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A Microscale Model for Air Pollutant Dispersion Simulation in Urban Areas: Presentation of the Model and Performance over a Single Building


doi: 10.1007/s00376-015-5152-1

  • A microscale air pollutant dispersion model system is developed for emergency response purposes. The model includes a diagnostic wind field model to simulate the wind field and a random-walk air pollutant dispersion model to simulate the pollutant concentration through consideration of the influence of urban buildings. Numerical experiments are designed to evaluate the model's performance, using CEDVAL (Compilation of Experimental Data for Validation of Microscale Dispersion Models) wind tunnel experiment data, including wind fields and air pollutant dispersion around a single building. The results show that the wind model can reproduce the vortexes triggered by urban buildings and the dispersion model simulates the pollutant concentration around buildings well. Typically, the simulation errors come from the determination of the key zones around a building or building cluster. This model has the potential for multiple applications; for example, the prediction of air pollutant dispersion and the evaluation of environmental impacts in emergency situations; urban planning scenarios; and the assessment of microscale air quality in urban areas.
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  • Aumond P., V. Masson, C. Lac, B. Gauvreau, S. Dupont, and M. Berengier, 2012: Including the drag effects of canopies: Real case large-eddy simulation studies. Bound.-Layer Meteor., 146, 65- 80.10.1007/s10546-012-9758-x02469f391f51022cfba7c3e40ce6d056http%3A%2F%2Fonlinelibrary.wiley.com%2Fresolve%2Freference%2FXREF%3Fid%3D10.1007%2Fs10546-012-9758-xhttp://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1007/s10546-012-9758-xWe use the mesoscale meteorological model Meso-NH, taking the drag force of trees into account under stable, unstable and neutral conditions in a real case study. Large-eddy simulations (LES) are carried out for real orography, using a regional forcing model and including the energy and water fluxes between the surface (mostly grass with some hedges of trees) and the atmosphere calculated using a state-of-the-art soil-vegetation-atmosphere-transfer model. The formulation of the drag approach consists of adding drag terms to the momentum equation and subgrid turbulent kinetic energy dissipation, as a function of the foliage density. Its implementation in Meso-NH is validated using Advanced Regional Prediction System simulation results and measurements from Shaw and Schumann (Boundary-Layer Meteorol, 61(1):47-64, ). The simulation shows that the Meso-NH model successfully reproduces the flow within and above homogeneous covers. Then, real case studies are used in order to investigate the three different boundary layers in a LES configuration (resolution down to 2 m) over the 'Lannemezan 2005' experimental campaign. Thus, we show that the model is able to reproduce realistic flows in these particular cases and confirm that the drag force approach is more efficient than the classical roughness approach in describing the flow in the presence of vegetation at these resolutions.
    Bagal N. L., E. R. Pardyjak, and M. J. Brown, 2004a: Improved upwind cavity parameterizations for a fast response urban wind model. Proc. 84th Annual Meeting Symp. Planning, Nowcasting and Forecasting Urban Zone, Seattle, WA, USA, American Meteorological Society, 5 pp.eae1a5eee6eba8a32080f24316b2af2fhttp%3A%2F%2Fams.confex.com%2Fams%2F84Annual%2Fwebprogram%2FPaper74016.htmlhttp://ams.confex.com/ams/84Annual/webprogram/Paper74016.htmlThe QUIC (Quick Urban & Industrial Complex) dispersion modeling system has been developed to provide high-resolution wind and concentration fields in cities. The fast response 3D urban wind model QUIC-URB explicitly solves for the flow field around buildings using a suite of empirical parameterizations and mass conservation. Previous evaluations of the model against single and multiple building wind tunnel data have shown weaknesses in several of the standard parameterizations. In particular, the upwind cavity associated with the horseshoe vortex does not compare well with experimental results. The cavity size is over predicted and the velocities within the cavity are quite poorly reproduced. In this work, the upwind cavity parameterization has been modified and evaluated against wind tunnel data for several rectangular building geometries. The upwind cavity has been divided into two regions: a displacement zone where a modified power law is implemented and a front eddy region where a simple vortex parameterization is specified. The models provide significant improvement over the previous standard parameterizations.
    Bagal N. L., B. Singh, E. R. Pardyjak, and M. J. Brown, 2004b: Implementation of rooftop recirculation parameterization into the QUIC fast response urban wind model. Proc. 5th AMS Urban Environ. Symp. Conf., Vancouver, B. C., American Meteorological Society. 27 pp.76b2fa7b39a4dc0670225529846ac4d1http%3A%2F%2Fams.confex.com%2Fams%2FAFAPURBBIO%2Fwebprogram%2FPaper80326.htmlhttp://ams.confex.com/ams/AFAPURBBIO/webprogram/Paper80326.htmlThe QUIC (Quick Urban & Industrial Complex) dispersion modeling system has been developed to provide high-resolution wind and concentration fields in cities. The fast response 3D urban wind model QUIC-URB explicitly solves for the flow field around buildings using a suite of empirical parameterizations and mass conservation. The current model does not capture the rooftop recirculation region associated with flow separation from the leading edge of the building. In this work, a model for rooftop recirculation is implemented using parameters for the length, height and strength of velocities for the recirculation region, which are a function of the aspect ratio of the building. An ellipsoidal region formed by the length and height parameters which are derived from Wilson (1979), represent the rooftop recirculation region. A logarithmic profile with modifications in the first half of the total height of the recirculation region is implemented as an initial wind field in the ellipsoidal region. After mass consistency is applied, this parameterization models the rooftop velocities quite well. In addition, the capacity to incorporate the effects of varying incident wind angles on rooftop flow has been added. In off angle flows, a delta wing type vortex forms on the rooftop with a core that is not perpendicular to the incident wind angle. This vortex is specified using a parameterization based on an empirical model by Banks et. al (2000). The length and height of the vortex along the corresponding building edges are calculated from the angle formed by the vortex core and the leading edge of the building. This vortex is extended beyond the downwind building face where it is forced to diffuse into the wake region. The modified model is an improved version of the previous model as it accounts for an improved modeling of flow in the near rooftop region. The modified model when evaluated with the experimental data for various building geometry cases and the incident wind perpendicular to the building face, matches the experimental data quite well.
    Boppana V. B. L., Z. T. Xie, and I. P. Castro, 2010: Large-eddy simulation of dispersion from surface sources in arrays of obstacles. Bound.-Layer Meteor., 135, 433- 454.10.1007/s10546-010-9489-99e45c0f73d26e5cb452ef746c3b7af54http%3A%2F%2Fwww.springerlink.com%2Fcontent%2F5w53q238407h0438%2Fhttp://www.springerlink.com/content/5w53q238407h0438/Towards meeting the objective of simulating heat transfer processes in urban areas, the study of dispersion from a scalar (ground) surface area source has been addressed as a first step, since dispersion from such a source is in some ways analogous to heat transfer from the surface. Two different urban-like geometries are considered: an array with cubes of equal height and an array with random height cuboids. Some point measurement dispersion experiments in a wind tunnel have previously been carried out in identical arrays using a naphthalene sublimation technique. Large-eddy simulations (LES) of these experiments have been performed as a validation study and the details, presented here, demonstrate the influence of the roughness morphology on the dispersion processes and the power of LES for obtaining physically important scalar turbulent flux information.
    Britter R. E., S. R. Hanna, 2003: Flow and dispersion in urban areas. Annual Review of Fluid Mechanics, 35, 469- 496.10.1146/annurev.fluid.35.101101.1611478badab4b-6c97-4725-9b62-48897e914611c21cba2ab7ccc6952980049fa8ca57a2http%3A%2F%2Fwww.annualreviews.org%2Fdoi%2Fabs%2F10.1146%2Fannurev.fluid.35.101101.161147refpaperuri:(df50c8ea29faf8ab6eb8831409e67f51)http://www.annualreviews.org/doi/abs/10.1146/annurev.fluid.35.101101.161147Increasing urbanization and concern about sustainability and quality of life issues have produced considerable interest in flow and dispersion in urban areas. We address this subject at four scales: regional, city neighborhood, and street. The flow is one over and through a complex array of structures. Most of the local fluid mechanical processes are understood; how these combine and what is the most appropriate framework to study and quantify the result is less clear. Extensive and structured experimental databases have been compiled recently in several laboratories. A number of major field experiments in urban areas have been completed very recently and more are planned. These have aided understanding as well as model development and evaluation.
    Britter R. E., S. R. Hanna, G. A. Briggs, and A. Robins, 2003: Short-range vertical dispersion from a ground level source in a turbulent boundary layer. Atmos. Environ., 37, 3885- 3894.10.1016/S1352-2310(03)00299-1249059c5ba4efc361556ee52a8ec7edbhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231003002991http://med.wanfangdata.com.cn/viewHTMLEn/PeriodicalPaper_JJ0210812815.aspxThe short-range vertical dispersion of a passive containment in a neutrally stratified turbulent boundary layer can be usefully characterised by a number of approaches, such as a vertical diffusivity, K z , a vertical Gaussian dispersion parameter, σ z , and an entrainment velocity, w e . σ z and w e are approximately related by w e ∝d σ z /d t . While the Gaussian model is widely used in most air quality applications, the entrainment velocity is widely used in wind tunnel studies and models for the dispersion of hazardous gases. This paper summarises experiments in three simulated rough-wall atmospheric boundary layers (two in the US and one in the UK), which lead to the conclusion that for passive releases the entrainment coefficient α in the formula ja:math is α =0.65±0.05. This value is consistent with a recent analysis of the Prairie Grass data (out to x =100m, where vertical profiles were obtained) that gave α =0.63±0.05. Somewhat surprisingly we were not able to locate a theoretical analysis that could reproduce these results without recourse to some empirical input.
    Cai X. M., 2000: Dispersion of a passive plume in an idealised urban convective boundary layer: A large-eddy simulation. Atmos. Environ., 34, 61- 72.10.1016/S1352-2310(99)00299-X898cce5cff37dc8301a0cc46cc36e3d3http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS135223109900299Xhttp://www.sciencedirect.com/science/article/pii/S135223109900299XThis large-eddy simulation study investigates the effects of an idealised surface inhomogeneity of sensible heat flux on dispersion of a passive plume emitted from elevated sources into an urban convective boundary layer (UCBL). The results show that when Deardorff's translation is made to introduce a wind with its direction aligned with the centreline of park blocks, dispersion of such a passive plume is strongly affected by turbulent structure associated with the patchy pattern of surface heat flux. In comparison with a case with a homogeneous surface, when the point source is located above the central line across the park areas, the mean plume heightis lower, the vertical dispersal parameteris smaller, the surface concentrationis generally higher, and pollutant is less dispersed in the vertical direction. When the point source is aligned with the built-up area, however, the opposite situation occurs. The difference in surface concentration at a same downwind distance can be as large as 100% among the cases over a homogeneous surface, the location above built-up surface and the location above park surface in the idealised urban area. The extent to which the patchy pattern of surface heat flux influences the plume dispersion depends on two parameters:/and/, whereis the distance between the centres of two adjacent parks,is the width of built-up area,is the size of park, andis the UCBL height. This study suggests that dispersion of a passive plume in a UCBL has different behaviour from that for a homogeneous surface in the previous studies. Estimate of pollutant concentration by the existing methods or models that are based on observations over a homogeneous surface cannot distinguish such differences revealed by the results in this paper.
    Cai X. M., M. Nasrullah, and Y. Huang, 2004: Fumigation of pollutants into a growing convective boundary layer over an inhomogeneous surface: A large eddy simulation. Atmos. Environ., 38, 3605- 3616.10.1016/j.atmosenv.2004.03.0494f80fb5b2b3ae9e926fe57e9a365555dhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231004003358http://www.sciencedirect.com/science/article/pii/S1352231004003358The effects of surface inhomogeneity of sensible heat flux on fumigation have been investigated by large-eddy simulation (LES) in the present study. The surface inhomogeneity consists of regularly aligned squares of park area surrounded by built-up area, the two types of surface having different values of sensible heat flux. The dynamics of such a CBL, named the urban CBL (UCBL), and the effects on dispersion of plumes initially placed inside the UCBL have been examined by Cai (Q. J. Royal Meteor. Soc. 125 (1999) 1427) and Cai (Atmos. Env. 34 (2000) 61) respectively. The present study delivers the following major findings. (i) The results of mean plume height and ground-level concentration (GLC) over the two landuse types for a fumigation case areto those for an “in-UCBL” dispersion case studied in Cai (Atmos. Env. 34 (2000) 61). In other words, for a fumigation case mean plume height is lower and GLC is higher over the built-up area (in comparison with those over the park area), whereas for an “in-UCBL” dispersion case mean plume height is higher and GLC is lower over the built-up area. (ii) In general, the two quantities have larger fluctuations over the built-up area than those over the park area. The above characteristics in (i) and (ii) are the consequence of stronger turbulence over the built-up area than that over the park area. (iii) The length scale of surface patchy pattern,, is the most effective surface parameter that affects the contrast of several variables (mean plume height, GLC, and dispersal parameters) between the two landuse types. (iv) For the same, the case with a larger value of skewness of surface pattern has a larger contrast of these variables between two landuse types during the early phase of fumigation.
