angles, and convective velocity scales (w*, for convective
conditions only) based on user-specified surface characteristics for
surface roughness (Zo), Bowen ratio (Bo), and
albedo (r). For unstable cases, the convective mixing height
(Zic) is calculated based on w*, and the mechanical
mixing height (Zim) is calculated for unstable and stable
conditions based on the friction velocity, u*.
    c. For applications involving simple or complex terrain,
AERSCREEN interfaces with AERMAP. AERSCREEN also interfaces with
BPIPPRM to provide the necessary building parameters for
applications involving building downwash using the Plume Rise Model
Enhancements (PRIME) downwash algorithm. AERSCREEN generates inputs
to AERMOD via MAKEMET, AERMAP, and BPIPPRM and invokes AERMOD in a
screening mode. The screening mode of AERMOD forces the AERMOD model
calculations to represent values for the plume centerline,
regardless of the source-receptor-wind direction orientation. The
maximum concentration output from AERSCREEN represents a worst-case
1-hour concentration. Averaging-time scaling factors of 1.0 for 3-
hour, 0.9 for 8-hour, 0.60 for 24-hour, and 0.10 for annual
concentration averages are applied internally by AERSCREEN to the
highest 1-hour concentration calculated by the model for non-area
type sources. For area type source concentrations for averaging
times greater than one hour, the concentrations are equal to the 1-
hour estimates.37 40

<IP_ADDRESS> CTSCREEN

    a. CTSCREEN 39 41 can be used to obtain conservative,
yet realistic, worst-case estimates for receptors located on terrain
above stack height. CTSCREEN accounts for the three-dimensional
nature of plume and

terrain interaction and requires detailed terrain data
representative of the modeling domain. The terrain data must be
digitized in the same manner as for CTDMPLUS and a terrain processor
is available.\42\ CTSCREEN is designed to execute a fixed matrix of
meteorological values for wind speed (u), standard deviation of
horizontal and vertical wind speeds ([sigma]v, [sigma]w), vertical
potential temperature gradient (d[theta]/dz), friction velocity
(u*), Monin-Obukhov length (L), mixing height (zi) as a
function of terrain height, and wind directions for both neutral/
stable conditions and unstable convective conditions. The maximum
concentration output from CTSCREEN represents a worst-case 1-hour
concentration. Time-scaling factors of 0.7 for 3-hour, 0.15 for 24-
hour and 0.03 for annual concentration averages are applied
internally by CTSCREEN to the highest 1-hour concentration
calculated by the model.

<IP_ADDRESS> Screening in Complex Terrain

    a. For applications utilizing AERSCREEN, AERSCREEN automatically
generates a polar-grid receptor network with spacing determined by
the maximum distance to model. If the application warrants a
different receptor network than that generated by AERSCREEN, it may
be necessary to run AERMOD in screening mode with a user-defined
network. For CTSCREEN applications or AERMOD in screening mode
outside of AERSCREEN, placement of receptors requires very careful
attention when modeling in complex terrain. Often the highest
concentrations are predicted to occur under very stable conditions,
when the plume is near or impinges on the terrain. Under such
conditions, the plume may be quite narrow in the vertical, so that
even relatively small changes in a receptor's location may
substantially affect the predicted concentration. Receptors within
about a kilometer of the source may be even more sensitive to
location. Thus, a dense array of receptors may be required in some
cases.
    b. For applications involving AERSCREEN, AERSCREEN interfaces
with AERMAP to generate the receptor elevations. For applications
involving CTSCREEN, digitized contour data must be preprocessed \42\
to provide hill shape parameters in suitable input format. The user
then supplies receptor locations either through an interactive
program that is part of the model or directly, by using a text
editor; using both methods to select receptor locations will
generally be necessary to assure that the maximum concentrations are
estimated by either model. In cases where a terrain feature may
``appear to the plume'' as smaller, multiple hills, it may be
necessary to model the terrain both as a single feature and as
multiple hills to determine design concentrations.
    c. Other screening techniques may be acceptable for complex
terrain cases where established procedures \43\ are used. The user
is encouraged to confer with the appropriate reviewing authority
(paragraph 3.0(b)) if any unforeseen problems are encountered, e.g.,
applicability, meteorological data, receptor siting, or terrain
contour processing issues.

4.2.2 Refined Models

    a. A brief description of each preferred model for refined
applications is found in addendum A. Also listed in that addendum
Are availability, the model input requirements, the standard options
that shall be selected when running the program, and output options.

<IP_ADDRESS> AERMOD

    a. For a wide range of regulatory applications in all types of
terrain, and for aerodynamic building downwash, the required model
is AERMOD.44 45 The AERMOD regulatory modeling system
consists of the AERMOD dispersion model, the AERMET meteorological
processor, and the AERMAP terrain processor. AERMOD is a steady-
state Gaussian plume model applicable to directly emitted air
pollutants that employs best state-of-practice parameterizations for
characterizing the meteorological influences and dispersion.
Differentiation of simple versus complex terrain is unnecessary with
AERMOD. In complex terrain, AERMOD employs the well-known dividing-
streamline concept in a simplified simulation of the effects of
plume-terrain interactions.
    b. The AERMOD Modeling System has been extensively evaluated
across a wide range of scenarios based on numerous field studies,
including tall stacks in flat and complex terrain settings, sources
subject to building downwash influences, and low-level non-buoyant
sources.\27\ These evaluations included several long-term field
studies associated with operating plants as well as several
intensive tracer studies. Based on these evaluations, AERMOD has
shown consistently good performance, with ``errors'' in predicted
versus observed peak concentrations, based on the Robust Highest
Concentration (RHC) metric, consistently within the range of 10 to
40 percent (cited in paragraph 4.1(e)).
    c. AERMOD incorporates the PRIME algorithm to account for
enhanced plume growth and restricted plume rise for plumes affected
by building wake effects.\46\ The PRIME algorithm accounts for
entrainment of plume mass into the cavity recirculation region,
including re-entrainment of plume mass into the wake region beyond
the cavity.
    d. AERMOD incorporates the Buoyant Line and Point Source (BLP)
Dispersion model to account for buoyant plume rise from line
sources. The BLP option utilizes the standard meteorological inputs
provided by the AERMET meteorological processor.
    e. The state-of-the-science for modeling atmospheric deposition
is evolving, new modeling techniques are continually being assessed,
and their results are being compared with observations.
Consequently, while deposition treatment is available in AERMOD, the
approach taken for any purpose shall be coordinated with the
appropriate reviewing authority (paragraph 3.0(b)).
    f. The AERMET meteorological processor incorporates the COARE
algorithms to derive marine boundary layer parameters for overwater
applications of AERMOD.47 48 AERMOD is applicable for
some overwater applications when platform downwash and shoreline
fumigation are adequately considered in consultation with the
Regional Office and appropriate reviewing authority. Where the
effects of shoreline fumigation and platform downwash need to be
assessed, the Offshore and Coastal Dispersion (OCD) model is the
applicable model (paragraph <IP_ADDRESS>).

<IP_ADDRESS> CTDMPLUS

    a. If the modeling application involves an elevated point source
with a well-defined hill or ridge and a detailed dispersion analysis
of the spatial pattern of plume impacts is of interest, CTDMPLUS is
available. CTDMPLUS provides greater resolution of concentrations
about the contour of the hill feature than does AERMOD through a
different plume-terrain interaction algorithm.

<IP_ADDRESS> OCD

    a. The OCD (Offshore and Coastal Dispersion) model is a
straight-line Gaussian model that incorporates overwater plume
transport and dispersion as well as changes that occur as the plume
crosses the shoreline. OCD can determine the impact of offshore
emissions from point, area, or line sources on the air quality of
coastal regions. OCD is also applicable for situations that involve
platform building downwash.

4.2.3 Pollutant Specific Modeling Requirements

<IP_ADDRESS> Models for Carbon Monoxide

    a. Models for assessing the impact of CO emissions are needed to
meet NSR requirements to address compliance with the CO NAAQS and to
determine localized impacts from transportations projects. Examples
include evaluating effects of point sources, congested roadway
intersections and highways, as well as the cumulative effect of
numerous sources of CO in an urban area.
    b. The general modeling recommendations and requirements for
screening models in section 4.2.1 and refined models in section
4.2.2 shall be applied for CO modeling. Given the relatively low CO
background concentrations, screening techniques are likely to be
adequate in most cases. In applying these recommendations and
requirements, the existing 1992 EPA guidance for screening CO
impacts from highways may be consulted.\49\

<IP_ADDRESS> Models for Lead

    a. In January 1999 (40 CFR part 58, appendix D), the EPA gave
notice that concern about ambient lead impacts was being shifted
away from roadways and toward a focus on stationary point sources.
Thus, models for assessing the impact of lead emissions are needed
to meet NSR requirements to address compliance with the lead NAAQS
and for SIP attainment demonstrations. The EPA has also issued
guidance on siting ambient monitors in the vicinity of stationary
point sources.\50\ For lead, the SIP should contain an air quality
analysis to determine the maximum rolling 3-month average lead
concentration resulting from major lead point sources, such as
smelters, gasoline additive plants, etc. The EPA has developed a
post-processor to calculate rolling 3-month average concentrations
from model output.\51\ General

guidance for lead SIP development is also available.\52\
    b. For major lead point sources, such as smelters, which
contribute fugitive emissions and for which deposition is important,
professional judgment should be used, and there shall be
coordination with the appropriate reviewing authority (paragraph
3.0(b)). For most applications, the general requirements for
screening and refined models of section 4.2.1 and 4.2.2 are
applicable to lead modeling.

<IP_ADDRESS> Models for Sulfur Dioxide

    a. Models for SO2 are needed to meet NSR requirements
to address compliance with the SO2 NAAQS and PSD
increments, for SIP attainment demonstrations,\53\ and for
characterizing current air quality via modeling.\54\ SO2
is one of a group of highly reactive gases known as ``oxides of
sulfur'' with largest emissions sources being fossil fuel combustion
at power plants and other industrial facilities.
    b. Given the relatively inert nature of SO2 on the
short-term time scales of interest (i.e., 1-hour) and the sources of
SO2 (i.e., stationary point sources), the general
modeling requirements for screening models in section 4.2.1 and
refined models in section 4.2.2 are applicable for SO2
modeling applications. For urban areas, AERMOD automatically invokes
a half-life of 4 hours \55\ to SO2. Therefore, care must
be taken when determining whether a source is urban or rural (see
section <IP_ADDRESS> for urban/rural determination methodology).

<IP_ADDRESS> Models for Nitrogen Dioxide

    a. Models for assessing the impact of sources on ambient
NO2 concentrations are needed to meet NSR requirements to
address compliance with the NO2 NAAQS and PSD increments.
Impact of an individual source on ambient NO2 depends, in
part, on the chemical environment into which the source's plume is
to be emitted. This is due to the fact that NO2 sources
co-emit NO along with NO2 and any emitted NO may react
with ambient ozone to convert to additional NO2 downwind.
Thus, comprehensive modeling of NO2 would need to
consider the ratio of emitted NO and NO2, the ambient
levels of ozone and subsequent reactions between ozone and NO, and
the photolysis of NO2 to NO.
    b. Due to the complexity of NO2 modeling, a multi-
tiered screening approach is required to obtain hourly and annual
average estimates of NO2.\56\ Since these methods are
considered screening techniques, their usage shall occur in
agreement with the appropriate reviewing authority (paragraph
3.0(b)). Additionally, since screening techniques are conservative
by their nature, there are limitations to how these options can be
used. Specifically, modeling of negative emissions rates should only
be done after consultation with the EPA Regional Office to ensure
that decreases in concentrations would not be overestimated. Each
tiered approach (see Figure 4-1) accounts for increasingly complex
considerations of NO2 chemistry and is described in
paragraphs c through e of this subsection. The tiers of
NO2 modeling include:
    i. A first-tier (most conservative) ``full'' conversion
approach;
    ii. A second-tier approach that assumes ambient equilibrium
between NO and NO2; and
    iii. A third-tier consisting of several detailed screening
techniques that account for ambient ozone and the relative amount of
NO and NO2 emitted from a source.
    c. For Tier 1, use an appropriate refined model (section 4.2.2)
to estimate nitrogen oxides (NOX) concentrations and
assume a total conversion of NO to NO2.
    d. For Tier 2, multiply the Tier 1 result(s) by the Ambient
Ratio Method 2 (ARM2), which provides estimates of representative
equilibrium ratios of NO2/NOX value based
ambient levels of NO2 and NOX derived from
national data from the EPA's Air Quality System (AQS).\57\ The
national default for ARM2 includes a minimum ambient NO2/
NOX ratio of 0.5 and a maximum ambient ratio of 0.9. The
reviewing agency may establish alternative minimum ambient
NO2/NOX values based on the source's in-stack
emissions ratios, with alternative minimum ambient ratios reflecting
the source's in-stack NO2/NOX ratios.
Preferably, alternative minimum ambient NO2/
NOX ratios should be based on source-specific data which
satisfies all quality assurance procedures that ensure data accuracy
for both NO2 and NOX within the typical range
of measured values. However, alternate information may be used to
justify a source's anticipated NO2/NOX in-
stack ratios, such as manufacturer test data, State or local agency
guidance, peer-reviewed literature, and/or the EPA's NO2/
NOX ratio database.
    e. For Tier 3, a detailed screening technique shall be applied
on a case-by-case basis. Because of the additional input data
requirements and complexities associated with the Tier 3 options,
their usage shall occur in consultation with the EPA Regional Office
in addition to the appropriate reviewing authority. The Ozone
Limiting Method (OLM),\58\ the Plume Volume Molar Ratio Method
(PVMRM),\59\ and the Generic Set Reaction Method (GRSM)
60 61 are three detailed screening techniques that may be
used for most sources. These three techniques use an appropriate
section 4.2.2 model to estimate NOX concentrations and
then estimate the conversion of primary NO emissions to
NO2 based on the ambient levels of ozone and the plume
characteristics. OLM only accounts for NO2 formation
based on the ambient levels of ozone while PVMRM and GRSM also
accommodate distance-dependent conversion ratios based on ambient
ozone. GRSM, PVMRM and OLM require explicit specification of the
NO2/NOX in-stack ratios and that ambient ozone
concentrations be provided on an hourly basis. GRSM requires hourly
ambient NOX concentrations in addition to hourly ozone.
    f. Alternative models or techniques may be considered on a case-
by-case basis and their usage shall be approved by the EPA Regional
Office (section 3.2). Such models or techniques should consider
individual quantities of NO and NO2 emissions,
atmospheric transport and dispersion, and atmospheric transformation
of NO to NO2. Dispersion models that account for more
explicit photochemistry may also be considered as an alternative
model to estimate ambient impacts of NOX sources.

