer is built using both irregular tetrahedral and hexahedral grids with finer resolutions close to the ground. The overall number of computational cells used is of the order of one million for most

5 372 S. Di Sabatino et al. cases. The bottom surface (i.e. ground) is specified as a rigid plane with a specified surface roughness. All simulations are carried out by using a simple velocity inlet condition (specifying velocity, turbulent kinetic energy k (TKE) and turbulent dissipation rate ɛ profiles), by specifying an operating pressure at a point in the middle of the flow domain and by using outflow conditions at the outlet i.e. the mass flow rate is the same as the inlet. All simulations are carried out as steady state solutions of the Navier Stokes equations and for the conservation of mass species. Second order discretisation schemes are used to increase the accuracy and reduce numerical diffusion. The specific methods are second order upwinding [1] for pressure, momentum, k and ɛ, the SIMPLE scheme is used for the pressure-velocity coupling. uses an iterative method to solve the algebraic system of equations. Starting from an initial guess the flow variables are recalculated in every iteration until each equation is solved up to an user specified error. The termination criterion is usually based on the residuals of the corresponding equations. Scaling of the residuals is usually done with the residuals after the first iteration. A termination criterion of 1 5 is used. Overall, a simulation took about 8 hours on double processors of an OPTERON machine. To get steady state solutions for each case considered in this paper, residuals reached the chosen level and were stopped after about 1 iterations. As certain quantities reached convergence at a different rate than other quantities, we checked that flow values remained unchanged with respect to the number of iterations. To quantify the influence of the grid resolution on the solution a grid convergence study is made. For this, different systematically and substantially refined grids are used. The grid refinement ratio is a minimum of 1.1 to allow the discretization error to be differentiated from other error sources (iterative convergence errors, computer round-off etc.) [22]. At the inlet, we use a velocity profile equal to the wind speed profile used in ADMS-Urban (Eq. 1) while turbulent kinetic energy and dissipation rate profiles are specified as follows: ( k = u 2 1 z ) (2) Cμ δ and ɛ = u 3 κz ( 1 z ) δ (3) where δ is the atmospheric boundary layer depth and C μ is a coefficient used to define the eddy viscosity in k ɛ models [15]. Before proceeding with full three-dimensional simulations as will be required when considering a portion of an urban area or street canyons, various simulations are performed using a three-dimensional (3D) flow domain to simulate the simplest atmospheric boundary layer flow over a surface with a specified surface roughness length. A value of C μ equal to.13 is used as suggested by Richards and Hoxey [2] to avoid high near-ground turbulence levels for flow over flat rough surfaces which are typically overestimated in CFD codes. Also this different C μ value overcome the general difficulty due to the decay of turbulence kinetic energy with the distance downstream often encountered in CFD atmospheric neutral boundary layer with the standard k ɛ model. As a consequence of the alteration of the C μ value, also σ ɛ (the turbulent dissipation rate Prandtl number) is adjusted to 3.22, in order to satisfy the transport equation for turbulent kinetic energy and turbulent dissipation rate in the k ɛ model. There are some uncertainties regarding the exact values of the simulated surface roughness due to s formulation for surface roughness. This relies on the use of an equivalent sand grain roughness, and a somewhat arbitrary roughness constant C s between and 1. Some tests are also performed with different grid sizes to find the optimum value for K s which is taken to be equal to 2, a value which is somewhere in the middle between the value of 1 and 32.6 found in literature ([23]). The size of the computational domain is 5, by 5, minthe horizontal and 8 m in the vertical. Figure 1 shows the velocity profiles obtained at different positions downwind of the inlet. From the figure it can be seen that the logarithmic velocity profile is maintained very well Inlet velocity Velocity at 2 m Velocity at 5 m u [m s 1 ] Fig. 1 Velocity profiles obtained with at different positions downwind of the inlet