    Castelli S. T., T. G. Reisin, 2011: Application of a modified version of RAMS model to simulate the flow and turbulence in the presence of buildings: The MUST COST732 exercise. International Journal of Environment and Pollution, 44, 394- 402.10.1504/IJEP.2011.038441ca186320a61cf32dcdbdeb28b922808ahttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F252764793_APPLICATION_OF_A_MODIFIED_VERSION_OF_RAMS_MODEL_TO_SIMULATE_THE_FLOW_AND_TURBULENCE_IN_PRESENCE_OF_BUILDINGS_THE_MUST_COST732_EXERCISEhttp://www.researchgate.net/publication/252764793_APPLICATION_OF_A_MODIFIED_VERSION_OF_RAMS_MODEL_TO_SIMULATE_THE_FLOW_AND_TURBULENCE_IN_PRESENCE_OF_BUILDINGS_THE_MUST_COST732_EXERCISEABSTRACT A modified version of the atmospheric model RAMS6.0 is used to simulate the flow in the MUST experiment, within the framework of the COST732 Action. A standard version of the k-turbulence closure model and its renormalisation group version, RNG k-蔚, were implemented in the model. Simulations were performed to test the suitability of using the model to reproduce the flow and turbulence in the presence of buildings in a complex configuration. Wind speed and turbulent kinetic energy profiles from measurements and simulations with different turbulence schemes were compared. Preliminary results emphasise the difference between the different turbulence schemes.
    Chung T. N. H., C. H. Liu, 2013: On the mechanism of air pollutant removal in two-dimensional idealized street canyons: A large-eddy simulation approach. Bound.-Layer Meteor., 148, 241- 253.10.1007/s10546-013-9811-4d2a02713f486ee27d3422009311cdb67http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs10546-013-9811-4http://link.springer.com/article/10.1007/s10546-013-9811-4Flow resistance, ventilation, and pollutant removal for idealized two-dimensional (2D) street canyons of different building-height to street-width (aspect) ratios \(AR\) are examined using the friction factor \(f\) , air exchange rate (ACH), and pollutant exchange rate (PCH), respectively, calculated by large-eddy simulation (LES). The flows are basically classified into three characteristic regimes, namely isolated roughness, wake interference, and skimming flow, as functions of the aspect ratios. The LES results are validated by various experimental and numerical datasets available in the literature. The friction factor increases with decreasing aspect ratio and reaches a peak at \(AR = 0.1\) in the isolated roughness regime and decreases thereafter. As with the friction factor, the ACH increases with decreasing aspect ratio in the wake interference and skimming flow regimes, signifying the improved aged air removal for a wider street canyon. The PCH exhibits a behaviour different from its ACH counterpart in the range of aspect ratios tested. Pollutants are most effectively removed from the street canyon with \(AR = 0.5\) . However, a minimum of PCH is found nearby at \(AR = 0.3\) , at which the pollutant removal is sharply weakened. Besides, the ACH and PCH are partitioned into the mean and turbulent components to compare their relative contributions. In line with our earlier Reynolds-averaged Navier鈥揝tokes calculations (Liu et al., Atmos Environ 45:4763鈥4769, 2011 ), the current LES shows that the turbulent components contribute more to both ACH and PCH, consistently demonstrating the importance of atmospheric turbulence in the ventilation and pollutant removal for urban areas.\(AR\)\(f\)\(AR = 0.1\)\(AR = 0.5\)\(AR = 0.3\)20112011
    Delay F., J. Bodin, 2001: Time domain random walk method to simulate transport by advection-dispersion and matrix diffusion in fracture networks. Geophys. Res. Lett., 28, 4051- 4054.10.1029/2001GL013698967726f5b8261e4d559bfdb92feb3c1ahttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2001GL013698%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2001GL013698/fullAbstract Top of page Abstract References A method is proposed to calculate in one step the residence time of a particle by advection-dispersion and matrix diffusion in a bond of a fracture network. The calculation is very rapid and avoids the discretization of Eulerian methods or the multiple leaps of classical Lagrangian approaches. The method is accurate in most flow conditions prevailing in fracture networks. Therefore, the method will be useful to evaluate the conditions in which the different transport mechanisms are of influence at the scale of the entire network.
    Di Sabatino, S., E. Solazzo, P. Paradisi, R. Britter, 2008: A simple model for spatially-averaged wind profiles within and above an urban canopy. Bound.-Layer Meteor., 127, 131- 151.10.1007/s10546-007-9250-128bdd412f8a35b0e337b5aa1504d4d4bhttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs10546-007-9250-1http://link.springer.com/10.1007/s10546-007-9250-1This paper deals with the modelling of the flow in the urban canopy layer. It critically reviews a well-known formula for the spatially-averaged wind profile, originally proposed by Cionco in 1965, and provides a new interpretation for it. This opens up a number of new applications for modelling mean wind flow over the neighbourhood scale. The model is based on a balance equation between the obstacle drag force and the local shear stress as proposed by Cionco for a vegetative canopy. The buildings within the canopy are represented as a canopy element drag formulated in terms of morphological parameters such as 位 f and 位 p (the ratios of plan area and frontal area of buildings to the lot area). These parameters can be obtained from the analysis of urban digital elevation models. The shear stress is parameterised using a mixing length approach. Spatially-averaged velocity profiles for different values of building packing density corresponding to different flow regimes are obtained and analysed. The computed solutions are compared with published data from wind-tunnel and water-tunnel experiments over arrays of cubes. The model is used to estimate the spatially-averaged velocity profile within and above neighbourhood areas of real cities by using vertical profiles of .
    Fujiwara C., K. Yamashita, M. Nakanishi, and Y. Fujiyoshi, 2011: Dust devil-like vortices in an urban area detected by a 3D scanning Doppler lidar. Journal of Applied Meteorology and Climatology, 50, 534- 547.10.1175/2010JAMC2481.1fb453bc3-7a1c-4246-8235-67a2e9b5c0b3521df3a3f0b84e655205901215dd0211http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F252345212_Dust_Devil-Like_Vortices_in_an_Urban_Area_Detected_by_a_3D_Scanning_Doppler_Lidarrefpaperuri:(2a8d54acff4cf6423911ad6448ce2ce4)http://www.researchgate.net/publication/252345212_Dust_Devil-Like_Vortices_in_an_Urban_Area_Detected_by_a_3D_Scanning_Doppler_LidarAbstract Atmospheric boundary layer (ABL) observations were conducted in an urban area (Sapporo, Japan) from April 2005 to July 2007 using a three-dimensional scanning coherent Doppler lidar. During this period, 50 dust devil–like vortices were detected in the area; they occurred during the daytime and were located at vertices or in the branches of convective cells (“fishnet” patterns of wind field). The diameters of the vortex cores ranged from 30 to 120 m, and maximum vorticity ranged from 0.15 to 0.26 s 611 . More than 60% of the vortices were cyclonic; the rest were anticyclonic. The tangential velocity component of the strongest vortex varied from 615.4 to +1.4 m s 611 , and the signal-to-noise ratio was weak in the core. Temporal changes were observed in the three-dimensional structures of two vortices from 1330 to 1354 (Japan standard time) 14 April 2005, and the temporal evolution of the stronger vortex was studied. The vortex initially formed along a low-level convergence line in a fishnet and developed vertically. Its vorticity increased with time in association with shrinkage in the core diameter.
    Goodin W. R., G. J. McRae, and J. H. Seinfeld, 1980: An objective analysis technique for constructing three-dimensional urban-scale wind fields. J. Appl. Meteor., 19, 98- 108.10.1175/1520-0450(1980)019<0098:AOATFC>2.0.CO;24673717e47060fd561e87b7b529f2a40http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F255903999_Objective_analysis_technique_for_constructing_three-dimensional_urban-scale_wind_fieldshttp://www.researchgate.net/publication/255903999_Objective_analysis_technique_for_constructing_three-dimensional_urban-scale_wind_fieldsAbstract An objective analysis procedure for generating mass-consistent, urban-scale three-dimensional wind fields is presented together with a comparison against existing techniques. The algorithm employs terrain following coordinates and variable vertical grid spacing. Initial estimates of the velocity field are developed by interpolating surface and upper level wind measurements. A local terrain adjustment technique, involving solution of the Poisson equation, is used to establish the horizontal components of the surface field. Vertical velocities are developed from successive solutions of the continuity equation followed by an iterative procedure which reduces anomalous divergence in the complete field. Major advantages of the procedure are that it is computationally efficient and allows boundary values to adjust in response to changes in the interior flow. The method has been successfully tested using field measurements and problems with known analytic solutions.
    Gousseau P., B. Blocken, and G. J. F. van Heijst, 2011: CFD simulation of pollutant dispersion around isolated buildings: On the role of convective and turbulent mass fluxes in the prediction accuracy. Journal of Hazardous Materials, 194, 422- 434.10.1016/j.jhazmat.2011.08.008218804209b1499d65199bd0f148d38831cb65969http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0304389411010132http://www.sciencedirect.com/science/article/pii/S0304389411010132Computational Fluid Dynamics (CFD) is increasingly used to predict wind flow and pollutant dispersion around buildings. The two most frequently used approaches are solving the Reynolds-averaged Navier-Stokes (RANS) equations and Large-Eddy Simulation (LES). In the present study, we compare the convective and turbulent mass fluxes predicted by these two approaches for two configurations of isolated buildings with distinctive features. We use this analysis to clarify the role of these two components of mass transport on the prediction accuracy of RANS and LES in terms of mean concentration. It is shown that the proper simulation of the convective fluxes is essential to predict an accurate concentration field. In addition, appropriate parameterization of the turbulent fluxes is needed with RANS models, while only the subgrid-scale effects are modeled with LES. Therefore, when the source is located outside of recirculation regions (case 1), both RANS and LES can provide accurate results. When the influence of the building is higher (case 2), RANS models predict erroneous convective fluxes and are largely outperformed by LES in terms of prediction accuracy of mean concentration. These conclusions suggest that the choice of the appropriate turbulence model depends on the configuration of the dispersion problem under study. It is also shown that for both cases LES predicts a counter-gradient mechanism of the streamwise turbulent mass transport, which is not reproduced by the gradient-diffusion hypothesis that is generally used with RANS models. Copyright 漏 2011 Elsevier B.V. All rights reserved.
    Gu Z. L., Y. W. Zhang, Y. Cheng, and S. C. Lee, 2011: Effect of uneven building layout on air flow and pollutant dispersion in non-uniform street canyons. Building and Environment, 46: 2657- 2665.10.1016/j.buildenv.2011.06.028f8f71f0a6e447c453af250910803a62dhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0360132311002083http://www.sciencedirect.com/science/article/pii/S0360132311002083Uneven building layouts and non-uniform street canyons are common in actual urban morphology. To study the effects of building layouts on air flow in non-uniform street canyons, various building arrangements are designed in this study. Simulations are carried out under four cases (i.e., a uniform street canyon as Case 1 and three non-uniform canyons as Cases 2&ndash;4) with parameter change of the occupying ratio of high buildings (ORHB) in the computational domain and their bilateral allocation as well as the combinations of stepup and/or stepdown notches. In the three non-uniform canyons, stepup and stepdown notches are separating (with ORHB of 25% for Case 2 and 75% for Case 4) or adjoining (with ORHB of 50% for Case 3). The air flow and pollutant dispersion in these street canyons are investigated using Large-eddy Simulation (LES). The air flow structures in the non-uniform street canyons are more complicated than in the uniform street canyon. Inside the non-uniform street canyons, the tilting, horizontal divergence and convergence of wind streamlines are found. Large-scale air exchanges of air mass inside and above the street canyons are found as well. At the pedestrian level, the concentrations of simulated pollutants (e.g., the mean and maximum concentrations) in the non-uniform street canyons are lower than those in the uniform one, suggesting that uneven building layouts are capable of improving the dispersion of pollutants in urban area. Further studies on Case 2&ndash;4 show that the separation of stepup and stepdown notches along the street increases the wind velocities in the vicinity of high buildings, while the adjoining of stepup and stepdown notches decreases the wind velocities. Low concentrations of pollutant at the pedestrian level are found in Case 2 compared to Cases 3 and 4. Thus, the separation of stepup and stepdown notches in non-uniform street canyons might be a good choice for uneven building layout arrangements from the point of view of pollutant dispersion and human health.
    Hanna S. R., 1971: A simple method of calculating dispersion from urban area sources. Journal of the Air Pollution Control Association, 21, 774- 777.10.1080/00022470.1971.10469595ee6f7bdb2d63e9b16421549edccee64fhttp%3A%2F%2Fwww.tandfonline.com%2Fdoi%2Fabs%2F10.1080%2F00022470.1971.10469595http://www.tandfonline.com/doi/abs/10.1080/00022470.1971.10469595Dr. Hanna is a research meteorologist in the Air Resources Atmospheric Turbulence and Diffusion Laboratory, National Oceanic and Atmospheric Administration, Oak Ridge, Tenn. This paper was presented April 6, 1971, at the Conference on Air Pollution Meteorology, sponsored by the American Meteorological Society in cooperation with the Air Pollution Control Association, at Raleigh, North Carolina.