[GRAPHIC] [TIFF OMITTED] TP23OC23.000

Figure 4-1: Multi-Tiered Approach for Estimating NO2 Concentrations

<IP_ADDRESS> Models for PM2.5

    a. PM2.5 is a mixture consisting of several diverse
components.\62\ Ambient PM2.5 generally consists of two
components: (1) the primary component, emitted directly from a
source; and (2) the secondary component, formed in the atmosphere
from other pollutants emitted from the source. Models for
PM2.5 are needed to meet NSR requirements to address
compliance with the PM2.5 NAAQS and PSD increments and
for SIP attainment demonstrations.
    b. For NSR modeling assessments, the general modeling
requirements for screening models in section 4.2.1 and refined
models in section 4.2.2 are applicable for the primary component of
PM2.5, while the methods in section 5.4 are applicable
for addressing the secondary component of PM2.5. Guidance
for PSD assessments is available for determining the best approach
to handling sources of primary and secondary PM2.5.\63\
    c. For SIP attainment demonstrations and regional haze
reasonable progress goal analyses, effects of a control strategy on
PM2.5 are estimated from the sum of the effects on the
primary and secondary components composing PM2.5. Model
users should refer to section 5.4.1 and associated SIP modeling
guidance \64\ for further details concerning appropriate modeling
approaches.
    d. The general modeling requirements for the refined models
discussed in section 4.2.2 shall be applied for PM2.5
hot-spot modeling for mobile sources. Specific guidance is available
for analyzing direct PM2.5 impacts from highways,
terminals, and other transportation projects.\65\

<IP_ADDRESS> Models for PM10

    a. Models for PM10 are needed to meet NSR
requirements to address compliance with the PM10 NAAQS
and PSD increments and for SIP attainment demonstrations.
    b. For most sources, the general modeling requirements for
screening models in section 4.2.1 and refined models in section
4.2.2 shall be applied for PM10 modeling. In cases where
the particle size and its effect on ambient concentrations need to
be considered, particle deposition may be used on a case-by-case
basis and their usage shall be coordinated with the appropriate
reviewing authority. A SIP development guide \66\ is also available
to assist in PM10 analyses and control strategy
development.
    c. Fugitive dust usually refers to dust put into the atmosphere
by the wind blowing over plowed fields, dirt roads, or desert or
sandy areas with little or no vegetation. Fugitive emissions include
the emissions resulting from the industrial process that are not
captured and vented through a stack, but may be released from
various locations within the complex. In some unique cases, a model
developed specifically for the situation may be needed. Due to the
difficult nature of characterizing and modeling fugitive dust and
fugitive emissions, the proposed procedure shall be determined in
consultation with the appropriate reviewing authority (paragraph
3.0(b)) for each specific situation before the modeling exercise is
begun. Re-entrained dust is created by vehicles driving over dirt
roads (e.g., haul roads) and dust-covered roads typically found in
arid areas. Such sources can be characterized as line, area or
volume sources.65 67 Emission rates may be based on site-
specific data or values from the general literature.
    d. Under certain conditions, recommended dispersion models may
not be suitable to appropriately address the nature of ambient
PM10. In these circumstances, the alternative modeling
approach shall be approved by the EPA Regional Office (section 3.2).
    e. The general modeling requirements for the refined models
discussed in section 4.2.2 shall be applied for PM10 hot-
spot modeling for mobile sources. Specific guidance is available for
analyzing direct PM10 impacts from highways, terminals,
and other transportation projects.\65\

5.0 Models for Ozone and Secondarily Formed Particulate Matter

5.1 Discussion

    a. Air pollutants formed through chemical reactions in the
atmosphere are referred to as secondary pollutants. For example,
ground-level ozone and a portion of PM2.5 are secondary
pollutants formed through photochemical reactions. Ozone and
secondarily formed particulate matter are closely related to each
other in that they share common sources of emissions and are formed
in the atmosphere from chemical reactions with similar precursors.
    b. Ozone formation is driven by emissions of NOX and
volatile organic compounds (VOCs). Ozone formation is a complicated
nonlinear process that requires favorable meteorological conditions
in addition to VOC and NOX emissions. Sometimes complex
terrain features also contribute to the build-up of precursors and
subsequent ozone formation or destruction.
    c. PM2.5 can be either primary (i.e., emitted
directly from sources) or secondary in nature. The fraction of
PM2.5 which is primary versus secondary varies by
location and season. In the United States, PM2.5 is
dominated by a variety of chemical species or components of
atmospheric particles, such as ammonium sulfate, ammonium nitrate,
organic carbon mass, elemental carbon, and other soil compounds and
oxidized metals. PM2.5 sulfate, nitrate, and ammonium
ions are predominantly the result of chemical reactions of the
oxidized products of SO2 and NOX emissions
with direct ammonia emissions.\68\
    d. Control measures reducing ozone and PM2.5
precursor emissions may not lead to proportional reductions in ozone
and PM2.5. Modeled strategies designed to reduce ozone or
PM2.5 levels typically need to consider the chemical
coupling between these pollutants. This coupling is important in
understanding processes that control the levels of both pollutants.
Thus, when feasible, it is important to use models that take into
account the chemical coupling between ozone and PM2.5. In
addition, using such a multi-pollutant modeling system can reduce
the resource burden associated with applying and evaluating separate
models for each pollutant and promotes consistency among the
strategies themselves.
    e. PM2.5 is a mixture consisting of several diverse
chemical species or components of

atmospheric particles. Because chemical and physical properties and
origins of each component differ, it may be appropriate to use
either a single model capable of addressing several of the important
components or to model primary and secondary components using
different models. Effects of a control strategy on PM2.5
is estimated from the sum of the effects on the specific components
comprising PM2.5.

5.2 Recommendations

    a. Chemical transformations can play an important role in
defining the concentrations and properties of certain air
pollutants. Models that take into account chemical reactions and
physical processes of various pollutants (including precursors) are
needed for determining the current state of air quality, as well as
predicting and projecting the future evolution of these pollutants.
It is important that a modeling system provide a realistic
representation of chemical and physical processes leading to
secondary pollutant formation and removal from the atmosphere.
    b. Chemical transport models treat atmospheric chemical and
physical processes such as deposition and motion. There are two
types of chemical transport models, Eulerian (grid based) and
Lagrangian. These types of models are differentiated from each other
by their frame of reference. Eulerian models are based on a fixed
frame of reference and Lagrangian models use a frame of reference
that moves with parcels of air between the source and receptor
point.\9\ Photochemical grid models are three-dimensional Eulerian
grid-based models that treat chemical and physical processes in each
grid cell and use diffusion and transport processes to move chemical
species between grid cells.\9\ These types of models are appropriate
for assessment of near-field and regional scale reactive pollutant
impacts from specific sources 7 10 11 12 or all
sources.13 14 15 In some limited cases, the secondary
processes can be treated with a box model, ideally in combination
with a number of other modeling techniques and/or analyses to treat
individual source sectors.
    c. Regardless of the modeling system used to estimate secondary
impacts of ozone and/or PM2.5, model results should be
compared to observation data to generate confidence that the
modeling system is representative of the local and regional air
quality. For ozone related projects, model estimates of ozone should
be compared with observations in both time and space. For
PM2.5, model estimates of speciated PM2.5
components (such as sulfate ion, nitrate ion, etc.) should be
compared with observations in both time and space.\69\
    d. Model performance metrics comparing observations and
predictions are often used to summarize model performance. These
metrics include mean bias, mean error, fractional bias, fractional
error, and correlation coefficient.\69\ There are no specific levels
of any model performance metric that indicate ``acceptable'' model
performance. The EPA's preferred approach for providing context
about model performance is to compare model performance metrics with
similar contemporary applications.64 69 Because model
application purpose and scope vary, model users should consult with
the appropriate reviewing authority (paragraph 3.0(b)) to determine
what model performance elements should be emphasized and presented
to provide confidence in the regulatory model application.
    e. There is no preferred modeling system or technique for
estimating ozone or secondary PM2.5 for specific source
impacts or to assess impacts from multiple sources. For assessing
secondary pollutant impacts from single sources, the degree of
complexity required to assess potential impacts varies depending on
the nature of the source, its emissions, and the background
environment. The EPA recommends a two-tiered approach where the
first tier consists of using existing technically credible and
appropriate relationships between emissions and impacts developed
from previous modeling that is deemed sufficient for evaluating a
source's impacts. The second tier consists of more sophisticated
case-specific modeling analyses. The appropriate tier for a given
application should be selected in consultation with the appropriate
reviewing authority (paragraph 3.0(b)) and be consistent with EPA
guidance.\70\

5.3 Recommended Models and Approaches for Ozone

    a. Models that estimate ozone concentrations are needed to guide
the choice of strategies for the purposes of a nonattainment area
demonstrating future year attainment of the ozone NAAQS.
Additionally, models that estimate ozone concentrations are needed
to assess impacts from specific sources or source complexes to
satisfy requirements for NSR and other regulatory programs. Other
purposes for ozone modeling include estimating the impacts of
specific events on air quality, ozone deposition impacts, and
planning for areas that may be attaining the ozone NAAQS.

5.3.1 Models for NAAQS Attainment Demonstrations and Multi-Source Air
Quality Assessments

    a. Simulation of ozone formation and transport is a complex
exercise. Control agencies with jurisdiction over areas with ozone
problems should use photochemical grid models to evaluate the
relationship between precursor species and ozone. Use of
photochemical grid models is the recommended means for identifying
control strategies needed to address high ozone concentrations in
such areas. Judgment on the suitability of a model for a given
application should consider factors that include use of the model in
an attainment test, development of emissions and meteorological
inputs to the model, and choice of episodes to model. Guidance on
the use of models and other analyses for demonstrating attainment of
the air quality goals for ozone is available.63 64 Users
should consult with the appropriate reviewing authority (paragraph
3.0(b)) to ensure the most current modeling guidance is applied.

