Lagrangian modeling of air pollutant transport

Imparte: Jonathan Kahl, University of Wisconsin, USA

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tmospheric trajectory models can reveal useful insights on source-receptor relationships and meteorological processes affecting transport and dispersion. In this talk I will describe some recent projects involving the use and application of trajectory models in air pollution studies. In one study, a systematic investigation was performed to quantify the sensitivity of single trajectory calculations to their starting elevation. An eight-year database of daily, 48-h back-trajectories using four different starting altitudes was created for ten sites across the continental United States, with trajectories initialized at four different elevations. Trajectory model calculations were found to be strongly sensitive to starting elevation, with a linear relationship observed between trajectory model sensitivity and difference in starting elevation. In another study, the value of upwind (Lagrangian) MODIS-Aerosol Optical Depth (AOD) predictors in empirical models of ground-level fine particle (PM2.5) concentrations are demonstrated. Using a daily averaged, gridded AOD product developed for the project, the Lagrangian AOD model was tested at the same ten sites. Multiple linear regression models that include Lagrangian AOD as predictors showed statistically significant improvement over the more commonly used simple linear regression models.