    Hanna S. R., R. Britter, and P. Franzese, 2003: A baseline urban dispersion model evaluated with Salt Lake City and Los Angeles tracer data. Atmos. Environ., 37, 5069- 5082.10.1016/j.atmosenv.2003.08.0142059564ec8e3a71e78f0a7f75f464826http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231003006654http://www.sciencedirect.com/science/article/pii/S1352231003006654A simple baseline urban dispersion model is suggested for use in simulating near-surface releases of tracer chemicals in the urban canopy layer. The model is based on the Gaussian plume or puff model, accounting for low wind speeds, nearly neutral stabilities, large turbulence intensities, and large initial mixing in urban areas. The performance characteristics of this baseline model can be easily determined and used for comparisons with more complex models. Two urban tracer data sets are used to demonstrate the baseline model's performance—the Salt Lake City (SLC) Urban 2000 data set, and the Los Angeles (LA) 2001 data set. The focus of the comparisons is on the maximum concentration, C max , on a given monitoring arc, normalized by the emission rate, Q . The C max / Q observations follow some straightforward similarity relations, such as a decrease with downwind distance, x , raised to the power 611.5 to 612.0, and a lack of dependence on wind speed during nighttime light wind scenarios when wind speeds are less than about 1.5m/s. The predictions of the simple baseline model are shown to agree with the observations from the 30 experimental trials in SLC and LA within a factor of about two to three.
    Hertwig D., G. C. Efthimiou, J. G. Bartzis, and B. Leitl, 2012: CFD-RANS model validation of turbulent flow in a semi-idealized urban canopy. Journal of Wind Engineering and Industrial Aerodynamics, 111, 61- 72.10.1016/j.jweia.2012.09.00362c840cbe2d42e8b0b518998d3d4b768http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0167610512002474http://www.sciencedirect.com/science/article/pii/S0167610512002474Urban flow fields computed by two steady Computational Fluid Dynamics models based on the Reynolds-averaged Navier Stokes equations (CFD-RANS) are compared to validation data measured in a boundary-layer wind-tunnel experiment. The numerical simulations were performed with the research code ADREA and the commercial code STAR-CD. Turbulent flow within and above a 1:225-scale wind-tunnel model representing a novel semi-idealized urban complexity represents the test case. In a systematic study the quality of the numerical predictions of mean wind fields is evaluated with a focus on the identification of model strengths and limitations. State-of-the-art validation metrics for numerical models were used to quantify the agreement between the data sets. Based on detailed spatial identification of locations of good or bad comparison the study showed how unsteady flow effects within street canyons are a major cause for discrepancies between numerical and experimental results.
    Inagaki A., M. C. L. Castillo, Y. Yamashita, M. Kand a, and H. Takimoto, 2012: Large-eddy simulation of coherent flow structures within a cubical canopy. Bound.-Layer Meteor., 142, 207- 222.10.1007/s10546-011-9671-85699a58d82c4c0ebb2dc27f5592cea91http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10546-011-9671-8http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1007/s10546-011-9671-8Instantaneous flow structures “within” a cubical canopy are investigated via large-eddy simulation. The main topics of interest are, (1) large-scale coherent flow structures within a cubical canopy, (2) how the structures are coupled with the turbulent organized structures (TOS) above them, and (3) the classification and quantification of representative instantaneous flow patterns within a street canyon in relation to the coherent structures. We use a large numerical domain (2,560m× 2,560m× 1,710m) with a fine spatial resolution (2.5m), thereby simulating a complete daytime atmospheric boundary layer (ABL), as well as explicitly resolving a regular array of cubes (40m in height) at the surface. A typical urban ABL is numerically modelled. In this situation, the constant heat supply from roof and floor surfaces sustains a convective mixed layer as a whole, but strong wind shear near the canopy top maintains the surface layer nearly neutral. The results reveal large coherent structures in both the velocity and temperature fields “within” the canopy layer. These structures are much larger than the cubes, and their shapes and locations are shown to be closely related to the TOS above them. We classify the instantaneous flow patterns in a cavity, specifically focusing on two characteristic flow patterns: flushing and cavity-eddy events. Flushing indicates a strong upward motion, while a cavity eddy is characterized by a dominant vortical motion within a single cavity. Flushing is clearly correlated with the TOS above, occurring frequently beneath low-momentum streaks. The instantaneous momentum and heat transport within and above a cavity due to flushing and cavity-eddy events are also quantified.
    Jiang D. H., H. N. Liu, and W. G. Wang, 2001: Test a modified surface wind interpolation scheme for complex terrain in a stable atmosphere. Atmos. Environ., 35, 4877- 4885.860383f1d7095980e9cba907d94939e3http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231001002655%2Fpdfft%3Fmd5%3Dfe8e5d175d2b2ef360fec182e90ed972%26pid%3D1-s2.0-S1352231001002655-main.pdf/s?wd=paperuri%3A%28f9618b823b6d6a32e433171c2336d748%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231001002655%2Fpdfft%3Fmd5%3Dfe8e5d175d2b2ef360fec182e90ed972%26pid%3D1-s2.0-S1352231001002655-main.pdf&ie=utf-8
    Jiang, W. M, H. B. Yu, X. Li, 1999: Random walk modeling of wake dispersion for the exhaust tower of an underground tunnel in urban area. Journal of Environmental Sciences, 11, 474- 479.10.3321/j.issn:1001-0742.1999.04.0159bf86b5bcf67daa4e2d6d92772adb2f3http%3A%2F%2Fwww.cnki.com.cn%2FArticle%2FCJFDTotal-HJKB199904014.htmhttp://d.wanfangdata.com.cn/Periodical_jes-e199904015.aspx
    Kaplan H., N. Dinar, 1996: A Lagrangian dispersion model for calculating concentration distribution within a built-up domain. Atmos. Environ., 30, 4197- 4207.10.1016/1352-2310(96)00144-60011f8dbc9631ddb26bdc5eaef7619fchttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F1352231096001446http://www.sciencedirect.com/science/article/pii/1352231096001446A Lagrangian model to study the dispersion of pollutants between urban buildings is described. The flow field is supplied by an objective analysis (Rockle (1990) Ph.D. thesis, Vom Fachbereich Mechanik, der Technischen Hochschule Darmstadt, Germany) and is adjusted to satisfy the continuity equation. From the resulting; mass consistent field the Lagrangian diffusion parameters are eliminated. A 3-D Lagrangian diffusion model in a nonhomogeneous field is applied to calculate the pollutant distribution between the buildings. Several examples are studied and compared to wind tunnel measurements.
    Luhar A. K., A. Venkatram, and S. M. Lee, 2006: On relationships between urban and rural near-surface meteorology for diffusion applications. Atmos. Environ., 40, 6541- 6553.10.1016/j.atmosenv.2006.05.067184159309a81404de0cb2bd57c6fa3fbhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231006005462http://www.sciencedirect.com/science/article/pii/S1352231006005462Dispersion of releases in urban areas can be estimated with information on micrometeorological variables in the urban boundary layer. However, this information is not generally available. On the other hand, meteorological measurements are routinely made in rural surroundings (e.g. airports). We examine empirical relationships between urban and rural meteorological variables using data from the Basel UrBan Boundary Layer Experiment (BUBBLE), conducted during June and July 2002 around Basel, Switzerland, and present two methods to estimate urban micrometeorology using measurements from rural sites. The first method is based on a two-dimensional internal boundary-layer model that uses rural variables as upwind inputs. It assumes that the urban Obukhov length is the same as that in the rural area in unstable conditions and that it is very large (neutral) in stable rural conditions. The second method uses a three-dimensional prognostic model called TAPM in which upwind rural observations are assimilated. Urban variables estimated from TAPM compare well with observations. This performance is slightly better than that of the internal boundary-layer model.
    Macdonald R. W., 2000. Modelling the mean velocity profile in the urban canopy layer. Bound.-Layer Meteor., 97, 25- 45.10.1023/A:1002785830512441d99d50b0bfec969aec97e09d91f56http%3A%2F%2Flink.springer.com%2F10.1023%2FA%3A1002785830512http://link.springer.com/10.1023/A:1002785830512A simple model originally derived for meanwind speed profiles in vegetative canopy flows ismodified for application to arrays ofthree-dimensional surface obstacles (cubes), whichcould be representative of a simple urban-typesurface. It is shown that for cube arrays that arenot too densely packed, the predicted exponentialvelocity profile provides an adequate fit to thespatially averaged velocity profile (u(z))within the obstacle canopy. Application of the model to a set of wind-tunnel dataallows for the evaluation of an empirical fittingparameter called the attenuation coefficient. This isrelated to the turbulence length scale, which can befound by manipulating the results of thegradient-diffusion model used to derive the velocityprofile. The results show a reduction of theturbulence length scale with increasing obstaclepacking density. By assuming a linear transition fromthis length scale at the top of the canopy to theclassical Prandtl length scale in the overlyinginertial sublayer, an acceptable model is obtained forthe full velocity profile within simple obstaclearrays, from the ground up to the overlyingsemi-logarithmic region.
    McElroy J. L., 1969: A comparative study of urban and rural dispersion. J. Appl. Meteor., 8, 19- 31.
    Meroney R. N., 2006: CFD prediction of cooling tower drift. Journal of Wind Engineering and Industrial Aerodynamics, 94, 463- 490.10.1016/j.jweia.2006.01.0159b8b015d3da263d62a1e0d74bbd8dd00http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0167610506000171http://www.sciencedirect.com/science/article/pii/S0167610506000171ABSTRACT Drift of small water droplets from mechanical and natural draft cooling tower installations can contain water treatment chemicals such that contact with plants, building surfaces and human activity can be hazardous. Prediction of drift deposition is generally provided by analytic models such as the US Environmental Protection Agency approved Industrial Source Complex Short Term Version 3 (ISCST3) or Seasonal-Annual Cooling Tower Impact (SACTI) codes; however, these codes are less suitable when cooling towers are located midst taller structures and buildings. A computational fluid dynamics (CFD) code including Lagrangian prediction of the gravity driven but stochastic trajectory descent of droplets is considered and compared to data from the 1977 Chalk Point Dye Tracer Experiment. The CFD program predicts plume rise, surface concentrations, plume centerline concentrations and surface drift deposition within the bounds of field experimental accuracy.
    Meroney R. N., 2008: Protocol for CFD prediction of cooling-tower drift in an urban environment. Journal of Wind Engineering and Industrial Aerodynamics, 96, 1789- 1804.10.1016/j.jweia.2008.02.029895b42e2c0dde41020f1e28bd8d0adc5http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0167610508000470http://www.sciencedirect.com/science/article/pii/S0167610508000470ABSTRACT
    Michioka T., A. Sato, and K. Sada, 2013: Large-eddy simulation coupled to mesoscale meteorological model for gas dispersion in an urban district. Atmos. Environ., 75, 153- 162.10.1016/j.atmosenv.2013.04.017262fefb352001b66382ca3f94e147911http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231013002628http://www.sciencedirect.com/science/article/pii/S1352231013002628A microscale large-eddy simulation (LES) model coupled to a mesoscale LES model is implemented to estimate a ground concentration considering the meteorological influence in an actual urban district. The microscale LES model is based on a finite volume method with an unstructured grid system to resolve the flow structure in a complex geometry. The Advanced Regional Prediction System (ARPS) is used for mesoscale meteorological simulation. To evaluate the performance of the LES model, 1-h averaged concentrations are compared with those obtained by field measurements, which were conducted for tracer gas dispersion from a point source on the roof of a tall building in Tokyo. The concentrations obtained by the LES model without combing the mesoscale LES model are in quite good agreement with the wind-tunnel experimental data, but overestimates the 1 h averaged ground concentration in the field measurements. On the other hand, the ground concentrations using the microscale LES model coupled to the mesoscale LES are widely distributed owing to large-scale turbulent motions generated by the mesoscale LES, and the concentrations are nearly equal to the concentrations from the field measurements.
    Oke T. R., 1988: Street design and urban canopy layer climate. Energy and Buildings, 11, 103- 113.10.1016/0378-7788(88)90026-60ddb5ae95e984e9f25f5f2aecb9d4fd5http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F0378778888900266http://www.sciencedirect.com/science/article/pii/0378778888900266This dilemma is investigated by reviewing the results of recent urban canyon field studies and of scale and mathematical modelling. By concentrating on quantifiable relations it appears that it may be possible to find a range of canyon geometries that are compatible with the apparently conflicting design objectives of mid-latitude cities. If this is correct, traditional European urban forms are climatically more favourable than more modern, especially North American, ones.