6 Flow and pollutant dispersion in street canyons k [m 2 s 2 ] Inlet k k at 2 m k at 5 m Inlet ε ε at 2 m ε at 5 m tend to become smaller along the domain near ground but they tend to increase above a height of 5 m. In the region near the ground (up to 1 m), the average TKE values reduce to approximately 75%, while at the ground they reduce to approximately 85% of the inlet values at distance of 5, m. This reduction over this large distance is much smaller than that found with previous CFD calculations using the standard k ɛ model as reported in [21]. Also this reduction in TKE values can be considered acceptable in the context of the modelling we are doing. As before, a check on the performance of the TKE along the entire domain should be done before proceeding with further calculations. The turbulent dissipation rate is maintained very well throughout the length of the computational domain. Based on those results the k ɛ model with the modifications already mentioned is adopted as the main turbulent model ε [m 2 s ] Fig. 2 k (Top) and ɛ (bottom) profiles obtained with at different positions downwind of the inlet throughout the length of the computational domain. This check should be done before doing any dispersion calculations from a single point or line source in the neutral atmospheric boundary as it likely affects the final concentration at the ground. Figure 2 shows the predicted k (top) and ɛ (bottom) profiles generated by the k ɛ model at various positions downwind of the inlet. The predicted TKE levels 3.3 Small Building Arrangement Flow Setup Several grids are tested for the different simulations performed in this study. For the comparison of FLU- ENT results with CEDVAL, a geometry of four square shaped rings of model buildings forming an intersection with an offset in one of the two lanes (two buildings are equipped with a slanted roof (45 o roof angle) is used. The geometry studied is shown in Fig. 3, while a part of the domain and the grid used for simulations are shown in Fig. 4. After performing a test to verify that the solution was domain independent and grid shape and size independent, a structured grid with refined elements near the Fig. 3 Sketch of the geometry of the CEDVAL experiment

7 374 S. Di Sabatino et al. where D CO is the diffusion coefficient for CO in the k mixture, μ t = ρc 2 μ is the turbulent viscosity, Y ɛ CO is the mass fraction of CO, ρ is the mixture density. In Eq. 4, Sc t = μ t /(ρ D t ) is the turbulent Schmidt number, where D t is the turbulent diffusivity. 3.5 Simple Traffic Source Dispersion Setup Fig. 4 Domain and grid used in to simulate the CED- VAL experiment ground, inside the intersection, is chosen. The refinement is shown in Fig. 4. The smallest dimensions of the elements near the ground and inside the intersection are equal to.5 m while they are about 2 m outside the intersection. The overall number of computational cells used is of the order of The bottom surface (i.e. ground) is specified as a rigid plane with a specified surface roughness, as for the simulations carried out in the previous section. Also the velocity inlet profile and turbulent kinetic energy and dissipation rate profiles are specified as in the previous section. 3.4 General Dispersion Setup Various models are available in to model dispersion of airborne material. In this study only the advection diffusion (AD) module is used. In turbulent flows, computes the mass diffusion which satisfies the conservation of mass as follows: ( J CO = ρ D CO + μ t Sc t ) Y CO (4) At first we study the case of CO dispersion from a simple traffic source i.e. a traffic source placed in the neutral atmospheric boundary layer without buildings in order to assess the validity of dispersion results. Again as in [21] a reduced computational domain size of 1, by 5 m in the horizontal and 15 minthe vertical is used for the gas dispersion simulations. The same inlet CO mass flow rate of 1 g/s set in ADMS- Urban model is used in. The reduced domain size allowed us to further refine the grid near the release and downstream of it especially in those regions where the plume is evolving. The grid elements are about.25 m in size near the traffic source, and grow with a ratio of 1.2. The traffic source is positioned perpendicular to the wind direction at 1 m downwind of the inlet section. The overall number of computational cells used is of the order of one million. The traffic source is simulated by separating a volume in the geometry at the required discharge position and by setting a CO source term (g/m 3 -s) for this volume. The area forming the base of this volume has the same dimensions of the traffic source set in ADMS-Urban, while the height of the volume is set to.5 m. Several tests were performed before choosing this value by verifying the independency of results from the specific choice of the height of the volume source. It was found that any height between.5 and 2 m would not influence the final concentration results. 3.6 Street Canyon Dispersion Setup We consider the case of CO dispersion from a traffic source placed in a street canyon of height H and width W. The aspect ratio of the street canyon is kept constant, W/H = 1, for all cases considered in this section. The street canyon geometry is built by placing at a distance of 1 m from the inlet two identical blocks each being 1 m long 1 mwideand2 mhigh.the same traffic source discussed in the previous section is used here. All simulations for this case use a domain and grid characteristics similar to those used to simulate the simple traffic source and the CEDVAL experiments.

