Numerical dispersion models for emission monitoring by spectroscopic remote sensing methods

Remote sensing techniques have been developed to measure concentrations of atmospheric pollutants in the vicinity of diffuse pollution sources. In order to derive emission rates from these measurements numerical dispersion models have to be used. Three main types of dispersion models are currently available: Gaussian dispersion models, Eulerian models, and Lagrangian models. Gaussian models base on an analytical solution of the diffusion equation which describes the horizontal and vertical mean and turbulent transport of airborne matter from a source. Eulerian models require the definition of a grid in the volume of interest. At these grid points the budget equations for mass, momentum heat, moisture, and pollutants will be solved numerically. Lagrangian models do not need a predescribed grid. Here the budget equations will be solved for particles or very small atmospheric volumes moving with the mean wind. For the application of Lagrangian models the wind field must be known a priori from measurements or from (Eulerian) model simulations. Advantages, disadvantages, and application of these models is discussed in this paper.