# Dispersion Modeling

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```					  Introduction to SCREEN3

NAU College of Engineering and Technology

smokestacks image from Univ. of Waterloo Environmental Sciences
Dispersion Models
   Repetitious solution of dispersion
equations
   Computer solves over and over again
   Based on principles of transport,
diffusion
   Complex mathematical equations
   Computer-aided simulation of
atmosphere based on inputs
   Best models need good quality and site-
specific data
2
Historically

Image from collection of Pittsburgh
Photographic Library, Carnegie
Library of Pittsburgh

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Types of Dispersion Models
   Gaussian Plume
   Analytical approximation of dispersion
   Numerical or CFDs
   Transport & diffusional flow fields
   Statistical & Empirical
   Based on experimental or field data
   Physical
   Flow visualization in wind tunnels, etc.

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Computational Fluid
Dynamics Modeling
image from MAGNUM Technologies, Inc

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CFD Building Effects

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Gaussian Dispersion
z

          Dh = plume rise

Dh                                    h = stack height
H = effective stack
H = h + Dh                      height
H
h                                x

C(x,y,z) Downwind at (x,y,z) ?
y
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Gaussian Plume

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SCREEN3 Model Assumptions
   Continuous pollutant emissions
   Conservation of mass in atmosphere
   Concentration profiles are represented by
Gaussian distribution—bell curve shape

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SCREEN3 designed for…
   Single source, short-term calculations
 Estimate maximum ground level
concentrations
 Distance to max. concentration from source
 Concentrations at user defined distances
 Simple downwash algorithm
   Max. concentrations in near & far wake
   Estimate concentrations in cavity
recirculation zone
   Source types
   All options for point & flare
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SCREEN3 designed for…(cont.)
   Full range of meteorological conditions accepted
   Stability classes
   Wind speeds
   Inversion break up
   Shoreline fumigation
   Determine plume rise for flare releases
   Includes effects of BID
   Buoyancy induced dispersion
   Simple area & volume sources

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Source Options

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Dispersion Model Structure
INPUT DATA: Operator
experience
EMISSIONS
METEROLOGY               RECEPTORS

Model does calculations

Model Output: Estimates of
Concentrations at Receptors

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Model Input Considerations
   Source type
   Point, area, volume, flare
   Stack or source emission data
   Pollutant emission data
   Stack- or source-specific data
   Temperature in stack
   Velocity out of stack
   Receptor data
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Input screen 1

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Input Considerations
   Actual pattern of dispersion depends on
atmospheric conditions prevailing during
release
   Major meteorological factors that influence
dispersion of pollutants
   Atmospheric stability (& temperature)
   Mixing height
   Wind speed & direction

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Meteorological inputs
   Appropriate meteorological
conditions
   Appropriate for location
   Appropriate for averaging time period
   Wind stability and speed
   Flat terrain or complex terrain
   Distances to points of interest
   Receptors
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Input screen: met data

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Discrete Distance input

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Automated Distances input

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Model Inputs not Covered Today
   Height of plume rise calculated
   Momentum and buoyancy
   Can significantly alter dispersion & location of
downwind maximum ground-level concentration
   Effects of nearby buildings estimated
   Downwash wake effects
   Can significantly alter dispersion & location of
downwind maximum ground-level concentration

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Building downwash option

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Run: screen update

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Model results: graphs

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View text results: editor

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Editor information

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Review
   Dispersion = expand & diffuse
   Picture
   Gaussian = even spreading directions
   Highest along axis
   Input data quality critical
   Screen3 limitation for reactive chemicals
   No reactions assumed to create or destroy

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SCREEN3
DEMONSTRATION

Software provided courtesy of

a division of Lakes Environmental Consultants Inc.   28
Exercise 1 input 1

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Exercise 1 input 2

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Run: Exercise 1

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Results exercise 1: graph

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Editor view1

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Editor: Model Summary

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Exercise 2 input 1

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Exercise 2 input 2

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Run: Exercise 2

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Exercise 2 results

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Editor: Exercise 2

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Editor: Model summary

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Exercise 3 input 1

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Exercise 3 results: graph

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Exercise 3: Model summary

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Model summary

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Input screen

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Editor: exercise 3

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