8 Flow and pollutant dispersion in street canyons Results and Discussion 4.1 Comparison with CEDVAL Dataset Figures 5 and 6 show velocity vectors on two vertical planes at y/h = 2 and y/h = 2 respectively (refer to Fig. 3). The plane y/h = 2 cuts the first lane before the intersection. Figure 5 shows that results are in good agreement with CEDVAL measurements. In particular, we observe the same flow pattern inside and outside the street canyon. Measurements shows that a small recirculation region forms just behind the first slanted roof. This is also predicted by. We also observe that both measurements and numerical simulations suggest that the slanted roof before the lane deviates airflow and no vortex is observed within the street canyon. The second plane y/h = 2 cuts the second lane after the intersection. In this case the downwind roof, taller than the upwind building acts as an obstacle to airflow and the vortex inside the street canyon is enhanced as shown in Fig. 6. Thisisalso shown by the numerical simulations. Fig. 6 Velocity vectors from CEDVAL measurements (top)and results (bottom)aty/h = 2 plane Figure 7 shows the comparison between and CEDVAL dimensionless velocity profiles versus dimensionless height z/h. Profiles are obtained along the same y/h = 2 and y/h = 2 planes in the middle of the street canyon. This figure shows that in the case of the presence of a vortex (corresponding to the plane y/h = 2) there is a very good agreement between results and CEDVAL measurements. In the other case the CFD model slightly underestimates the velocity within the canyon near the ground. Velocity underestimations have been already found when using the k ɛ model to simulate flow in presence of buildings with roofs [17]. However it should be noted that in the framework of this work the most interesting case is when the vortex forms within the street canyon. This is the case when the k ɛ model performs best. 4.2 Simple Traffic Source Fig. 5 Velocity vectors from CEDVAL measurements (top)and results (bottom)aty/h = 2 plane From [21] it is known that dispersion spread using the algebraic Reynolds stress model (RSM) [1]

9 376 S. Di Sabatino et al. z/h z/h CEDVAL u/u ref CEDVAL y/h= _ 2 y/h=2.4 _ u/u ref Fig. 7 Velocity profiles from CEDVAL measurements (solid line) and results (cross symbols) at the y/h = 2 plane (top)andatthey/h = 2 plane (bottom) for a high level point source is smaller than that predicted by ADMS-Urban. We found that this is also the case for both the point source and the simple traffic source using the k ɛ model. Therefore we performed some sensitivity tests for the choice of the most suitable Schmidt number in order to artificially increase the plume dispersion. From the sensitivity test the value of.4 was the most appropriate one. We used this for all subsequent dispersion simulations except for the street canyon simulation with the wind perpendicular to it. For this case the best choice for the Schmidt number was.4 as will be discussed later. Figure 8 shows the comparison between and ADMS-Urban CO concentrations for the case of a simple traffic source. The profiles are plotted as a function of x both at ground level and at z = 1 m. Figure shows that there is a good agreement between and ADMS- Urban results at ground level for x < 3 m, while for x > 3 m CO concentrations are about 2 times higher than ADMS-Urban ones. A similar observation can be made for results at z = 1 m. maximum concentration value is the same as ADMS- Urban even though its position is about 7 mcloserto the source. CO concentrations both at ground level and at z = 1 m tend to the same asymptotic value at large distance from the source. The same behaviour is shown by ADMS-Urban results even if asymptotic value is higher than the ADMS-Urban one. Figure 9 shows the CO contour plots obtained by (left) and ADMS-Urban (right) on two horizontal planes, at ground level (a) and at z = 1 m (c), and on a vertical plane parallel to the wind flow direction (b). The ADMS-Urban prediction of the vertical and horizontal spread (Fig. 9) reachesawidthof5 m at a downwind distance of about 3 m whereas the width of the predicted plume is 35 matthe same position. Despite the diminished Schmidt number the plume spread is still smaller than that predicted by ADMS-Urban both in the horizontal and in the vertical. However, the discrepancy between the two models is much smaller than that reported by [21] for which the discrepancy between the models was about 7%. Also it should be pointed out that we obtained those results by using both a k ɛ and AD model which typically run much faster than a combination of an RSM model and a Lagrangian particle tracking (LP) model as used by [21]. 4.3 Traffic Source in a Street Canyon In this section dispersion from a traffic source placed within a street canyon is studied for different wind directions. At first we consider the case of wind direction perpendicular to the orientation of the street canyon. This is the case for which a full bi-dimensional flow ground level ground level z=1 m z=1 m x [m] Fig. 8 ADMS-Urban and CO concentration profiles as a function of x coordinate, downwind of the source

