Dispersion In Urban Areas Research Articles

Should we care about the level of detail in trees when running urban microscale simulations?

Due to lack of information and long geometry generation times, tree geometries are usually oversimplified or even ignored in Computational Fluid Dynamic (CFD) simulations that predict wind and pollutant dispersion in urban areas. Nevertheless, trees are known to impact local wind patterns and air quality levels. Thus, in this paper we explore the effects that tree models automatically reconstructed at diverse Level of Detail (LoD) (1, 2 and 3) have in numerical wind predictions. We address this by comparing the non-dimensional velocity magnitude differences between simulations with multiple tree LoDs. To further understand these differences in changing environmental contexts we use three morphologies: an isolated tree, an idealized street, canyon, and a real urban geometry from Rotterdam, The Netherlands The numerical results show that the velocity magnitude differences between the cases with LoD1 tree models and those with LoD2 tree models can be over 1.0m/s while the differences between LoD2 and LoD3 cases are rather limited, usually lower than 0.2m/s. Consequently, through this study we highlight the importance of using tree models in LoD2 or LoD3 at least for CFD simulations of wind flows in urban areas. To further support this conclusion we also analyze the impact of changing wind directions and tree Leaf Area Density (LAD) values in the impact of tree LoDs on wind. The differences found in this work linked to the level of realism in your tree models can support future studies where researchers want to make an informed choice.

Numerical study on the gaseous radioactive pollutant dispersion in urban area from the upstream wind: Impact of the urban morphology

The radioactive pollutant could migrate to the downstream urban area under the action of atmospheric dispersion due to the turbulent mixing under actual pollution accidents. A scenario in which radioactive contaminants from the upstream (for example, a nearshore nuclear power plant accident) migrates to the downstream urban blocks have been considered in this study. Numerical simulations using computational fluid dynamics (CFD) are then conducted to investigate the effects of the urban morphology (building packing density and layout) on the atmospheric dispersion of radioactive pollutants in this scenario. The building packing density and structure can significantly affect urban areas' mean flow pattern and the turbulent kinetic energy (TKE). The flow pattern and the TKE distribution influence the radioactive pollution dispersion. It is found that the radioactive pollution at the urban canyons is significantly affected by the vertical transport at the canyon. A comparison of the distributions of radioactive and traditional non-radioactive pollutants is also provided.

Computational fluid dynamics analysis of pollutant dispersion around a high-rise building: Impact of surrounding buildings

Understanding the detailed mechanism of pollutant dispersion in urban areas is a critical aspect of air quality control in residential areas. In this study, wind tunnel measurements and high-resolution computational fluid dynamic (CFD) based on large eddy simulation (LES) were used for the detailed analysis of the pollutant dispersion around a high-rise building for an isolated case and also a case when the central high-rise building was surrounded by several low-rise buildings. The pollutant was released on the leeward façade of the high-rise building at the pedestrian height. It was shown that the presence of the surrounding buildings significantly alters the pollutant transfer mechanism around the high-rise building. In the surrounded case, the pollutant in the wake region shifted vertically toward the roof of the high-rise building. As a result, at a location of 2/3 of the high-rise building height, the mean concentration is five times larger than the isolated case, which means the residents on higher floors experience more pollution concentration in the case of the surrounded case even though they are located much higher than the canyon rooftop and the pollutant source. In both cases, the contribution of the turbulent diffusive flux in the streamwise direction is comparable with the mean convective flux while they have opposite directions. Detailed analyses of the pollutant mass transfer rate budget around different control volumes around the high-rise buildings highlighted the effect of the surrounding buildings. It is concluded that there is a risk of underestimating pollutant concentrations around a high-rise building, especially near the upper floors if the surrounding buildings are not properly considered.

Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion

Mapping near-field pollutant concentration is essential to track plume dispersion in urban areas. By solving most of the turbulence spectrum, large-eddy simulations (LES) can accurately represent concentration spatial variability. Synthesizing this large amount of information to improve the accuracy of lower-fidelity operational models is particularly appealing. Due to the computational cost of LES, it becomes a challenge in multi-query contexts, to quantify how tracer dispersion changes with atmospheric and source parametric variations. We propose a data-driven reduced-order model (ROM) combining proper orthogonal decomposition (POD) and probabilistic Gaussian process regression (GPR) to efficiently predict tracer concentration field first- and second-order statistics. The novelty of this work lies in the original approach used to efficiently tune the GPR based on the available reduced-basis information and properties. In practice, the GPR hyperparameters are optimized through the hierarchy of POD modes thanks to Bayesian optimization informed by POD. The robustness of the approach is also addressed under small data constraint. A detailed analysis of our model performance is carried out in the case of a turbulent atmospheric boundary-layer flow over a surface-mounted obstacle for which the convective character of the system together with the uncertainty of the pollutant source location make it very challenging for ROM. The model is able to capture the wide range of spatial scales across the POD modes. The ROM accuracy remains acceptable for a minimal budget of about one hundred LES snapshots, providing a first estimation moving towards more realistic atmospheric dispersion applications.

Modelling Radiative and Convective Thermal Exchanges over a European City Center and Their Effects on Atmospheric Dispersion

Micro-meteorological studies of urban flow and pollution dispersion often assume a neutral atmosphere and often the three-dimensional variation in temperature fields and flow around buildings is neglected in most building energy balance models. The aim of this work is to present the results of development and validation of a three-dimensional tool coupling thermal energy balance of the buildings and modelling of the atmospheric flow and dispersion in urban areas. To do so, a 3D microscale atmospheric radiative scheme has been developed in the atmospheric module of the computational fluid dynamics (CFD) code Code_Saturne adapted to detailed building geometries. The full coupling of the radiative transfer and fluid dynamics models has been validated with idealized cases. In this paper, our focus is to simulate and compare with measurements the diurnal evolution of the brightness surface temperatures and the momentum and energy fluxes for a neighborhood in the city center of Toulouse, in the southwest part of France. This is performed by taking into account the 3D effects of the flow around the buildings and all thermal exchanges, in real meteorological conditions, and compare them to aircraft infrared images and in situ measurements on a meteorological mast. The calculation mesh developed for the city center and the simulation conditions for the selected day of the field campaign are presented. The results are evaluated with the measurements from the Canopy and Aerosol Particles Interactions in TOulouse Urban Layer experiment (CAPITOUL). In addition, the second purpose of this work is to investigate a hypothetical release of passive pollutant dispersion in the same area of Toulouse under different thermal transfer conditions for the street and the buildings surfaces: neutral and 3D radiative transfer heating. The presence of heat transfer continually modifies the airflow field while the airflow in the neutral case reaches a stationary state. Compared to the neutral case, taking into account the thermal transfer enhances the turbulence kinetic energy and vertical velocity (especially at the roof level) due to buoyancy forces. The simulation results also show that the thermal effects considerably alter the plume shape.

Hazardous atmospheric dispersion in urban areas: A Deep Learning approach for emergency pollution forecast

Today, Computational Fluid Dynamics approaches have a high level of spatial/temporal accuracy in modelling atmospheric transport and dispersion in very complex environments. Several numerical models require, however, heavy computational resources and prolonged simulation time up to several days. This time constraint is specifically crucial for intervention planning in case of accidental or malevolent toxic releases in a city. In this paper, we propose to use synthetic data generated by a realistic 3-D transport/dispersion simulator, to train a learning framework called MCxM. The latter relies on a sequence of masking and correction operations to progressively apply the spatial constraints and underlying physics of transport and dispersion. The learning phase uses the urban geometry of the French city Grenoble. We then test the effectiveness of the trained MCxM in a different French city: Paris. The results show that the MCxM's forecasts are virtually instantaneous and generalize successfully to unseen conditions.

Impact of Indoor-Outdoor Temperature Difference on Building Ventilation and Pollutant Dispersion within Urban Communities

Mechanical ventilation consumes a huge amount of global energy. Natural ventilation is a crucial solution for reducing energy consumption and enhancing the capacity of atmospheric self-purification. This paper evaluates the impacts of indoor-outdoor temperature differences on building ventilation and indoor-outdoor air pollutant dispersion in urban areas. The Computational Fluid Dynamics (CFD) method is employed to simulate the flow fields in the street canyon and indoor environment. Ventilation conditions of single-side ventilation mode and cross-ventilation mode are investigated. Air change rate, normalized concentration of traffic-related air pollutant (CO), intake fraction and exposure concentration are calculated to for ventilation efficiency investigation and exposure assessment. The results show that cross ventilation increases the air change rate for residential buildings under isothermal conditions. With the indoor-outdoor temperature difference, heating could increase the air change rate of the single-side ventilation mode but restrain the capability of the cross-ventilation mode in part of the floors. Heavier polluted areas appear in the upstream areas of single-side ventilation modes, and the pollutant can diffuse to middle-upper floors in cross-ventilation modes. Cross ventilation mitigates the environmental health stress for the indoor environment when indoor-outdoor temperature difference exits and the personal intake fraction is decreased by about 66% compared to the single-side ventilation. Moreover, the existence of indoor-outdoor temperature differences can clearly decrease the risk of indoor personal exposure under both two natural ventilation modes. The study numerically investigates the building ventilation and pollutant dispersion in the urban community with natural ventilation. The method and the results are helpful references for optimizing the building ventilation plan and improving indoor air quality.

