The relationship between the convective boundary layer and dispersion from tall stacks

This paper discusses the structure of the convective planetary boundary layer in relation to dispersion of pollutants. As tall stack plumes are brought down to the ground primarily during unstable conditions, we have investigated the conditions under which the PBL is convective. Our analysis indicates that the convective PBL occurs more often than is commonly thought. The second part of the paper describes some aspects of dispersion in the convective PBL. We propose a simple model capable of providing factor-of-two type estimates of ground-level concentrations. We also discuss some of the factors which complicate attempts to obtain more accurate results. In this context, we point out the problems associated with using model-predicted ensemble means to estimate observed concentrations.     

A turbulent energy model for diffusion in the convective boundary layer

A turbulent energy model developed by the authors to describe atmospheric flows is used to study diffusion in the convective boundary layer. The model is based on the turbulent energy transport equation coupled with eddy diffusivity expressions for momentum and heat transfer. The diffusion model assumes equality of the eddy diffusivity for heat and mass and Gaussian diffusion in the cross-stream direction. The model is shown to reproduce satisfactorily the main features of diffusion in convective flows, and its predictions compare well with the measurements of the laboratory experiments of Willis and Deardorff, as well as with field data.     

Air pollution in a convective boundary layer

Detailed profiles of the concentration and the concentration flux for pollutants released continuously from a point source near the ground are presented. Although there are no observational data of the concentration flux and the covariance of temperature and concentration, the distributions of concentration and eddy diffusivity derived from this study are in good agreement with those of laboratory experiments. This study also shows that the covariance of temperature and concentration is important in producing a countergradient concentration flux in a convective boundary layer.     

A simple and fast model to compute concentration moments in a convective boundary layer

Recently, a modified meandering plume model for concentration fluctuations in a convective boundary layer has been formulated (Atmos. Environ. 34 (2000) 3599). This model is based on a hybrid Eulerian–Lagrangian approach and it accounts for the skewed and inhomogeneous turbulence characteristics of the convective flow. Using the same hypotheses, but eliminating the need for Lagrangian particle model, we propose a generalised approach, that only requires the knowledge of mean concentration field. The proposed model is independent from the method used to obtain the mean concentration field. The evaluation of the concentration fluctuation field needs a computational time of only few seconds on a standard PC. Therefore, the model is suitable for practical applications.     

The vertical distribution of NO3 in the atmospheric boundary layer

Recent urban measurements suggest that NO₃ concentrations vary significantly with altitude in the lowest few hundred metres of the atmosphere. Calculations using a one-dimensional boundary layer model show that NO₃ concentrations are small near the ground and increase with altitude to a maximum near the top of the nocturnal boundary layer (NBL). These results show that the NBL is not well mixed, and that where there are surface sources and sinks two-box models of the NBL are inadequate, and surface measurements are not representative and may lead to an underestimate of the oxidising capacity of the atmosphere.     

Regional source identification using Lagrangian stochastic particle dispersion and HYSPLIT backward-trajectory models

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.     

A Lagrangian study of the boundary layer transport of pollutants in the northeastern United States

This paper describes the Lagrangian aircraft sampling of a unique segment of the Baltimore/Washington urban plume as it was transported along the Northeast Corridor on 14 August 1980. Plume cross-sectional analyses of NO_x and O₃ at four downwind distances are presented and discussed relative to physical processes and chemical interactions/transformations. The analyses indicated (1) longrange transport of O₃, at least to 400 km, along the Northeast Corridor, (2) significant O₃, scavenging when the NO₂ concentrations exceed 20 ppb and (3) O₃ concentrations within the urban plume during the daylight hours increased as much as 100 ppb, while the background concentrations increased only 30 ppb.In addition, the time of the highest 1-h average O₃ concentrations at surface monitoring sites beneath the urban plume increased with increasing distances from Baltimore to New York City. Significant surface O₃ peaks were observed under cloudy skies downwind of New York City after midnight and are believed to be the result of transport from the corridor region SW of New York City.     

Shared- and distributed-memory parallelization of a Lagrangian atmospheric dispersion model

This paper describes parallelization of a 3-D Lagrangian stochastic atmospheric dispersion model using both distributed- and shared-memory methods. Shared-memory parallelism is implemented through the use of OpenMP compiler directives. Distributed-memory parallelism relies on the MPI message-passing library. One or both (using MPI for inter-node and OpenMP for intra-node communication) of the parallel modes can be used depending upon the requirements of the problem and the computational platform available. The distributed-memory version achieves a nearly linear decrease in execution time as the number of processors is increased. As the number of particles per processor is lowered, performance is limited by the decrease in work per processor and by the need to produce one set of output files. The shared-memory version achieves a speedup factor of ∼1.4 running on machines with four processors.     

A new Lagrangian particle model for the simulation of dense gas dispersion

A Lagrangian stochastic model (MicroSpray), able to simulate the airborne dispersion in complex terrain and in presence of obstacles, was modified to simulate the dispersion of dense gas clouds. This is accomplished by taking into account the following processes: negative buoyancy, gravity spreading and the particle's reflection at the bottom computational boundary. Elevated and ground level sources, continuous and instantaneous emissions, time varying sources, plumes with initial momentum (horizontal, vertical or oblique in any direction), plumes without initial momentum are considered. MicroSpray is part of the model system MSS, which also includes the diagnostic MicroSwift model for the reconstruction of the 3-D wind field in presence of obstacles and orography. To evaluate the MSS ability to simulate the dispersion of heavy gases, its simulation performances are compared in detail to two field experiments (Thorney Island and Kit Fox) and to a chlorine railway accident (Macdona). Then, a comprehensive analysis considering several experiments of the Modelers Data Archive is presented. The statistical analysis on the overall available data reveals that the performance of the new MicroSpray version for dense gas releases is generally reliable. For instance, the agreement between concentration predictions and observations is within a factor of two in the 72% up to 99% of the occurrences for the case studies considered. The values of other performance measures, such as correlation coefficient, geometric mean bias and geometric variance, mostly set in the ranges indicated as good-model performances in the specialized literature.     

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.     

Numerical simulations of air pollutant dispersion in a stratified planetary boundary layer

A numerical model is described for computing pollutant concentration distributions downwind from a source. It is based on the three-dimensional dispersion equation governing the time-dependent advective and diffusive transport of air pollutants and is solved numerically by a mixed Lagrangian-Eulerian finite-difference scheme. The model includes the vertical wind shear, the turning of the actual wind, and vertical variations of the vertical eddy diffusivity. In this paper the model is used to simulate the pollutant dispersion process in a stratified planetary boundary layer. The vertical profiles of horizontal mean wind and vertical eddy diffusivities are calculated numerically from a planetary boundary layer model. The influence of the ground roughness and the atmospheric stability on the pollutant distribution is investigated. The results indicate that both parameters essentially determine the air pollutant dispersion process.