Prediction of atmospheric dispersion of pollutants in an airport environment

In this article we discuss the development of a methodology to predict atmospheric turbulent dispersion of pollutants generated from air traffic in an airport. It is based on the Lagrangian stochastic model (LSM), developed by Das and Durbin [2005. A Lagrangian stochastic model for dispersion in stratified turbulence, Physics of Fluids 17, 025109]. The approach is via the backward trajectory formulation of the model. The sources and receptors in an airport type problem are modeled as spheres and procedures have been derived for concentration calculation by both forward and backward trajectory methods. Some tests are performed to highlight certain features of the method. The turbulence statistics that are required as input are provided in terms of similarity profiles. The airport domain is partitioned to make the required search algorithms efficient. Pollutant concentration profiles are calculated over a range of meteorological data.     

A statistical approach for estimating uncertainty in dispersion modeling: An example of application in southwestern USA

A method based on a statistical approach of estimating uncertainty in simulating the transport and dispersion of atmospheric pollutants is developed using observations and modeling results from a tracer experiment in the complex terrain of the southwestern USA. The method takes into account the compensating nature of the error components by representing all terms, except dispersion error and variance of stochastic processes. Dispersion error and the variance of the stochastic error are estimated using the maximum likelihood estimation technique applied to the equation for the fractional error. Mesoscale Model 5 (MM5) and a Lagrangian random particle dispersion model with three optional turbulence parameterizations were used as a test bed for method application. Modeled concentrations compared well with the measurements (correlation coefficients on the order of 0.8). The effects of changing two structural components (the turbulence parameterization and the model grid vertical resolution) on the magnitude of the dispersion error also were examined. The expected normalized dispersion error appears to be quite large (up to a factor of three) among model runs with various turbulence schemes. Tests with increased vertical resolution of the atmospheric model (MM5) improved most of the dispersion model statistical performance measures, but to a lesser extent compared to selection of a turbulence parameterization. Method results confirm that structural components of the dispersion model, namely turbulence parameterizations, have the most influence on the expected dispersion error.     

A fluctuating plume model for concentration fluctuations in a plant canopy

A one-particle Lagrangian model for continuous releases in the non-Gaussian inhomogeneous turbulence of a canopy layer is derived based on the fluctuating plume model of Franzese [2003. Lagrangian stochastic modeling of a fluctuating plume in the convective boundary layer. Atmos. Environ. 37, 1691–1701.]. The model equations are filtered by a time-dependent low-pass filter applied to the turbulent kinetic energy in order to obtain a fluctuating plume model able to simulate the vertical meandering of the cloud centroid through non-stationary Lagrangian equations. The model satisfies the well-mixed condition. The relative dispersion of particles and the concentration fluctuation statistics of a passive tracer inside a modeled vegetal canopy are studied. The probability density function of the concentration relative to the plume centroid is parameterized and the mean and variance fields of concentration are simulated and compared with wind-tunnel data and numerical simulations. A skewed, reflected probability density function for the vertical position of the plume centroid is considered.     

On the influence of the urban roughness sublayer on turbulence and dispersion

The concept of the urban roughness sublayer is discussed and this lowest atmospheric layer over a rough surface is shown to have a non-negligible vertical extension over typical urban surfaces. The existing knowledge on the turbulence and flow structure within an urban roughness sublayer is reviewed, focusing on the height dependence of turbulent fluxes and a scaling approach for turbulence statistics, such as velocity variances, in the above-roof part of the roughness sublayer. Finally, the implication of this turbulence and flow structure upon dispersion characteristics is investigated. The most prominent difference of explicitly taking into account the roughness sublayer in a dispersion simulation (as compared to assuming a `constant flux layer') is a clearly enhanced ground level concentration far downwind from the source. For the example of a tracer release experiment over a (sub) urban surface (Copenhagen) it is shown that introducing the roughness sublayer clearly improves the model performance.     

