Overview of thePuff-Particle Model (PPM)Presented at:9thConference on Air Quality ModelingOctober 9-10, 2008Presented by:Joseph ScireTRC Environmental Corporation**Acknowledgement • The PPM was developed by Dr. Peter de Haan as** **part of his Ph.D thesis at the Swiss Federal Institu te** **of Technology in Zurich, Switzerland. This presentation is based on a summary of his work as** **presented in the following references: .** **de Haan, P., J.S. Scire, D.G. Strimaitis and M.W. Rot ach, 1999: Introduction of a** **Puff-Particle approach for near-source dispersion i nto the CALPUFF model.** **23rd NATO/CCMS ITM, Bulgaria. Air pollution Modelin g and Its Application XIII,** **S.-E. Gryning and E. Batchvarova (eds.), Plenum Press, New York** **de Haan, P. and J.S. Scire, 1999: Prediction of hig her moments of near-source** **concentration by simulating the meandering of pollut ant puffs. Preprints 13** **th Conference Boundary Layers and Turbulence, 10-15 Ja** **n 1999, Dallas, TX. American Meteorological Society.** **Overview • Puff-Particle Model (PPM) is a module introduced i** **nto the CALPUFF Lagrangian puff dispersion model to** **provide a more detailed treatment of near-field dispersion** **• PPM combines the advantages of both puff and particle dispersion models** **• Allows the model to predict both the mean concentration as well as the concentration distribu** **tion about the mean (higher moments of the probability** **density function) Introduction** **• Puff models such as CALPUFF offer advantages over steady-state plume models such as:** **• Accounting for spatial variability of meteorologic al and** **dispersion conditions, causality effects, low wind speed** **dispersion, memory of previous hour’s emissions, sp atial** **variability in dispersion rates, etc. • Lagrangian stochastic particle models are:** **• State-of-the-science approach, especially for simu lation of** **inhomogenous (convective) turbulence • Computationally demanding** **• More difficult to deal with wet and dry deposition , chemistry** **Puff Models Types • Ensemble average puff model** **• Puff consists of a center of mass and a 3-D distri bution of total** **mass around the center • Represents the ensemble average of the concentrati** **on distribution belong to a “piece” of the pollutant re** **lease • Cluster dispersion puff model** **• Puff is a physical cluster of particles • “Relative” dispersion (due to turbulent eddies smal** **ler than the puff) contribute to puff cluster growth** **• Larger eddies move puff as a whole without changin g the** **relative separation of particles within the cluster (meander** **component) Puff-Particle Model** **• Instantaneous puff releases require use of relativ e** **dispersion but update frequency of flow field is to o low** **to resolve turbulent eddies not covered by relative dispersion concept** **• PPM uses a full stochastic Lagrangian particle dispersion model to determine the puff trajectory** **• Kinematic turbulent energy associated with eddies smaller than the puff size are removed since they a** **re already accounted for the in relative dispersion** **• Every puff carries along it position along with th e** **position and turbulent velocity components of the stochastic particle to which it belongs** **Puff-Particle Model • Effect of meandering caused by turbulent eddies la** **rger than the puff but not resolved by the flow is simul** **ated by the puff center trajectories** **• Two contributions of dispersion process: • Relative dispersion (small eddies)** **• Meander (large eddies) • Stochastic path artificially produces the meanderi** **ng behavior, but it is necessary to account for the sp** **atial and temporal correlation of turbulence** **• Neighboring puffs should show similar meandering Implementation of PPM into CALPUFF** **• To every newly released puff a “mirror ensemble” is attached. This mirror ensemble consists of a user** **defined number of puff-particles. • Time step broken into sub-steps (sampling steps) i** **n CALPUFF. For each sub-step the mirror ensemble is** **advected with a PPM time step (~1-10 seconds). For every PPM time step, new particle trajectories are** **computed, from which the puff trajectories are deri ved.** **• At end of sampling step, mirror ensemble’s first a nd** **second moments of mass distribution are used to compute the parent puff’s size and position and the** **n handed back to main CALPUFF routine** **Implementation of PPM into CALPUFF • CALPUFF then computes any physical process** **changing the puff’s mass or chemical composition (b ut** **not its size or location) • At some point, the size of the particle-puffs in t** **he mirror ensemble will be large enough so that most of the** **energy spectrum will be within the puff-particle • Relative dispersion ~ same as absolute dispersion** **• Parent puff location and size is recomputed, the m irror** **ensemble is deleted and the parent puff is restored • Parent puff treated in normal CALPUFF way using ab** **solute dispersion** **Testing and Evaluation • The PPM was evaluated using measurements from** **three tracer experiments. • Copenhagen** **• 9 hours measurements under convective conditions • 115m release height, suburban area** **• Lillestrom • 8 observations, 15-minute averaging times** **• Strongly stable winter conditions • 36m release height, suburban area** **• Kincaid • Mostly convective conditions** **• 171 hours of measurements • 187m power plant stack, rural environment** **• Datasets are “reference datasets” developed as part of** **European short-range dispersion model harmonization workshops** **Description of Evaluation Results • Copenhagen** **• Good agreement of arcwise maximum concentrations wi th little** **overall bias and nearly all data points within fact or of two of** **observations • Some underprediction of cross-wind integrated conce** **ntration (CIC)** **• Very similar results to those obtained with a full Lagrangian** **particle dispersion model (LPFM) • Lillestrom** **• Generally good prediction of arc-maximum concentra tions** **• Some displacement of location of peak concentratio ns** **• Kincaid • Used QI=3 (highest quality) data** **• Agreement between predicted and observed arc-maxim um** **concentrations was very good, with s light tendency toward** **overprediction of the highest concentration values**