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J. C. Weil

Abstract

Most diffusion models currently used in air quality applications are substantially out of date with understanding of turbulence and diffusion in the planetary boundary layer. Under a Cooperative Agreement with the Environmental Protection Agency, the American Meteorological Society organized a workshop to help improve the basis of such models, their physics and hopefully their performance. Reviews and recommendations were made on models in three areas: diffusion in the convective boundary layer (CBL), diffusion in the stable boundary layer (SBL), and model uncertainty.

Progress has been made in all areas, but it is most significant and ready for application to practical models in the case of the CBL. This has resulted from a clear understanding of the vertical structure and diffusion in the CBL, as demonstrated by laboratory experiments, numerical simulations, and field observations. All of these investigations have shown the importance of the convective scaling parameter: w *, the convective velocity scale and zi, the CBL height. This knowledge and the non-Gaussian nature of vertical diffusion have already been incorporated in some applied models and show much promise. The workshop has made a number of recommendations concerning the use of this information, with perhaps the most important being the use of w *, zi directly in expressions for the dispersion parameters (σy, σz).

Understanding of turbulence structure and diffusion in the SBL is less complete and not yet ready for general use in applications. However, some promising new developments include a similarity framework for turbulence structure over ideal terrain and models to predict vertical dispersion in terms of the local structure. Further development and testing of these models are required, with new data sets—laboratory, numerical, and field—being especially beneficial.

As for model uncertainty, it is recommended that natural variability estimates ultimately become an integral part of air quality predictions. Some general frameworks for these estimates include the meandering plume and Eulerian similarity models, with the former being of more immediate utility. However, further evaluation of these models is necessary before they can be recommended for applications.

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J. C. Weil, R. I. Sykes, and A. Venkatram

Abstract

Over the past decade, much attention has been devoted to the evaluation of air-quality models with emphasis on model performance in predicting the high concentrations that are important in air-quality regulations. This paper stems from our belief that this practice needs to be expanded to 1) evaluate model physics and 2) deal with the large natural or stochastic variability in concentration. The variability is represented by the root-mean- square fluctuating concentration (σc about the mean concentration (C) over an ensemble—a given set of meteorological, source, etc. conditions. Most air-quality models used in applications predict C, whereas observations are individual realizations drawn from an ensemble. For σcC large residuals exist between predicted and observed concentrations, which confuse model evaluations.

This paper addresses ways of evaluating model physics in light of the large σc the focus is on elevated point-source models. Evaluation of model physics requires the separation of the mean model error-the difference between the predicted and observed C—from the natural variability. A residual analysis is shown to be an elective way of doing this. Several examples demonstrate the usefulness of residuals as well as correlation analyses and laboratory data in judging model physics.

In general, σc models and predictions of the probability distribution of the fluctuating concentration (c′), Ω(c′, are in the developmental stage, with laboratory data playing an important role. Laboratory data from point-source plumes in a convection tank show that Ω(c′ approximates a self-similar distribution along the plume center plane, a useful result in a residual analysis. At pmsent,there is one model—ARAP—that predicts C, σc, and &Omega(c for point-source plumes. This model is more computationally demanding than other dispersion models (for C only) and must be demonstrated as a practical tool. However, it predicts an important quantity for applications— the uncertainty in the very high and infrequent concentrations. The uncertainty is large and is needed in evaluating operational performance and in predicting the attainment of air-quality standards.

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J. C. Weil, L. A. Corio, and R. P. Brower

Abstract

A probability density function (PDF) dispersion model is presented for buoyant plumes in the convective boundary layer (CBL), where the mean concentration field C is obtained from the PDFs p y and p z of tracer particle position in the lateral y and vertical z directions. The p y is assumed to be Gaussian, whereas the p z is derived from the the vertical velocity PDF, which is skewed. Three primary sources contribute to the modeled C field: 1) the “direct” or real source at the stack, 2) an “indirect” source to account for the slow downward dispersion of lofting plumes from the CBL top, and 3) a “penetrated” source to treat material that initially penetrates the elevated inversion but later fumigates into the CBL. Image sources are included to satisfy the zero-flux conditions at the ground and the CBL top.

Comparisons between the modeled crosswind-integrated concentration fields C y and convection tank data show fair to good agreement in the lower half of the CBL. In particular, the C y profiles at the surface agree with the data over a wide range of the dimensionless buoyancy flux F∗ and show a systematic decrease in C y with F∗.

Comparisons between the modeled and observed ground-level concentrations around several power plants exhibit good agreement on average and are considerably better than those obtained with a standard Gaussian plume model. A residual analysis suggests some areas for future model development.

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Steven G. Perry, Alan J. Cimorelli, Robert J. Paine, Roger W. Brode, Jeffrey C. Weil, Akula Venkatram, Robert B. Wilson, Russell F. Lee, and Warren D. Peters

Abstract

The performance of the American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) Improvement Committee’s applied air dispersion model against 17 field study databases is described. AERMOD is a steady-state plume model with significant improvements over commonly applied regulatory models. The databases are characterized, and the performance measures are described. Emphasis is placed on statistics that demonstrate the model’s abilities to reproduce the upper end of the concentration distribution. This is most important for applied regulatory modeling. The field measurements are characterized by flat and complex terrain, urban and rural conditions, and elevated and surface releases with and without building wake effects. As is indicated by comparisons of modeled and observed concentration distributions, with few exceptions AERMOD’s performance is superior to that of the other applied models tested. This is the second of two articles, with the first describing the model formulations.

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Alan J. Cimorelli, Steven G. Perry, Akula Venkatram, Jeffrey C. Weil, Robert J. Paine, Robert B. Wilson, Russell F. Lee, Warren D. Peters, and Roger W. Brode

Abstract

The formulation of the American Meteorological Society (AMS) and U.S. Environmental Protection Agency (EPA) Regulatory Model (AERMOD) Improvement Committee’s applied air dispersion model is described. This is the first of two articles describing the model and its performance. Part I includes AERMOD’s characterization of the boundary layer with computation of the Monin–Obukhov length, surface friction velocity, surface roughness length, sensible heat flux, convective scaling velocity, and both the shear- and convection-driven mixing heights. These parameters are used in conjunction with meteorological measurements to characterize the vertical structure of the wind, temperature, and turbulence. AERMOD’s method for considering both the vertical inhomogeneity of the meteorological characteristics and the influence of terrain are explained. The model’s concentration estimates are based on a steady-state plume approach with significant improvements over commonly applied regulatory dispersion models. Complex terrain influences are provided by combining a horizontal plume state and a terrain-following state. Dispersion algorithms are specified for convective and stable conditions, urban and rural areas, and in the influence of buildings and other structures. Part II goes on to describe the performance of AERMOD against 17 field study databases.

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