    Parente A., C. Gorl, J. van Beeck, and C. Benocci, 2011: Improved k-蔚 model and wall function formulation for the RANS simulation of ABL flows. Journal of Wind Engineering and Industrial Aerodynamics, 99, 267- 278.10.1016/j.jweia.2010.12.0176681b534-3f9c-4a34-85d7-b7c4c86b9a00e6a84a2b365cd1f1bd785e212766c7a3http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS016761051100002Xrefpaperuri:(a646c241a3a4499d54bbc17e5828604d)http://www.sciencedirect.com/science/article/pii/S016761051100002XThe simulation of Atmospheric Boundary Layer (ABL) flows is usually performed using the commercial CFD codes with RANS turbulence modelling and standard sandrain rough wall functions. Such approach generally results in the undesired decay of the velocity and turbulent profiles specified at the domain inlet, before they reach the section of interest within the computational domain. This behaviour is a direct consequence of the inconsistency between the fully developed ABL inlet profiles and the wall function formulation.The present paper addresses the aforementioned issue and proposes a solution to it. A modified formulation of the Richards and Hoxey wall function for turbulence production is presented to avoid the well-documented over-prediction of the turbulent kinetic energy at the wall. Moreover, a modification of the standard urbulence model is proposed to allow specific arbitrary sets of fully developed profiles at the inlet section of the computational domain.The methodology is implemented and tested in the commercial code FLUENT v6.3 by means of the User Defined Functions (UDF). Results are presented for two neutral boundary layers over flat terrain, at wind tunnel and full scale, and for the flow around a bluff-body immersed into a wind-tunnel ABL. The potential of the proposed methodology in ensuring the homogeneity of velocity and turbulence quantities throughout the computational domain is demonstrated.
    Perret L., E. Savory, 2013: Large-scale structures over a single street canyon immersed in an urban-type boundary layer. Bound.-Layer Meteor., 148, 111- 131.10.1007/s10546-013-9808-zbcfc09c507f1042e59207e1d5813f718http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10546-013-9808-zhttp://link.springer.com/10.1007/s10546-013-9808-zAn analysis of the dynamics of the flow over a street canyon immersed in an atmospheric boundary layer is presented, using particle image velocimetry measurements in a wind tunnel. Care was taken to generate a 1:200 model scale urban type boundary layer that is correctly scaled to the size of the canyon buildings. Using proper orthogonal decomposition (POD) of the velocity field and conditional averaging techniques, it is first shown that the flow above the opening of the canyon consists of a shear layer separating from the upstream obstacle, animated by a coherent flapping motion and generating large-scale vortical structures. These structures are alternately injected into the canyon or shed off the obstacle into the outer flow. It is shown that unsteady fluid exchanges between the canyon and the outer flow are mainly driven by the shear layer. Finally, using POD, the non-linear interaction between the large-scale structures of the oncoming atmospheric boundary layer and the flow over the canyon is demonstrated.
    Pol S. U., N. L. Bagal, B. Singh, M. J. Brown and E. Pardyjak, 2006: Implementation of a new rooftop recirculation parameterization into the QUIC fast response urban wind model. Proc. 6th AMS Symposium Urban Environment, Atlanta, G. A. JP1. 2, American Meteorological Society, 227 pp.76b2fa7b39a4dc0670225529846ac4d1http%3A%2F%2Fams.confex.com%2Fams%2FAFAPURBBIO%2Fwebprogram%2FPaper80326.htmlhttp://ams.confex.com/ams/AFAPURBBIO/webprogram/Paper80326.htmlThe QUIC (Quick Urban & Industrial Complex) dispersion modeling system has been developed to provide high-resolution wind and concentration fields in cities. The fast response 3D urban wind model QUIC-URB explicitly solves for the flow field around buildings using a suite of empirical parameterizations and mass conservation. The current model does not capture the rooftop recirculation region associated with flow separation from the leading edge of the building. In this work, a model for rooftop recirculation is implemented using parameters for the length, height and strength of velocities for the recirculation region, which are a function of the aspect ratio of the building. An ellipsoidal region formed by the length and height parameters which are derived from Wilson (1979), represent the rooftop recirculation region. A logarithmic profile with modifications in the first half of the total height of the recirculation region is implemented as an initial wind field in the ellipsoidal region. After mass consistency is applied, this parameterization models the rooftop velocities quite well. In addition, the capacity to incorporate the effects of varying incident wind angles on rooftop flow has been added. In off angle flows, a delta wing type vortex forms on the rooftop with a core that is not perpendicular to the incident wind angle. This vortex is specified using a parameterization based on an empirical model by Banks et. al (2000). The length and height of the vortex along the corresponding building edges are calculated from the angle formed by the vortex core and the leading edge of the building. This vortex is extended beyond the downwind building face where it is forced to diffuse into the wake region. The modified model is an improved version of the previous model as it accounts for an improved modeling of flow in the near rooftop region. The modified model when evaluated with the experimental data for various building geometry cases and the incident wind perpendicular to the building face, matches the experimental data quite well.
    Ren C., E. Y. Y. Ng, and L. Katzschner, 2011: Urban climatic map studies: A review. Int. J. Climatol., 31, 2213- 2233.10.1002/joc.22373c2a8c713065b727e81aafa418212b64http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fjoc.2237%2Fpdfhttp://onlinelibrary.wiley.com/doi/10.1002/joc.2237/pdfAbstract Since their introduction 40 years ago, worldwide interest in urban climatic map (UCMap) studies has grown. Today, there are over 15 countries around the world processing their own climatic maps, developing urban climatic guidelines, and implementing mitigation measures for local planning practices. Facing the global issue of climate change, it is also necessary to include the changing climatic considerations holistically and strategically in the planning process, and to update city plans. This paper reviews progress in UCMap studies. The latest concepts, key methodologies, selected parameters, map structure, and the procedures of making UCMaps are described in the paper. The mitigation measures inspired by these studies and the associated urban climatic planning recommendations are also examined. More than 30 relevant studies around the world have been cited, and both significant developments and existing problems are discussed. The thermal environment and air ventilation condition within the urban canopy layer (UCL) of the city are important in the analytical processes of the climatic-environmental evaluation. Possible mitigation measures and planned actions include decreasing anthropogenic heat release, improving air ventilation at the pedestrian level, providing more shaded areas, increasing greenery, creating air paths, and controlling building morphologies. Further developments have and will continue to focus on the spatial analysis of human thermal comfort in urban outdoor environments and on the impacts and adaptations of climate change. Mapmakers must continue to share lessons and experiences with city planners and policy makers, especially in the rapidly expanding cities of developing countries and regions. Copyright 2010 Royal Meteorological Society
    R枚ckle R., 1990: Determination of flow relationships in the field of complex building structures. PhD dissertation, Fachberich Mechanik, der Technischen Hochschule Darmstadt, Germany.
    Ross D. G., I. N. Smith, P. C. Manins, and D. G. Fox, 1988: Diagnostic wind field modeling for complex terrain: model development and testing. J. Appl. Meteor., 27, 785- 796.10.1175/1520-0450(1988)0272.0.CO;22024be6cadc63397987755c92f2767edhttp%3A%2F%2Fwww.researchgate.net%2Fpublication%2F234462662_Diagnostic_Wind_Field_Modeling_for_Complex_Terrain_Model_Development_and_Testinghttp://www.researchgate.net/publication/234462662_Diagnostic_Wind_Field_Modeling_for_Complex_Terrain_Model_Development_and_TestingABSTRACT A three dimensional diagnostic wind field model is shown to be capable of generating potential flow solutions associated with simple terrain features. This is achieved by modifying an initially uniform background wind to make the flow divergence free. Atmospheric stability effects can be incorporated by considering the relative degree of adjustment that is allowed between the horizontal and vertical components of the wind.A framework for developing a Froude-number-dependent expression for this ratio is proposed and evaluated by comparing modeled streamline deflections of flow past an ideal hill with results from wind tunnel and tow tank experiments.
    Saneinejad S., P. Moonen, T. Defraeye, D. Derome, and J. Carmeliet, 2012: Coupled CFD,radiation and porous media transport model for evaluating evaporative cooling in an urban environment. Journal of Wind Engineering and Industrial Aerodynamics, 104-106, 455- 463.10.1016/j.jweia.2012.02.006690f7a91-0eb1-4c0c-b9b0-ed698330672adf63ca2ba10ea99fe61c04cbacbdc7b6http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS016761051200030Xrefpaperuri:(52a983198b133d201760f8daa786d2de)http://www.sciencedirect.com/science/article/pii/S016761051200030XUrban heat islands affect the energy use for cooling in an urban environment, as well as human comfort and health. Water evaporation from moist surfaces could potentially reduce the local temperature in urban areas, a process known as evaporative cooling. This paper introduces a coupled model to study the effect of evaporative cooling on the temperature conditions in an urban street canyon. A computational model for determining convective heat and mass exchanges between the canyon walls and the air is proposed. The model couples three sub-models: (i) a Computational Fluid Dynamics (CFD) model, which solves heat and vapor transfer in the air, (ii) a Building Envelope Heat and Moisture (BE-HAM) transport model which solves heat and moisture transfer within the porous building walls and (iii) a radiation model (RAD) which determines the radiative heat exchange between the surfaces. An efficient coupling strategy has been developed and applied to investigate the drying of a wet windward wall of a street canyon. The effect of evaporation on the reduction of the surface and air temperatures in a street canyon is analyzed and the influence of these temperature reductions on the Physiological Equivalent Temperature (PET) is shown to be important.
    Shi R. F., G. X. Cui, Z. S. Wang, C. X. Xu, and Z. S. Zhang, 2008: Large eddy simulation of wind field and plume dispersion in building array. Atmos. Environ., 42, 1083- 1097.10.1016/j.atmosenv.2007.10.071ed0a5f7d6cc90412fd44ea82e10df1afhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231007009910http://www.sciencedirect.com/science/article/pii/S1352231007009910This paper presents numerical simulation of wind field and contaminant dispersion in the flow over a group of buildings by large eddy simulation (LES) with higher accuracy finite volume method (FVM) for numerical discretization of governing equation and with immersed boundary method (IBM) at building surfaces. The paper is aimed at proposing a numerical method which can deal with the complex turbulent flow and contaminant dispersion inside a group of buildings. The geometry of the building layout and flow parameters are taken from the wind tunnel experiments by Davidson et al. [1996. Wind tunnel simulations of plume dispersion through groups of obstacles. Atmospheric Environment 30 (22), 3715鈥3731] so that the feasibility and reliability of the numerical method and code can be examined by comparison between numerical results and experimental measurements with confidence. The flow patterns and contaminant dispersion inside the group of buildings are shown in agreement with the description of Davidson et al. [1996. Wind tunnel simulations of plume dispersion through groups of obstacles. Atmospheric Environment 30 (22), 3715-3731]. The numerical prediction of the statistical properties of contaminant dispersion, e.g. the mean concentration distributions, lateral and vertical spreads of the contaminant an so forth, is in good agreement with wind tunnel experiment measurements. The numerical study of this testing case reveals a number of issues which should be considered in application of LES to such complex turbulent flows in environment engineering, e.g. spatial resolution, reasonable subgrid stress model and the turbulent intensity in atmospheric boundary layer.
    Singh B., B. S. Hansen, M. J. Brown, and E. R. Pardyjak, 2008: Evaluation of the QUIC-URB fast response urban wind model for a cubical building array and wide building street canyon. Environmental Fluid Mechanics, 8, 281- 312.10.1007/s10652-008-9084-5636f12ed9a5eef14f32428eac09563d9http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10652-008-9084-5http://link.springer.com/10.1007/s10652-008-9084-5This paper describes the QUIC-URB fast response urban wind modeling tool and evaluates it against wind tunnel data for a 702×0211 cubical building array and wide building street canyon. QUIC-URB is based on the R02ckle diagnostic wind modeling strategy that rapidly produces spatially resolved wind fields in urban areas and can be used to drive urban dispersion models. R02ckle-type models do not solve transport equations for momentum or energy; rather, they rely heavily on empirical parameterizations and mass conservation. In the model-experiment comparisons, we test two empirical building flow parameterizations within the QUIC-URB model: our implementation of the standard R02ckle (SR) algorithms and a set of modified R02ckle (MR) algorithms. The MR model attempts to build on the strengths of the SR model and introduces additional physically based, but simple parameterizations that significantly improve the results in most regions of the flow for both test cases. The MR model produces vortices in front of buildings, on rooftops and within street canyons that have velocities that compare much more favorably to the experimental results. We expect that these improvements in the wind field will result in improved dispersion calculations in built environments.
    Soulhac L., P. Salizzoni, F. -X. Cierco, and R. Perkins, 2011: The model SIRANE for atmospheric urban pollutant dispersion; Part I, presentation of the model. Atmos. Environ., 45, 7379- 7395.10.1016/j.atmosenv.2011.07.0085830d08d3fdecbc6bb09251950fe93e9http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231011007096http://www.sciencedirect.com/science/article/pii/S1352231011007096ABSTRACT In order to control and manage urban air quality, public authorities require an integrated approach that incorporates direct measurements and modelling of mean pollutant concentrations. These have to be performed by means of operational modelling tools, that simulate the transport of pollutants within and above the urban canopy over a large number of streets. The operational models must be able to assess rapidly a large variety of situations and with limited computing resources. SIRANE is an operational urban dispersion model based on a simplified description of the urban geometry that adopts parametric relations for the pollutant transfer phenomena within and out of the urban canopy. The streets in a city district are modelled as a network of connected street segments. The flow within each street is driven by the component of the external wind parallel to the street, and the pollutant is assumed to be uniformly mixed within the street. The model contains three main mechanisms for transport in and out of a street: advection along the street axis, diffusion across the interface between the street and the overlying air flow and exchanges with other streets at street intersections. The dispersion of pollutants advected or diffused out of the streets is taken into account using a Gaussian plume model, with the standard deviations sy and sz parameterised by the similarity theory. The input data for the final model are the urban geometry, the meteorological parameters, the background concentration of pollutants advected into the model domain by the wind and the emissions within each street in the network.