5.3.2 Models for Single-Source Air Quality Assessments

    a. Depending on the magnitude of emissions, estimating the
impact of an individual source's emissions of NOX and VOC
on ambient ozone is necessary for obtaining a permit. The simulation
of ozone formation and transport requires realistic treatment of
atmospheric chemistry and deposition. Models (e.g., Lagrangian and
photochemical grid models) that integrate chemical and physical
processes important in the formation, decay, and transport of ozone
and important precursor species should be applied. Photochemical
grid models are primarily designed to characterize precursor
emissions and impacts from a wide variety of sources over a large
geographic area but can also be used to assess the impacts from
specific sources.7 11 12
    b. The first tier of assessment for ozone impacts involves those
situations where existing technical information is available (e.g.,
results from existing photochemical grid modeling, published
empirical estimates of source specific impacts, or reduced-form
models) in combination with other supportive information and
analysis for the purposes of estimating secondary impacts from a
particular source. The existing technical information should provide
a credible and representative estimate of the secondary impacts from
the project source. The appropriate reviewing authority (paragraph
3.0(b)) and appropriate EPA guidance 70 71 should be
consulted to determine what types of assessments may be appropriate
on a case-by-case basis.
    c. The second tier of assessment for ozone impacts involves
those situations where existing technical information is not
available or a first tier demonstration indicates a more refined
assessment is needed. For these situations, chemical transport
models should be used to address single-source impacts. Special
considerations are needed when using these models to evaluate the
ozone impact from an individual source. Guidance on the use of
models and other analyses for demonstrating the impacts of single
sources for ozone is available.\70\ This guidance document provides
a more detailed discussion of the appropriate approaches to
obtaining estimates of ozone impacts from a single source. Model
users should use the latest version of the guidance in consultation
with the appropriate reviewing authority (paragraph 3.0(b)) to
determine the most suitable refined approach for single-source ozone
modeling on a case-by-case basis.

5.4 Recommended Models and Approaches for Secondarily Formed PM2.5

    a. Models that estimate PM2.5 concentrations are
needed to guide the choice of strategies for the purposes of a
nonattainment area demonstrating future year attainment of the
PM2.5 NAAQS. Additionally, models that estimate
PM2.5 concentrations are needed to assess impacts from
specific sources or source complexes to satisfy requirements for NSR
and other regulatory programs. Other purposes for PM2.5
modeling include estimating the impacts of specific events on air
quality,

visibility, deposition impacts, and planning for areas that may be
attaining the PM2.5 NAAQS.

5.4.1 Models for NAAQS Attainment Demonstrations and Multi-Source Air
Quality Assessments

    a. Models for PM2.5 are needed to assess the adequacy
of a proposed strategy for meeting the annual and 24-hour
PM2.5 NAAQS. Modeling primary and secondary
PM2.5 can be a multi-faceted and complex problem,
especially for secondary components of PM2.5 such as
sulfates and nitrates. Control agencies with jurisdiction over areas
with secondary PM2.5 problems should use models that
integrate chemical and physical processes important in the
formation, decay, and transport of these species (e.g.,
photochemical grid models). Suitability of a modeling approach or
mix of modeling approaches for a given application requires
technical judgment as well as professional experience in choice of
models, use of the model(s) in an attainment test, development of
emissions and meteorological inputs to the model, and selection of
days to model. Guidance on the use of models and other analyses for
demonstrating attainment of the air quality goals for
PM2.5 is available.63 64 Users should consult
with the appropriate reviewing authority (paragraph 3.0(b)) to
ensure the most current modeling guidance is applied.

5.4.2 Models for Single-Source Air Quality Assessments

    a. Depending on the magnitude of emissions, estimating the
impact of an individual source's emissions on secondary particulate
matter concentrations may be necessary for obtaining a permit.
Primary PM2.5 components shall be simulated using the
general modeling requirements in section <IP_ADDRESS>. The simulation of
secondary particulate matter formation and transport is a complex
exercise requiring realistic treatment of atmospheric chemistry and
deposition. Models should be applied that integrate chemical and
physical processes important in the formation, decay, and transport
of these species (e.g., Lagrangian and photochemical grid models).
Photochemical grid models are primarily designed to characterize
precursor emissions and impacts from a wide variety of sources over
a large geographic area and can also be used to assess the impacts
from specific sources.7 10 For situations where a project
source emits both primary PM2.5 and PM2.5
precursors, the contribution from both should be combined for use in
determining the source's ambient impact. Approaches for combining
primary and secondary impacts are provided in appropriate guidance
for single source permit related demonstrations.\70\
    b. The first tier of assessment for secondary PM2.5
impacts involves those situations where existing technical
information is available (e.g., results from existing photochemical
grid modeling, published empirical estimates of source specific
impacts, or reduced-form models) in combination with other
supportive information and analysis for the purposes of estimating
secondary impacts from a particular source. The existing technical
information should provide a credible and representative estimate of
the secondary impacts from the project source. The appropriate
reviewing authority (paragraph 3.0(b)) and appropriate EPA guidance
70 71 should be consulted to determine what types of
assessments may be appropriate on a case-by-case basis.
    c. The second tier of assessment for secondary PM2.5
impacts involves those situations where existing technical
information is not available or a first tier demonstration indicates
a more refined assessment is needed. For these situations, chemical
transport models should be used for assessments of single-source
impacts. Special considerations are needed when using these models
to evaluate the secondary particulate matter impact from an
individual source. Guidance on the use of models and other analyses
for demonstrating the impacts of single sources for secondary
PM2.5 is available.\70\ This guidance document provides a
more detailed discussion of the appropriate approaches to obtaining
estimates of secondary particulate matter concentrations from a
single source. Model users should use the latest version of this
guidance in consultation with the appropriate reviewing authority
(paragraph 3.0(b)) to determine the most suitable single-source
modeling approach for secondary PM2.5 on a case-by-case
basis.

6.0 Modeling for Air Quality Related Values and Other Governmental
Programs

6.1 Discussion

    a. Other Federal government agencies and State, local, and
Tribal agencies with air quality and land management
responsibilities have also developed specific modeling approaches
for their own regulatory or other requirements. Although such
regulatory requirements and guidance have come about because of EPA
rules or standards, the implementation of such regulations and the
use of the modeling techniques is under the jurisdiction of the
agency issuing the guidance or directive. This section covers such
situations with reference to those guidance documents, when they are
available.
    b. When using the model recommended or discussed in the
Guideline in support of programmatic requirements not specifically
covered by EPA regulations, the model user should consult the
appropriate Federal, State, local, or Tribal agency to ensure the
proper application and use of the models and/or techniques. These
agencies have developed specific modeling approaches for their own
regulatory or other requirements. Most of the programs have, or will
have when fully developed, separate guidance documents that cover
the program and a discussion of the tools that are needed. The
following paragraphs reference those guidance documents, when they
are available.

6.2 Air Quality Related Values

    a. The 1990 CAA Amendments give FLMs an ``affirmative
responsibility'' to protect the natural and cultural resources of
Class I areas from the adverse impacts of air pollution and to
provide the appropriate procedures and analysis techniques. The CAA
identifies the FLM as the Secretary of the department, or their
designee, with authority over these lands. Mandatory Federal Class I
areas are defined in the CAA as international parks, national parks
over 6,000 acres, and wilderness areas and memorial parks over 5,000
acres, established as of 1977. The FLMs are also concerned with the
protection of resources in federally managed Class II areas because
of other statutory mandates to protect these areas. Where State or
Tribal agencies have successfully petitioned the EPA and lands have
been redesignated to Class I status, these agencies may have
equivalent responsibilities to that of the FLMs for these non-
Federal Class I areas as described throughout the remainder of
section 6.2.
    b. The FLM agency responsibilities include the review of air
quality permit applications from proposed new or modified major
pollution sources that may affect these Class I areas to determine
if emissions from a proposed or modified source will cause or
contribute to adverse impacts on air quality related values (AQRVs)
of a Class I area and making recommendations to the FLM. AQRVs are
resources, identified by the FLM agencies, that have the potential
to be affected by air pollution. These resources may include
visibility, scenic, cultural, physical, or ecological resources for
a particular area. The FLM agencies take into account the particular
resources and AQRVs that would be affected; the frequency and
magnitude of any potential impacts; and the direct, indirect, and
cumulative effects of any potential impacts in making their
recommendations.
    c. While the AQRV notification and impact analysis requirements
are outlined in the PSD regulations at 40 CFR 51.166(p) and 40 CFR
52.21(p), determination of appropriate analytical methods and
metrics for AQRV's are determined by the FLM agencies and are
published in guidance external to the general recommendations of
this paragraph.
    d. To develop greater consistency in the application of air
quality models to assess potential AQRV impacts in both Class I
areas and protected Class II areas, the FLM agencies have developed
the Federal Land Managers' Air Quality Related Values Work Group
Phase I Report (FLAG).\72\ FLAG focuses upon specific technical and
policy issues associated with visibility impairment, effects of
pollutant deposition on soils and surface waters, and ozone effects
on vegetation. Model users should consult the latest version of the
FLAG report for current modeling guidance and with affected FLM
agency representatives for any application specific guidance which
is beyond the scope of the Guideline.

6.2.1 Visibility

    a. Visibility in important natural areas (e.g., Federal Class I
areas) is protected under a number of provisions of the CAA,
including sections 169A and 169B (addressing impacts primarily from
existing sources) and section 165 (new source review). Visibility
impairment is caused by light scattering and light absorption
associated with particles and gases in the atmosphere. In most areas
of the country, light scattering by PM2.5 is the most

significant component of visibility impairment. The key components
of PM2.5 contributing to visibility impairment include
sulfates, nitrates, organic carbon, elemental carbon, and crustal
material.\72\
    b. Visibility regulations (40 CFR 51.300 through 51.309) require
State, local, and Tribal agencies to mitigate current and prevent
future visibility impairment in any of the 156 mandatory Federal
Class I areas where visibility is considered an important attribute.
In 1999, the EPA issued revisions to the regulations to address
visibility impairment in the form of regional haze, which is caused
by numerous, diverse sources (e.g., stationary, mobile, and area
sources) located across a broad region (40 CFR 51.308 through
51.309). The state of relevant scientific knowledge has expanded
significantly since that time. A number of studies and reports
73 74 have concluded that long-range transport (e.g., up
to hundreds of kilometers) of fine particulate matter plays a
significant role in visibility impairment across the country.
Section 169A of the CAA requires States to develop SIPs containing
long-term strategies for remedying existing and preventing future
visibility impairment in the 156 mandatory Class I Federal areas,
where visibility is considered an important attribute. In order to
develop long-term strategies to address regional haze, many State,
local, and Tribal agencies will need to conduct regional-scale
modeling of fine particulate concentrations and associated
visibility impairment.
    c. The FLAG visibility modeling recommendations are divided into
two distinct sections to address different requirements for: (1)
near field modeling where plumes or layers are compared against a
viewing background, and (2) distant/multi-source modeling for plumes
and aggregations of plumes that affect the general appearance of a
scene.\72\ The recommendations separately address visibility
assessments for sources proposing to locate relatively near and at
farther distances from these areas.\72\

<IP_ADDRESS> Models for Estimating Near-Field Visibility Impairment

    a. To calculate the potential impact of a plume of specified
emissions for specific transport and dispersion conditions (``plume
blight'') for source-receptor distances less than 50 km, a screening
model and guidance are available.72 75 If a more
comprehensive analysis is necessary, a refined model should be
selected. The model selection, procedures, and analyses should be
determined in consultation with the appropriate reviewing authority
(paragraph 3.0(b)) and the affected FLM(s).

<IP_ADDRESS> Models for Estimating Visibility Impairment for Long-Range
Transport

    a. Chemical transformations can play an important role in
defining the concentrations and properties of certain air
pollutants. Models that take into account chemical reactions and
physical processes of various pollutants (including precursors) are
needed for determining the current state of air quality, as well as
predicting and projecting the future evolution of these pollutants.
It is important that a modeling system provide a realistic
representation of chemical and physical processes leading to
secondary pollutant formation and removal from the atmosphere.
    b. Chemical transport models treat atmospheric chemical and
physical processes such as deposition and motion. There are two
types of chemical transport models, Eulerian (grid based) and
Lagrangian. These types of models are differentiated from each other
by their frame of reference. Eulerian models are based on a fixed
frame of reference and Lagrangian models use a frame of reference
that moves with parcels of air between the source and receptor
point.\9\ Photochemical grid models are three-dimensional Eulerian
grid-based models that treat chemical and physical processes in each
grid cell and use diffusion and transport processes to move chemical
species between grid cells.\9\ These types of models are appropriate
for assessment of near-field and regional scale reactive pollutant
impacts from specific sources 7 10 11 12 or all
sources.13 14 15
    c. Development of the requisite meteorological and emissions
databases necessary for use of photochemical grid models to estimate
AQRVs should conform to recommendations in section 8 and those
outlined in the EPA's Modeling Guidance for Demonstrating Attainment
of Air Quality Goals for Ozone, PM2.5, and Regional
Haze.64 Demonstration of the adequacy of prognostic
meteorological fields can be established through appropriate
diagnostic and statistical performance evaluations consistent with
recommendations provided in the appropriate guidance.\64\ Model
users should consult the latest version of this guidance and with
the appropriate reviewing authority (paragraph 3.0(b)) for any
application-specific guidance that is beyond the scope of this
subsection.