Monte Carlo simulation of nitrogen oxides dispersion from a vehicular exhaust plume and its sensitivity studies

The pollutant dispersion behavior from the vehicular exhaust plume has a direct impact on human health, particularly to the drivers, bicyclists, motorcyclists, pedestrians, people working nearby and vehicle passengers. A two-dimensional pollutant dispersion numerical model was developed based on the joint-scalar probability density function (PDF) approach coupled with a k–ε turbulence model to simulate the initial dispersion process of nitrogen oxides, temperature and flow velocity distributions from a vehicular exhaust plume. A Monte Carlo algorithm was used to solve the PDF transport equations in order to obtain the dispersion distribution of nitrogen oxides concentration. The model was then validated by a series of sensitivity experimental studies in order to assess the effects of vehicular exhaust tailpipe velocities, wind speeds and chemistry on the initial dispersion of NO and NO₂ mass concentrations from the vehicular exhaust plume. The results show that the mass concentrations of nitrogen oxides decrease along the centerline of the vehicular exhaust plume in the downstream distance. The dispersion process can be enhanced when the vehicular exhaust tailpipe velocity is much larger than the wind speed. The oxidation reaction of NO plays an important role when the wind speed is large and the vehicular exhaust exit velocity is small, which leads to chemical reduction of NO, and the formation and accumulation of NO₂ in the exhaust plume. It is also found that the effect of vehicular exhaust-induced turbulence in the vicinity of the exhaust tailpipe exit is more dominant than the effect of wind turbulence, while the wind turbulence gradually shows a significant role for the dispersion of nitrogen oxides along with the development of exhaust plume. The range of dispersion of nitrogen oxides in the radial direction is increased along with the development of vehicular exhaust plume.     

Comparison of PDFs, closure schemes and turbulence parameterisations in Lagrangian stochastic models

Six one-dimensional models, based on the Ito-type stochastic equation, are presented and compared. Four of these take into account up to the fourth order moment of vertical velocity fluctuations, and two up to the third order moment. Four models make use of a bi-Gaussian probability density function (PDF) and the other two are based on a Gram-Charlier series expansion truncated to the third or fourth order. All the models were run with a parameterisation of input turbulence (i.e. w², w³, and τ profiles). Concerning the fourth order moment w⁴, two different parameterisations were considered. Comparisons are made between ground-level concentrations, plume height and plume width observed in the Willis and Deardorff water tank experiments and those predicted by the different models here considered. The goal of this study was to find the models that give greater confidence in their applicability in dispersion studies and to verify the importance of considering the fourth order moment. The main conclusions are: simulation results largely depend on the turbulence parameterisation chosen; the Gram-Charlier PDF gives the best agreement with observations; some combinations of models and turbulence parameterisations perform well in simulating the shape of the ground-level concentration (g.1.c.) trend but fail in correctly simulating the form of the plume (plume height and vertical width); in the case of the Gram-Charlier PDF, the fourth order model reproduced the vertical plume width better than the third order one, whereas the two schemes yielded similar g.1.c. distributions.     

Comparison of a new Eulerian model with a modified Lagrangian approach for particle distribution and deposition indoors

Understanding of aerosol dispersion characteristics has many scientific and engineering applications. It is recognized that Eulerian or Lagrangian approach has its own merits and limitations. A new Eulerian model has been developed and it adopts a simplified drift–flux methodology in which external forces can be incorporated straightforwardly. A new near-wall treatment is applied to take into account the anisotropic turbulence for the modified Lagrangian model. In the present work, we present and compare both Eulerian and Lagrangian models to simulate particle dispersion in a small chamber. Results reveal that the standard k–ε Lagrangian model over-predicts particle deposition compared to the present turbulence-corrected Lagrangian approach. Prediction by the Eulerian model agrees well with the modified Lagrangian model.     

