Dispersion In Urban Environment Research Articles

CFD- and BPNN- based investigation and prediction of air pollutant dispersion in urban environment

This study employed a Computational Fluid Dynamics (CFD)-based back propagation neural network (BPNN) to investigate and predict the pollutant dispersion in an ideal urban environment. The training and test datasets were generated using the Reynolds-averaged Navier–Stokes (RANS) model, which involved 60 cases by varying reference inflow speeds, NO & NO2, and ambient O3 concentrations. The results indicated that the ambient flow was gradually decoupled from the reversed flow at each building's leeward zone further downstream, leading to heightened difficulty in pollutant elimination. Upstream buildings located in front of the emission source were rarely affected by pollutants, regardless of changes in Damköhler numbers (Da). While varying DaNO provides a limited influence, confining DaO3 can effectively reduce hazardous pollutants' impact around the emission source vicinity, on the urban streets, and in the further downstream area of the building array. A budget analysis showed that increasing DaO3 primarily enlarged the chemical reactions’ effects on NO increase within the urban residential area. Finally, the BPNN model demonstrated a high accuracy in predicting wind speed and NO concentration within a few seconds. These findings provide valuable insights for designing effective strategies to mitigate hazardous pollutants’ impacts in urban environments.

Evaluation of Turbulence and Dispersion in Multiscale Atmospheric Simulations over Complex Urban Terrain during the Joint Urban 2003 Field Campaign

Abstract This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment. Significance Statement Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments.

A Review on Atmospheric Dispersion System for Air Pollutants Integrated with GIS in Urban Environment

The objective of this article is to present comprehensive findings and analysis of studies performed on air pollutant dispersion in urban environments. It captures India’s rising environmental pollution due to urbanization, industrialization, and population growth. Dispersion of pollutants due to the wind in the lower Atmospheric Boundary Layer (ABL) is a major concern nowadays. The dispersion field around the buildings is a critical parameter to analyze and it primarily depends on the correct simulation of the wind flow structure. Therefore, studies performed on this in past years are being reviewed. Additionally, a brief review of different air dispersion models that are integrated with the Geographic Information System (GIS) has been studied in this article to assess the exposure. The results of these studies provide the urban air dispersion model aligning to three sub-models i.e., Emission, Weather Prediction, and Dispersion models. Various factors like wind speed, wind direction, cloud cover, traffic emission, disposal of waste, transportation, and others are considered. This study also captures the problems and risks being faced while creating a model, and its possible mitigation approaches.

Recent developments and issues of small-scale wind turbines in urban residential buildings- A review

To address the future growth in energy demands, complete renewable power generation from environmental assets demands unique methodologies from every investigator. Wind energy harvesting in residential areas is one such approach. But the urban landscape and the physical challenges restrict the erection of small-scale wind turbines. Further, a significant degree of uncertainty leads to a lack of awareness of how dispersion in urban environments impacts turbine performance. This research looks into the potential productivity of wind turbine installations in urban areas, considering turbulence. These unique ambient conditions prevailing in a dynamic environment compared to flat terrains have made the harnessing of wind energy very difficult. Many researchers are still trying to find an effective methodology in these constrained circumstances. Since the characteristics of small-scale wind turbine models are undetermined, large-scale turbines fared well compared to small-scale wind turbine models. The feasibility and success of small and medium turbines in community homes were investigated in this study. The origins of the very uncertain nature of atmospheric boundary layer limitations are discussed in this work and subsequent successful developments by different researchers.