Three-dimensional spatial modelling of traffic-induced urban air pollution using the Graz Lagrangian model and GIS

Air pollution is a significant global problem that affects climate, human, and ecosystem health. Traffic emissions are a major source of atmospheric pollution in large cities. The aim of this research was to support air quality analysis by spatially modelling traffic-induced air pollution dispersion in urban areas at the street level. The dispersion model called the Graz Lagrangian model (GRAL model) was adapted to determine the NOx concentration level based on traffic, meteorology, buildings, and street configuration data in one of Tehran’s high traffic routes. In this case, meteorological parameters such as wind speed and direction were considered significant factors. Later, using local and general auto-correlation analyses, temporal and spatial variations in the concentration of NOx were measured at different altitudes. The results showed that the average output concentration of NOx pollutants at different altitudes ranges from 64.5 to 426.6 ppb. The resulting Moran index equals to 0.7–0.9 which indicates a high level of positive spatial auto-correlation. The analysis of the local Moran index represents the overcame pollution clusters with high levels of concentration at low to medium heights and the rise in clusters with low pollution at higher heights, while there is no clear clustering in the middle sections. In addition, the study of pollutant concentration variations over time has shown that pollution peaks occur at 07:00–08:00 and 21:00–22:00.

Simulation of air pollution dispersion in Dhaka city street canyon

Pollutant dispersion in urban areas has been a key concern in densely populated cities because pollutants can negatively impact human health and the environment. Topography and urban obstructions, such as buildings, greatly affect the atmospheric fluid flow, leading to the dispersion process. Street canyons are usually formed in highly populous cities due to the close proximity of buildings to streets to serve commercial purposes. It can also be termed as pollutant traps that exact adverse effects on urban life. Street canyons cause changes in pollutant dispersion, particularly in the case of vehicle exhaust air pollutants, which cannot be transported by the wind owing to the presence of buildings acting as impediments regardless of the wind flow. Hence, it is imperative to completely understand the behavior of pollutants within the confined urban surroundings for further improving the urban air quality. This study explores the effects of street canyons on the busiest street of Dhaka city in Bangladesh. The Reynolds-averaged Navier–Stokes based dispersion model is used to investigate the air quality phenomenon. Building height, width, building distance, and road width have been taken into consideration in the simulation. It is expected that the findings of the current study would be helpful for urban planners and designers of Dhaka city.

Simulation of a dense gas chlorine release with a Lagrangian particle dispersion model (LPDM)

In 2015, 2016, nine chlorine releases experiments took place at Dugway Proving Ground (Utah, USA). Five trials conducted in 2015 featured a grid of CONEX shipping containers around the release tank to simulate an idealized urban area. The trials were a great occasion to collect data describing the dynamics of “high molecular weight” species in an idealized urban environment.This paper proposes a comparison of the PMSS (Parallel Micro-Swift Micro-Spray) suite against the experimental data from four of the trials, following the choice made by an international model inter-comparison team. PMSS combines two MPI parallelized urban modelling subsystems: (1) PSWIFT phenomenologically accounts for 3D wind flow and turbulence around buildings, and (2) PSPRAY simulates air-based contaminant dispersion in urban areas using a stochastic Lagrangian Particle Dispersion Model.For this comparison, PMSS is deployed first on a hypothetic configuration and compared to the Code_Saturne CFD model, and then using the data of the trials 1, 5, 6 and 7 performed during JRII (Jack Rabbit II) 2015 and 2016. The initial momentum due to the tank discharge and the dense gas and droplet effects of the pressurized chlorine release are modelled using a dedicated radial source term jet and the Glendening equations. In trials 1 and 5, the influence of the CONEX shipping containers on the wind flow and the dispersion is explicitly considered. While spread through a large interval, the chlorine concentrations are computed both in the near field and in the far field (up to 11 km) using PMSS nesting capabilities. The results of the model are compared with the experimental data and sensitivity tests are also presented.