    Soulhac L., P. Salizzoni, P. Mejean, D. Didier, and I. Rios, 2012: The model SIRANE for atmospheric urban pollutant dispersion; Part II, validation of the model on a real case study. Atmos. Environ., 49, 320- 337.10.1016/j.atmosenv.2011.11.031de75b276cf6a87e104546127428b16eehttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231011012143http://www.sciencedirect.com/science/article/pii/S1352231011012143We analyse the performance of the model SIRANE by comparing its outputs to field data measured within an urban district. SIRANE is the first urban dispersion model based on the concept of street network, and contains specific parametrical law to explicitly simulate the main transfer mechanisms within the urban canopy. The model validation is performed by means of field data collected during a 15 days measurement campaign in an urban district in Lyon, France. The campaign provided information on traffic fluxes and cars emissions, meteorological conditions, background pollution levels and pollutant concentration in different location within the district. This data set, together with complementary modelling tools needed to estimate the spatial distribution of traffic fluxes, allowed us to estimate the input data required by the model. The data set provide also the information essential to evaluate the accuracy of the model outputs.
    Vardoulakis S., Coauthors, 2011: Numerical model inter-comparison for wind flow and turbulence around single-block buildings. Environmental Modeling & Assessment, 16, 169- 181.10.1007/s10666-010-9236-00d94de29457550e15ad0e43c2c4abf0ehttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs10666-010-9236-0http://link.springer.com/10.1007/s10666-010-9236-0Wind flow and turbulence within the urban canopy layer can influence the heating and ventilation of buildings, affecting the health and comfort of pedestrians, commuters and building occupants. In addition, the predictive capability of pollutant dispersion models is heavily dependent on wind flow models. For that reason, well-validated microscale models are needed for the simulation of wind fields within built-up urban microenvironments. To address this need, an inter-comparison study of several such models was carried out within the European research network ATREUS. This work was conducted as part of an evaluation study for microscale numerical models, so they could be further implemented to provide reliable wind fields for building energy simulation and pollutant dispersion codes. Four computational fluid dynamics (CFD) models (CHENSI, MIMO, VADIS and FLUENT) were applied to reduced-scale single-block buildings, for which quality-assured and fully documented experimental data were obtained. Simulated wind and turbulence fields around two surface-mounted cubes of different dimensions and wall roughness were compared against experimental data produced in the wind tunnels of the Meteorological Institute of Hamburg University under different inflow and boundary conditions. The models reproduced reasonably well the general flow patterns around the single-block buildings, although over-predictions of the turbulent kinetic energy were observed near stagnation points in the upwind impingement region. Certain discrepancies between the CFD models were also identified and interpreted. Finally, some general recommendations for CFD model evaluation and use in environmental applications are presented.
    Venkatram A., M. Princevac, 2008: Using measurements in urban areas to estimate turbulent velocities for modeling dispersion. Atmos. Environ., 42, 3833- 3841.10.1016/j.atmosenv.2007.12.06167ddc00823f98e7ee851921318d8cf5fhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231008000083http://www.sciencedirect.com/science/article/pii/S1352231008000083This study extends a study [Princevac, M., Venkatram, A., 2007. Estimating micrometeorological inputs for modeling dispersion in urban areas during stable conditions. Atmospheric Environment,.] in which mean winds and temperatures measured at one or two levels on towers located in urban areas were fitted to Monin&ndash;Obukhov similarity equations to obtain estimates of micrometeorological variables required in modeling dispersion in the stable boundary layer. This study shows that such methods are also useful in unstable conditions: measurements of the mean wind speed and the standard deviation of temperature fluctuations,, at one level on a tower yield estimates of surface heat flux, surface friction velocity, and standard deviations of turbulent velocities that are within a factor of two of values observed at two urban sites over 80% of the time.
    van de Walle, B., M. Turoff, 2008: Decision support for emergency situations. Information Systems and e-Business Management, 6, 295- 316.10.1007/978-3-540-48716-6_39e16f29c28c6382010895caf1f5dfc93http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10257-008-0087-zhttp://link.springer.com/10.1007/s10257-008-0087-zEmergency situations occur unpredictably and cause individuals and organizations to shift their focus and attention immediately to deal with the situation. When disasters become large scale, all the limitations resulting from a lack of integration and collaboration among all the involved organizations begin to be exposed and further compound the negative consequences of the event. Often in large-scale disasters the people who must work together have no history of doing so; they have not developed a trust or understanding of one another鈥檚 abilities, and the totality of resources they each bring to bear have never before been exercised. As a result, the challenges for individual or group decision support systems (DSS) in emergency situations are diverse and immense. In this contribution, we present recent advances in this area and highlight important challenges that remain.
    Walton A., A. Y. S. Cheng, 2002: Large-eddy simulation of pollution dispersion in an urban street canyon閳ユ柡锟芥摨art II: Idealised canyon simulation. Atmos. Environ., 36, 3615- 3627.10.1016/S1352-2310(02)00260-1f6fcca95-6c22-4ee4-892a-616e792fb699eb55c0c0da74c023042d7e0850050cd6http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1352231002002601refpaperuri:(98ed9693401d5dafe90f36ffa18bd66b)http://www.sciencedirect.com/science/article/pii/S1352231002002601Three-dimensional large-eddy simulations are performed with the dynamic sub-grid scale model for an idealised urban canyon with pollution modelled as a passive scalar. In addition to concentration distributions, turbulence statistics for the canyon are presented. Higher turbulence intensities are predicted in the core of the vortex compared to the widely used –model. This results in a more homogeneous distribution of pollutants, in agreement with experimental studies reported in the literature. Regions of enhanced turbulence are also observed near the walls leading to a lateral dispersion of pollutants along the canyon. The centre of the vortex is observed to precess around the canyon and also meanders along the length of the canyon. Puffs of pollution are ejected from the top of canyons intermittently rather than smoothly, with a characteristic time scale of the order of 30–60 s.
    Walton A., A. Y. S. Cheng, and W. C. Yeung, 2002: Large-eddy simulation of pollution dispersion in an urban street canyon閳ユ柡锟芥摨art I: Comparison with field data. Atmos. Environ., 36, 3601- 3613.
    Wang P., H. L. Mu, 2011: Random-walk model simulation of air pollutant dispersion in atmospheric boundary layer in China. Environmental Monitoring and Assessment, 172, 507- 515.10.1007/s10661-010-1350-6201459939b9971c6ffbcd18660eea8bac1593fabhttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs10661-010-1350-6http://link.springer.com/10.1007/s10661-010-1350-6In this study, the land-sea breeze circulation model coupled with a random-walk model is developed by the analysis of the formation and the mechanism of the land-sea breeze. Based on the data of the land-sea circulation in Dalian, China, the model simulated the diurnal variation of pressure, flow, temperature, and turbulent kinetic energy field and also provides a basis for solving the air pollutant concentration in the land-sea breeze circulation so as to estimate the economic cost attributable to the atmospheric pollution. The air pollutant concentration in the background of land-sea circulation is also simulated by a Gaussian dispersion model, and the results revealed that the land-sea circulation model coupled with the random-walk model gives a reasonable description of air pollutant dispersion in coastal areas.
    Xie Z. T., O. Coceal, and I. P. Castro, 2008: Large-eddy simulation of flows over random urban-like obstacles. Bound.-Layer Meteor., 129, 1- 23.10.1007/s10546-008-9290-128b8cb1645fb28c5027cfbe934be061ehttp%3A%2F%2Flink.springer.com%2F10.1007%2Fs10546-008-9290-1http://link.springer.com/10.1007/s10546-008-9290-1Further to our previous large-eddy simulation (LES) of flow over a staggered array of uniform cubes, a simulation of flow over random urban-like obstacles is presented. To gain a deeper insight into the effects of randomness in the obstacle topology, the current results, e.g. spatially-averaged mean velocity, Reynolds stresses, turbulence kinetic energy and dispersive stresses, are compared with our previous LES data and direct numerical simulation data of flow over uniform cubes. Significantly different features in the turbulence statistics are observed within and immediately above the canopy, although there are some similarities in the spatially-averaged statistics. It is also found that the relatively high pressures on the tallest buildings generate contributions to the total surface drag that are far in excess of their proportionate frontal area within the array. Details of the turbulence characteristics (like the stress anisotropy) are compared with those in regular roughness arrays and attempts to find some generality in the turbulence statistics within the canopy region are discussed.
    Zhang N., W. M. Jiang, 2006: A large eddy simulation on the effect of building on atmospheric pollutant dispersion. Chinese J. Atmos. Sci., 30, 361- 371 (in Chinese).10.1016/S1003-6326(06)60040-X4ef8b2e998e3140d3b0d31776b28ee45http%3A%2F%2Fen.cnki.com.cn%2Farticle_en%2Fcjfdtotal-dqxk200602003.htmhttp://en.cnki.com.cn/article_en/cjfdtotal-dqxk200602003.htmThe effect of buildings on wind flow fields plays a very important role in urban meteorology and air pollutant dispersion.To understand the urban flow characteristics will help us improve the understanding and prediction in micro/local scale meteorology.Large eddy simulation is one of the most sophisticated methods.A large eddy simulation model is used to simulate the flow structure around a bluff building.TKE(Turbulent Kinetic Energy) sub-grid closure scheme is employed in the model.The numerical simulation results are compared with the wind tunnel experiment EDVAL(Compilation of Experimental Data for VALidation of microscale dispersion models).The results are compared both in whole and in detail.The large eddy simulation model can describe the reverse flow above the building top and the cavity structure well.The numerical simulated horizontal and vertical wind speeds are compared with the physical simulation results as well.It shows that the flow characteristics around the building can be illustrated in detail by the large eddy simulation model.Based on the flow results,a Largarian particle model is employed to simulate the atmospheric pollutant dispersion around the building.The large eddy simulation model can provide detailed and reliable wind and turbulence fields,which can help the dispersion model describe the air pollutant diffusion better.A series of cases of air pollutant dispersion from a virtual source are simulated by the numerical models to understand how the source location impacts the pollutant dispersion around a bluff building.The results show that a minute change of the source location may lead to a great change of the pollutant distribution because the distortion of flow due to the effect of the building, especially for the surface pollutant concentration in the wake area.In the first three cases the source location is set above the building roof.When the point source location is set at Z/H =1.05(H is the building height),the pollutant is carried by the downwash stream of the cavity structure and the concentration is largest in these three cases.When the point source location is changed from Z/H=1.05 to Z/H=1.25,most of the pollutant will skip the cavity flow behind the building and the surface concentration in the wake area decreases greatly,the peak value of the surface concentration is behind the wake.When the source is moved to Z/H=1.28,most of the pollutant is cleaned from the simulation domain,the surface concentration is nearly zero.When the source is in the wake behind the building,the pollutant stays in local area causing a high surface concentration.A great part of pollutant will also be carried downward due to the circulation flow behind the building and cause a great surface pollutant concentration.
    Zhang Y. W., Z. L. Gu, Y. Cheng, and S. C. Lee, 2011: Effect of real-time boundary wind conditions on the air flow and pollutant dispersion in an urban street canyon-large eddy simulations. Atmos. Environ., 45, 3352- 3359.a213ebccc391e621f8d7a93cd6bd8cbbhttp%3A%2F%2Fwww.irgrid.ac.cn%2Fhandle%2F1471x%2F628178%3Fmode%3Dfull%26submit_simple%3DShow%2Bfull%2Bitem%2Brecord/s?wd=paperuri%3A%2817cb0c6695302adb9d5c97a92a0c0126%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.irgrid.ac.cn%2Fhandle%2F1471x%2F628178%3Fmode%3Dfull%26submit_simple%3DShow%2Bfull%2Bitem%2Brecord&ie=utf-8
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Manuscript received: 19 June 2015
Manuscript revised: 30 July 2015
Manuscript accepted: 17 August 2015
通讯作者: 陈斌, bchen63@163.com
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A Microscale Model for Air Pollutant Dispersion Simulation in Urban Areas: Presentation of the Model and Performance over a Single Building