Uncertainty Quantification of Inflow on Passive Scalar Dispersion in Urban Environment

Uncertainty Quantification of Inflow on Passive Scalar Dispersion in Urban Environment

CFD simulations of wind flow and pollutant dispersion in a street canyon with traffic flow: Comparison between RANS and LES

• CFD simulations for a street canyon with moving traffic are systematically validated. • Among the five RANS models, SKE has the best performance against wind tunnel data. • All RANS models overestimate pollutant concentration near moving traffics. • LES outperforms the RANS simulation in predicting mean velocity and concentration. • Pollutant concentrations are strongly affected by turbulent diffusion fluxes. The dispersion of traffic pollutants in street canyons takes place under the joint influence of the urban morphology and traffic-induced air motions. Computational fluid dynamics (CFD) are increasingly used to explore air quality problems in cities, however, numerical setup guidance for studies considering moving traffics remains missing. This study presents a systematic evaluation and comparison of steady Reynolds-averaged Navier-Stokes (RANS) and large-eddy simulation (LES) in reproducing the wind flow and pollutant dispersion in a street canyon under traffic flow using the quasi-steady method. The RANS simulations are performed with five turbulence models, and the standard k-ε (SKE) turbulence model is found to yield the best agreement with the wind-tunnel data. The LES simulation with the wall-adapting local eddy-viscosity subgrid-scale model outperforms all RANS models in simulating the mean velocity and mean pollutant concentration, and can accurately predict the turbulent velocity. The counter-gradient turbulent mass flux captured by LES reveals that the gradient-diffusion hypothesis used in steady RANS fails in the regions near moving traffics, which strongly affects the predicted mean concentration. The findings of this study can support future CFD studies on pollutant dispersion in urban environments.

Magnetic Resonance Imaging measurements of scalar dispersion for a scaled urban transient release

Atmospheric dispersion in urban environments is highly complex, and a fundamental understanding requires a combination of field tests, laboratory experiments, and simulations. In this study, a transient release dispersion experiment was performed involving a scaled model of Oklahoma City, with the intention of being relevant for Joint Urban 2003 (JU2003) related studies. Flow over the city model was fully turbulent and housed in a water channel, with a characteristic building Reynolds number of 6200. The transient release was achieved by injecting a passive scalar at street level following a square waveform with a frequency of 1 Hz. Magnetic Resonance Imaging (MRI) techniques were leveraged to obtain phase-averaged velocity and concentration measurements over 12 intervals throughout the transient release cycle, with average uncertainties of 5.4% and 2.2% of the bulk velocity and injected concentration, respectively. Velocity and concentration measurements are obtained on three-dimensional grids of roughly 2 million data points each. An investigation of three-dimensional flow and dispersion features is presented, and a control volume encompassing an intersection within the city model is analyzed by applying the scalar transport equation to the experimental data. The phase-averaged measurements enable the calculation of scalar flux time series as well as the transient storage term. Scalar flux and transient storage indicate complex dispersion characteristics, and can potentially be used to infer the turbulent diffusion of contaminant. The MRI datasets can provide a reference for other atmospheric dispersion modelers and experimentalists, and the corresponding analyses demonstrate particular advantages of MRI data for model evaluation.

Effect of building cross-section shape on air pollutant dispersion around buildings

This research investigates the effect of building cross-section shape on the air pollutant dispersion around an isolated building. A Computational Fluid Dynamic (CFD) model was developed and validated based on detailed wind tunnel experimental data. Both Large Eddy Simulation (LES) and Realizable k-ε models were used for turbulence closure and LES was found to provide a better agreement with experimental results than the RANS model, particularly in replicating pollutant dispersion characteristics associated with wind-structure interaction. The LES model was then utilized to study the effect of building cross-section shape. Four building cross-section shapes, square, chamfered, curved and circular, along with different air pollutant emission locations were studied. It has been found that the area around an isolated building can be categorized into three emission regions, namely the windward critical emission region, the leeward critical emission region and the side non-critical emission region. The air pollutant emissions from the leeward critical emission region affect the building the most. Furthermore, the size of this region depends on the building cross-section shape, with the circular and curved buildings having a smaller leeward critical emission region than those for the chamfered and square buildings. Evidently, air pollutant dispersion around a building is dominated by wind-structure interaction which in turn is dependent on the building cross-sectional shape. These characteristics can be utilized to mitigate air pollutant dispersion in urban environments in conjunction with careful consideration of the air pollutant emission location relative to a building.