Online update of model state and parameters of a Monte Carlo atmospheric dispersion model by using ensemble Kalman filter

For an atmospheric dispersion model designed for the assessment of nuclear accident consequences, some uncertain model parameters, such as source term and weather conditions, may influence the reliability of model predictions. In this respect, good estimations of both model state and uncertain parameters are required. In this paper, an ensemble Kalman filter (EnKF) based method for simultaneous state and parameter estimation, using off-site radiation monitoring data, is presented. This method is based on a stochastic state space model, which resembles the parameter errors with stochastic quantities. Three imperfect parameters, including the source release rate, wind direction and turbulence intensity were perturbed simultaneously, and multiple parameter estimation were performed. Having been tested against both simulated and real radiation monitoring data, the method was found to be able to realistically reconstruct the real scene of dispersion, as well as the uncertain parameters. The estimated parameters given by EnKF nicely converge to the true values, and the method also tracks the temporal variation of those parameters.     

CFD modelling of small particle dispersion: The influence of the turbulence kinetic energy in the atmospheric boundary layer

When considering the modelling of small particle dispersion in the lower part of the Atmospheric Boundary Layer (ABL) using Reynolds Averaged Navier Stokes simulations, the particle paths depend on the velocity profile and on the turbulence kinetic energy, from which the fluctuating velocity components are derived to predict turbulent dispersion. It is therefore important to correctly reproduce the ABL, both for the velocity profile and the turbulence kinetic energy profile.For RANS simulations with the standard k–ε model, Richards and Hoxey (1993. Appropriate boundary conditions for computational wind engineering models using the k–ε turbulence model. Journal of Wind Engineering and Industrial Aerodynamics 46–47, 145–153.) proposed a set of boundary conditions which result in horizontally homogeneous profiles. The drawback of this method is that it assumes a constant profile of turbulence kinetic energy, which is not always consistent with field or wind tunnel measurements. Therefore, a method was developed which allows the modelling of a horizontally homogeneous turbulence kinetic energy profile that is varying with height.By comparing simulations performed with the proposed method to simulations performed with the boundary conditions described by Richards and Hoxey (1993. Appropriate boundary conditions for computational wind engineering models using the k–ε turbulence model. Journal of Wind Engineering and Industrial Aerodynamics 46–47, 145–153.), the influence of the turbulence kinetic energy on the dispersion of small particles over flat terrain is quantified.     

A lagrangian dispersion model for calculating concentration distribution within a built-up domain

A 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.     

Data assimilation in the atmospheric dispersion model for nuclear accident assessments

Uncertainty factors in atmospheric dispersion models may influence the reliability of model prediction. The ability of a model in assimilating measurement data will be helpful to improve model prediction. In this paper, data assimilation based on ensemble Kalman filter (EnKF) is introduced to a Monte Carlo atmospheric dispersion model (MCADM) designed for assessment of consequences after an accident release of radionuclides. Twin experiment has been performed in which simulated ground-level dose rates have been assimilated. Uncertainties in the source term and turbulence intensity of wind field are considered, respectively. Methodologies and preliminary results of the application are described. It is shown that it is possible to reduce the discrepancy between the model forecast and the true situation by data assimilation. About 80% of error caused by the uncertainty in the source term is reduced, and the value for that caused by uncertainty in the turbulence intensity is about 50%.     

One-dimensional simulation of solute transfer in saturated–unsaturated porous media using the discontinuous finite elements method

A one-dimensional transport model for simulating water flow and solute transport in homogeneous–heterogeneous, saturated–unsaturated porous media is presented. The model is composed of a combination of accurate numerical algorithms for solving the nonlinear Richard's and advection–dispersion equations (ADE). The mixed form of Richard's equation is solved using a standard finite element method (FEM) with primary variable switching. The transport equation is solved using operator splitting, with the discontinuous finite element method (DFE) for discretization of the advective term. A slope limiting procedure for DFE avoids numerical instabilities but creates very limited numerical dispersion for high Peclet numbers. An implicit finite differences scheme (FD) is used for the dispersive term.The unsaturated flow and transport model (Wamos-T) is applied to a variety of rigorous problems including transient flow, heterogeneous medium and abrupt variations of velocity in magnitude and direction due to time-varying boundary conditions. It produces accurate and mass-conservative solutions for a very large range of grid Peclet numbers. The Wamos-T model is a good and robust alternative for the simulation of mass transport in unsaturated domain.     

