Model airshed

Urban Airshed Model (UAM) is a du-ee-dimensional grid based photochemical sinuudtion model for urban scale domains. 

Area sources of either a selected chemical or a precursor present a common problem for modeling. In particular, the rich and complex patterns of hydrocarbon emissions from general urban and industrial sources either include or might produce through atmospheric photochemical reactions some of the species on the analysis list. The treatment of such species in photochemical airshed modeling is difficult (8, 9). The effort required for any one such exercise is substantial, and the effort required for a comprehensive analysis of all urban regions relevant to this program would be prohibitive. 

Step 2 required identification of source impacts by airshed modeling. Wind speed, direction, mixing height, and emission data bases designed to represent conditions on PACS sampling days were used to insure that the CMB impact estimates could be directly compared to model predictions for each sampllne site. 

Point source plume trapping assumptions used in the Eugene airshed modeling were verified by comparing alternative modeling assumptions of sulfate emission impacts to measured sulfate levels. 

FIGURE 16.16 Peak ozone isopleths calculated for downtown Los Angeles (DTLA) and Rubidoux, approximately 100 km east and downwind of DTLA under typical meteorological conditions. Spatially uniform reductions of VOC and NOx were employed in an airshed model by Milford el at. (1989). The top shows isopleths in two dimensions as presented by Milford et at. (f989), and the bottom shows these data extrapolated to three dimensions (from Finlayson-Pitts and Pitts, f993). 

FIGURE 16.17 (a) Elements of a typical airshed model (from McRae et al., 1982a). (b) Elements of the chemical module (from D. Dabdub and J. Seinfeld, personal communication, 1996). 

The box model is closely related to the more complex airshed models described below in that it is based on the conservation of mass equation and includes chemical submodels that represent the chemistry more accurately than many plume models, for example. However, it is less complex and hence requires less computation time. It has the additional advantage that it does not require the detailed emissions, meteorological, and air quality data needed for input and validation of the airshed models. However, the resulting predictions are... 

FIGURE 16.21 Schematic illustration of the grid used and treatment of atmospheric processes in one Eulerian airshed model (adapted from Ames et al., 1978). 

CIT = California Institute of Technology/Carnegie Institute of Technology UAM = Urban Airshed Model RADM 2 = Regional Acid Deposition Model. G-P = gas-particle. 

Al-Wali, K. I., and P. J. Samson, Preliminary Sensitivity Analysis of Urban Airshed Model Simulations to Temporal and Spatial Availability of Boundary Layer Wind Measurements, Atmos. Environ., 30, 2027-2042 (1996). 

California Air Resources Board, Initial Statement of Proposed Rulemaking, Amendments to the Low-Emission Vehicle Program, September 25, 1992, and Supplement, Establishment of Reactivity Adjustment Factors and Speciated Vehicle Data and/or Airshed Modeling Results, November 13, 1992a. Available from the California Air Resources Board, 9528 Telstar Ave., El Monte, CA 91731. 

Chock, D. P G. Yarwood, A. M. Dunker, R. E. Morris, A. K. Pollack, and C. H. Schleyer, Sensitivity of Urban Airshed Model Results for Test Fuels to Uncertainties in Light-Duty Vehicle and Biogenic Emissions and Alternative Chemical Mechanisms. Auto/Oil Air Quality Improvement Research Program, Atmos. Environ., 29, 3067-3084 (1995). 

McNair, L. A., A. G. Russell, M. T. Odnian, B. E. Croes, and L. Kao, Airshed Model Evaluation of Reactivity Adjustment Factors Calculated with the Maximum Incremental Reactivity Scale for Transitional-Low Emission Vehicles, J. Air Waste Manage. Assoc., 44, 900-907 (1994). 

Sistla, G N. Zhou, W. Hao, J.-Y. Ku, S. T. Rao, R. Bornstein, F. Freedman, and P. Thunis, Effects of Uncertainties in Meteorological Inputs on Urban Airshed Model Predictions and Ozone Control Strategies, Atmos. Environ., 30, 2011-2025 (1996). 

Several types of models are commonly used to describe the dispersion of atmospheric contaminants. Among these are the box, plume, and puff models. None are suitable, however, for describing the coupled transport and reaction phenomena that characterize atmospheres in which chemical reaction processes are important. Simulation models that have been proposed for the prediction of concentrations of photochemically formed pollutants in an urban airshed are reviewed here. The development of a generalized kinetic mechanism for photochemical smog suitable for inclusion in an urban airshed model, the treatment of emissions from automobiles, aircraft, power plants, and distributed sources, and the treatment of temporal and spatial variations of primary meteorological parameters are also discussed. 

T Trban airshed models are mathematical representations of atmospheric transport, dispersion, and chemical reaction processes which when combined with a source emissions model and inventory and pertinent meteorological data may be used to predict pollutant concentrations at any point in the airshed. Models capable of accurate prediction will be important aids in urban and regional planning. These models will be used for ... 