Quantifying inflow uncertainties in RANS simulations of urban pollutant dispersion

Numerical simulations of flow and pollutant dispersion in urban environments have the potential to support design and policy decisions that could reduce the population's exposure to air pollution. Reynolds-averaged Navier-Stokes simulations are a common modeling technique for urban flow and dispersion, but several sources of uncertainty in the simulations can affect the accuracy of the results. The present study proposes a method to quantify the uncertainty related to variability in the inflow boundary conditions. The method is applied to predict flow and pollutant dispersion in downtown Oklahoma City and the results are compared to field measurements available from the Joint Urban 2003 measurement campaign. Three uncertain parameters that define the inflow profiles for velocity, turbulence kinetic energy and turbulence dissipation are defined: the velocity magnitude and direction, and the terrain roughness length. The uncertain parameter space is defined based on the available measurement data, and a non-intrusive propagation approach that employs 729 simulations is used to quantify the uncertainty in the simulation output. A variance based sensitivity analysis is performed to identify the most influential uncertain parameters, and it is shown that the predicted tracer concentrations are influenced by all three uncertain variables. Subsequently, we specify different probability distributions for the uncertain inflow variables based on the available measurement data and calculate the corresponding means and 95% confidence intervals for comparison with the field measurements at 35 locations in downtown Oklahoma City.

Simulation of gaseous pollutant dispersion around an isolated building using the k–ω SST (shear stress transport) turbulence model

ABSTRACTThe dispersion of gaseous pollutant around buildings is complex due to complex turbulence features such as flow detachment and zones of high shear. Computational fluid dynamics (CFD) models are one of the most promising tools to describe the pollutant distribution in the near field of buildings. Reynolds-averaged Navier-Stokes (RANS) models are the most commonly used CFD techniques to address turbulence transport of the pollutant. This research work studies the use of closure model for the gas dispersion around a building by fully resolving the viscous sublayer for the first time. The performance of standard model is also included for comparison, along with results of an extensively validated Gaussian dispersion model, the U.S. Environmental Protection Agency (EPA) AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model). This study’s CFD models apply the standard and the turbulence models to obtain wind flow field. A passive concentration transport equation is then calculated based on the resolved flow field to simulate the distribution of pollutant concentrations. The resultant simulation of both wind flow and concentration fields are validated rigorously by extensive data using multiple validation metrics. The wind flow field can be acceptably modeled by the model. However, the model fails to simulate the gas dispersion. The model outperforms in both flow and dispersion simulations, with higher hit rates for dimensionless velocity components and higher “factor of 2” of observations (FAC2) for normalized concentration. All these validation metrics of model pass the quality assurance criteria recommended by The Association of German Engineers (Verein Deutscher Ingenieure, VDI) guideline. Furthermore, these metrics are better than or the same as those in the literature. Comparison between the performances of and AERMOD shows that the CFD simulation is superior to Gaussian-type model for pollutant dispersion in the near wake of obstacles. AERMOD can perform as a screening tool for near-field gas dispersion due to its expeditious calculation and the ability to handle complicated cases. The utilization of to simulate gaseous pollutant dispersion around an isolated building is appropriate and is expected to be suitable for complex urban environment.Implications: Multiple validation metrics of turbulence model in CFD quantitatively indicated that this turbulence model was appropriate for the simulation of gas dispersion around buildings. CFD is, therefore, an attractive alternative to wind tunnel for modeling gas dispersion in urban environment due to its excellent performance, and lower cost.

