Formulation of turbulence diffusion relationships under stable atmospheric conditions and its effect on pollution dispersion

Abstract

In this article, we formulate Monin–Obukhov similarity theory (MOST)-based relationships, for normalized standard deviations of wind velocity components under the local scaling framework, and investigate their applicability under stable and highly stable atmospheric conditions. We used the fast response data collected using an ultrasonic anemometer over a flat terrain of Kalpakkam in India and a complex hilly terrain at Cadarache, France, for arriving at these formulations. The study shows that after filtering of the submesoscale motions from the sonic anemometer data, the turbulence diffusion relationships follow local scaling, under stable conditions. The study further indicates that these relationships follow similar behavior for the sites taken for this study. At neutral conditions, the values of the scaled standard deviations are found to be 1.9 ± 0.07, 1.8 ± 0.06 and 1.3 ± 0.02, for longitudinal, crosswind and vertical component, respectively, for the complex terrain and 1.8 ± 0.03, 1.9 ± 0.06 and 1.1 ± 0.04, respectively, for the flat terrain. The research also investigates the effect of the new diffusion relationships in simulating atmospheric dispersion, using the Lagrangian particle dispersion model FLEXPART-WRF. Simulations using these new diffusion relationships show a higher dose estimate relative to the model default Hanna’s method, in the case of radioactivity dispersion. Detailed comparisons of the simulated dose rate estimates against measurements using Environmental Radiation Monitors (ERM) indicate that the new relationships give better correlation (r2 = 0.62) under stable conditions over model default relationships (r2 = 0.50).