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NONROAD – is computerized database developed by the EPA to provide state and local
pollution control agencies with the ability and resources to create and forecast emission
inventories for the nonroad category of emission sources. These ncluded all-terrain recreational
vehicles and marine equ pment, agricultural and construction equipment, lawn and garden
equipment, locomotives and a variety of other off-road vehicles and equipment. The required
inputs vary by the type of vehicle/equipment for which emission estimates are
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AIR QUALITY PROCEDURES FOR CIVILIAN AIRPORTS & AIR FORCE BASES
(THE "AIR QUALITY HANDBOOK")
ADDENDUM (Continued)
EPA Office of Transportation and Air Quality.
4. (p. 31)
4.1 (p. 31)
􀁹
􀁹
EDMS is now the FAA
4.2 (p. 32)
.
4.2.1 (p. 32)
4.2.2 (p. 32)
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NONROAD is used in support of airport air quality assessments primarily for the estimation of
emissions from construction-related equipment and aircraft ground support equipment (GSE).
Still in the draft form, updates to this model are developed periodically and are available from the
DISPERSION ASSESSMENT
Introduction
Describes the purpose and applicability of dispersion modeling and introduces the concept of
screening models.
For additional information and guidance on dispersion models, refer to the following publications:
40 CFR Chapter 1, Appendix W to Part 51 – Guideline on Air Quality Models (April 15, 2003
or the most recent edition.) (Reference 94)
Contains recommendations and supporting information on the selection and applications of
air quality models.
Federal Register, Vol. 63, No. 70 (April 13, 1998), Emissions and Dispersion Modeling
System Policy for Airport Air Quality Analysis; Interim Guidance to FAA Orders 1050.1D and
5050.4A, DOT, FAA.
Provides an explanation of FAA’s policy on the application and use of EDMS for airport air quality
assessments. required model for air quality analysis of aviation sources:
aircraft, auxiliary power units, and ground support equipment.
Dispersion Inputs
Introduces the section sub-headings that follow.
No changes
Emission Sources
Refers to the emissions inventory for information.
No changes.
Meteorology
Describes the importance of wind speed, wind direction, atmospheric stability and m xing height.
In accordance with EPA’s Appendix W – Guideline to Air Quality Models, “The meteorological data
used as input to a dispersion model should be selected on the basis of spatial and climatological
(e.g. tempora ) representativeness as well as the ability of the individual parameters selected to
characterize the transport and dispersion conditions in the area concern”. Temporal
representativeness is defined as a function of the year-to-year variations in weather conditions. The
EPA guidelines further advise, “The model user should acquire enough meteorological data to
ensure that worst-case meteorological conditions are adequately represented in the model results”.
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AIR QUALITY PROCEDURES FOR CIVILIAN AIRPORTS & AIR FORCE BASES
(THE "AIR QUALITY HANDBOOK")
ADDENDUM (Continued)
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results. of
4.2.3 (p. 33)
􀂃 ,
4.2.4
are
􀂃
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In order to meet these objectives, Appendix W calls for the use of up to five years of representative
meteorological data. Five years of National Weather Service (NWS) meteorological data or at least
one year of site-specific (e.g. data collected at, or in the immediate vicinity of, the facility) may be
used. When site-specific data is availab e, these data are preferred. Appendix W provides further
guidance when using less than 5 years of meteorological data: “when sufficient and representative
data exist for less than a 5 year period from a nearby NWS site…then the highest concentration
estimate should be considered the design value.”
EPA is currently developing a screening process for its AERMOD atmospheric dispers on model that
will enable the analysis of simulated worst-case meteorological conditions. As an interim measure,
the modeler may coordinate with the reviewing agencies during the early stages of an a r quality
analysis to supplement and streamline this process.
In many cases, the reviewing agencies have already identified the yearly data set(s) that represent
worst-case or other appropriate meteorological conditions for the study area. In other cases, the
modeler may attempt to demonstrate which year(s) of meteorological data is characteristic of worstcase
　
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