Validation and optimization of SST k-ω turbulence model for pollutant dispersion within a building array

The prediction of the dispersion of air pollutants in urban areas is of great importance to public health, homeland security, and environmental protection. Computational Fluid Dynamics (CFD) emerges as an effective tool for pollutant dispersion modelling. This paper reports and quantitatively validates the shear stress transport (SST) k-ω turbulence closure model and its transitional variant for pollutant dispersion under complex urban environment for the first time. Sensitivity analysis is performed to establish recommendation for the proper use of turbulence models in urban settings. The current SST k-ω simulation is validated rigorously by extensive experimental data using hit rate for velocity components, and the “factor of two” of observations (FAC2) and fractional bias (FB) for concentration field. The simulation results show that current SST k-ω model can predict flow field nicely with an overall hit rate of 0.870, and concentration dispersion with FAC2 = 0.721 and FB = 0.045. The flow simulation of the current SST k-ω model is slightly inferior to that of a detached eddy simulation (DES), but better than that of standard k-ε model. However, the current study is the best among these three model approaches, when validated against measurements of pollutant dispersion in the atmosphere. This work aims to provide recommendation for proper use of CFD to predict pollutant dispersion in urban environment.

LES and RANS comparison of flow and pollutant dispersion in urban environment

This study investigates air pollution dispersion in urban areas by means of Computational Fluid Dynamics (CFD). The commercial CFD software FLUENT was used to implement two different turbulence simulation methods (RANS and LES), in domains similar to complex urban environments. Particularly, different combinations of roof shapes were studied and simulation results of pollutant (ethane) concentrations were compared against experimental data. The building height (H) to the neighbour building distance (B) ratio was also taken into consideration. Previous studies showed that both RANS and LES models are accurate enough to predict pollutant concentrations fields in B/H = 1. In the present study the incapability of RANS models to predict accurately pollutant concentration in B/H = 0.5 for all roof shapes configurations is revealed.

The influence of an obstacle on flow and pollutant dispersion in neutral and stable boundary layers

Predicting pollutant dispersion in urban environments requires accurate treatment of obstacle geometry, inflow turbulence and temperature differences. This paper considers both the influence of thermal stratification and the presence of a single obstacle on pollutant dispersion in turbulent boundary layers (TBLs). Turbulent flow over a fence with line sources of pollutant in its vicinity is simulated by means of Large-Eddy Simulations. Separate ‘driver’ simulations are done to generate the inflow TBL for several levels of stratification. Using these inflow TBLs the flow development and pollutant dispersion behind the fence, up to 100 fence heights, h, is investigated. It is shown that the decay of velocity and temperature deficit is independent of stability, while the decay of Reynolds stress and concentration excess decreases with increasing stability. For neutral cases the influence of the obstacle is gone after approximately 75h, while for stable cases near the ground the flow is still accelerated compared to the undisturbed case. The fence does cause a local reduction of stratification and thereby increased pollutant dispersion. However, neglecting the effect of buoyancy results in an underestimation of pollutant concentration by a factor 2.5 at 75h downstream of the emission source for the most stable case.

Quantifying inflow uncertainties for CFD simulations of the flow in downtown Oklahoma City

Computational Fluid Dynamics (CFD) methods are widely used to investigate wind flow and dispersion in urban environments. Validation with field experiments that represent the full complexity of the problem should be performed to assess the predictive capabilities of the computations. In this context it will be necessary to quantify the effect of uncertainties in simulations of the full-scale problem. The present study aims at quantifying the uncertainty related to the variability in the inflow boundary conditions for Reynolds-averaged Navier–Stokes (RANS) simulations of the flow in downtown Oklahoma City to address validation with the Joint Urban 2003 field measurements. Three uncertain inflow parameters were defined: the wind speed and wind direction at a reference height, and the aerodynamic roughness in the logarithmic velocity inlet profile. An ensemble of 729 RANS simulations were performed to determine the polynomial chaos expansion coefficients that define the response surfaces for the velocity magnitude and direction at 13 field measurement stations, and the results are compared to the experimental data. For the velocity magnitude the mean experimental velocity magnitude is encompassed within the 95% confidence interval for the magnitudes predicted by the Uncertainty Quantification study in all stations. For the velocity direction this holds in 11 out of 13 locations. The study demonstrates the significant potential of applying advanced uncertainty quantification methods to address validation with field measurements and to develop a more realistic approach to the definition of inflow boundary conditions in atmospheric CFD simulations.