The Gaussian plume and puff models, which describe the concentration distribution of an inert species downwind of a point, line, or area source, characterize the next level of complexity of airshed models. In the usual applications of these models ... 

The third level of complexity in airshed modeling involves the solution of the partial differential equations of conservation of mass. While the computational requirements for this class of models are much greater than for the box model or the plume and puff models, this approach permits the inclusion of chemical reactions, time-varying meteorological conditions, and complex source emissions patterns. However, since this model consists only of the conservation equations, variables associated with the momentum and energy equations—e.g., wind fields and the vertical temperature structure—must be treated as inputs to the model. The solution of this class of models will be examined here. 

Portions of the material described here are derived from a comprehensive airshed modeling program in which the authors are participating (17). This chapter focuses on urban airshed models however novel models have been proposed for urban air pollution problems of a more restricted scale— particularly, the prediction of concentrations in the vicinity of major local sources, notably freeways, airports, power plants, and refineries. In discussing plume and puff models earlier we pointed out one such class of models. Other work is the model proposed by Eschenroeder (18) to predict concentrations of inert species in the vicinity of roadways and the modeling of chemically reacting plumes, based on the Lagrangian similarity hypothesis, as presented by Friedlander and Seinfeld (19). 

Several approaches to airshed modeling based on the numerical solution of the semi-empirical equations of continuity (7) are now discussed. We stress that the solution of these equations yields the mean concentration of species i and not the actual concentration, which is a random variable. We emphasize the models capable ot describing concentration changes in an urban airshed over time intervals of the order of a day although the basic approaches also apply to long time simulations on a regional or continental scale. 

We divide the airshed models discussed here into two basic categories, moving cell models and fixed coordinate models. In the moving cell approach a hypothetical column of air, which may or may not be well mixed vertically, is followed through the airshed as it is advected by the wind. Pollutants are injected into the column at its base, and chemical reactions may take place within the column. In the fixed coordinate approach the airshed is divided into a three-dimensional grid. 

We stress that the moving-cell approach is not a full airshed model nor is it intended as such. Rather, it is a technique for computing concentration histories along a given air trajectory. It is not feasible to use this approach to predict concentrations as a function of time and location throughout an airshed since a large number of trajectory calculations would be required. 

Fixed Coordinate Approaches. In the fixed coordinate approach to airshed modeling, the airshed is divided into a three-dimensional grid for the numerical solution of some form of (7), the specific form depending upon the simplifying assumptions made. We classify the general methods for solution of the continuity equations by conventional finite difference methods, particle in cell methods, and variational methods. Finite difference methods and particle in cell methods are discussed here. Variational methods involve assuming the form of the concentration distribution, usually in terms of an expansion of known functions, and evaluating coeflBcients in the expansion. There is currently active interest in the application of these techniques (23) however, they are not yet suflBciently well developed that they may be applied to the solution of three-dimensional time-dependent partial differential equations, such as (7). For this reason we will not discuss these methods here. 

If we divide the airshed into L cells and consider N species, LN ordinary differential equations of the form (15) constitute the airshed model. As might be expected, this model bears a direct relation to the partial differential equations of conservation (7). If we allow the cell size to become small, it can be shown that (15) is the same as the first-order spatial finite difference representation of (7) in which turbulent diffusive transport is neglected—i.e,. 

Chemical reaction processes account for the production of a variety of contaminant species in the atmosphere. Each of the basic airshed models above includes reaction phenomena in the conservative equations. The reaction term, denoted by R accounts for the rate of production of species i by chemical reaction and depends generally on the concentrations of each N species. The conservation equations are thus coupled through the Ri terms, the functional form of each term being determined through the specification of a particular kinetic mechanism for the atmospheric reactions. 

There will be instances where the use of an airshed model will be limited to the prediction of concentrations of inert species. However, when chemical reaction processes are important, it is essential to include an adequate description of these phenomena in the model. Here we outline the requirements that an appropriate kinetic mechanism must meet, survey pertinent model development efforts, and present an example of a mechanism that possesses many of the attributes that a suitable model must display. 

An example of a generalized mechanism suitable for inclusion in an urban airshed model is presented below. In particular, we wish to illustrate the scope of such a mechanism and the level of detail that must be included to ensure accuracy while avoiding undue complexity. We have selected the mechanism proposed by Hecht and Seinfeld (45) for this purpose. This mechanism fulfills the requirements for suitability summarized earlier, has predicted accurately the concentration-time behavior of pollutant species in a smog chamber for a variety of hydrocarbon-NO mixtures, and can be included in any of the airshed models described without difficulty. We note, however, that the mechanism is not a unique description of atmospheric chemistry modified and improved versions may well be developed during the next few years. 

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