Turbulence and dispersion modeling near highways

Traffic-induced turbulence plays a dominant role in the dispersion of pollutants near highways. The formulations for velocity deficit and turbulence in vehicle wakes, developed from theoretical and physical modeling studies of Eskridge and his colleagues at US EPA about 20 years ago, are discussed. The vehicle wake parameterizations incorporated in ROADWAY-2, a near-highway pollutant dispersion model, and its evaluation results are described. The first field measurements of velocities and turbulence in the vehicle wake, using a towed array of 3-D sonic anemometers, are analyzed, and the results are presented and discussed. Specific recommendations are made for additional work in field measurements, laboratory studies, and mathematical model development and evaluation.     

The Fifth International Clean Air Congress

ABSTRACT

The main objective of this study was to investigate the capabilities of the receptor-oriented inverse mode Lagrangian Stochastic Particle Dispersion Model (LSPDM) with the 12-km resolution Mesoscale Model 5 (MM5) wind field input for the assessment of source identification from seven regions impacting two receptors located in the eastern United States. The LSPDM analysis was compared with a standard version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) single-particle backward-trajectory analysis using inputs from MM5 and the Eta Data Assimilation System (EDAS) with horizontal grid resolutions of 12 and 80 km, respectively. The analysis included four 7-day summertime events in 2002; residence times in the modeling domain were computed from the inverse LSPDM runs and HYPSLIT-simulated backward trajectories started from receptor-source heights of 100, 500, 1000, 1500, and 3000 m. Statistics were derived using normalized values of LSPDM- and HYSPLIT-predicted residence times versus Community Multiscale Air Quality model-predicted sulfate concentrations used as baseline information. From 40 cases considered, the LSPDM identified first- and second-ranked emission region influences in 37 cases, whereas HYSPLIT-MM5 (HYSPLIT-EDAS) identified the sources in 21 (16) cases. The LSPDM produced a higher overall correlation coefficient (0.89) compared with HYSPLIT (0.55–0.62). The improvement of using the LSPDM is also seen in the overall normalized root mean square error values of 0.17 for LSPDM compared with 0.30–0.32 for HYSPLIT. The HYSPLIT backward trajectories generally tend to underestimate near-receptor sources because of a lack of stochastic dispersion of the backward trajectories and to overestimate distant sources because of a lack of treatment of dispersion. Additionally, the HYSPLIT backward trajectories showed a lack of consistency in the results obtained from different single vertical levels for starting the backward trajectories. To alleviate problems due to selection of a backward-trajectory starting level within a large complex set of 3-dimensional winds, turbulence, and dispersion, results were averaged from all heights, which yielded uniform improvement against all individual cases.

IMPLICATIONS Backward-trajectory analysis is one of the standard procedures for determining the spatial locations of possible emission sources affecting given receptors, and it is frequently used to enhance receptor modeling results. This analysis simplifies some of the relevant processes such as pollutant dispersion, and additional methods have been used to improve receptor-source relationships. A methodology of inverse Lagrangian stochastic particle dispersion modeling was used in this study to complement and improve standard backward-trajectory analysis. The results show that inverse dispersion modeling can identify regional sources of haze in national parks and other regions of interest.     

A Lagrangian stochastic model for predicting concentration fluctuations in urban areas

A combined Lagrangian stochastic model with a micromixing sub-model is used to estimate the fluctuating concentrations observed in two wind tunnel experiments. The Lagrangian stochastic model allows fluid trajectories to be simulated in the inhomogeneous flow, while the mixing model accounts for the dissipation of fluctuations using the interaction by exchange with the mean (IEM) mechanism. The model is first tested against the open terrain, wind tunnel data of Fackrell, J.E. and Robins, A.E. [1982. Concentration fluctuations and fluxes in plumes from point sources in a turbulent boundary layer. Journal of Fluid Mechanics 117, 1–26] and shows good agreement with the observed mean concentrations and fluctuation intensities. The model is then compared with the wind tunnel simulation of a two-dimensional street canyon by Pavageau, M. and Schatzmann, M. [1999. Wind tunnel measurements of concentration fluctuations in an urban street canyon. Atmospheric Environment 33, 3961–3971]. Despite the limitations of the k–ε turbulence scheme and the IEM mixing mechanism, the model reproduces the fluctuation intensity pattern within the canyon well. Overall, the comparison with both sets of wind tunnel experiments are encouraging, and the simplicity of the model means that predictions in a complex, three-dimensional geometry can be produced in a practicable amount of time.     

