Overview of the 

Puff-Particle Model (PPM) 

Presented at:

9th Conference on Air Quality Modeling

October 9-10, 2008

Presented by:

Joseph Scire

TRC Environmental Corporation
Acknowledgement

• The PPM was developed by Dr. Peter de Haan 

part of his Ph.D thesis at the Swiss Federal Ins

of Technology in Zurich, Switzerland. This presentation is based on a summary of his wopresented in the following references: . de Haan, P., J.S. Scire, D.G. Strimaitis and M.W. Rotach, 1999: Introdu

Puff-Particle approach for near-source dispersion into the CALPUFF 23rd NATO/CCMS ITM, Bulgaria. Air pollution Modeling and Its ApplS.-E. Gryning and E. Batchvarova (eds.), Plenum Press, New York

de Haan, P. and J.S. Scire, 1999: Prediction of higher moments of near-

concentration by simulating the meandering of pollutant puffs. Prepri

Conference Boundary Layers and Turbulence, 10-15 Jan 1999, Dalla

American Meteorological Society.
Overview

• Puff-Particle Model (PPM) is a module introdu

the CALPUFF Lagrangian puff dispersion modprovide a more detailed treatment of near-field dispersion

• PPM combines the advantages of both puff an

particle dispersion models

• Allows the model to predict both the mean 

concentration as well as the concentration distabout the mean (higher moments of the probadensity function)
Introduction

• Puff models such as CALPUFF offer advantag

steady-state plume models such as:

• Accounting for spatial variability of meteorological an

dispersion conditions, causality effects, low wind spedispersion, memory of previous hour’s emissions, spvariability in dispersion rates, etc.

• Lagrangian stochastic particle models are:

• State-of-the-science approach, especially for simula

inhomogenous (convective) turbulence

• Computationally demanding• More difficult to deal with wet and dry deposition, ch
Puff Models Types

• Ensemble average puff model

• Puff consists of a center of mass and a 3-D distribut

mass around the center

• Represents the ensemble average of the concentrat

distribution belong to a “piece” of the pollutant releas

• Cluster dispersion puff model

• Puff is a physical cluster of particles• “Relative” dispersion (due to turbulent eddies smalle

puff) contribute to puff cluster growth

• Larger eddies move puff as a whole without changin

relative separation of particles within the cluster (mecomponent)
Puff-Particle Model

• Instantaneous puff releases require use of rela

dispersion but update frequency of flow field is to resolve turbulent eddies not covered by reladispersion concept

• PPM uses a full stochastic Lagrangian particle 

dispersion model to determine the puff trajecto

• Kinematic turbulent energy associated with ed

smaller than the puff size are removed since thalready accounted for the in relative dispersion

• Every puff carries along it position along with t

position and turbulent velocity components of tstochastic particle to which it belongs
Puff-Particle Model

• Effect of meandering caused by turbulent eddi

than the puff but not resolved by the flow is simby the puff center trajectories

• Two contributions of dispersion process:

• Relative dispersion (small eddies)• Meander (large eddies)

• Stochastic path artificially produces the meand

behavior, but it is necessary to account for the and temporal correlation of turbulence

• Neighboring puffs should show similar meandering
Implementation of PPM into CALP

• To every newly released puff a “mirror ensemb

attached. This mirror ensemble consists of a udefined number of puff-particles.

• Time step broken into sub-steps (sampling ste

CALPUFF. For each sub-step the mirror enseadvected with a PPM time step (~1-10 secondevery PPM time step, new particle trajectories computed, from which the puff trajectories are 

• At end of sampling step, mirror ensemble’s firs

second moments of mass distribution are used compute the parent puff’s size and position anhanded back to main CALPUFF routine
Implementation of PPM into CALP

• CALPUFF then computes any physical proces

changing the puff’s mass or chemical composi

not its size or location)

• At some point, the size of the particle-puffs in t

ensemble will be large enough so that most of energy spectrum will be within the puff-particle

• Relative dispersion ~ same as absolute dispersion• Parent puff location and size is recomputed, the mir

ensemble is deleted and the parent puff is restored

• Parent puff treated in normal CALPUFF way using a

dispersion
Testing and Evaluation

• The PPM was evaluated using measurements 

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 a

European short-range dispersion model harmoworkshops
Description of Evaluation Resu

• Copenhagen

• Good agreement of arcwise maximum concentration

overall bias and nearly all data points within factor oobservations

• Some underprediction of cross-wind integrated conc

(CIC)

• Very similar results to those obtained with a full Lag

particle dispersion model (LPFM)

• Lillestrom

• Generally good prediction of arc-maximum concentr• Some displacement of location of peak concentratio

• Kincaid

• Used QI=3 (highest quality) data• Agreement between predicted and observed arc-ma

concentrations was very good, with s light tendency overprediction of the highest concentration values