database).\5\
----------------------------------------------------------------------------------------------------------------
                                              Nearby Source(s).\6\
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................  Maximum allowable           Annual level when           Actual operating
                                     emission limit or           actually operating,         factor averaged
                                     federal enforceable         averaged over the           over the most
                                     permit limit.\5\            most recent 2               recent 2 years.\3\
                                                                 years.\3\                   \8\
Short term (<=24 hours)...........  Maximum allowable           Temporally                  Continuous
                                     emission limit or           representative level        operation, i.e.,
                                     federal enforceable         when actually               all hours of each
                                     permit limit.\6\            operating,                  time period under
                                                                 reflective of the           consideration (for
                                                                 most recent 2               all hours of the
                                                                 years.\3\ \7\               meteorological
                                                                                database).\5\
----------------------------------------------------------------------------------------------------------------
                                             Other Source(s) \8\ \9\
----------------------------------------------------------------------------------------------------------------
  The ambient impacts from Non-nearby or Other Sources (e.g., natural sources, minor sources and, distant major
 source and unidentified sources) can be represented by air quality monitoring data unless adequate data do not
                                                     exist.
----------------------------------------------------------------------------------------------------------------
\1\ For purposes of emissions trading, NSR, or PSD, other model input criteria may apply. See Section 8.2 for
  more information regarding attainment demonstrations of primary PM2.5.
\2\ Terminology applicable to fuel burning sources; analogous terminology (e.g., lb/throughput) may be used for
  other types of sources.
\3\ Unless it is determined that this period is not representative.
\4\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
  causing the highest concentration.
\5\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24-hours) and the
  source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
  modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 p.m. each day, only these hours will
  be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating
\6\ See Section 8.3.3.
\7\ Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or
  other information and should be determined through consultation with the appropriate reviewing authority
  (Paragraph 3.0(b)).
\8\ For those permitted sources not in operation or that have not established an appropriate factor, continuous
  operation (i.e., 8760) should be used.
\9\ See Section 8.3.2.

             Table 8-2--Point Source Model Emission Input for NAAQS Compliance in PSD Demonstrations
----------------------------------------------------------------------------------------------------------------
                                                                                Operating factor
          Averaging time            Emissions limit  (lb/   x   Operating level  (lb/   x     (e.g., hr/yr. hr/
                                          MMBtu) \1\                  MMBtu) \2\                    day)
----------------------------------------------------------------------------------------------------------------
                                      Proposed Major New or Modified Source
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................  Maximum allowable           Design capacity or          Continuous operation
                                     emission limit or           federally                   (i.e., 8760
                                     federal enforceable         enforceable permit          hours).\2\
                                     permit limit.               condition.
Short term (<=24 hours)...........  Maximum allowable           Design capacity or          Continuous
                                     emission limit or           federally                   operation, i.e.,
                                     federal enforceable         enforceable permit          all hours of each
                                     permit limit.               condition.\3\               time period under
                                                                                consideration (for
                                                                                all hours of the
                                                                                meteorological
                                                                                database).\2\
----------------------------------------------------------------------------------------------------------------
                                            Nearby Source(s) \4\ \5\
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................  Maximum allowable           Annual level when           Actual operating
                                     emission limit or           actually operating,         factor averaged
                                     federal enforceable         averaged over the           over the most
                                     permit limit.\5\            most recent 2               recent 2 years.\6\
                                                                 years.\6\                   \8\
Short term (<=24 hours)...........  Maximum allowable           Annual level when           Continuous
                                     emission limit or           actually operating,         operation, i.e.,
                                     federal enforceable         averaged over the           all hours of each
                                     permit limit.\5\            most recent 2               time period under
                                                                 years.\6\ \7\               consideration (for
                                                                                all hours of the
                                                                                meteorological
                                                                                database).\2\
----------------------------------------------------------------------------------------------------------------
                                             Other Source(s) \5\ \9\
----------------------------------------------------------------------------------------------------------------
  The ambient impacts from Non-nearby or Other Sources (e.g., natural sources, minor sources and,distant major
sources, and unidentified sources) can be represented by air quality monitoring data unless adequate data do not
                                                     exist.
----------------------------------------------------------------------------------------------------------------
\1\ Terminology applicable to fuel burning sources; analogous terminology (e.g., lb/throughput) may be used for
  other types of sources.

