Dispersion atmospheric stability

Parameters Affeeting Gas Dispersion A wide variety of parameters affect the dispersion of gases. These include (1) wind speed, (2) atmospheric stability, (3) local terrain characteristics, (4) height of the release above the ground, (5) release geometry, i.e. from a point, line, or area source, ( momentum of the material released, and (7) buoyancy of the material released. 

TABLE 26-28 Atmospheric Stability Classes for Use with the Pasquill-Gifford Dispersion Model... 

The Britter and McQiiaid model was developed by performing a dimensional analysis and correlating existing data on dense cloud dispersion. The model is best suited for instantaneous or continuous ground-level area or volume source releases of dense gases. Atmospheric stability was found to have little effect on the results and is not a part of the model. Most of the data came from dispersion tests in remote, rural areas, on mostly flat terrain. Thus, the results would not be apphcable to urban areas or highly mountainous areas. 

From Figures 2 and 3, the Pasquill-Gifford dispersion coefficients are obtained for a downwind distance of 2000 meters and for atmospheric stability Class B. 

Atmospheric stability and mechanical turbulence (important near to the ground) are used to derive the vertical and horizontal dispersion coefficients. Table 45.2 shows Pasquill s stability categories used to derive the coefficients by reference to standard graphs. 

The third factor affecting dispersion is turbulence. Mechanical turbulence is caused by the roughness of the Earth s surface. Away from the surface, convective turbulence (heated air rising and cooler air falling) becomes increasingly important. The amount of turbulence and the height to which it operates depends on the surface roughness, wind speed and atmospheric stability. 

The dispersion coefficients are a function of atmospheric conditions and the distance downwind from the release. The atmospheric conditions are classified according to six different stability classes, shown in Table 5-1. The stability classes depend on wind speed and quantity of sunlight. During the day, increased wind speed results in greater atmospheric stability, whereas at night the reverse is true. This is due to a change in vertical temperature profiles from day to night. 

There exist a number of correlations for the dispersion parameters a-y and CT as a function of downwind distance x and atmospheric stability. These correlations have been reviewed extensively and repeatedly, and the reader is referred to Gifford (1976), Weber (1976), American Meteorological Society Workshop (1977), Doran et al. (1978), Irwin (1979), and Sedefian and Bennett (1980) for comprehensive summaries of the available formulas. In particular, these references provide one with the relations necessary to select <7y and cr for a specific application. We do not endeavor to survey these relations here. 

In another review, Hoffert discussed the social motivations for modeling air quality for predictive purposes and elucidated the components of a model. Meteorologic factors were summarized in terms of windfields and atmospheric stability as they are traditionally represented mathematically. The species-balance equation was discussed, and several solutions of the equation for constant-diffusion coefficient and concentrated sources were suggested. Gaussian plume and puff results were related to the problems of developing multiple-source urban-dispersion models. Numerical solutions and box models were then considered. The review concluded with a brief outline of the atmospheric chemical effects that influence the concentration of pollutants by transformation. 

Notice that the wind speed does not appear explicitly in Eq. (26-61). It is implicit through the dispersion coefficients since these are a function of distance downwind from the initial release and the atmospheric stability conditions. 

Some AQ models rely on standard meteorological products which usually do not include turbulence, atmospheric stability, mixing height, and dispersion coefficients... 

Meteorology Wind speed Wind direction Temperature Solar radiation Net radiation Precipitation etc. Continuously To estimate the dispersion factor, wind speed, wind direction and atmospheric stabilities are statistically analysed according to the JAEC s Guide. 

One commonly used suite of models that is based on Gaussian plume modeling is the Industrial Source Complex (ISC) Dispersion Models (US EPA, 1995). This suite includes both a short-term model (ISCST), which calculates the hourly air pollutant concentrations in an area surrounding a source, as well as a long-term model (ISCLT), which calculates the average air pollutant concentrations over a year or longer. ISCLT uses meteorological data summarized by frequency for 16 radial sectors (22.5° each) this data format is referred to as a stability array (STAR). Within each sector of STAR, joint frequencies of wind direction, wind speed, and atmospheric stability class are provided. 

The classic text describing atmospheric dispersion phenomena is Pasquill s Atmospheric Diffusion (Pasquill, 1962). On page 181, Pasquill includes a number of diagrams (attributed to Church (1949) and the United States Weather Bureau (1955)) illustrating the characteristic forms of smoke-plumes from chimneys under different conditions of atmospheric stability. These diagrams have been copied here as Figures 1-5. 

The stability of the atmosphere is clearly a major influence on the dispersion of a smoke-plume. If the plume comprised a CWA rather than a harmless tracer, then the atmospheric stability would have an important effect on the extent of the downwind hazard area and on the concentrations of agent experienced within that hazard area. Atmospheric stability can be determined by direct observation - as in Figures 1-5 -or by estimation of the Monin-Obukhov length-scale, L (m), which is a height proportional to the height above a surface at which thermal effects first dominate shear (momentum) effects (Pasquill, 1962), as defined in Equation (1) ... 

Atmospheric dispersion modelling requires an accurate and reliable description of the weather, including descriptions of the prevailing airflow, the atmospheric stability and the effects of flow structures at the spatial and temporal scales of interest. 

Leuning, R. (2000). Estimation of scalar source/sink di.stributions in plant canopies using Lagrangian dispersion analysis corrections for atmospheric stability and comparison with a multilayer canopy model Boundary-Layer Meteorol. 96, 293-314. 

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