  • 1. Institute for Climate and Global Change Research and School of Atmospheric Sciences, Nanjing University, Nanjing 210093
  • 2. Sichuan Environmental Monitoring Center, Chengdu 610091
  • 3. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089

Abstract: A microscale air pollutant dispersion model system is developed for emergency response purposes. The model includes a diagnostic wind field model to simulate the wind field and a random-walk air pollutant dispersion model to simulate the pollutant concentration through consideration of the influence of urban buildings. Numerical experiments are designed to evaluate the model's performance, using CEDVAL (Compilation of Experimental Data for Validation of Microscale Dispersion Models) wind tunnel experiment data, including wind fields and air pollutant dispersion around a single building. The results show that the wind model can reproduce the vortexes triggered by urban buildings and the dispersion model simulates the pollutant concentration around buildings well. Typically, the simulation errors come from the determination of the key zones around a building or building cluster. This model has the potential for multiple applications; for example, the prediction of air pollutant dispersion and the evaluation of environmental impacts in emergency situations; urban planning scenarios; and the assessment of microscale air quality in urban areas.

1. Introduction
  • Urbanization is a worldwide process through which human beings change the natural world. The natural/vegetated land surface is converted to an urban land surface composed of buildings. Large amounts of material and energy are consumed in urban areas and pollutants and waste heat are released as a result. With urbanization taking place all around the world, many related environmental problems occur over urban areas from the regional to the building scale (e.g. Britter and Hanna, 2003; Britter et al., 2003; Ren et al., 2011). Air pollutant dispersion at the microscale is very important because it is closely related to the comfort and health of residents in populated urban areas. However, the characteristics of pollutant dispersion in urban areas at the local scale and microscale is complicated because of the complex wind field disturbed by buildings of various shapes (Walton et al., 2002; Hanna et al., 2003; Shi et al., 2008; Xie et al., 2008; Boppana et al., 2010; Fujiwara et al., 2011; Gu et al., 2011; Zhang et al., 2011; Chung and Liu, 2013; Perret and Savory, 2013). The SIRANE model (Soulhac et al., 2011, 2012) improved the conventional Gaussian model by integrating a box model for street canyons and considering the fluxes at street intersections.

