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

The dispersion coefficients are key parameters in most of the air quality models and their use in low wind speed conditions involves some degree of uncertainty. Various estimates of dispersion parameters are discussed for low wind conditions. Specifically, schemes viz., standard, split sigma, split sigma theta, segmented plume (I) and (II), short-term averaging and Umin approach are studied through intercomparison. The concentration formula used in this intercomparison is obtained from the steady-state advection-diffusion equation. The results show that with hourly data of wind velocity and standard deviation of horizontal wind direction (σθ), split sigma and split sigma theta schemes perform much better than the traditional standard method as they take into account the wind direction fluctuations for the horizontal dispersion. On the other hand, with high-frequency data of wind velocity and σθ, schemes like segmented plume (I) and (II) and short-term averaging simulate the observations much better especially in terms of the multiple peak nature of the concentration distribution. The influence of wind fluctuations from instantaneous plumes is captured by the high-frequency data. Further, short-term averaging scheme has the advantage of not requiring σθ and still performing well. The difference in these results and those obtained from the Gaussian plume solution has been found to be marginal.