APPLICATION OF A LAGRANGIAN RANDOM PARTICLE MODEL TO FORWARD AND INVERSE AIR QUALITY MODELING

Two methods of using a Lagrangian random particle dispersion model in air quality simulations will be presented. For the purpose of source-oriented modeling, a hybrid Lagrangian dispersion and Eulerian chemistry were coupled to study the effects of ozone in a coastal region. The Lagrangian model was providing transport and dispersion of ozone and ozone precursors, while a chemical module was treating chemical transformations within a Eulerian framework. The Lagrangian particles had chemical dimensions that were updated after each chemical model step. This hybrid model allowed for the treatment of air parcels from numerous sources and reproduced their non-linear chemical evolution. For the purpose of receptor-oriented modeling, the Lagrangian random particle model was adapted to be run in an inverse mode to determine the most probable sources impacting particular receptors. This inverse modeling improved source location estimates of the backtrajectory analysis and standard receptor models. These two methods indicate that the Lagrangian random particle model can be a useful tool in improving the accuracy of air quality models. Moreover, due to the fact that the Lagrangian framework can be applied to the highest resolutions, these approaches arc appropriate for any complexity of regional, mesoscale, and microscalc domains