\2\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24-hours) and the
  source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
  modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 p.m. each day, only these hours will
  be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating
\3\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
  causing the highest concentration.
\4\ Includes existing facility to which modification is proposed if the emissions from the existing facility
  will not be affected by the modification. Otherwise use the same parameters as for major modification.
\5\ See Section 8.3.3.
\6\ Unless it is determined that this period is not representative.
\7\ Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or
  other information and should be determined through consultation with the appropriate reviewing authority
  (Paragraph 3.0(b)).
\8\ For those permitted sources not in operation or that have not established an appropriate factor, continuous
  operation (i.e., 8760) should be used.
\9\ See Section 8.3.2.

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
9.2.4). 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 in the vicinity
of the source(s) under consideration for emissions limits that are
not adequately represented by ambient monitoring data. Typically,
sources that cause a significant concentration gradient in the
vicinity of the source(s) under consideration for emissions limits
are not adequately represented by background ambient monitoring. 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.\91\ 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-sources impacts for ozone and secondary
PM2.5.
    d. For PSD assessments in general 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.

8.3.2 Recommendations for Isolated Single Source

    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.
    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 the most similar characteristics as the project area. If there
are no monitors located in the vicinity of the new or modify 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 activities that are not typical or expected to occur again
in the future (e.g., construction, roadway repairs, forest fires, or
unusual agricultural activities). There may also be cases where
scaling (multiplying the monitored concentrations with a scaling
factor) or adjusting (adding or subtracting a constant value the
monitored concentrations) of data from specific days or hours. Such
adjustments would make the monitored background concentrations more
temporally and/or spatially representative of area around the new or
modifying source for the purposes of the regulator 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).\92\
    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. Determination of the appropriate background concentrations
should be consistent with appropriate EPA modeling guidance
59 92 and justified in the modeling protocol that is
vetted with the appropriate reviewing authority (paragraph 3.0(b)).
    e. 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.
    f. 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) identification and characterization of
contributions from nearby sources through explicit modeling, and (2)
characterization of contributions from other sources through
adequately representative ambient monitoring data. 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.
Since an ambient monitor is limited to characterizing air quality at
a fixed location, sources that causes a significant concentration
gradient in the vicinity of the source(s) under consideration for
emissions limits are not likely to be adequately characterized by
the monitored data due to the high degree of variability of the
source's impact.
    i. The pattern of concentration gradients can vary significantly
based on the averaging period being assessed. In general,
concentration gradients will be smaller and more spatially uniform
for annual averages than for short-term averages, especially for
hourly averages. The spatial distribution of annual impacts around a
source will often have a single peak downwind of the source based on
the prevailing wind direction, except in cases where terrain or
other geographic effects are important. By contrast, the spatial
distribution of peak short-term impacts will typically show several
localized concentration peaks with more significant gradient.
    ii. Concentration gradients associated with a particular source
will generally be largest between that source's location and the
distance to the maximum ground-level concentrations from that
source. Beyond the maximum impact distance, concentration gradients
will generally be much smaller and more spatially uniform. Thus, the
magnitude of a concentration gradient will be greatest in the
proximity of the source and will generally not be significant at
distances greater than 10 times the height of the stack(s) at that
source without consideration of terrain influences.
    iii. The number of nearby sources to be explicitly modeled in
the air quality analysis is expected to be few except in unusual
situations. In most cases, the few nearby sources will be located
within 10 to 20 km from the source(s) under consideration. Owing to
both the uniqueness of each modeling situation and the large number
of variables involved in identifying nearby sources, no attempt is
made here to comprehensively define a ``significant concentration
gradient.'' Rather, identification of nearby sources calls for the
exercise of professional judgement by the appropriate reviewing
authority (paragraph 3.0(b)). This guidance is not intended to alter
the exercise of that judgement 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 appendix 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). 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, 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 sources, minor and distance major
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
Guidance on the Use of Models and Other Analyses for Demonstrating
Attainment of Air Quality Goals for Ozone, PM2.5, and
Regional Haze \93\. 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, data from a nearby National Weather
Service (NWS) or comparable station, or prognostic meteorological
data. The implementation of ASOS (automated surface observing
stations) in recent years should not preclude the use of NWS-ASOS
data if such a station is determined to be representative of the
modeled area.\94\
    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 \95\ shall be used to preprocess all meteorological
data, be it observed or prognostic, for use with AERMOD in
regulatory applications. The AERMINUTE \96\ processor, in most
cases, should be used to process 1-minute ASOS wind data for input
into AERMET when processing NWS ASOS sites in AERMET. When
processing prognostic meteorological data for AERMOD, the Mesoscale
Model Interface Program (MMIF) \93\ should be used to process data
for input into AERMET. Other methods of processing prognostic
meteorological data for input into AERMET should be approved by the
appropriate reviewing authority. Additionally, the following
meteorological preprocessors are recommended by the EPA: PCRAMMET
\97\, MPRM \98\, and METPRO \99\. 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.\100\
    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 101 102,
where applicable, for determining surface characteristics when
processing measured meteorological data through AERMET. In areas
where it is not possible to use AERSURFACE output, surface
characteristics can determined using techniques that apply the same
analysis as AERSURFACE. 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.103 104 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 \76\.
    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 \97\ which 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 meteorological data, at least 1 year of site-
specific, or at least 3 years of prognostic meteorological data are
required. If 1 year or more, up to 5 years, of site-specific data is
available, these data are preferred for use in air quality analyses.
Such data should have been 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 LRT 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 ``hourly'' weather observation was a human observer-
based observation reflecting a single 2-minute average generally
taken about 10 minutes before the hour. However, beginning with
January 2000 for first-order stations and March 2005 for all
stations, NCEI has archived the rolling 2-minute average winds at
every minute for ASOS sites. The AERMINUTE processor \96\ was
developed to reduce calm and missing hours by taking advantage of
the availability of the 1-minute ASOS wind data to calculate full
hourly average winds to replace standard hourly observations and
reduce the number of calm and missing winds in AERMET processing.
---------------------------------------------------------------------------