10 Flow and pollutant dispersion in street canyons 377 Fig. 9 CO concentration contour plots from (left) and ADMS-Urban (right) at horizontal planes at ground level (a), at z = 1 m (c) and on a vertical plane parallel to the wind direction (b) a b c vortex is expected to form. Figure 1 shows the CO contour plot on a vertical plane in the middle of the street canyon, parallel to the wind direction. We can observe that the vortex occupies most of the street canyon. Figure 11 shows the comparison between and ADMS-Urban concentration predictions. CO concentrations are plotted as a function of z along the three vertical lines shown in Fig. 1,respectively at x = 5, x = and x = 5 m. These positions are chosen as they represent, for our case, the leeward side, the middle of the canyon and windward side. Analysis of flow and concentrations in those positions is expected to give information on the main physical mechanism occurring in a street canyon. Looking at Fig. 11 we observe that overall CO concentration predictions are larger than those obtained with ADMS- Urban. In particular, at z = 1 m predictions are about 2 times larger than ADMS-Urban concentrations. However, predictions at the lee side at z = 1 m are about 44% larger than those at the windward side whereas ADMS-Urban predicts concentrations on the lee side are about 33% larger than those Fig. 1 CO concentration contour plot within the street canyon. Vertical lines indicate three positions at the lee side (x = 5 m), in the middle of the street canyon (x = m) and at the windward side (x = 5 m)

11 378 S. Di Sabatino et al x 1 4 x= m x 1 4 x=5 m x= _ 5 m x 1 4 Fig. 11 CO concentration profiles from ADMS-Urban and FLU- ENT for the case of wind direction perpendicular to the street canyon axis. Profiles refers to the lee side (top), middle of the street canyon (middle) and windward side (bottom) at the other side of the canyon. This difference can be considered acceptable given the different nature of the two models. Within a canyon both the size of the region occupied by the vortex and momentum and scalar exchange rate between the inside and the outside region of the street canyon are very important in dispersion calculations. For the skimming flow regime pollutants are trapped within the street canyon as in this condition the exchange rate is very small. In the mean velocity within the street canyon is about ten times smaller than velocity above it. The vortex occupies the whole region within the canyon, as shown in Fig. 12. ADMS-Urban assumes a recirculation zone depending only on the street canyon aspect ratio and the wind velocity in the region. In there is no prescription of what the recirculation zone should be and the vortex is the results of the specified flow boundary conditions. results for this case are obtained using a very small Schmidt number that is.4. This was done to increase diffusion within the street canyon. concentration predictions using a Schmidt number of.4 are about 1 times larger than those predicted by ADMS-Urban. We then analyse the case of wind direction forming an angle of 45 o with respect to the axis of the street canyon. Figure 13 shows CO concentration profiles as a function of z along the same three vertical lines shown in Fig. 1. These results are obtained with a Schmidt number equal to.4. predictions are smaller than those obtained with ADMS-Urban. In particular, at z = 1 m predictions are about two times lower than ADMS-Urban concentrations. In this case the vortex within the street canyon is stretched along the street canyon by the non zero wind component parallel to the street canyon as shown by the velocity vectors plotted in Fig. 14. The plot refers to the horizontal plane at z = 5 m. Different arrangements Fig. 12 Vortex inside the street canyon

