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sources being modeled. This typically includes output from computer-based emission models,
which should be properly formatted as input to the dispersion model. Additional information,
such as source location coordinates and source geometry, is required by dispersion models.
Second, local meteorological information is required, which typically is supplied by publicly
available databases. For those locations with variable topography on or near the site, some
dispersion models can accept as input a local topographic data set. However, the terrain around
airports is usually flat enough to allow the modeler to ignore the effects of topography on
pollutant dispersion.
Finally, receptor locations are identified by the user. These are the locations for which pollutant
concentrations will be calculated by the model. If an overall view of the air quality on and off the
site is required, then a grid of receptors may be used. If a small number of “sensitive” locations is
the only area of interest, computational requirements may be reduced by specifying receptors
Dispersion Model
I-6
only at those locations. “Sensitive” locations generally include any areas where the public is
likely to be present — a key component for NAAQS assessments. Receptors may be located any
distance away from the emission sources, but most models rely on relatively simple mathematical
models that restrict the distance from the source for which a dispersion calculation may be
considered accurate. Selection of receptor locations should follow the guidelines set forth below
or as required by the appropriate regulatory agency.
For each time period specified, the contribution of each emission source to the concentration at
each receptor must be calculated. Therefore if 50 separate sources are modeled at an airport, with
100 receptors specified, then 5,000 dispersion calculations must be made for each time period
being modeled. This can lead to a very large computational requirement if a large number of time
periods is modeled.
The output of a dispersion model is the average concentration of a pollutant or set of pollutants at
each receptor over a specified time period, typically corresponding to the time periods required
by NAAQS assessments. Depending on the pollutants modeled, the concentrations are given as a
one-hour average, eight-hour average, 24-hour average, or annual arithmetic mean.
Computer-based dispersion models typically require the user to provide only the requested input
data in the proper format; no understanding of the mathematics behind the model is necessary.
However, the basics of dispersion modeling are presented below so that the reader may
appreciate the techniques and limitations of these models.
Most dispersion models use a relatively simple mathematical approximation to estimate the
steady-state concentration of pollutants at a receptor resulting from a single emission source,
such as a boiler stack. Multiple emission sources are treated individually. A Gaussian
approximation, as given in Equation I-1, has been found to simulate adequately the steady-state
dispersion of pollutants from a continuous point source:
Where:
C - point concentration at receptor, mg/m3
H - effective height of emissions, m
Q - mass flow of contaminants from receptor, mg/s
u - wind speed, m/s
x,y,z - ground level coordinates of receptor, m
sy - standard deviation of plume concentration distribution in y plane, m
sz - standard deviation of plume concentration distribution in z plane, m
Equation I-1: Gaussian Approximation
A common simplification is to assume that all receptors are at ground level. This allows
simplification of Equation I-1 to Equation I-2:
C (x; y; z;H) =
Q
2 u
exp -
1
2
y
exp -
1
2
z-H
+ exp -
1
2
z+ H
y z
2
y
2
z
2
p s s s s s z
æ
è ç
ö
ø ÷
é
ë
êê
ù
û
úú
æ
è ç
ö
ø ÷
é
ë êê
ù
û úú
æ
è ç
ö
ø ÷
é
ë êê
ù
û úú
ìí ï
îï
üý ï
þï
I-7
Equation I-2: Gaussian Approximation (Receptors at Ground Level)
This model of pollutant dispersion makes several key assumptions. The main assumption is that
the plume of dispersed pollutants follows a Gaussian distribution in both the horizontal and
　
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