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One major approach to modeling dispersion of pollutants inside confined spaces describes the turbulent transport of material as the product of an eddy diffusivity and the local concentration gradient. This paper examines the applicability of this eddy diffusivity/gradient model by (1) describing the conditions under which this approach is an appropriate representation of turbulent transport, and (2) re-analysis of data provided in studies that have successfully applied gradient transport to describe tracer concentrations. We find that the solutions of the mass conservation equation based on gradient transport provide adequate descriptions of concentration measurements from two studies representative of two types of sources: instantaneous and continuous release of aerosols. We then provide the rationale for the empirical success of the gradient transport model. The solutions of the gradient transport model allow us to examine the relationship between the ventilation rate and the spatial and temporal behavior of the dose of material associated with aerosol releases in a room. We conclude with the associated implications on mitigation of exposure to aerosols such as airborne virus or bacteria.

Transportation infrastructure (including roadway traffic, ports, and airports) is critical to the nation's economy. With a growing economy, aircraft activity is expected to grow across the world. In the US, airport-related emissions, while generally small, are not an insignificant source of air pollution and related adverse health effects. However, currently there is a lack of tools that can easily be applied to study near-source pollution and explore the benefits of improvements to air quality and exposures. Screening-level air quality modelling is a useful tool for examining urban-scale air quality impacts of airport operations. Spatially-resolved aircraft emissions are needed for the screening-level modelling. In order to create spatially-resolved aircraft emissions, we developed a bottom-up emissions estimation methodology that includes data from a global chorded inventory dataset from the aviation environmental design tool (AEDT). The initial implementation of this method was performed for Los Angeles International Airport (LAX). This paper describes a new emissions estimation methodology for aircraft emissions in support of community-scale assessments of air quality around airports and presents an illustration of its application at the Los Angeles International Airport during the LAX 2011/2012 Air Quality Source Apportionment Study.

The Canopy Horizontal Array Turbulence Study (CHATS) experimental study was conducted at a Cilker Orchards's walnut blocks in Dixon, California, before and after leaves emerged, to help improve understanding, simulation capabilities, and modeling of coupled vegetation-atmosphere-land surface interactions. The campaign took place over 12 weeks in the spring of 2007 and was broken into three 4-week phases. Canopy-induced mechanical and thermodynamical vertical variation of turbulence transport characteristics and the linkages between canopy-scale motions and the larger scale planetary boundary layer (PBL) turbulence were measured. The observations focused on measurements characterizing stratification influences on the spatial structure of canopy-induced turbulence, the trace gas source/sink distribution associated with vegetation, and the overall impact of canopy-induced processes on trace gas transport.

The predictability of passive scalar dispersion is of both theoretical interest and practical importance, for example for high‐resolution numerical weather prediction and air quality modeling. However, the implications for the numerical modeling of urban areas remain relatively unexplored. Using obstacle‐resolving large‐eddy simulations (LES), we conducted twin experiments, with and without a velocity perturbation, to investigate how the presence of urban roughness affects error growth in streamwise velocity (u) and passive scalar (θ) fields, as well as the differences between error evolutions in u and θ fields. The predictability limit is characterized using the signal‐to‐noise ratio (SNR) as a continuous metric to indicate when error reaches saturation. The presence of urban roughness decreases Tp$$ {T}_{\mathrm{p}} $$ of the passive scalar by around 20% compared to cases without them. The error statistics of θ indicate that urban roughness‐induced flow structures and different scalar source locations affect the scalar dispersion and relative fluctuations, which subsequently dictate the evolution of the SNR. Analysis of the passive scalar error energy (ϵθ2) budget indicates that the contributions from advective transport by the velocity and velocity error dominate. The error energy spectra of both u and θ exhibit a −5/3 slope in flat‐wall cases, but not in the presence of urban roughness, thereby highlighting the deviation from the assumption of locally isotropic turbulence. This study reveals that urban roughness can decrease the predictability of the passive scalar and destroy the similarity between the error statistics of the velocity and the passive scalar. [ABSTRACT FROM AUTHOR]

A slightly simplified form of Thomson's Lagrangian stochastic model (LSM) is presented for dispersion applications in three-dimensional (3D) flow fields. It is found that the Lagrangian velocity of a particle in 3D inhomogeneous Gaussian turbulence can be decomposed into the local Eulerian mean velocity [U.sub.Ei] at the particle position and a velocity perturbation [u'.sub.i]. The Eulerian mean wind can be predicted by 3D wind field models, whereas the [u'.sub.i] is obtained from Thomson's model and depends on the turbulence field. The [U.sub.Ei], [u'.sub.i] decomposition was used earlier in a two-dimensional particle model for a canopy (by Flesch and Wilson) and in models with 3D mean winds but with [u'.sub.i] based on LSM forms differing from that of Thomson. This note shows that the [U.sub.Ei], [u'.sub.i] decomposition is consistent with Thomson's LSM for general 3D flow fields and is a simpler solution that should lead to improved computational efficiency for dispersion applications.