Large-eddy simulation of approaching-flow stratification on dispersion over arrays of buildings

The study investigates thermal stratification effects of approach flows on dispersion in urban environments. This is in some ways analogous to a well-developed non-neutral flow (e.g. through a large urban area) approaching a neighbourhood-scale urban region, where the effect of the local heat transfer was assumed less important. A generic urban-type geometry, i.e. a group of staggered cubes, was taken as the first test case. The DAPPLE site, which was about a one-km2 region near the intersection of Marylebone Road and Gloucester Place in central London, was taken as the second test case. Only weakly unstable conditions (i.e. bulk Richardson number Rb≥−0.2) of approach flows were considered, with adiabatic boundary conditions at the ground and building surfaces. A number of numerical experiments were performed. The modelled mean concentration for Rb = −0.1 gave the best agreement with the field data at all DAPPLE stations. This suggests that stratification effects on dispersion in weakly unstable conditions (e.g. in London) are not negligible.

Assessment of near-field pollutant dispersion: Effect of upstream buildings

The prediction of pollutant dispersion in urban environment is an extremely complex phenomenon, particularly in the vicinity of a cluster of buildings. Dispersion of effluents released from stacks located on building roofs are severely affected by adjacent surroundings. This paper investigates the impact of an upstream building on the near field of a pollutant source in terms of dilution distribution on the roof of an emitting building. The study was carried out using Computational Fluid Dynamics (CFD) approach with Realizable k–ε for turbulent flow modeling. A limited number of cases were also modeled in a wind tunnel for validation purposes. The study shows that when the source is located within the recirculation zone, dilution is highly sensitive to the height of the upstream building and much less sensitive to the width and length of the upstream building. It is also shown that dilution value has an asymptotic behavior which defines the particular point where dilution becomes independent of the upstream building configuration. Some discrepancies between CFD and wind tunnel data were found, specifically for extreme configurations e.g. significantly taller upstream building. These differences are mainly due to the inherent unsteady fluctuations in the wake of buildings which are not detectable by RANS.

Large-eddy simulation of stratification effects on dispersion in urban environments

This paper focuses on investigating thermal buoyancy effects on dispersion of approach flows and local heat transfer from/to buildings by taking the DAPPLE site as the test case but for varying wind direction and magnitude. Firstly, based on the BT tower data, only weakly unstable conditions (i.e. Richardson number |Ri|<1) of approach flows were considered, with adiabatic boundary conditions at the ground and building surfaces. It was found that the predicted concentration in the far field was significantly reduced. Secondly, numerical experiments were performed, i.e. using various Richardson numbers. It was found that the modelled mean concentration for bulk Richardson number −0.9 was in reasonable agreement with field data at all stations. Thirdly, to understand the effects of weakly local unstable conditions, non-adiabatic boundary conditions at ground and building surfaces were implemented as well as weakly unstable conditions in the approach flows. No significant difference was found comparing with those using adiabatic boundary conditions. These simulations suggest that stratification effects on dispersion in weakly unstable conditions (e.g. in London) are not negligible.

Improved parameterisation for the numerical modelling of air pollution within an urban street canyon

