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vertical planes, as illustrated in Figure I-2.
Figure I-2: Coordinate System Showing Gaussian Distributions in the Horizontal and
Vertical Planes
Wind tunnel tests have shown this to be an adequate representation of some pollutant plumes,
although in practice large variations from the ideal have been observed. The standard deviation
factors sy and sz represent the degree of “spreading” of pollutants horizontally and vertically
during plume movement. High standard deviation values would result from an unstable, turbulent
atmosphere, whereas low values would occur in less turbulent atmospheric conditions. These
factors are calculated using the Pasquill-Gifford stability classification system, described in
paragraph 2.2.2.
Other assumptions include a constant wind speed from the negative-x direction, with no
adjustment for changing wind speed with height; continuous emissions from a single point source
at a rate indicated by the factor Q; upwind pollutant concentration of zero; no removal of
pollutants by settling and deposition on the ground (i.e. total reflection of the plume at the earth’s
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I-8
surface); and no removal of pollutants by chemical transformation to other species. The
combination of these assumptions allows a calculation of steady-state concentration at any
specified receptor downwind of the source. The assumption of zero upwind concentration of
each pollutant allows the modeling of only those emissions resulting from the sources being
modeled. When performing an NAAQS assessment, however, it is necessary to add those
dispersion model results to ambient or background pollutant levels measured for the area to
determine whether or not the source will produce enough additional pollution to cause a violation
of the NAAQS.
In modeling a complex emission system, such as an airport, a dispersion model is likely to
represent sources as point, line, and area emissions, based on the type of sources identified in the
emissions inventory. Point sources include stationary emission points such as sand piles, boiler
stacks, and painting operations. Modeling of these sources is straightforward, using the Gaussian
point source approximation given above. In some cases, mobile sources may also be modeled as
point sources. For example, the takeoff roll and climb of an aircraft are most easily modeled as a
series of “puffs,” each of which is treated as a single Gaussian point source with a duration of a
few seconds.
Roadways may be modeled as line sources or a series of point sources. The line source
approximation involves the solving of an integral along the specified line, which results in a
greater computational requirement than the approximation as a series of point sources. Both
calculations are based on the Gaussian model, with similar results expected in most cases. A
dispersion model may include adjustments for factors associated with roadways that affect
turbulence, such as surface roughness. Surface roughness of the area near the roadway has an
effect on dispersion because the source of emissions is very close to the ground, and may be
required as input to some dispersion models.
Parking lots may be modeled as area sources, a series of line sources, or as a series of point
sources. Because each type of model relies upon the same Gaussian approximation, results
should be independent of the type of model used as long as the receptor is located far enough
away from the parking lot. The key factors in choosing one source model over another are the
difference in computational demand and whether EPA has “approved” the model for use on the
particular type of application.
Dispersion models using a Gaussian approximation of pollutants have been applied for many
years to emissions from stacks at industrial and utility sites. For those cases, the important issues
in dispersion modeling have been incorporating estimates of plume rise and downwash of the
plume at the stack tip and nearby buildings. At airports, stack emissions make up a very small
component of the total emissions, with the majority arising instead from mobile sources such as
aircraft, passenger vehicles, and ground support equipment. However, the Gaussian
　
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