6.2.2 Models for Estimating Deposition Impacts

    a. For many Class I areas, AQRVs have been identified that are
sensitive to atmospheric deposition of air pollutants. Emissions of
NOX, sulfur oxides, NH3, mercury, and
secondary pollutants such as ozone and particulate matter affect
components of ecosystems. In sensitive ecosystems, these compounds
can acidify soils and surface waters, add nutrients that change
biodiversity, and affect the ecosystem services provided by forests
and natural areas.\72\ To address the relationship between
deposition and ecosystem effects, the FLM agencies have developed
estimates of critical loads. A critical load is defined as, ``A
quantitative estimate of an exposure to one or more pollutants below
which significant harmful effects on specified sensitive elements of
the environment do not occur according to present knowledge.'' \76\
    b. The FLM deposition modeling recommendations are divided into
two distinct sections to address different requirements for: (1)
near field modeling, and (2) distant/multi-source modeling for
cumulative effects. The recommendations separately address
deposition assessments for sources proposing to locate relatively
near and at farther distances from these areas.\72\ Where the source
and receptors are not in close proximity, chemical transport (e.g.,
photochemical grid) models generally should be applied for an
assessment of deposition impacts due to one or a small group of
sources. Over these distances, chemical and physical transformations
can change atmospheric residence time due to different propensity
for deposition to the surface of different forms of nitrate and
sulfate. Users should consult the latest version of the FLAG report
\72\ and relevant FLM representatives for guidance on the use of
models for deposition. Where source and receptors are in close
proximity, users should contact the appropriate FLM for application-
specific guidance.

6.3 Modeling Guidance for Other Governmental Programs

    a. Dispersion and photochemical grid modeling may need to be
conducted to ensure that individual and cumulative offshore oil and
gas exploration, development, and production plans and activities do
not significantly affect the air quality of any State as required
under the Outer Continental Shelf Lands Act (OCSLA). Air quality
modeling requires various input datasets, including emissions
sources, meteorology, and pre-existing pollutant concentrations. For
sources under the reviewing authority of the Department of Interior,
Bureau of Ocean Energy Management (BOEM), guidance for the
development of all necessary Outer Continental Shelf (OCS) air
quality modeling inputs and appropriate model selection and
application is available from the BOEM's website: https://www.boem.gov/about-boem/regulations-guidance/guidance-portal.
    b. The Federal Aviation Administration (FAA) is the appropriate
reviewing authority for air quality assessments of primary pollutant
impacts at airports and air bases. The Aviation Environmental Design
Tool (AEDT) is developed and supported by the FAA, and is
appropriate for air quality assessment of primary pollutant impacts
at airports or air bases. AEDT has adopted AERMOD for treating
dispersion. Application of AEDT is intended for estimating the
change in emissions for aircraft operations, point source, and
mobile source emissions on airport property and quantify the
associated pollutant level- concentrations. AEDT is not intended for
PSD, SIP, or other regulatory air quality analyses of point or
mobile sources at or peripheral to airport property that are
unrelated to airport operations. The latest version of AEDT may be
obtained from the FAA at: https://aedt.faa.gov.

7.0 General Modeling Considerations

7.1 Discussion

    a. This section contains recommendations concerning a number of
different issues not explicitly covered in other sections of the
Guideline. The topics covered here are not specific to any one
program or modeling area, but are common to dispersion modeling
analyses for criteria pollutants.

7.2 Recommendations

7.2.1 All sources

<IP_ADDRESS> Dispersion Coefficients

    a. For any dispersion modeling exercise, the urban or rural
determination of a source

is critical in determining the boundary layer characteristics that
affect the model's prediction of downwind concentrations.
Historically, steady-state Gaussian plume models used in most
applications have employed dispersion coefficients based on
Pasquill-Gifford \77\ in rural areas and McElroy-Pooler \78\ in
urban areas. These coefficients are still incorporated in the BLP
and OCD models. However, the AERMOD model incorporates a more up-to-
date characterization of the atmospheric boundary layer using
continuous functions of parameterized horizontal and vertical
turbulence based on Monin-Obukhov similarity (scaling)
relationships.\44\ Another key feature of AERMOD's formulation is
the option to use directly observed variables of the boundary layer
to parameterize dispersion.44 45
    b. The selection of rural or urban dispersion coefficients in a
specific application should follow one of the procedures suggested
by Irwin \79\ to determine whether the character of an area is
primarily urban or rural (of the two methods, the land use procedure
is considered more definitive.):
    i. Land Use Procedure: (1) Classify the land use within the
total area, Ao, circumscribed by a 3 km radius circle
about the source using the meteorological land use typing scheme
proposed by Auer; \80\ (2) if land use types I1, I2, C1, R2, and R3
account for 50 percent or more of Ao, use urban
dispersion coefficients; otherwise, use appropriate rural dispersion
coefficients.
    ii. Population Density Procedure: (1) Compute the average
population density, p per square kilometer with Ao as
defined above; (2) If p is greater than 750 people per square
kilometer, use urban dispersion coefficients; otherwise use
appropriate rural dispersion coefficients.
    c. Population density should be used with caution and generally
not be applied to highly industrialized areas where the population
density may be low and, thus, a rural classification would be
indicated. However, the area is likely to be sufficiently built-up
so that the urban land use criteria would be satisfied. Therefore,
in this case, the classification should be ``urban'' and urban
dispersion parameters should be used.
    d. For applications of AERMOD in urban areas, under either the
Land Use Procedure or the Population Density Procedure, the user
needs to estimate the population of the urban area affecting the
modeling domain because the urban influence in AERMOD is scaled
based on a user-specified population. For non-population oriented
urban areas, or areas influenced by both population and industrial
activity, the user will need to estimate an equivalent population to
adequately account for the combined effects of industrialized areas
and populated areas within the modeling domain. Selection of the
appropriate population for these applications should be determined
in consultation with the appropriate reviewing authority (paragraph
3.0(b)) and the latest version of the AERMOD Implementation
Guide.\81\
    e. It should be noted that AERMOD allows for modeling rural and
urban sources in a single model run. For analyses of whole urban
complexes, the entire area should be modeled as an urban region if
most of the sources are located in areas classified as urban. For
tall stacks located within or adjacent to small or moderate sized
urban areas, the stack height or effective plume height may extend
above the urban boundary layer and, therefore, may be more
appropriately modeled using rural coefficients. Model users should
consult with the appropriate reviewing authority (paragraph 3.0(b))
and the latest version of the AERMOD Implementation Guide \81\ when
evaluating this situation.
    f. Buoyancy-induced dispersion (BID), as identified by
Pasquill,\82\ is included in the preferred models and should be used
where buoyant sources (e.g., those involving fuel combustion) are
involved.

<IP_ADDRESS> Complex Winds

    a. Inhomogeneous local winds. In many parts of the United
States, the ground is neither flat nor is the ground cover (or land
use) uniform. These geographical variations can generate local winds
and circulations, and modify the prevailing ambient winds and
circulations. Typically, geographic effects are more apparent when
the ambient winds are light or calm, as stronger synoptic or
mesoscale winds can modify, or even eliminate the weak geographic
circulations.\83\ In general, these geographically induced wind
circulation effects are named after the source location of the
winds, e.g., lake and sea breezes, and mountain and valley winds. In
very rugged hilly or mountainous terrain, along coastlines, or near
large land use variations, the characteristics of the winds are a
balance of various forces, such that the assumptions of steady-state
straight-line transport both in time and space are inappropriate. In
such cases, a model should be chosen to fully treat the time and
space variations of meteorology effects on transport and dispersion.
The setup and application of such a model should be determined in
consultation with the appropriate reviewing authority (paragraph
3.0(b)) consistent with limitations of paragraph 3.2.2(e). The
meteorological input data requirements for developing the time and
space varying three-dimensional winds and dispersion meteorology for
these situations are discussed in paragraph <IP_ADDRESS>(c). Examples of
inhomogeneous winds include, but are not limited to, situations
described in the following paragraphs:
    i. Inversion breakup fumigation. Inversion breakup fumigation
occurs when a plume (or multiple plumes) is emitted into a stable
layer of air and that layer is subsequently mixed to the ground
through convective transfer of heat from the surface or because of
advection to less stable surroundings. Fumigation may cause
excessively high concentrations, but is usually rather short-lived
at a given receptor. There are no recommended refined techniques to
model this phenomenon. There are, however, screening procedures \40\
that may be used to approximate the concentrations. Considerable
care should be exercised in using the results obtained from the
screening techniques.
    ii. Shoreline fumigation. Fumigation can be an important
phenomenon on and near the shoreline of bodies of water. This can
affect both individual plumes and area-wide emissions. When
fumigation conditions are expected to occur from a source or sources
with tall stacks located on or just inland of a shoreline, this
should be addressed in the air quality modeling analysis. The EPA
has evaluated several coastal fumigation models, and the evaluation
results of these models are available for their possible application
on a case-by-case basis when air quality estimates under shoreline
fumigation conditions are needed.\84\ Selection of the appropriate
model for applications where shoreline fumigation is of concern
should be determined in consultation with the appropriate reviewing
authority (paragraph 3.0(b)).
    iii. Stagnation. Stagnation conditions are characterized by calm
or very low wind speeds, and variable wind directions. These
stagnant meteorological conditions may persist for several hours to
several days. During stagnation conditions, the dispersion of air
pollutants, especially those from low-level emissions sources, tends
to be minimized, potentially leading to relatively high ground-level
concentrations. If point sources are of interest, users should note
the guidance provided in paragraph (a) of this subsection. Selection
of the appropriate model for applications where stagnation is of
concern should be determined in consultation with the appropriate
reviewing authority (paragraph 3.0(b)).

<IP_ADDRESS> Gravitational Settling and Deposition

    a. Gravitational settling and deposition may be directly
included in a model if either is a significant factor. When
particulate matter sources can be quantified and settling and dry
deposition are problems, use professional judgment along with
coordination with the appropriate reviewing authority (paragraph
3.0(b)). AERMOD contains algorithms for dry and wet deposition of
gases and particles.\85\ For other Gaussian plume models, an
``infinite half-life'' may be used for estimates of particle
concentrations when only exponential decay terms are used for
treating settling and deposition. Lagrangian models have varying
degrees of complexity for dealing with settling and deposition and
the selection of a parameterization for such should be included in
the approval process for selecting a Lagrangian model. Eulerian grid
models tend to have explicit parameterizations for gravitational
settling and deposition as well as wet deposition parameters already
included as part of the chemistry scheme.

7.2.2 Stationary Sources

<IP_ADDRESS> Good Engineering Practice Stack Height

    a. The use of stack height credit in excess of Good Engineering
Practice (GEP) stack height or credit resulting from any other
dispersion technique is prohibited in the development of emissions
limits by 40 CFR 51.118 and 40 CFR 51.164. The definition of GEP
stack height and dispersion technique are contained in 40 CFR
51.100. Methods and procedures for making the appropriate stack
height calculations, determining stack height credits and an example
of applying those

techniques are found in several references,86 87 88 89
that provide a great deal of additional information for evaluating
and describing building cavity and wake effects.
    b. If stacks for new or existing major sources are found to be
less than the height defined by the EPA's refined formula for
determining GEP height, then air quality impacts associated with
cavity or wake effects due to the nearby building structures should
be determined. The EPA refined formula height is defined as H +
1.5L.\88\ Since the definition of GEP stack height defines excessive
concentrations as a maximum ground-level concentration due in whole
or in part to downwash of at least 40 percent in excess of the
maximum concentration without downwash, the potential air quality
impacts associated with cavity and wake effects should also be
considered for stacks that equal or exceed the EPA formula height
for GEP. The AERSCREEN model can be used to obtain screening
estimates of potential downwash influences, based on the PRIME
downwash algorithm incorporated in the AERMOD model. If more refined
concentration estimates are required, AERMOD should be used (section
4.2.2).