Numerical modelling for application to wind flow and dispersion in urban environments has noticeably progressed in recent years, to currently represent a widely used tool for simulating mechanical processes governing air pollution in complex geometries. In particular, Computational Fluid Dynamic (CFD) techniques based on RANS (Reynolds-Averaged Navier–Stokes equations) models, are extensively used to produce detailed simulations of the wind flow and turbulence in the urban canopy. However, several studies have indicated that RANS models, and in particular the widely used standard k– ɛ turbulence model, are sensitive to the particular form of inlet profiles for turbulence and velocity. In the present study, simulations of the wind flow and dispersion within an idealised street canyon were carried out using the standard k– ɛ turbulence model provided by the commercial software FLUENT. The aim of this study was to improve the standard k– ɛ model performance by modifying the model parameters according to the chosen form of inlet profiles for velocity and turbulence. Capability of the model to reproduce real wind flow fields, turbulence and concentration patterns was evaluated by comparing the model results against recently published wind tunnel data. Results for turbulent kinetic energy and concentration showed that the redefinition of the default dispersive parameters can significantly enhance the model performance. The newly proposed parameterisations of the standard k– ɛ turbulence model can be readily implemented within commercial CFD software packages, offering a reliable modelling tool for application to urban air pollution and other environmental studies.

Source Inversion for Contaminant Plume Dispersion in Urban Environments Using Building-Resolving Simulations

AbstractThe ability to determine the source of a contaminant plume in urban environments is crucial for emergency-response applications. Locating the source and determining its strength based on downwind concentration measurements, however, are complicated by the presence of buildings that can divert flow in unexpected directions. High-resolution flow simulations are now possible for predicting plume evolution in complex urban geometries, where contaminant dispersion is affected by the flow around individual buildings. Using Bayesian inference via stochastic sampling algorithms with a high-resolution computational fluid dynamics model, an atmospheric release event can be reconstructed to determine the plume source and release rate based on point measurements of concentration. Event-reconstruction algorithms are applied first for flow around a prototype isolated building (a cube) and then using observations and flow conditions from Oklahoma City, Oklahoma, during the Joint Urban 2003 field campaign. Stochastic sampling methods (Markov chain Monte Carlo) are used to extract likely source parameters, taking into consideration measurement and forward model errors. In all cases the steady-state flow field generated by a 3D Navier–Stokes finite-element code (FEM3MP) is used to drive thousands of forward-dispersion simulations. To enhance computational performance in the inversion procedure, a reusable database of dispersion simulation results is created. It is possible to successfully invert the dispersion problems to determine the source location and release rate to within narrow confidence intervals even with such complex geometries. The stochastic methodology here is general and can be used for time-varying release rates and reactive flow conditions. The results of inversion indicate the probability of a source being found at a particular location with a particular release rate, thus inherently reflecting uncertainty in observed data or the lack of enough data in the shape and size of the probability distribution. A composite plume showing concentrations at the desired confidence level can also be constructed using the realizations from the reconstructed probability distribution. This can be used by emergency responders as a tool to determine the likelihood of concentration at a particular location being above a threshold value.

Urban Dispersion Modeling: Comparison with Single-Building Measurements

Abstract Two models have been developed to predict airflow and dispersion in urban environments. The first model, the Realistic Urban Spread and Transport of Intrusive Contaminants (RUSTIC) model, is a fast-running urban airflow code that rapidly converges to a numerical solution of a modified set of the compressible Navier–Stokes equations. RUSTIC uses the k–ω turbulence model with a buoyancy production term to handle atmospheric stability effects. The second model, “MESO,” is a Lagrangian particle transport and dispersion code that predicts concentrations of a released chemical or biological agent in urban or rural areas. As a preliminary validation of the models, concentrations simulated by MESO are compared with experimental data from wind-tunnel testing of dispersion around both a multistory rectangular building and a single-story L-shaped building. For the rectangular building, trace gas is forced out at the base of the downwind side, whereas for the L-shaped building, trace gas is forced out of a side door in the inner corner of the “L.” The MESO–RUSTIC combination is set up with the initial conditions of the wind-tunnel experiment, and the steady-state concentrations simulated by the models are compared with the wind-tunnel data. For the multistory building, a dense set of detector locations was available downwind at ground level. For the L-shaped building, concentration data were available at three heights in a lateral plane at a distance of one building height downwind of the lee side. A favorable comparison between model simulations and test data is shown for both buildings.