A novel approach to simulate pollutant dispersion in the built environment: Transport-based recurrence CFD

The large-scale practical application of Large-Eddy Simulation (LES) for predicting long-term wind flow and pollutant dispersion in urban areas is inhibited mainly by the associated very large computational costs. To overcome this difficulty, the present study, for the first time, applies transport-based recurrence Computational Fluid Dynamics (rCFD) to simulate atmospheric pollutant dispersion around a building. A novel diffusion model is proposed to accurately predict pollutant transport with rCFD. To illustrate the feasibility and advantages of rCFD, pollutant dispersion around an isolated cubical building with a rooftop vent, immersed in neutral atmospheric boundary layer flow is used as a case study and both LES and rCFD simulations are conducted. It is shown that rCFD simulation results agree well with those from LES both in terms of mean and fluctuating concentrations while the simulation wall-clock time drops from 222 h to 16 min. The application of four evaluation metrics (FAC2, FB, NMSE and R) indicates very good agreement between LES and rCFD results. Another major advantage of rCFD is that different pollutant events can be simulated promptly once the database has been stored for a given flow configuration, as shown by the comparison of LES and rCFD results for two other cases with different release locations. This study extends the application of transport-based rCFD to pollutant dispersion in the built environment and indicates that rCFD is a promising approach to facilitate the large-scale practical application of LES for this type of applications.

CFD dispersion study based on a variable Schmidt formulation for flows around different configurations of ground-mounted buildings

Computational fluid dynamics (CFD) has become a reference tool for the investigation of pollutant emission and dispersion in urban areas, and for the assessment of the associated risk. In this framework, specific focus is given to the estimation of the downwind and ground level concentration of air pollutants coming from emission sources such as vehicular traffic, industrial plants or accidental events. Pollutant dispersion in the Atmospheric Boundary Layer (ABL) is strongly impacted by the turbulence. This leads to a complex coupled problem, considering the reciprocal influence of the two phenomena. A key role in pollutant dispersion is played by the turbulent Schmidt number Sct which directly affects the turbulent dispersion coefficient and, consequently, the concentration field. No universally-accepted formulation for the turbulent Schmidt number exists in the literature, although its impact on the prediction of pollutant dispersion is recognised. Stemming from a brief review of the existing literature and knowledge on the topic, this paper aims to propose a novel approach for the optimal determination of Sct, also through the use of uncertainty quantification. The proposed Sct is based on the local turbulece level, and is validated on different idealized test cases, representative of typical urban configurations.

Propagation of toxic substances in the urban atmosphere: A complex network perspective

The accidental or malicious release of toxic substances in the urban atmosphere is a major environmental and safety problem, especially in large cities. Computational fluid dynamics codes and simplified modelling tools have been used in the last decades to model pollutant dispersion in urban areas. These studies have shown that propagation is strongly influenced by the layout of buildings and, therefore, by the street topology of the city. This work presents a novel approach to the study of toxic propagation within the urban canopy based on the theory of complex networks. The urban canopy is modelled as a network, where the streets and the street intersections represent respectively the links and the nodes of the network. The direction and the weights of the links contain the geometrical characteristics of the street canyons and their wind conditions, so that all the key variables involved in pollutant dispersion are represented in a single mathematical structure that is a weighted and directed complex network. Thanks to this mathematical interpretation, propagation is modelled as a spreading process on a network and a depth-first search algorithm is used to rapidly delimit the zone of influence of a source node. This zone is the set of streets that are contaminated from the source. As a case study, the proposed model is applied to the urban tissue of the city of Lyon. The algorithm simulates a toxic release in all the nodes of the network and computes the number of people affected by each propagation process. In this way, the nodes with the most dangerous spreading potential are identified and vulnerability maps of the city are constructed. Moreover, various wind and concentration scenarios are easily implemented. The results highlight how the proposed method is effective in assessing the most vulnerable points in a city with a computational time that is up to three orders of magnitude lower than that of existing models. Moreover, the proposed approach paves the way to future applications of tools and metrics from the complex network theory for a deeper comprehension of the mechanisms that drive pollutant dispersion in urban areas.