<IP_ADDRESS> Plume Rise

    a. The plume rise methods of Briggs 90 91 are
incorporated in many of the preferred models and are recommended for
use in many modeling applications. In AERMOD,44 45 for
the stable boundary layer, plume rise is estimated using an
iterative approach, similar to that in the CTDMPLUS model. In the
convective boundary layer, plume rise is superposed on the
displacements by random convective velocities.\92\ In AERMOD, plume
rise is computed using the methods of Briggs, except in cases
involving building downwash, in which a numerical solution of the
mass, energy, and momentum conservation laws is performed.\93\ No
explicit provisions in these models are made for multistack plume
rise enhancement or the handling of such special plumes as flares.
    b. Gradual plume rise is generally recommended where its use is
appropriate: (1) in AERMOD; (2) in complex terrain screening
procedures to determine close-in impacts; and (3) when calculating
the effects of building wakes. The building wake algorithm in AERMOD
incorporates and exercises the thermodynamically based gradual plume
rise calculations as described in paragraph (a) of this subsection.
If the building wake is calculated to affect the plume for any hour,
gradual plume rise is also used in downwind dispersion calculations
to the distance of final plume rise, after which final plume rise is
used. Plumes captured by the near wake are re-emitted to the far
wake as a ground-level volume source.
    c. Stack tip downwash generally occurs with poorly constructed
stacks and when the ratio of the stack exit velocity to wind speed
is small. An algorithm developed by Briggs \91\ is the recommended
technique for this situation and is used in preferred models for
point sources.
    d. On a case-by-case basis, refinements to the preferred model
may be considered for plume rise and downwash effects and shall
occur in agreement with the appropriate reviewing authority
(paragraph 3.0(b)) and approval by the EPA Regional Office based on
the requirements of section 3.2.2.

7.2.3 Mobile Sources

    a. Emissions of primary pollutants from mobile sources can be
modeled with an appropriate model identified in section 4.2.
Screening of mobile sources can be accomplished by using screening
meteorology, e.g., worst-case meteorological conditions. Maximum
hourly concentrations computed from screening modeling can be
converted to longer averaging periods using the scaling ratios
specified in the AERSCREEN User's Guide.\37\
    b. Mobile sources can be modeled in AERMOD as either line (i.e.,
elongated area) sources or as a series of volume sources. Line
sources can be represented in AERMOD with the following source
types: LINE, AREA, VOLUME or RLINE. However, since mobile source
modeling usually includes an analysis of very near-source impacts,
the results can be highly sensitive to the characterization of the
mobile emissions. Important characteristics for both line/area and
volume sources include the plume release height, source width, and
initial dispersion characteristics, and should also take into
account the impact of traffic-induced turbulence that can cause
roadway sources to have larger initial dimensions than might
normally be used for representing line sources.
    c. The EPA's quantitative PM hot-spot guidance \65\ and Haul
Road Workgroup Final Report \67\ provide guidance on the appropriate
characterization of mobile sources as a function of the roadway and
vehicle characteristics. The EPA's quantitative PM hot-spot guidance
includes important considerations and should be consulted when
modeling roadway links. Area and line sources, which can be
characterized as AREA, LINE, and RLINE source types in AERMOD, or
volume sources, may be used for modeling mobile sources. However,
experience in the field has shown that area sources (characterized
as AREA, LINE, or RLINE source types) may be easier to characterize
correctly compared to volume sources. If volume sources are used, it
is particularly important to ensure that roadway emissions are
appropriately spaced when using volume source so that the emissions
field is uniform across the roadway. Additionally, receptor
placement is particularly important for volume sources that have
``exclusion zones'' where concentrations are not calculated for
receptors located ``within'' the volume sources, i.e., less than
2.15 times the initial lateral dispersion coefficient from the
center of the volume.\65\ Therefore, placing receptors in these
``exclusion zones'' will result in underestimates of roadway
impacts.

8.0 Model Input Data

    a. Databases and related procedures for estimating input
parameters are an integral part of the modeling process. The most
appropriate input data available should always be selected for use
in modeling analyses. Modeled concentrations can vary widely
depending on the source data or meteorological data used. This
section attempts to minimize the uncertainty associated with
database selection and use by identifying requirements for input
data used in modeling. More specific data requirements and the
format required for the individual models are described in detail in
the user's guide and/or associated documentation for each model.

8.1 Modeling Domain

8.1.1 Discussion

    a. The modeling domain is the geographic area for which the
required air quality analyses for the NAAQS and PSD increments are
conducted.

8.1.2 Requirements

    a. For a NAAQS or PSD increments assessment, the modeling domain
or project's impact area shall include all locations where the
emissions of a pollutant from the new or modifying source(s) may
cause a significant ambient impact. This impact area is defined as
an area with a radius extending from the new or modifying source to:
(1) the most distant location where air quality modeling predicts a
significant ambient impact will occur, or (2) the nominal 50 km
distance considered applicable for Gaussian dispersion models,
whichever is less. The required air quality analysis shall be
carried out within this geographical area with characterization of
source impacts, nearby source impacts, and background
concentrations, as recommended later in this section.
    b. For SIP attainment demonstrations for ozone and
PM2.5, or regional haze reasonable progress goal
analyses, the modeling domain is determined by the nature of the
problem being modeled and the spatial scale of the emissions that
impact the nonattainment or Class I area(s). The modeling domain
shall be designed so that all major upwind source areas that
influence the downwind nonattainment area are included in addition
to all monitor locations that are currently or recently violating
the NAAQS or close to violating the NAAQS in the nonattainment area.
Similarly, all Class I areas to be evaluated in a regional haze
modeling application shall be included and sufficiently distant from
the edge of the modeling domain. Guidance on the determination of
the appropriate modeling domain for photochemical grid models in
demonstrating attainment of these air quality goals is
available.\64\ Users should consult the latest version of this
guidance for the most current modeling guidance and the appropriate
reviewing authority (paragraph 3.0(b)) for any application specific
guidance that is beyond the scope of this section.

8.2 Source Data

8.2.1 Discussion

    a. Sources of pollutants can be classified as point, line, area,
and volume sources. Point sources are defined in terms of size and
may vary between regulatory programs. The line sources most
frequently considered are roadways and streets along which there are
well-defined movements of motor vehicles. They may also be lines of
roof vents or stacks, such as in aluminum refineries. Area

and volume sources are often collections of a multitude of minor
sources with individually small emissions that are impractical to
consider as separate point or line sources. Large area sources are
typically treated as a grid network of square areas, with pollutant
emissions distributed uniformly within each grid square. Generally,
input data requirements for air quality models necessitate the use
of metric units. As necessary, any English units common to
engineering applications should be appropriately converted to
metric.
    b. For point sources, there are many source characteristics and
operating conditions that may be needed to appropriately model the
facility. For example, the plant layout (e.g., location of stacks
and buildings), stack parameters (e.g., height and diameter), boiler
size and type, potential operating conditions, and pollution control
equipment parameters. Such details are required inputs to air
quality models and are needed to determine maximum potential
impacts.
    c. Modeling mobile emissions from streets and highways requires
data on the road layout, including the width of each traveled lane,
the number of lanes, and the width of the median strip.
Additionally, traffic patterns should be taken into account (e.g.,
daily cycles of rush hour, differences in weekday and weekend
traffic volumes, and changes in the distribution of heavy-duty
trucks and light-duty passenger vehicles), as these patterns will
affect the types and amounts of pollutant emissions allocated to
each lane and the height of emissions.
    d. Emission factors can be determined through source-specific
testing and measurements (e.g., stack test data) from existing
sources or provided from a manufacturing association or vendor.
Additionally, emissions factors for a variety of source types are
compiled in an EPA publication commonly known as AP-42.\94\ AP-42
also provides an indication of the quality and amount of data on
which many of the factors are based. Other information concerning
emissions is available in EPA publications relating to specific
source categories. The appropriate reviewing authority (paragraph
3.0(b)) should be consulted to determine appropriate source
definitions and for guidance concerning the determination of
emissions from and techniques for modeling the various source types.

8.2.2 Requirements

    a. For SIP attainment demonstrations for the purpose of
projecting future year NAAQS attainment for ozone, PM2.5,
and regional haze reasonable progress goal analyses, emissions which
reflect actual emissions during the base modeling year time period
should be input to models for base year modeling. Emissions
projections to future years should account for key variables such as
growth due to increased or decreased activity, expected emissions
controls due to regulations, settlement agreements or consent
decrees, fuel switches, and any other relevant information. Guidance
on emissions estimation techniques (including future year
projections) for SIP attainment demonstrations is
available.64 95
    b. For the purpose of SIP revisions for stationary point
sources, the regulatory modeling of inert pollutants shall use the
emissions input data shown in Table 8-1 for short-term and long-term
NAAQS. To demonstrate compliance and/or establish the appropriate
SIP emissions limits, Table 8-1 generally provides for the use of
``allowable'' emissions in the regulatory dispersion modeling of the
stationary point source(s) of interest. In such modeling, these
source(s) should be modeled sequentially with these loads for every
hour of the year. As part of a cumulative impact analysis, Table 8-1
allows for the model user to account for actual operations in
developing the emissions inputs for dispersion modeling of nearby
sources, while other sources are best represented by air quality
monitoring data. Consultation with the appropriate reviewing
authority (paragraph 3.0(b)) is advisable on the establishment of
the appropriate emissions inputs for regulatory modeling
applications with respect to SIP revisions for stationary point
sources.
    c. For the purposes of demonstrating NAAQS compliance in a PSD
assessment, the regulatory modeling of inert pollutants shall use
the emissions input data shown in Table 8-2 for short and long-term
NAAQS. The new or modifying stationary point source shall be modeled
with ``allowable'' emissions in the regulatory dispersion modeling.
As part of a cumulative impact analysis, Table 8-2 allows for the
model user to account for actual operations in developing the
emissions inputs for dispersion modeling of nearby sources, while
other sources are best represented by air quality monitoring data.
For purposes of situations involving emissions trading, refer to
current EPA policy and guidance to establish input data.
Consultation with the appropriate reviewing authority (paragraph
3.0(b)) is advisable on the establishment of the appropriate
emissions inputs for regulatory modeling applications with respect
to PSD assessments for a proposed new or modifying source.
    d. For stationary source applications, changes in operating
conditions that affect the physical emission parameters (e.g.,
release height, initial plume volume, and exit velocity) shall be
considered to ensure that maximum potential impacts are
appropriately determined in the assessment. For example, the load or
operating condition for point sources that causes maximum ground-
level concentrations shall be established. As a minimum, the source
should be modeled using the design capacity (100 percent load). If a
source operates at greater than design capacity for periods that
could result in violations of the NAAQS or PSD increments, this load
should be modeled. Where the source operates at substantially less
than design capacity, and the changes in the stack parameters
associated with the operating conditions could lead to higher ground
level concentrations, loads such as 50 percent and 75 percent of
capacity should also be modeled. Malfunctions which may result in
excess emissions are not considered to be a normal operating
condition. They generally should not be considered in determining
allowable emissions. However, if the excess emissions are the result
of poor maintenance, careless operation, or other preventable
conditions, it may be necessary to consider them in determining
source impact. A range of operating conditions should be considered
in screening analyses. The load causing the highest concentration,
in addition to the design load, should be included in refined
modeling.
    e. Emissions from mobile sources also have physical and temporal
characteristics that should be appropriately accounted. For example,
an appropriate emissions model shall be used to determine emissions
profiles. Such emissions should include speciation specific for the
vehicle types used on the roadway (e.g., light duty and heavy duty
trucks), and subsequent parameterizations of the physical emissions
characteristics (e.g., release height) should reflect those
emissions sources. For long-term standards, annual average emissions
may be appropriate, but for short-term standards, discrete temporal
representation of emissions should be used (e.g., variations in
weekday and weekend traffic or the diurnal rush-hour profile typical
of many cities). Detailed information and data requirements for
modeling mobile sources of pollution are provided in the user's
manuals for each of the models applicable to mobile
sources.65 67
BILLING CODE 6560-50-P

[GRAPHIC] [TIFF OMITTED] TP23OC23.001

[GRAPHIC] [TIFF OMITTED] TP23OC23.002

BILLING CODE 6560-50-C

8.3 Background Concentrations

8.3.1 Discussion

    a. Background concentrations are essential in constructing the
design concentration, or total air quality concentration, as part of
a cumulative impact analysis for NAAQS and PSD increments (section
9.2.3). To assist applicants and reviewing authorities with
appropriately characterizing background concentrations, EPA has
developed the Draft Guidance on Developing Background Concentrations
for Use in Modeling Demonstrations.\96\ The guidance provides a
recommended framework composed of steps that should be used in
parallel with the recommendations made in this section. Generally,
background air quality should not include the ambient impacts of the
project source under consideration. Instead, it should include:
    i. Nearby sources: These are individual sources located in the
vicinity of the source(s) under consideration for emissions limits
that are not adequately represented by ambient monitoring data. The
ambient contributions from these nearby sources are thereby
accounted for by explicitly modeling their emissions (section 8.2).
    ii. Other sources: That portion of the background attributable
to natural sources, other unidentified sources in the vicinity of
the project, and regional transport contributions from more distant
sources (domestic and international). The ambient contributions from
these sources are typically accounted for through use of ambient
monitoring data or, in some cases, regional-scale photochemical grid
modeling results.
    b. The monitoring network used for developing background
concentrations is expected to conform to the same quality assurance
and other requirements as those networks established for PSD
purposes.\97\ Accordingly, the air quality monitoring data should be
of sufficient completeness and follow appropriate data validation

procedures. These data should be adequately representative of the
area to inform calculation of the design concentration for
comparison to the applicable NAAQS (section 9.2.2).
    c. For photochemical grid modeling conducted in SIP attainment
demonstrations for ozone, PM2.5 and regional haze, the
emissions from nearby and other sources are included as model inputs
and fully accounted for in the modeling application and predicted
concentrations. The concept of adding individual components to
develop a design concentration, therefore, do not apply in these SIP
applications. However, such modeling results may then be appropriate
for consideration in characterizing background concentrations for
other regulatory applications. Also, as noted in section 5, this
modeling approach does provide for an appropriate atmospheric
environment to assess single-source impacts for ozone and secondary
PM2.5.
    d. For NAAQS assessments and SIP attainment demonstrations for
inert pollutants, the development of the appropriate background
concentration for a cumulative impact analysis involves proper
accounting of each contribution to the design concentration and will
depend upon whether the project area's situation consists of either
an isolated single source(s) or a multitude of sources. For PSD
increment assessments, all impacts after the appropriate baseline
dates (i.e., trigger date, major source baseline date, and minor
source baseline date) from all increment-consuming and increment-
expanding sources should be considered in the design concentration
(section 9.2.2).