The Pentagon, and its 25,000+ occupants, represents a likely target for a future terrorist attack using chemical, biological, or radiological material released into the atmosphere. Motivated by this, a building-protection system, called Pentagon Shield, is being developed and deployed by a number of government, academic, and private Organizations. The system consists of a variety of data-assimilation and forecast models that resolve processes from the mesoscale to the city scale to the building scale, and assimilate meteorological and contaminant data that are measured by remote and in situ sensors. This paper reports on a field program that took place in 2004 in the area of the Pentagon, where the aim was to provide meteorological data and concentration data from tracer releases, and to support the development and evaluation of the system. In particular, the results of the field program are being used to improve our understanding of urban meteorological processes, verify the overall effectiveness of the operational building protection system, and verify the skill of the component meteorological, and transport and dispersion, modeling systems. Based on the experience gained in this project, it will be more straightforward to develop similar systems to protect other high-profile facilities against the accidental or intentional release of hazardous material into the atmosphere.

A Lagrangian particle dispersion model (LPDM) is used to study fumigation of pollutants in and above the entrainment zone into a growing convective boundary layer. Probability density functions of particle location with height and time are calculated from particle trajectories driven by the sum of the resolved-scale velocity from a large-eddy simulation (LES) model and the stochastic subgrid-scale (SGS) velocity. The crosswind-integrated concentration (CWIC) fields show good agreement with water tank experimental data. A comparison of the LPDM output with an Eulerian diffusion model output based on the same LES flow shows qualitative agreement with each other except that a greater overshoot maximum of the ground-level concentration occurs in the Eulerian model. The dimensionless CWICs near the surface for sources located above the entrainment zone collapse to a nearly universal curve provided that the profiles are time shifted, where the shift depends on the source heights. The dimensionless CWICs for sources located within the entrainment zone show a different behavior. Thus, fumigation from sources above the entrainment zone and within the entrainment zone should be treated separately. An examination of the application of Taylor's translation hypothesis to the fumigation process showed the importance of using the mean boundary layer wind speed as a function of time rather than the initial mean boundary layer wind speed, because the mean boundary layer wind speed decreases as the simulation proceeds. The LPDM using LES is capable of accurately simulating fumigation of particles into the convective boundary layer. This technique provides more computationally efficient simulations than Eulerian models.

A Lagrangian dispersion model driven by velocity fields from large-eddy simulations (LESs) is presented for passive particle dispersion in the planetary boundary layer (PBL). In this combined LES-Lagrangian stochastic model (LSM), the total velocity is divided into resolved or filtered and unresolved or subfilter-scale (SFS) velocities. The random SFS velocity is modeled using an adaptation of Thomson's LSM ill which the ensemble-mean velocity and velocity variances are replaced by the resolved velocity and SFS variances, respectively. The random SFS velocity forcing has an amplitude determined by the SFS fraction of the total turbulent kinetic energy (TKE): the fraction is about 0.15 in the bulk of the simulated convective boundary layer (CBL) used here and reaches values as large as 0.31 and 0.37 in the surface layer and entrainment layer, respectively. For the proposed LES-LSM, the modeled crosswind-integrated concentration (CWIC) fields are in good agreement with the 1) surface-layer similarity (SLS) theory for a surface source in the CBL and 2) convection tank measurements of the CWIC for an elevated release in the CBL surface layer. The second comparison includes the modeled evolution of the vertical profile shape with downstream distance, which shows the attainment of an elevated CWIC maximum and a vertically well-mixed CWIC far downstream, in agreement with the tank data. For the proposed model, the agreement with the tank data and SLS theory is better than that obtained with an earlier model in which the SFS fraction of the TKE is assumed to be 1, and significantly better than a model that neglects the SFS velocities altogether.

A study of the relative dispersion of ice crystals in 30 seeded cumulus clouds showed that the observed mean relative dispersion tallied with expected values derived from a Lagrangian stochastic two particle model. Batchelor's theoretical results for relative dispersion are valid for long times but fail for short times after seeding time. Probability density functions (pdfs) of the dispersion values were also calculated and used to show that pdfs become more symmetric from small to large times.