Bayesian inversion of inflow direction and speed in urban dispersion simulations

Computational fluid dynamics (CFD) is a powerful tool to investigate airflow and pollutant dispersion in urban areas and can provide critical information for urban planning and public health assessment. Although CFD approaches can reproduce detailed airflow and dispersion results in complex areas, it is extremely sensitive to inflow parameters. The conventional way to set inflow conditions is to measure the mean wind direction at a single upwind detector and adopt it as the inflow direction in the CFD model. In this paper, a novel method is proposed to provide an alternative way to obtain the inflow parameters in urban dispersion simulations by using multiple near-field wind detectors within urban areas and a probabilistic inverse method, the Bayesian inference. A field release experiment in a real built-up area is adopted as the test case. Simulation results of both wind and concentration are compared between the proposed and conventional methods. The proposed method provides the probability distributions of inflow parameters, which well reproduce the near-field airflow field in the urban area. With respect to the dispersion simulation, the statistical analysis shows better performance of the proposed method in the point estimates of concentrations than the conventional method. In addition, the proposed method yields the probability distributions of predicted concentrations, which offers a rigorous quantification of uncertainties in urban dispersion simulations.

CFD Simulation of Automotive Pollutant Dispersion in High-Rise Building Urban Environment Under Deeply Stable Atmospheric Condition

The layer of atmosphere adjacent to the earth’s surface which is affected by friction, heat transfer and pollution from the surface is called the atmospheric boundary layer (ABL). At nighttime, the earth’s surface become colder than the upper atmospheric layers. The thermal stratification with heavy cold layers close to the ground and light hot ones upwards dampens mixing currents in the atmosphere; a condition named as stable ABL. The absence of mixing at night causes the pollutants released from ground sources, such as automotive transportation, to settle in the layers close to the earth which affects human health. This research is a CFD investigation of the effect of building density on pollutant dispersion in urban areas under severe atmospheric stability condition. Three plane area densities were examined; 35, 25 and 15%. Carbon dioxide was considered as the pollutant. Large eddy simulation (LES) was utilized in the simulation. The results have proven the positive effect of building structures in dispersing pollutants. However, high building densities above 25% trap high concentrations of pollutants at the pedestrian level. The research may offer recommendations for the city planners and legislators about traffic pollution and architectural planning.

On the urban geometry generalization for CFD simulation of gas dispersion from chimneys: Comparison with Gaussian plume model

This article is a contribution to the geometry generalization for computational fluid dynamics (CFD) simulations of gas dispersion in urban areas. The CO2 emission from a natural gas-fueled thermal power plant is simulated using different generic urban patterns. The buildings distribution, the built density and the height of the buildings were analyzed. Both buildings distribution and built density did not show significant effect on the gas concentration over the city, whereas the average height of the buildings shows a clear influence on the vertical profile of the gas concentration. The results of the general patterns were compared with a real city (Munich, Germany), obtaining agreement between both generalized and real city geometries. Additionally, the vertical profiles of gas concentration were compared with the Gaussian plume model, and a new equation for computing the vertical dispersion parameter is proposed for urban environments as a function of the averaged buildings height.

Accuracy assessment of noise mapping on the main street

Among the sounds constituting a part of our daily lives, the undesired and disturbing ones are named noise. The noises can be defined as the acoustic energy that affects the physiological and psychological welfare of humans due to its direct or indirect effects on the human health. But, the traffic noise, which is one of the disturbing noises having wide dispersion in urban areas, affects the health of a gradually increasing number of people. The number of studies on measurement, dispersion, and mapping of traffic noise in developed and developing countries is gradually increasing. It can be seen that the studies concentrate on mapping based on the city centers and neighborhoods, where the traffic is intense. In this article, unlike the regional noise measurement studies, the transversal cross sections were established with a certain interval according to the land status along the main street, and the noise measurement points were determined using the global navigation satellite system (GNSS). The daily mean noise levels (Lden) were calculated by performing the measurements at daytime, evening, and nighttime. Using ArcGIS 10.4 software, the accuracy of noise maps created via inverse distance weighting (IDW), radial basis function (RBF), and ordinary kriging (OK) geostatistical analysis interpolation methods and the dispersion effect of traffic noise were compared. According to the visual method and error corrections, it was found that the best noise map was obtained using the RBF method. It is believed that the map provides accurate and reliable results because of the flat land surface, higher noise values measured on the road region, decrease in the noise at the points farther from the road region, and soft transitions between the noise values depending on the distance from the road. In order to take measures in order to decrease or eliminate noise on the main street, the local administrators and decision-makers can make use of these maps, the accuracy of which has been confirmed.