8.3.2 Recommendations for Isolated Single Sources

    a. In areas with an isolated source(s), determining the
appropriate background concentration should focus on
characterization of contributions from all other sources through
adequately representative ambient monitoring data. The application
of EPA's recommended framework for determining an appropriate
background concentration should be consistent with appropriate EPA
modeling guidance 63 96 and justified in the modeling
protocol that is vetted with the appropriate reviewing authority
(paragraph 3.0(b)).
    b. The EPA recommends use of the most recent quality assured air
quality monitoring data collected in the vicinity of the source to
determine the background concentration for the averaging times of
concern. In most cases, the EPA recommends using data from the
monitor closest to and upwind of the project area. If several
monitors are available, preference should be given to the monitor
with characteristics that are most similar to the project area. If
there are no monitors located in the vicinity of the new or
modifying source, a ``regional site'' may be used to determine
background concentrations. A regional site is one that is located
away from the area of interest but is impacted by similar or
adequately representative sources.
    c. Many of the challenges related to cumulative impact analyses
arise in the context of defining the appropriate metric to
characterize background concentrations from ambient monitoring data
and determining the appropriate method for combining this monitor-
based background contribution to the modeled impact of the project
and other nearby sources. For many cases, the best starting point
would be use of the current design value for the applicable NAAQS as
a uniform monitored background contribution across the project area.
However, there are cases in which the current design value may not
be appropriate. Such cases include but are not limited to:
    i. For situations involving a modifying source where the
existing facility is determined to impact the ambient monitor, the
background concentration at each monitor can be determined by
excluding values when the source in question is impacting the
monitor. In such cases, monitoring sites inside a 90[deg] sector
downwind of the source may be used to determine the area of impact.
    ii. There may be other circumstances which would necessitate
modifications to the ambient data record. Such cases could include
removal of data from specific days or hours when a monitor is being
impacted by activities that are not typical or not expected to occur
again in the future (e.g., construction, roadway repairs, forest
fires, or unusual agricultural activities). There may also be cases
where it may be appropriate to scale (multiplying the monitored
concentrations with a scaling factor) or adjust (adding or
subtracting a constant value the monitored concentrations) data from
specific days or hours. Such adjustments would make the monitored
background concentrations more temporally and/or spatially
representative of the area around the new or modifying source for
the purposes of the regulatory assessment.
    iii. For short-term standards, the diurnal or seasonal patterns
of the air quality monitoring data may differ significantly from the
patterns associated with the modeled concentrations. When this
occurs, it may be appropriate to pair the air quality monitoring
data in a temporal manner that reflects these patterns (e.g.,
pairing by season and/or hour of day).\98\
    iv. For situations where monitored air quality concentrations
vary across the modeling domain, it may be appropriate to consider
air quality monitoring data from multiple monitors within the
project area.
    d. Considering the spatial and temporal variability throughout a
typical modeling domain on an hourly basis and the complexities and
limitations of hourly observations from the ambient monitoring
network, the EPA does not recommend hourly or daily pairing of
monitored background and modeled concentrations except in rare cases
of relatively isolated sources where the available monitor can be
shown to be representative of the ambient concentration levels in
the areas of maximum impact from the proposed new source. The
implicit assumption underlying hourly pairing is that the background
monitored levels for each hour are spatially uniform and that the
monitored values are fully representative of background levels at
each receptor for each hour. Such an assumption clearly ignores the
many factors that contribute to the temporal and spatial variability
of ambient concentrations across a typical modeling domain on an
hourly basis. In most cases, the seasonal (or quarterly) pairing of
monitored and modeled concentrations should sufficiently address
situations to which the impacts from modeled emissions are not
temporally correlated with background monitored levels.
    e. In those cases where adequately representative monitoring
data to characterize background concentrations are not available, it
may be appropriate to use results from a regional-scale
photochemical grid model, or other representative model application,
as background concentrations consistent with the considerations
discussed above and in consultation with the appropriate reviewing
authority (paragraph 3.0(b)).

8.3.3 Recommendations for Multi-Source Areas

    a. In multi-source areas, determining the appropriate background
concentration involves: (1) characterization of contributions from
other sources through adequately representative ambient monitoring
data, and (2) identification and characterization of contributions
from nearby sources through explicit modeling. A key point here is
the interconnectedness of each component in that the question of
which nearby sources to include in the cumulative modeling is
inextricably linked to the question of what the ambient monitoring
data represents within the project area.
    b. Nearby sources: All sources in the vicinity of the source(s)
under consideration for emissions limits that are not adequately
represented by ambient monitoring data should be explicitly modeled.
EPA's recommended framework for determining an appropriate
background concentration \96\ should be applied to identify such
sources and accurately account for their ambient impacts through
explicit modeling.
    i. The determination of nearby sources relies on the selection
of adequately representative ambient monitoring data (section
8.3.2). The EPA recommends determining the representativeness of the
monitoring data through a visual assessment of the modeling domain
considering any relevant nearby sources and their respective air
quality data. The visual assessment should consider any relevant air
quality data such as the proximity of nearby sources to the project
source and the ambient monitor, the nearby source's level of
emissions with respect to the ambient data, and the dispersion
environment (i.e., meteorological patterns, terrain, etc.) of the
modeling domain.
    ii. Nearby sources not adequately represented by the ambient
monitor through visual assessment should undergo further qualitative
and quantitative analysis before being explicitly modeled. The EPA
recommends evaluating any modeling, monitoring, or emissions data
that may be available for the identified nearby sources with respect
to possible exceedances of the appropriate SIL or violations to the
NAAQS.
    iii. The number of nearby sources to be explicitly modeled in
the air quality analysis is expected to be few except in unusual

situations. The determination of nearby sources through the
application of EPA's recommended framework calls for the exercise of
professional judgment by the appropriate reviewing authority
(paragraph 3.0(b)) and should be consistent with appropriate EPA
modeling guidance.63 96 This guidance is not intended to
alter the exercise of that judgment or to comprehensively prescribe
which sources should be included as nearby sources.
    c. For cumulative impact analyses of short-term and annual
ambient standards, the nearby sources as well as the project
source(s) must be evaluated using an appropriate addendum A model or
approved alternative model with the emission input data shown in
Table 8-1 or 8-2.
    i. When modeling a nearby source that does not have a permit and
the emissions limits contained in the SIP for a particular source
category is greater than the emissions possible given the source's
maximum physical capacity to emit, the ``maximum allowable emissions
limit'' for such a nearby source may be calculated as the emissions
rate representative of the nearby source's maximum physical capacity
to emit, considering its design specifications and allowable fuels
and process materials. However, the burden is on the permit
applicant to sufficiently document what the maximum physical
capacity to emit is for such a nearby source.
    ii. It is appropriate to model nearby sources only during those
times when they, by their nature, operate at the same time as the
primary source(s) or could have impact on the averaging period of
concern. Accordingly, it is not necessary to model impacts of a
nearby source that does not, by its nature, operate at the same time
as the primary source or could have impact on the averaging period
of concern, regardless of an identified significant concentration
gradient from the nearby source. The burden is on the permit
applicant to adequately justify the exclusion of nearby sources to
the satisfaction of the appropriate reviewing authority (paragraph
3.0(b)). The following examples illustrate two cases in which a
nearby source may be shown not to operate at the same time as the
primary source(s) being modeled: (1) Seasonal sources (only used
during certain seasons of the year). Such sources would not be
modeled as nearby sources during times in which they do not operate;
and (2) Emergency backup generators, to the extent that they do not
operate simultaneously with the sources that they back up. Such
emergency equipment would not be modeled as nearby sources.
    d. Other sources. That portion of the background attributable to
all other sources (e.g., natural, minor, distant major, and
unidentified sources) should be accounted for through use of ambient
monitoring data and determined by the procedures found in section
8.3.2 in keeping with eliminating or reducing the source-oriented
impacts from nearby sources to avoid potential double-counting of
modeled and monitored contributions.

8.4 Meteorological Input Data

8.4.1 Discussion

    a. This subsection covers meteorological input data for use in
dispersion modeling for regulatory applications and is separate from
recommendations made for photochemical grid modeling.
Recommendations for meteorological data for photochemical grid
modeling applications are outlined in the latest version of EPA's
Modeling Guidance for Demonstrating Attainment of Air Quality Goals
for Ozone, PM2.5, and Regional Haze.\64\ In cases where Lagrangian
models are applied for regulatory purposes, appropriate
meteorological inputs should be determined in consultation with the
appropriate reviewing authority (paragraph 3.0(b)).
    b. The meteorological data used as input to a dispersion model
should be selected on the basis of spatial and climatological
(temporal) representativeness as well as the ability of the
individual parameters selected to characterize the transport and
dispersion conditions in the area of concern. The representativeness
of the measured data is dependent on numerous factors including, but
not limited to: (1) the proximity of the meteorological monitoring
site to the area under consideration; (2) the complexity of the
terrain; (3) the exposure of the meteorological monitoring site; and
(4) the period of time during which data are collected. The spatial
representativeness of the data can be adversely affected by large
distances between the source and receptors of interest and the
complex topographic characteristics of the area. Temporal
representativeness is a function of the year-to-year variations in
weather conditions. Where appropriate, data representativeness
should be viewed in terms of the appropriateness of the data for
constructing realistic boundary layer profiles and, where
applicable, three-dimensional meteorological fields, as described in
paragraphs (c) and (d) of this subsection.
    c. The meteorological data should be adequately representative
and may be site-specific data (land-based or buoy data for overwater
applications), data from a nearby National Weather Service (NWS) or
comparable station, or prognostic meteorological data. The
implementation of NWS Automated Surface Observing Stations (ASOS) in
the early 1990's should not preclude the use of NWS ASOS data if
such a station is determined to be representative of the modeled
area.\99\
    D. Model input data are normally obtained either from the NWS or
as part of a site-specific measurement program. State climatology
offices, local universities, FAA, military stations, industry, and
pollution control agencies may also be sources of such data. In
specific cases, prognostic meteorological data may be appropriate
for use and obtained from similar sources. Some recommendations and
requirements for the use of each type of data are included in this
subsection.

