Comparison of some sigma schemes for estimation of air pollutant dispersion in moderate and low winds

Abstract

One of the most important parameters in plume dispersion modeling is the plume growth (dispersion coefficients σ ). Different models for estimating dispersion parameters are discussed to establish the relative importance of one over the others. Comparisons were made between power law functions, standard, split sigma and split sigma theta methods. We use the double Gaussian expression for calculating concentration in this comparison. The results show that, with low wind speed (<2 m/s), split sigma and split sigma theta methods give much better results than other methods, while, with wind speed greater than 2 m/s, the power law function methods give more plausible results.

The Influence of Eddy Diffusivity Variation on the Atmospheric Diffusion Equation

“…Now, the statistical method is presented and comparison among analytical, statically and observed results will be offered [20]. Table 3, we find that the predicted concentrations Equation (26) and Equation (40) for 135 I lies inside factor of 2 with observed data.…”

Exploratory study of atmospheric methane enhancements derived from natural gas use in the Houston urban area

“…S1), for a particular emission rate Q, the concentration of a pollutant observed at a specific distance downwind (x,y,z) will depend on the wind speed (in the x direction) and the dispersion coefficients in the y and z directions (σy and σz, respectively). The values of σy and σz can be calculated for urban conditions employing the coefficients and equations established by Briggs-Gifford and McElroy (Beychok, 2005;Essa et al, 2005) (Table S9 and S10).…”

“…Four groups, corresponding to the established leakage probabilities categories (high, medium A, medium B and low) and comprising over 31,000 CH4 and C2H6 concentration data points, were employed for this analysis. LDA is a data classification technique derived from the Fisher's linear discriminant method, that determines the linear combinations of variables that best discriminate distinct groups of data (Balakrishnama and Ganapathiraju, 1998;Fisher, 1936). These linear combinations are known as the canonical linear discriminant functions (CLDF), and the statistical significance of their discriminant capacity is evaluated based on the Chi-square statistic.…”

“…Briggs-Gifford coefficients and equations for calculation of dispersion coefficients in the y and z directions (σy and σz, respectively) for urban conditions (Beychok, 2005 Table S10. McElroy's coefficients and equations for calculation of dispersion coefficients in the y and z directions (σy and σz, respectively) for urban conditions (Essa et al, 2005) Table S14. Discriminant functions at group centroids for linear discriminant analysis based on CH4 and C2H6 concentrations in the selected residential zones.…”

On the Algorithms Used to Compute the Standard Deviation of Wind Direction

“…Thus, the lateral spread of a plume is linked to the angular standard deviation through the equation s Lat 5 tan(s u )xf(x), which for small s u reduces to s Lat 5 s u xf(x). Even though there are various proposed forms for f(x) (Irwin 1983), the use of schemes involving the horizontal standard deviation of wind direction provides the best way of estimating the cross-wind dispersion, especially for low wind speeds (Cirillo and Poli 1992;Essa et al 2005;Sharan et al 1995;Yadav and Sharan 1996). A practical application of this scheme is given by the work of Yamartino and Wiegand (1986), who use the standard deviation of wind direction in their Canyon Plume box model to determine the level of mixing and turbulence within the canyon.…”