Prediction of PM10 concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration

At urban traffic intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly during the green-light period. The changes of driving patterns (i.e., idle, acceleration, deceleration and cruising patterns) generally produce uncertain emission. Additionally, the movement of pedestrians and the influence of wind further result in the random dispersion of pollutants. It is, therefore, too complex to simulate the effects of such dynamics on the resulting emission using conventional deterministic causal models.For this reason, a modified semi-empirical box model for predicting the PM₁₀ concentrations on roadsides is proposed in this paper. The model constitutes three parts, i.e., traffic, emission and dispersion components. The traffic component is developed using a generalized force traffic model to obtain the instantaneous velocity and acceleration when vehicles move through intersections. Hence the distribution of vehicle emission in street canyon during the green-light period is calculated. Then the dispersion component is investigated using a semi-empirical box model combining average wind speed, box height and background concentrations. With these considerations, the proposed model is applied and evaluated using measured data at a busy traffic intersection in Mong Kok, Hong Kong. In order to test the performance of the model, two situations, i.e., the data sets within a sunny day and between two sunny days, were selected to examine the model performance. The predicted values are generally well coincident with the observed data during different time slots except several values are overestimated or underestimated. Moreover, two types of vehicles, i.e., buses and petrol cars, are separately taken into account in the study. Buses are verified to contribute most to the emission in street canyons, which may be useful in evaluating the impact of vehicle emissions on the ambient air quality when there is a significant change in a specific vehicular population.     

One-dimensional simulation of solute transfer in saturated-unsaturated porous media using the discontinuous finite elements method

A one-dimensional transport model for simulating water flow and solute transport in homogeneous-heterogeneous, saturated-unsaturated porous media is presented. The model is composed of a combination of accurate numerical algorithms for solving the nonlinear Richard's and advection-dispersion equations (ADE). The mixed form of Richard's equation is solved using a standard finite element method (FEM) with primary variable switching. The transport equation is solved using operator splitting, with the discontinuous finite element method (DFE) for discretization of the advective term. A slope limiting procedure for DFE avoids numerical instabilities but creates very limited numerical dispersion for high Peclet numbers. An implicit finite differences scheme (FD) is used for the dispersive term. The unsaturated flow and transport model (Wamos-T) is applied to a variety of rigorous problems including transient flow, heterogeneous medium and abrupt variations of velocity in magnitude and direction due to time-varying boundary conditions. It produces accurate and mass-conservative solutions for a very large range of grid Peclet numbers. The Wamos-T model is a good and robust alternative for the simulation of mass transport in unsaturated domain.     

Large-eddy simulation of pollution dispersion in an urban street canyon—Part I: comparison with field data

Large-eddy simulations (LESs) are applied to the problem of pollution dispersion within the urban canopy layer, specifically street canyons. The objective is to study the turbulence structure and hence the physical dispersion mechanisms of pollutants. LESs are implemented by incorporating the dynamic sub-grid scale stress model into the commercial computational fluids dynamics code CFX. To gain confidence in the approach, simulations are performed for a canyon-like geometry (roof garden) for which experimental measurements were also made. The experimental campaign consisted of using sonic anemometers to measure mean flow and turbulence intensities at a high sample rate of 60 Hz. Good agreement between simulations and experimental data are obtained. Real geometric features, such as non-uniform wall heights, result in a very much three-dimensional flow distribution. Comparisons with the k–ε model show that LESs are able to predict more accurately the turbulence statistics of the flow.