12 Flow and pollutant dispersion in street canyons x 1 4 x= m x 1 4 x=5 m x= _ 5 m x 1 4 Fig. 13 CO concentration profiles from ADMS-Urban and FLU- ENT for case with wind direction at 45 o with respect to the street canyon axis. Profiles correspond to x = 5 m(top), x = m (middle) andx = 5 m of buildings should be analysed in order to further investigate these differences between these results and those obtained above for the case of wind direction perpendicular to the axis of the street canyon. We also study the case for which the wind direction is parallel to the axis of the street canyon. Figure 15 shows CO concentration profiles as a function of z along the same three vertical lines shown in Fig. 1. In this case the flow is parallel to the axis of the street canyon. No vortex is expected to form within the street canyon and this is confirmed by results. Figure 15 shows that CO concentrations decrease almost exponentially as the height increases and becomes negligible at the top of the street canyon. This is a further confirmation of the fact that material is transported and diffused along the street canyon. As in the case of wind direction oblique to street canyon axis, at z = 1 m predictions are about two times lower than ADMS-Urban concentrations. To better compare results with those obtained with ADMS-Urban we have spatially averaged the former over planes (yz) parallel to the street canyon axis. This is most probably a useful way of comparing results from a CFD model and those calculated with a box model such as the street canyon module in ADMS-Urban. Table 1 shows the comparison between averaged FLU- ENT concentrations with the constant concentration values predicted by ADMS-Urban inside the street canyon for the three wind directions discussed earlier. From Table 1 we can conclude that the averaged values obtained by are in good agreement with ADMS-Urban predictions. In many cases, FLU- ENT simulations give more information about the CO distribution within the street canyon, and these details could help in making assumptions about flow and Fig. 14 Velocity vectors in the vicinity of the street canyon entrance

13 38 S. Di Sabatino et al x 1 4 x= m x 1 4 x=5 m x= _ 5 m x 1 4 Fig. 15 CO concentration profiles from ADMS-Urban and for the case with wind direction parallel to the street canyon axis. Profiles correspond to x = 5 m(top), x = m (middle) andx = 5 m dispersion processes for larger scale models. Ambient wind effect is an extensively studied problem in streetcanyon research. The most commonly met case in the literature is with the wind perpendicular to the street axis, because this is the worst situation for air pollutants to dilute and escape from street canyons. We demonstrate that for oblique (45 o ) and parallel wind directions changes in ambient wind direction could make large differences in the mean flow recirculation and hence the pollutant distributions. The investigations on the influence of wind direction presented in this paper by both CFD and integral approaches give reasonable agreement between these data sets. 5 Conclusions CFD has the potential to be an important tool for simulating gas dispersion in areas with many buildings in close proximity, even if an extensive validation is needed for each site modelled. The analysis carried out in this paper shows that using a general purpose CFD model such as for atmospheric Table 1 Averaged CO concentrations in (mg/m 3 )fromadms- Urban and x = 5 m x = x = 5 m 9 o ADMS-Urban o ADMS-Urban o ADMS-Urban concentrations have been averaged over the yz plane at three different x positions: x = 5 m (leeward), x = (middle of the canyon) and x = 5 m (windward). dispersion requires a number of considerations about the grid resolution, surface roughness, inlet conditions, discretization methods and the selection of the appropriate turbulence and dispersion models. From this study a number of considerations can be made. To successfully reproduce the atmospheric boundary layer and obtain a well maintained turbulent kinetic energy is more important to give the correct values for K s, C μ and σ ɛ, than the grid refinement. For the gas dispersion simulations, the strong dependence of the gas concentration on the velocity profile gives a good agreement of CFD results with ADMS-Urban for the simple traffic source by using a modified k ɛ model. However when using the Advection Diffusion model it is important to choose the most adequate Schmidt number which in full scale dispersion simulation in the atmosphere is much smaller than that typically used in engineering type of applications. It is found that a Schmidt number of.4 should be used for the simulation of simple traffic source and in street canyons except for the case when the wind direction is perpendicular to the street canyon axis. For this last case a Schmidt number equal to.4 gives the best agreement with ADMS-Urban. A more detailed study of the correlation between grid resolution and concentration is still needed. The turbulence k-ɛ model may be accurate for the simulation of dispersion in a street canyon provided that an appropriate choice for coefficients in the turbulence model and the Schmidt number in the diffusion model are made. The above considerations can be applied to other CFD-Gaussian type model comparison but further tests should still be made. Acknowledgements The authors kindly acknowledge CERC for making available ADMS-Urban for this study. We also thank Dr. D. Carruthers for useful discussions.

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