8.4.2 Recommendations and Requirements

    a. AERMET \100\ shall be used to preprocess all meteorological
data, be it observed or prognostic, for use with AERMOD in
regulatory applications. The AERMINUTE \101\ processor, in most
cases, should be used to process 1-minute ASOS wind data for input
to AERMET when processing NWS ASOS sites in AERMET. When processing
prognostic meteorological data for AERMOD, the Mesoscale Model
Interface Program (MMIF) \109\ should be used to process data for
input to AERMET, both for land-based applications and overwater
applications. Other methods of processing prognostic meteorological
data for input to AERMET should be approved by the appropriate
reviewing authority. Additionally, the following meteorological
preprocessors are recommended by the EPA: PCRAMMET,\102\ MPRM,\103\
and METPRO.\104\ PCRAMMET is the recommended meteorological data
preprocessor for use in applications of OCD employing hourly NWS
data. MPRM is the recommended meteorological data preprocessor for
applications of OCD employing site-specific meteorological data.
METPRO is the recommended meteorological data preprocessor for use
with CTDMPLUS.\105\
    b. Regulatory application of AERMOD necessitates careful
consideration of the meteorological data for input to AERMET. Data
representativeness, in the case of AERMOD, means utilizing data of
an appropriate type for constructing realistic boundary layer
profiles. Of particular importance is the requirement that all
meteorological data used as input to AERMOD should be adequately
representative of the transport and dispersion within the analysis
domain. Where surface conditions vary significantly over the
analysis domain, the emphasis in assessing representativeness should
be given to adequate characterization of transport and dispersion
between the source(s) of concern and areas where maximum design
concentrations are anticipated to occur. The EPA recommends that the
surface characteristics input to AERMET should be representative of
the land cover in the vicinity of the meteorological data, i.e., the
location of the meteorological tower for measured data or the
representative grid cell for prognostic data. Therefore, the model
user should apply the latest version AERSURFACE,106 107
where applicable, for determining surface characteristics when
processing measured land-based meteorological data through AERMET.
In areas where it is not possible to use AERSURFACE output, surface
characteristics can be determined using techniques that apply the
same analysis as AERSURFACE. In the case of measured meteorological
data for overwater applications, AERMET calculates the surface
characteristics and AERSURFACE outputs are not needed. In the case
of prognostic meteorological data, the surface characteristics
associated with the prognostic meteorological model output for the
representative grid cell should be used.108 109
Furthermore, since the spatial scope of each variable could be
different, representativeness should be judged for each variable
separately. For example, for a variable such as wind direction, the
data should ideally be collected near plume height to be adequately
representative, especially for sources located in complex

terrain. Whereas, for a variable such as temperature, data from a
station several kilometers away from the source may be considered to
be adequately representative. More information about meteorological
data, representativeness, and surface characteristics can be found
in the AERMOD Implementation Guide.\81\
    c. Regulatory application of CTDMPLUS requires the input of
multi-level measurements of wind speed, direction, temperature, and
turbulence from an appropriately sited meteorological tower. The
measurements should be obtained up to the representative plume
height(s) of interest. Plume heights of interest can be determined
by use of screening procedures such as CTSCREEN.
    d. Regulatory application of OCD requires meteorological data
over land and over water. The over land or surface data, processed
through PCRAMMET \102\ or MPRM,\103\ that provides hourly stability
class, wind direction and speed, ambient temperature, and mixing
height, are required. Data over water requires hourly mixing height,
relative humidity, air temperature, and water surface temperature.
Missing winds are substituted with the surface winds. Vertical wind
direction shear, vertical temperature gradient, and turbulence
intensities are optional.
    e. The model user should acquire enough meteorological data to
ensure that worst-case meteorological conditions are adequately
represented in the model results. The use of 5 years of adequately
representative NWS or comparable meteorological data, at least 1
year of site-specific (either land-based or overwater based), or at
least 3 years of prognostic meteorological data, are required. If 1
year or more, up to 5 years, of site-specific data are available,
these data are preferred for use in air quality analyses. Depending
on completeness of the data record, consecutive years of NWS, site-
specific, or prognostic data are preferred. Such data must be
subjected to quality assurance procedures as described in section
<IP_ADDRESS>.
    f. Objective analysis in meteorological modeling is to improve
meteorological analyses (the ``first guess field'') used as initial
conditions for prognostic meteorological models by incorporating
information from meteorological observations. Direct and indirect
(using remote sensing techniques) observations of temperature,
humidity, and wind from surface and radiosonde reports are commonly
employed to improve these analysis fields. For long-range transport
applications, it is recommended that objective analysis procedures,
using direct and indirect meteorological observations, be employed
in preparing input fields to produce prognostic meteorological
datasets. The length of record of observations should conform to
recommendations outlined in paragraph 8.4.2(e) for prognostic
meteorological model datasets.

8.4.3 National Weather Service Data

<IP_ADDRESS> Discussion

    a. The NWS meteorological data are routinely available and
familiar to most model users. Although the NWS does not provide
direct measurements of all the needed dispersion model input
variables, methods have been developed and successfully used to
translate the basic NWS data to the needed model input. Site-
specific measurements of model input parameters have been made for
many modeling studies, and those methods and techniques are becoming
more widely applied, especially in situations such as complex
terrain applications, where available NWS data are not adequately
representative. However, there are many modeling applications where
NWS data are adequately representative and the applications still
rely heavily on the NWS data.
    b. Many models use the standard hourly weather observations
available from the National Centers for Environmental Information
(NCEI).\b\ These observations are then preprocessed before they can
be used in the models. Prior to the advent of ASOS in the early
1990's, the standard ``hourly'' weather observation was a human-
based observation reflecting a single 2-minute average generally
taken about 10 minutes before the hour. However, beginning in
January 2000 for first-order stations and in March 2005 for all
stations, the NCEI has archived the 1-minute ASOS wind data (i.e.,
the rolling 2-minute average winds) for the NWS ASOS sites. The
AERMINUTE processor \101\ was developed to reduce the number of calm
and missing hours in AERMET processing by substituting standard
hourly observations with full hourly average winds calculated from
1-minute ASOS wind data.
---------------------------------------------------------------------------

    \b\ Formerly the National Climatic Data Center (NCDC).
---------------------------------------------------------------------------

<IP_ADDRESS> Recommendations

    a. The preferred models listed in addendum A all accept, as
input, the NWS meteorological data preprocessed into model
compatible form. If NWS data are judged to be adequately
representative for a specific modeling application, they may be
used. The NCEI makes available surface and upper air meteorological
data online and in CD-ROM format. Upper air data are also available
at the Earth System Research Laboratory Global Systems Divisions
website and from NCEI. For the latest websites of available surface
and upper air data see reference 100.
    b. Although most NWS wind measurements are made at a standard
height of 10 m, the actual anemometer height should be used as input
to the preferred meteorological processor and model.
    c. Standard hourly NWS wind directions are reported to the
nearest 10 degrees. Due to the coarse resolution of these data, a
specific set of randomly generated numbers has been developed by the
EPA and should be used when processing standard hourly NWS data for
use in the preferred EPA models to ensure a lack of bias in wind
direction assignments within the models.
    d. Beginning with year 2000, NCEI began archiving 2-minute
winds, reported every minute to the nearest degree for NWS ASOS
sites. The AERMINUTE processor was developed to read those winds and
calculate hourly average winds for input to AERMET. When such data
are available for the NWS ASOS site being processed, the AERMINUTE
processor should be used, in most cases, to calculate hourly average
wind speed and direction when processing NWS ASOS data for input to
AERMOD.\99\
    e. Data from universities, FAA, military stations, industry and
pollution control agencies may be used if such data are equivalent
in accuracy and detail (e.g., siting criteria, frequency of
observations, data completeness, etc.) to the NWS data, they are
judged to be adequately representative for the particular
application, and have undergone quality assurance checks.
    f. After valid data retrieval requirements have been met,\110\
large number of hours in the record having missing data should be
treated according to an established data substitution protocol
provided that adequately representative alternative data are
available. Data substitution guidance is provided in section 5.3 of
reference 110.\110\ If no representative alternative data are
available for substitution, the absent data should be coded as
missing using missing data codes appropriate to the applicable
meteorological pre-processor. Appropriate model options for treating
missing data, if available in the model, should be employed.

8.4.4 Site-Specific Data

<IP_ADDRESS> Discussion

    a. Spatial or geographical representativeness is best achieved
by collection of all of the needed model input data in close
proximity to the actual site of the source(s). Site-specific
measured data are, therefore, preferred as model input, provided
that appropriate instrumentation and quality assurance procedures
are followed, and that the data collected are adequately
representative (free from inappropriate local or microscale
influences) and compatible with the input requirements of the model
to be used. It should be noted that, while site-specific
measurements are frequently made ``on-property'' (i.e., on the
source's premises), acquisition of adequately representative site-
specific data does not preclude collection of data from a location
off property. Conversely, collection of meteorological data on a
source's property does not of itself guarantee adequate
representativeness. For help in determining representativeness of
site-specific measurements, technical guidance \110\ is available.
Site-specific data should always be reviewed for representativeness
and adequacy by an experienced meteorologist, atmospheric scientist,
or other qualified scientist in consultation with the appropriate
reviewing authority (paragraph 3.0(b)).

<IP_ADDRESS> Recommendations

    a. The EPA guidance \110\ provides recommendations on the
collection and use of site-specific meteorological data.
Recommendations on characteristics, siting, and exposure of
meteorological instruments and on data recording, processing,
completeness requirements, reporting, and archiving are also
included. This publication should be used as a supplement to other
limited guidance on these subjects.5 97 111 112 Detailed
information on quality assurance is

also available.\113\ As a minimum, site-specific measurements of
ambient air temperature, transport wind speed and direction, and the
variables necessary to estimate atmospheric dispersion should be
available in meteorological datasets to be used in modeling. Care
should be taken to ensure that meteorological instruments are
located to provide an adequately representative characterization of
pollutant transport between sources and receptors of interest. The
appropriate reviewing authority (paragraph 3.0(b)) is available to
help determine the appropriateness of the measurement locations.
    i. Solar radiation measurements. Total solar radiation or net
radiation should be measured with a reliable pyranometer or net
radiometer sited and operated in accordance with established site-
specific meteorological guidance.110 113
    ii. Temperature measurements. Temperature measurements should be
made at standard shelter height (2m) in accordance with established
site-specific meteorological guidance.\110\
    iii. Temperature difference measurements. Temperature difference
(DT) measurements should be obtained using matched thermometers or a
reliable thermocouple system to achieve adequate accuracy. Siting,
probe placement, and operation of DT systems should be based on
guidance found in Chapter 3 of reference 110 and such guidance
should be followed when obtaining vertical temperature gradient
data. AERMET may employ the Bulk Richardson scheme, which requires
measurements of temperature difference, in lieu of cloud cover or
insolation data. To ensure correct application and acceptance,
AERMOD users should consult with the appropriate reviewing authority
(paragraph 3.0(b)) before using the Bulk Richardson scheme for their
analysis.
    iv. Wind measurements. For simulation of plume rise and
dispersion of a plume emitted from a stack, characterization of the
wind profile up through the layer in which the plume disperses is
desirable. This is especially important in complex terrain and/or
complex wind situations where wind measurements at heights up to
hundreds of meters above stack base may be required in some
circumstances. For tall stacks when site-specific data are needed,
these winds have been obtained traditionally using meteorological
sensors mounted on tall towers. A feasible alternative to tall
towers is the use of meteorological remote sensing instruments
(e.g., acoustic sounders or radar wind profilers) to provide winds
aloft, coupled with 10-meter towers to provide the near-surface
winds. Note that when site-specific wind measurements are used,
AERMOD, at a minimum, requires wind observations at a height above
ground between seven times the local surface roughness height and
100 m. (For additional requirements for AERMOD and CTDMPLUS, see
addendum A.) Specifications for wind measuring instruments and
systems are contained in reference 110.
    b. All processed site-specific data should be in the form of
hourly averages for input to the dispersion model.
    i. Turbulence data. There are several dispersion models that are
capable of using direct measurements of turbulence (wind
fluctuations) in the characterization of the vertical and lateral
dispersion (e.g., CTDMPLUS or AERMOD). When turbulence data are used
to directly characterize the vertical and lateral dispersion, the
averaging time for the turbulence measurements should be 1-hour. For
technical guidance on processing of turbulence parameters for use in
dispersion modeling, refer to the user's guide to the meteorological
processor for each model (see section 8.4.2(a)).
    ii. Stability categories. For dispersion models that employ P-G
stability categories for the characterization of the vertical and
lateral dispersion, the P-G stability categories, as originally
defined, couple near-surface measurements of wind speed with
subjectively determined insolation assessments based on hourly cloud
cover and ceiling height observations. The wind speed measurements
are made at or near 10 m. The insolation rate is typically assessed
using observations of cloud cover and ceiling height based on
criteria outlined by Turner.\77\ It is recommended that the P-G
stability category be estimated using the Turner method with site-
specific wind speed measured at or near 10 m and representative
cloud cover and ceiling height. Implementation of the Turner method,
as well as considerations in determining representativeness of cloud
cover and ceiling height in cases for which site-specific cloud
observations are unavailable, may be found in section 6 of reference
110. In the absence of requisite data to implement the Turner
method, the solar radiation/delta-T (SRDT) method or wind
fluctuation statistics (i.e., the [sigma]E and
[sigma]A methods) may be used.
    iii. The SRDT method, described in section <IP_ADDRESS> of reference
110, is modified slightly from that published from earlier work
\114\ and has been evaluated with three site-specific
databases.\115\ The two methods of stability classification that use
wind fluctuation statistics, the [sigma]E and
[sigma]A methods, are also described in detail in section
6.4.4 of reference 110 (note applicable tables in section 6). For
additional information on the wind fluctuation methods, several
references are available.116 117 118 119
    c. Missing data substitution. After valid data retrieval
requirements have been met,\110\ hours in the record having missing
data should be treated according to an established data substitution
protocol provided that adequately representative alternative data
are available. Such protocols are usually part of the approved
monitoring program plan. Data substitution guidance is provided in
section 5.3 of reference 110. If no representative alternative data
are available for substitution, the absent data should be coded as
missing, using missing data codes appropriate to the applicable
meteorological pre-processor. Appropriate model options for treating
missing data, if available in the model, should be employed.

