Lagrangian-stochastic modeling of pollutant dispersion in the urban boundary layer over complex terrain, and its comparison to Haifa 2009 tracer campaign
Abstract:Lagrangian stochastic particle models provide a well-established theoretical framework for the description of pollutant dispersion in different atmospheric boundary layer scenarios. Such modeling depends on an adequate description of the turbulent processes. Usually turbulent structure in the surface layer is described in terms of Monin-Obukhov similarity theory (MOST) using universal relationships between scaling parameters. These relationships have been shown to be valid in the case of horizontal homogeneity for stationary turbulence. The description of the turbulent processes above rough surfaces, such as over canopies, is a more complex case. For the urban canopy it was found that under developed stationary turbulence conditions MOST relations are approximately valid (in some cases, with extensions). An even more complex case is that of rough surfaces over topography, as no similarity theory has been established to properly describe the turbulence exchange over heterogeneous surfaces in complex terrain.
The Haifa 2009 urban tracer meteorological and dispersion campaign is discussed. We model near-neutral stratification using the full velocity fluctuation covariance matrix and the convective stratification using skewed velocity probability density function. Simulations show good agreement with direct tracer measurements. Turbulence parametrizations were adopted based on their accordance with our measurements and with other measurements and fits from the literature. The characteristics of turbulence in the urban area over topography are discussed. It is shown that in conditions of developed stationary turbulence, at most areas, there is in an agreement with MOST predictions. In contrast, in very low wind conditions the turbulent nocturnal boundary layer is not necessarily stationary and is spatially non-homogeneous, as manifested in the pollutant pattern.