    Numerical simulation is an important method widely used for the urban atmospheric environment and many models have been developed for microscale pollutant dispersion. The "urbanized" Gaussian model is a conventional method that tries to consider the impact of buildings by modifying the horizontal and vertical diffusion parameters (McElroy, 1969; Hanna, 1971). This method works well when the building density is quite low (Hanna et al., 2003; Luhar et al., 2006; Venkatram and Princevac, 2008), but fails in areas with high-rise buildings.

    The abilities of computational fluid dynamics (CFD) methods (e.g., large-eddy simulation, direct numerical simulation) are similar in terms of their representation of the wind flow characteristics around buildings and urban canyons (Cai, 2000; Walton and Cheng, 2002; Walton et al., 2002; Cai et al., 2004; Meroney, 2006, 2008; Shi et al., 2008; Gousseau et al., 2011; Zhang et al., 2011; Aumond et al., 2012; Hertwig et al., 2012; Inagaki et al., 2012; Saneinejad et al., 2012; Michioka et al., 2013). However, such methods are usually quite expensive computationally, and less effective in an emergency response setting.

    Regarding emergency responses at the urban neighborhood scale (e.g., toxic gas leakages, airborne aerosol emissions), information on air pollutant dispersion and evaluations of the likely harm should be supported over a very short timeframe (about 10-30 minutes) for a decision to made (van de Walle and Turoff, 2008). A fast method is needed to simulate the wind flow/air dispersion around building clusters with relatively high accuracy and less computational cost. A few models have been developed for this purpose, e.g., QUIC (The Quick Urban and Industrial Complex dispersion model system) developed by the Los Alamos National Laboratory (www.lanl.gov/projects/quic/index.shtml) (Singh et al., 2008). In this paper, an urban microscale air pollution dispersion simulation model (hereafter, UMAPS) is established and evaluated with wind tunnel experiments.

2. The model
  • The model (UMAPS) is a building-resolved air pollutant dispersion model system, which includes a diagnosis model for wind fields around urban buildings (Wind Information Field Fast Analysis Model, WIFFA) and a random-walk air pollutant dispersion model (Nanjing University Random-Walk Dispersion Model, NJU-RWM) to simulate the pollutant transport in urban canopies or canyons.

  • WIFFA is responsible for calculating the wind fields for the dispersion model. WIFFA includes two modules, a first-guess wind field interpolation model and a mass conservation wind model. The first-guess wind field interpolation model supplies the initial conditions for the mass conservation wind model, based on building morphology information and background wind speed/direction. The mass conservation model calculates a more realistic wind field based on the mass continuity equation.

    The impact of the building is considered via the method of the QUICmodel (http://www.lanl.gov/projects/quic/quicurb. shtml), in which the wind field around a building is characterized by several key zones, including the upwind displacement zone, the upwind cavity, the leeside cavity, the wake zone, and the rooftop recirculation zone due to the prevailing wind direction, and the reference wind speed is used for the interpolation in different zones. Both the wind fields in the leeside cavity and wake zone are determined by the method of (Röckle, 1990). Wind fields in the upwind displacement zone and upwind cavity are estimated by the method of Bagal et al. (2004a, b), and the interpolation method of (Pol et al., 2006) is used for the rooftop recirculation zone.

    The interaction among buildings causes the wind flow in a street canyon to be more complicated than around a single building. (Oke, 1988) classified the wind flow in a street canyon into three types: isolated roughness flow, wake interference flow, and skimming flow. For the isolated roughness flow, the interpolation method for a single building is used in our model. For the skimming flow and the wake interference flow, the method of (Kaplan and Dinar, 1996) is used.

    The shapes of buildings in an urban area in the real world are far more complicated than a cube or rectangle. In UMAPS, all buildings are simplified to be a rectangle characterized by the maximum building length, width and height, to take advantage of the idealized interpolation schemes introduced above. Also, all the above interpolation methods work under the assumption that the inlet wind flow is perpendicular to the building wall. When the inlet wind flow is not perpendicular to the building wall, an adjustment is made using the method of (Kaplan and Dinar, 1996).

    Two schemes are used for wind profile interpolation in WIFFA. The power profile method (Röckle, 1990) is used as the QUICK-URB model when the building coverage is low (buildings covering a fraction less than or equal to 35%, in the current experiments) and the buildings distribution is sparse. The interpolation equation is as follows: \begin{equation} \label{eq1} u_0(z)=u_0(z_{ ref})\left(\dfrac{z}{z_{ ref}}\right)^p , (1)\end{equation} where u0(z ref) is the reference wind speed, z ref is the reference height, p is the power index, z is the vertical height, and u0(z) is the interpolated wind speed at the height of z. When the building intensity is high (coverage greater than 35%), the urban canopy profile method (Macdonald, 2000) is used, because the power method usually overestimates the wind speed below the height of buildings. The equation of the urban canopy profile is as follows: \begin{equation} \label{eq2} u_0(z)=\left\{ \begin{array}{l@{\quad}l} u_{ can}\dfrac{\ln\frac{z-d}{z_0}}{\ln\frac{H_{ can}}{z_0}} & z>H_{ can}\\[5.5mm] u_{ can}\exp\left[\alpha(z)\left(\dfrac{z}{H_{ can}}-1\right)\right] & z\le H_{ can} \end{array} \right., (2)\end{equation} where H can is the height of the canopy (in this paper, its value is set to the average building height of the whole simulation domain), u can is the wind speed at the top of the urban canopy, d is the displacement height (in this paper, it is set as 0.7H can), z0 is the roughness length (about 0.1-0.2H can), and α(z) is the decay exponent, which is a function of z and the building intensity of the horizontal section at the height of z (Macdonald, 2000).

    After the first-guess interpolation, an initial wind field is created and the wind speed at the grids that are inside the buildings are set to zero, but the interaction of the wind fields between "building-impact" grids and background grids are not considered. The mass conservation equation is taken into account to obtain a more realistic wind field from the first-guess result. Mass-conservation wind models have been widely used to simulate the wind field over complex terrain for air pollutant dispersion (Goodin et al., 1980; Ross et al., 1988; Jiang et al., 2001). The model used in UMAPS was originally developed by (Jiang et al., 2001), and the building influence is considered as a very sharp topography.

  • The random-walk method is widely used in air pollutant dispersion simulations, which tracks tracer particles through advection by the mean wind field and diffusion by atmospheric turbulence. The turbulence movement is estimated by calculating the probability distribution of particle movement, which is simulated by a random number. A large number of particles are used to statistically simulate the distribution of pollutant mass, and concentrations are calculated by the distribution of tracer particles. This method is also widely used in urban dispersion simulations (e.g., Delay and Bodin, 2001; Wang and Mu, 2011). NJU-RWM is a random-walk model developed by (Jiang et al., 1999). The model has been modified to consider the influence of buildings and verified by (Zhang and Jiang, 2006).

3. Wind tunnel experiment database and numerical case design
  • The CEDVAL (Compilation of Experimental Data for Validation of Microscale Dispersion Models, http://www.mi.uni-hamburg.de/CEDVAL_Validation_Data.427.0.html) database is selected for the model evaluation in this paper. The CEDVAL experiments were carried out at Hamburg University, and include mean wind field, turbulence, and air pollutant concentration measurements for single buildings and building clusters. This database is widely used for the development and evaluation of microscale numerical models (Di Sabatino et al., 2008; Castelli and Reisin, 2011; Parente et al., 2011; Vardoulakis et al., 2011).

    The A1-1 and A1-5 wind tunnel experiments in CEDVAL are used to evaluate the performance of the wind field simulation by UMAPS around a single building; the numerical experiments are named SA1-1 and SA1-5, respectively. The model uses Cartesian coordinates and a regular cubic grid is deployed. The horizontal simulation domain is 450 m in the inlet wind velocity direction (x direction), 200 m in the crosswind direction (y direction), and 100 m in the vertical direction (z direction). The grid resolution is 1 m. The inlet wind profile for the numerical experiments is set as the same power-exponent profiles as in the wind tunnel setting, as follows: \begin{equation} \label{eq3} u(z)=U_{ ref}\left(\dfrac{z}{H_{ ref}}\right)^p , (3)\end{equation} where H ref is the reference height, which is 100 m in A1-1/SA1-1 and 125 m in A1-5/SA1-5; U ref is the inlet wind speed at the height of H ref, which is 6.0 m s-1 in A1-1/SA1-1 and 5.85 m s-1 in A1-5; and p is the power exponent parameter, which is 0.21 in all experiments.

    In the A1-5 wind tunnel experiment, four sources are placed on the ground near the leeside wall of the building. The pollutant concentration observations are represented by dimensionless concentration, defined as K=c m× U ref× H2/Q s, where c m is the pollutant concentration, U ref is the reference wind speed, as in Eq. (4), and Q s is the total mass of pollutant release. H is the building height. In the numerical experiments, the wind field is simulated by WIFFA, and then NJU-RWM is deployed to simulate the air pollutant dispersion. A total of 400 000 particles are released to simulate the pollutant dispersion in NJU-RWM.