8.4.5 Prognostic Meteorological Data

<IP_ADDRESS> Discussion

    a. For some modeling applications, there may not be a
representative NWS or comparable meteorological station available
(e.g., complex terrain), and it may be cost prohibitive or
infeasible to collect adequately representative site-specific data.
For these cases, it may be appropriate to use prognostic
meteorological data, if deemed adequately representative, in a
regulatory modeling application. However, if prognostic
meteorological data are not representative of transport and
dispersion conditions in the area of concern, the collection of
site-specific data is necessary.
    b. The EPA has developed a processor, the MMIF,\108\ to process
MM5 (Mesoscale Model 5) or WRF (Weather Research and Forecasting)
model data for input to various models including AERMOD. MMIF can
process data for input to AERMET or AERMOD for a single grid cell or
multiple grid cells. MMIF output has been found to compare favorably
against observed data (site-specific or NWS).\120\ Specific guidance
on processing MMIF for AERMOD can be found in reference 109109. When
using MMIF to process prognostic data for regulatory applications,
the data should be processed to generate AERMET inputs and the data
subsequently processed through AERMET for input to AERMOD. If an
alternative method of processing data for input to AERMET is used,
it must be approved by the appropriate reviewing authority
(paragraph 3.0(b)).

<IP_ADDRESS> Recommendations

    a. Prognostic model evaluation. Appropriate effort by the
applicant should be devoted to the process of evaluating the
prognostic meteorological data. The modeling data should be compared
to NWS observational data or other comparable data in an effort to
show that the data are adequately replicating the observed
meteorological conditions of the time periods modeled. An
operational evaluation of the modeling data for all model years
(i.e., statistical, graphical) should be completed.\64\ The use of
output from prognostic mesoscale meteorological models is contingent
upon the concurrence with the appropriate reviewing authority
(paragraph 3.0(b)) that the data are of acceptable quality, which
can be demonstrated through statistical comparisons with
meteorological observations aloft and at the surface at several
appropriate locations.\64\
    b. Representativeness. When processing MMIF data for use with
AERMOD, the grid cell used for the dispersion modeling should be
adequately spatially representative of the analysis domain. In most
cases, this may be the grid cell containing the emission source of
interest. Since the dispersion modeling may involve multiple sources
and the domain may cover several grid cells, depending on grid
resolution of the prognostic model, professional judgment may be
needed to select the appropriate grid cell to use. In such cases,
the selected grid cells should be adequately representative of the
entire domain.
    c. Grid resolution. The grid resolution of the prognostic
meteorological data should be considered and evaluated
appropriately, particularly for projects involving complex terrain.
The operational evaluation of the modeling data should consider
whether a finer grid resolution is needed to ensure that the data
are representative. The use of output from prognostic mesoscale
meteorological

models is contingent upon the concurrence with the appropriate
reviewing authority (paragraph 3.0(b)) that the data are of
acceptable quality.

8.4.6 Marine Boundary Layer Environments

<IP_ADDRESS> Discussion

    a. Calculations of boundary layer parameters for the marine
boundary layer present special challenges as the marine boundary
layer can be very different from the boundary layer over land. For
example, convective conditions can occur in the overnight hours in
the marine boundary layer while typically over land, stable
conditions occur at night. Also, surface roughness in the marine
environment is a function of wave height and wind speed and less
static with time than surface roughness over land.
    b. While the Offshore and Coastal Dispersion Model (OCD) is the
preferred model for overwater applications, there are applications
where the use of AERMOD is applicable. These include applications
that utilize features of AERMOD not included in OCD (e.g.,
NO2 chemistry). Such use of AERMOD would require
consultation with the Regional Office and appropriate reviewing
authority to ensure that platform downwash and shoreline fumigation
are adequately considered in the modeling demonstration.
    c. For the reasons stated above, a standalone pre-processor to
AERMOD, called AERCOARE \47\ was developed to use the Coupled Ocean
Atmosphere Response Experiment (COARE) bulk-flux algorithms \48\ to
bypass AERMET and calculate the boundary layer parameters for input
to AERMOD for the marine boundary layer. AERCOARE can process either
measurements from water-based sites such as buoys or prognostic
data. To better facilitate the use of the COARE algorithms for
AERMOD, EPA has included the COARE algorithms into AERMET thus
eliminating the need for a standalone pre-processor and ensuring the
algorithms are updated as part of routine AERMET updates.

<IP_ADDRESS> Recommendations

    a. Measured data. For applications in the marine environment
that require the use of AERMOD, measured surface data, such as from
a buoy or other offshore platform, should be processed in AERMET
with the COARE processing option following recommendations in the
AERMET User's Guide \100\ and AERMOD Implementation Guide.\81\ For
applications in the marine environment that require the use of OCD,
users should use the recommended meteorological pre-processor MPRM.
    b. Prognostic data. For applications in the marine environment
that require the use of AERMOD and prognostic data, the prognostic
data should be processed via MMIF for input to AERMET following
recommendations in paragraph <IP_ADDRESS>(b) and the guidance found in
reference 109.

8.4.7 Treatment of Near-Calms and Calms

<IP_ADDRESS> Discussion

    a. Treatment of calm or light and variable wind poses a special
problem in modeling applications since steady-state Gaussian plume
models assume that concentration is inversely proportional to wind
speed, depending on model formulations. Procedures have been
developed to prevent the occurrence of overly conservative
concentration estimates during periods of calms. These procedures
acknowledge that a steady-state Gaussian plume model does not apply
during calm conditions, and that our knowledge of wind patterns and
plume behavior during these conditions does not, at present, permit
the development of a better technique. Therefore, the procedures
disregard hours that are identified as calm. The hour is treated as
missing and a convention for handling missing hours is recommended.
With the advent of the AERMINUTE processor, when processing NWS ASOS
data, the inclusion of hourly averaged winds from AERMINUTE will, in
some instances, dramatically reduce the number of calm and missing
hours, especially when the ASOS wind are derived from a sonic
anemometer. To alleviate concerns about these issues, especially
those introduced with AERMINUTE, the EPA implemented a wind speed
threshold in AERMET for use with ASOS derived
winds.99 100 Winds below the threshold will be treated as
calms.
    b. AERMOD, while fundamentally a steady-state Gaussian plume
model, contains algorithms for dealing with low wind speed (near
calm) conditions. As a result, AERMOD can produce model estimates
for conditions when the wind speed may be less than 1 m/s, but still
greater than the instrument threshold. Required input to AERMET for
site-specific data, the meteorological processor for AERMOD,
includes a threshold wind speed and a reference wind speed. The
threshold wind speed is the greater of the threshold of the
instrument used to collect the wind speed data or wind direction
sensor.\110\ The reference wind speed is selected by the model as
the lowest level of non-missing wind speed and direction data where
the speed is greater than the wind speed threshold, and the height
of the measurement is between seven times the local surface
roughness length and 100 m. If the only valid observation of the
reference wind speed between these heights is less than the
threshold, the hour is considered calm, and no concentration is
calculated. None of the observed wind speeds in a measured wind
profile that are less than the threshold speed are used in
construction of the modeled wind speed profile in AERMOD.

<IP_ADDRESS> Recommendations

    a. Hourly concentrations calculated with steady-state Gaussian
plume models using calms should not be considered valid; the wind
and concentration estimates for these hours should be disregarded
and considered to be missing. Model predicted concentrations for 3-,
8-, and 24-hour averages should be calculated by dividing the sum of
the hourly concentrations for the period by the number of valid or
non-missing hours. If the total number of valid hours is less than
18 for 24-hour averages, less than 6 for 8-hour averages, or less
than 3 for 3-hour averages, the total concentration should be
divided by 18 for the 24-hour average, 6 for the 8-hour average, and
3 for the 3-hour average. For annual averages, the sum of all valid
hourly concentrations is divided by the number of non-calm hours
during the year. AERMOD has been coded to implement these
instructions. For hours that are calm or missing, the AERMOD hourly
concentrations will be zero. For other models listed in addendum A,
a post-processor computer program, CALMPRO \121\ has been prepared,
is available on the EPA's SCRAM website (section 2.3), and should be
used.
    b. Stagnant conditions that include extended periods of calms
often produce high concentrations over wide areas for relatively
long averaging periods. The standard steady-state Gaussian plume
models are often not applicable to such situations. When stagnation
conditions are of concern, other modeling techniques should be
considered on a case-by-case basis (see also section <IP_ADDRESS>).
    c. When used in steady-state Gaussian plume models other than
AERMOD, measured site-specific wind speeds of less than 1 m/s but
higher than the response threshold of the instrument should be input
as 1 m/s; the corresponding wind direction should also be input.
Wind observations below the response threshold of the instrument
should be set to zero, with the input file in ASCII format. For
input to AERMOD, no such adjustment should be made to the site-
specific wind data, as AERMOD has algorithms to account for light or
variable winds as discussed in section <IP_ADDRESS>(a). For NWS ASOS
data, see the AERMET User's Guide \100\ for guidance on wind speed
thresholds. For prognostic data, see the latest guidance \109\ for
thresholds. Observations with wind speeds less than the threshold
are considered calm, and no concentration is calculated. In all
cases involving steady-state Gaussian plume models, calm hours
should be treated as missing, and concentrations should be
calculated as in paragraph (a) of this subsection.

9.0 Regulatory Application of Models

9.1 Discussion

    a. Standardized procedures are valuable in the review of air
quality modeling and data analyses conducted to support SIP
submittals and revisions, NSR, or other EPA requirements to ensure
consistency in their regulatory application. This section recommends
procedures specific to NSR that facilitate some degree of
standardization while at the same time allowing the flexibility
needed to assure the technically best analysis for each regulatory
application. For SIP attainment demonstrations, refer to the
appropriate EPA guidance 53 64 for the recommended
procedures.
    b. Air quality model estimates, especially with the support of
measured air quality data, are the preferred basis for air quality
demonstrations. A number of actions have been taken to ensure that
the best air quality model is used correctly for each regulatory
application and that it is not arbitrarily imposed.
     First, the Guideline clearly recommends that the most
appropriate model be used in each case. Preferred models are
identified, based on a number of factors, for many uses.

     Second, the preferred models have been subjected to a
systematic performance evaluation and a scientific peer review.
Statistical performance measures, including measures of difference
(or residuals) such as bias, variance of difference and gross
variability of the difference, and measures of correlation such as
time, space, and time and space combined, as described in section
2.1.1, were generally followed.
     Third, more specific information has been provided for
considering the incorporation of new models into the Guideline
(section 3.1), and the Guideline contains procedures for justifying
the case-by-case use of alternative models and obtaining EPA
approval (section 3.2).
    c. Air quality modeling is the preferred basis for air quality
demonstrations. Nevertheless, there are rare circumstances where the
performance of the preferred air quality model may be shown to be
less than reasonably acceptable or where no preferred air quality
model, screening model or technique, or alternative model are
suitable for the situation. In these unique instances, there is the
possibility of assuring compliance and establishing emissions limits
for an existing source solely on the basis of observed air quality
data in lieu of an air quality modeling analysis. Comprehensive air
quality monitoring in the vicinity of the existing source with
proposed modifications will be necessary in these cases. The same
attention should be given to the detailed analyses of the air
quality data as would be applied to a model performance evaluation.
    d. The current levels and forms of the NAAQS for the six
criteria pollutants can be found on the EPA's NAAQS website at
https://www.epa.gov/criteria-air-pollutants. As required by the CAA,
the NAAQS are subjected to extensive review every 5 years and the
standards, including the level and the form, may be revised as part
of that review. The criteria pollutants have either long-term
(annual or quarterly) and/or short-term (24-hour or less) forms that
are not to be exceeded more than a certain frequency over a period
of time (e.g., no exceedance on a rolling 3-month average, no more
than once per year, or no more than once per year averaged over 3
years), are averaged over a period of time (e.g., an annual mean or
an annual mean averaged over 3 years), or are some percentile that
is averaged over a period of time (e.g., annual 99th or 98th
percentile averaged over 3 years). The 3-year period for ambient
monitoring design values does not dictate the length of the data