A New Scheme for the Simulation of Microscale Flow and Dispersion in Urban Areas by Coupling Large-Eddy Simulation with Mesoscale Models

A coupling scheme is proposed for the simulation of microscale flow and dispersion in which both the mesoscale field and small-scale turbulence are specified at the boundary of a microscale model. The small-scale turbulence is obtained individually in the inner and outer layers by the transformation of pre-computed databases, and then combined in a weighted sum. Validation of the results of a flow over a cluster of model buildings shows that the inner- and outer-layer transition height should be located in the roughness sublayer. Both the new scheme and the previous scheme are applied in the simulation of the flow over the central business district of Oklahoma City (a point source during intensive observation period 3 of the Joint Urban 2003 experimental campaign), with results showing that the wind speed is well predicted in the canopy layer. Compared with the previous scheme, the new scheme improves the prediction of the wind direction and turbulent kinetic energy (TKE) in the canopy layer. The flow field influences the scalar plume in two ways, i.e. the averaged flow field determines the advective flux and the TKE field determines the turbulent flux. Thus, the mean, root-mean-square and maximum of the concentration agree better with the observations with the new scheme. These results indicate that the new scheme is an effective means of simulating the complex flow and dispersion in urban canopies.

Large‐eddy simulation of vortex streets and pollutant dispersion behind high‐rise buildings

Understanding turbulent flow and pollutant dispersion in urban areas is one of the important problems in urban meteorology and environmental fluid mechanics. In this study, we examine turbulent flow and pollutant dispersion in a densely built‐up area in Seoul, South Korea, using the parallelized large‐eddy simulation model (PALM). In particular, we focus on vortex streets and associated pollutant dispersion behind high‐rise buildings. The turbulence recycling method is used to produce inflow profiles. Vortices are generated near the high‐rise buildings and propagate downstream forming vortex streets behind the high‐rise buildings. To investigate characteristics of the vortex streets, spectral and correlation analyses are performed. The spectral analysis reveals that vortices have a non‐dimensional vortex shedding frequency of 0.1–0.2, and this periodicity is weakened due to the influence of other buildings. The correlation analysis shows that vortices appear frequently in regions of negative pressure perturbation. The vertical turbulent momentum fluxes induced by ejections and sweeps largely contribute to the total vertical turbulent momentum flux downstream of the high‐rise buildings. Especially, ejections in the wake region are stronger compared to other regions because ejections are induced by vortices near the top of the high‐rise buildings. It is found that pollutant dispersion is interrupted by both low‐rise and high‐rise buildings. Strong updraughts behind the high‐rise buildings transport pollutant upward and increase the mean pollutant concentration at upper levels. Vortices forming the vortex streets play a role in pollutant mixing in such a way that the vortices eject air of high pollutant concentration from the wake region behind the high‐rise buildings and entrain air of low pollutant concentration into the wake region. The mixing by vortices is verified by the correlation between vorticity and pollutant concentration.

Google-Earth Based Visualizations for Environmental Flows and Pollutant Dispersion in Urban Areas.

In the present study, we address the development and application of an efficient tool for conversion of results obtained by an integrated computational fluid dynamics (CFD) and computational reaction dynamics (CRD) approach and their visualization in the Google Earth. We focus on results typical for environmental fluid mechanics studies at a city scale that include characteristic wind flow patterns and dispersion of reactive scalars. This is achieved by developing a code based on the Java language, which converts the typical four-dimensional structure (spatial and temporal dependency) of data results in the Keyhole Markup Language (KML) format. The visualization techniques most often used are revisited and implemented into the conversion tool. The potential of the tool is demonstrated in a case study of smog formation due to an intense traffic emission in Rotterdam (The Netherlands). It is shown that the Google Earth can provide a computationally efficient and user-friendly means of data representation. This feature can be very useful for visualization of pollution at street levels, which is of great importance for the city residents. Various meteorological and traffic emissions can be easily visualized and analyzed, providing a powerful, user-friendly tool for traffic regulations and urban climate adaptations.