  • To evaluate the performance of the model system, the following statistical parameters are employed: \begin{eqnarray} \label{eq4} { MN}&=&\overline{X_i}(i={ o,p}) ;(4)\\ \label{eq5} E&=&X_{ o}-X_{ p} ;(5)\\ \label{eq6} { RE}&=&|(\overline{X_{ o}}-\overline{X_{ p}})/\overline{X_{ o}}| ;(6)\\ \label{eq7} { RMSE}&=&\sqrt{\overline{(X_{ o}-X_{ p})^2}} ;(7)\\ \label{eq8} R&=&\dfrac{\overline{(X_{ o}-\overline{X_{ o}})(X_{ p}-\overline{X_{ p}})}}{\sigma_{{ X}_{ o}}\sigma_{{ X}_{ p}}} ;(8)\\ \label{eq9} { FAC2}&=&\dfrac{N\left(0.5\le\dfrac{X_{ p}}{X_{ o}}\le2.0\right)}{N} ;(9)\\ \label{eq10} { HR}(A)&=&\dfrac{1}{N}\sum_i^Ni_{ i}i_{ i}\left\{ \begin{array}{l@{\quad}l} 1 & \left|\dfrac{P_{ i}-O_{ i}}{O_{ i}}\right|\le A\\[3mm] 0 & { otherwise} \end{array} \right..(10) \end{eqnarray} Here, X o is the observed variable (wind speed, wind components, or pollutant concentration) and X p is the respective modeled one. MN is the mean value; E is the mean error between simulations and observations, RE is the relative simulation error; RMSE is the root-mean-square error; R is the correlation coefficient; FAC2 is the factor of two of observations; N(0.5≤ X p/X o≤ 2.0) is the data number under the condition (0.5≤ X p/X o≤ 2.0); N is the total data number; HR is the hit rate; and A is the threshold value of relative error.

4. Results
  • The simulated results are first interpolated to the measurement points of the wind tunnel experiments for the evaluation. The CEDVAL A1-1 and A1-5 experiments relate to the wind fields and pollution dispersion around a single building; the building size is 20 m in the x direction, 30 m in the y direction, and the height is 35 m. The simulation results show that UMAPS captures the wind field structures well compared to the wind tunnel experiments. In both the numerical simulations and wind tunnel observations, the displacement point occurs at about x/H=-1.0 to 1.3, and the stagnation point occurs at z/H=0.7. The reattachment point is at the location of x/H=2.2, and the wind speed in the windward vortex is less than 2.0 m s-1 (Fig. 1). These results are consistent with the simulations reported in (Singh et al., 2008). The vertical leeside cavity vortex and the horizontal double-eyed vortex are represented well in the simulations (Fig. 2).

    Figure 1.  The wind velocities at the crossing section $y/H=0$: (a) CEDVAL observations; (b) numerical simulations. A: wind cavity; B: roof-top circulation; C: leeside cavity and wake zone.

    Figure 2.  The wind velocities at the crossing section of $z/H=0.28$: (a) CEDVAL observations; (b) numerical simulations. A: wind cavity; B: lateral wall zone; C: leeside cavity and wake zone.

    A clockwise vortex appears and the vortex eye occurs at the location of (x/H=0.9 and z/H=0.9) in the vertical section. In the horizontal section, the eyes of the symmetric vortexes occur at (x/H=0.7, y/H=0.6), compared to (x/H=0.7, y/H=0.4) in the wind tunnel experiments. The wind speed in the leeside cavity is less than 1.5 m s-1, and the wind speed increases to 3.0 m s-1 at heights greater than the leeside cavity.

    To analyze the model performance in a more detailed way, the evaluation parameters are calculated not only for the whole y/H=0 section, but also for the key zones, including the windward zone, the leeside zone, and the rooftop zone, as shown in Fig. 1. The model simulation for this section is good, with a mean RE of 6.4% and R=0.96. Table 1 lists the statistical parameters of wind speed in different zones, and shows that the model performs better in the windward zone and rooftop zone, as compared to the leeside zone. The RE of the leeside zone is 21.3%, compared to a 5.4% in the windward zone and 3.7% in the rooftop zone. Figure 3 illustrates the vertical profile of u and w at different locations in the y/H=0 section. The simulation represents the blocking of wind by the building, the upward motion before the building, and the downward motion behind the building. For the y/H=0 section, the model overestimates the total wind speed and u component slightly, with a maximum E of 0.34 m s-1. The larger simulation errors of u occur in the leeside profiles at the level between z/H=0.8 and 1.2. This area is the transition area from the leeside cavity and wake zone to the background flow, and the model describes a sharper transition compared to the tunnel experiment.

    Figure 3.  The vertical profiles of wind components ($u$ and $w$) at the plane of $y/H=0$ in A1-1 and SA1-1.

    For the wind tunnel observations and numerical simulations of wind fields in the z/H=0.28 section (Fig. 2), the RE of the whole section is 1.4% and R=0.91. Three key zones are again selected for a more detailed evaluation (the windward zone, leeside zone and lateral-wall zone), as shown in Fig. 2, and the related evaluation parameters are listed in Table 2. The largest simulation error happens in the windward area, where the average wind speed of the wind tunnel experiment is 2.55 m s-1, while that of the simulation is 1.88 m s-1. The RE is 25.1%, compared to 5.2% in the leeside zone, 4.9% in the lateral-wall zone, and 1.4% for the whole section. Figure 4 illustrates the horizontal profile of u and v at different locations at x/H=-1.6,-1,-0.5,0,0.5,1,2, and 3. The modeled horizontal wind components are consistent with the simulations. The largest error of u is of 0.25 m s-1, which occurs at x/H=3.0; and the largest RMSEs of u are about 0.63 m s-1 and 0.61 m s-1, occurring at x/H=-1.0 and x/H=3.0, where the frontal eddy and leeside vortex occur, respectively. The largest MD of v is only 0.01 m s-1, but with a large RMSE of about 1.17 m s-1, which happens at x/H=-0.5. The errors of the windward zone come from the overestimation of the area of the frontal eddy, based on (Röckle, 1990).

    Figure 4.  The horizontal profiles of wind components ($u$ and $v$) at the plane of $z/H=0.28$ in A1-1 and SA1-1.

    Figures 5 and 6 illustrate the horizontal distribution of the dimensionless concentration K in the horizontal sections of z/H=0.08, z/H=0.28 and y/H=0 in A1-5 and SA1-5. High concentration occurs in the leeside cavity circulation and lateral-wall-side circulation, and the maximum concentration appears in these area instead of the middle axis of the circulations. This is because the vortex structure in the leeside cavity may cause the pollutant to be concentrated and a flow reversal in the background wind direction would bring the pollutant windward into the lateral-wall-side circulations. Behind the leeside cavity, the concentration decreases with distance dramatically, and the decreasing trend in numerical simulations is higher than that in the wind tunnel experiments.

    In the wind tunnel experiments and numerical simulation results, the pollutant concentration in the z/H=0.08 section is higher than that in the z/H=0.28 section because the pollutant source is on the ground. The maximum of the dimensionless concentration K is 70.4 for z/H=0.08 and 21.5 for z/H=0.28 in the wind tunnel experiment, but 92.7 and 32.1 in the numerical simulations. This demonstrates the model overestimates the peak value of the pollutant concentration but underestimates the dispersion area. The maximum appears just at the corner of the leeside wall and lateral side wall in the wind tunnel experiment, but it appears at the location just behind the leeside wall in the simulation. This is due to the overestimation of the lateral-wall-side circulations in WIFFA.

    For the results of the vertical section, both the wind tunnel experiment and the numerical simulations show that high concentration occurs near the leeside wall area in the leeside cavity. Under the combined influence of the leeside cavity vortex and rooftop vortex, high concentration also occurs over the building roof. The largest simulation error appears in the transition zone from the leeside cavity vortex to the background wind flow. In this area, the model underestimates the pollutant concentration due to the overestimation of wind speed.

    In the y/H=0 section, both the wind tunnel experiment and numerical simulation show the highest concentration appearing in the ground corner of the leeside wall, with the maximum being 66.7 in the wind tunnel experiment and 62.5 in the numerical simulation. On the roof top level, both in the wind tunnel experiment and the numerical simulation, there is a high concentration at the leeside corner, with a maximum K of 1.66 in the experiments and 0.82 in the numerical simulation results. The numerical simulation shows a low pollutant concentration at the lowest model layer (at the height of 1 m). This is because, in the RWM, the tracer particle will bounce back when it encounters the surface or building walls. Such an influence can increase when the vertical resolution is coarse and add several buffer levels between the ground and the lowest layer.

    Table 3 shows the evaluation parameters for the dimensionless pollutant concentration. The model slightly underestimates the concentration for all three sections. The RE is 10.8% for the vertical section and 35.1% for the horizontal section. However, the model represents the horizontal concentration distribution better; the R values of section z/H=0.08 and 0.28 are 0.77 and 0.70, respectively, which are greater than the value of 0.60 for section y/H=0. For all sections, the FAC2s and HRs are greater than 50% and 60%, which have been used as threshold values for model evaluations in previous research (e.g., Vardoulakis et al., 2011; Parente et al., 2011). This means that the model is reliable for pollutant dispersion simulation.

    Figure 5.  The dimensionless pollutant concentration in the horizontal section: (a) wind tunnel experiment result in A1-5 at $z/H=0.08$ (the circles indicate the locations of sources); (b) numerical simulation result in SA1-5 at $z/H=0.08$; (c) wind tunnel experiment result in A1-5 at $z/H=0.28$; (d) numerical simulation result in SA1-5 at $z/H=0.28$.

    Figure 6.  The dimensionless pollutant concentration in the vertical section $y/H=0$: (a) wind tunnel experiment result in A1-5; (b) numerical simulation result in SA1-5.

5. Summary
  • UMAPS is a microscale air pollutant model system developed for air pollutant dispersion simulation under emergency release conditions. It includes a diagnostic wind field model (WIFFA) and a random-walk air pollutant dispersion model (NJU-RWM) to simulate the wind fields and pollutant concentration in detail, through consideration of the influence of urban buildings. The wind field model is composed of two parts: an interpolation model, to obtain the first-guess fields of different zones around a building or street canyon; and a mass conservation wind model, to obtain a detailed wind field in the whole simulation domain. NJU-RWM reproduces the air pollutant dispersion by releasing tracer particles.

    The CEDVAL database is used to evaluate the model's performance. The wind field and pollutant dispersion experiments around a single building are used to evaluate the simulation results. The simulation error, relative error, correlation coefficient, and root-square simulation error are used to evaluate the model's performance. The comparisons show that the model can reproduce the wind fields and pollutant dispersion around a typical rectangular building. Generally, the model overestimates the wind speed and underestimates the pollutant concentration. The largest uncertainty relates to the determination of the size of the key zones and the simplification of the complex building shape. This indicates that the definition parameters of the key zones around the building are important for model performance. Evaluations of the model's performance over more complex and realistic conditions will be carried out in the next stage of model development.

    UMAPS is a simple and fast model, which does not demand much computational resource and can work on a personal computer. It also works well with operational meteorological observations or numerical weather predictions. This model has the potential for multiple applications; for example, to predict air pollutant dispersion and evaluate environmental impacts in emergency response situations, in urban planning scenarios, and for assessing microscale air quality in urban areas.

Reference

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