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

<IP_ADDRESS> Recommendations

    a. The preferred models listed in appendix 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. NEIS makes
available surface 105 106 and upper air \107\
meteorological data online and in CD-ROM format. Upper air data are
also available at the Earth System Research Laboratory Global
Systems Divisions Web site (http://esrl.noaa.gov/gsd).
    b. Although most NWS wind measurements are made at a standard
height of 10 meters, 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. A specific set of randomly generated numbers has
been developed for use with the preferred EPA models and should be
used with standard NWS data to ensure a lack of bias in wind
direction assignments within the models.
    d. Beginning with year 2000, NCDC began archiving 2-minute
winds, reported every minute for NWS ASOS sites. The AERMINUTE
processor was developed to read those winds and calculate hourly
average winds for input into 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.\94\

    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,\108\
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 108. 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 \108\ is available. Site-specific data should
always be reviewed for representativeness and adequacy by an
experienced meteorologist, atmospheric scientist, or other qualified
scientist.

<IP_ADDRESS> Recommendations

    a. The EPA guidance \108\ 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 91 109 110 Detailed
information on quality assurance is also available.\111\ 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.
    b. All processed site-specific data should be in the form of
hourly averages for input into the dispersion model. These data
include surface wind speed, transport direction, dilution wind
speed, and turbulence measurements [sigma]A and
[sigma]E (for use in stability determinations and direct
input into the dispersion model). The hourly average turbulence
measurements should be the square root of the arithmetic average of
the 15-minute average variances (square of [sigma]A or
[sigma]E).
    c. Missing data substitution. After valid data retrieval
requirements have been met,\108\ 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 108. 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.
    d. 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.108 111
    e. Temperature measurements. Temperature measurements should be
made at standard shelter height (2m) in accordance with established
site-specific meteorological guidance.\108\
    f. 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 108 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.
    g. 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 meters. (For additional requirements for AERMOD and CTDMPLUS,
see appendix A.) Specifications for wind measuring instruments and
systems are contained in reference 108.
    h. Turbulence. 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, AERMOD). For specific requirements for
CTDMPLUS, AERMOD, see appendix A. For technical guidance on
measurement and processing of turbulence parameters, see reference
108. When turbulence data are used in this manner to directly
characterize the vertical and lateral dispersion, the averaging time
for the turbulence measurements should be 1 hour. However, since
AERMOD incorporates an algorithm to account for horizontal plume
meander under low wind conditions, the methodology outlined in
paragraph <IP_ADDRESS>(b) should be used to calculate hourly averages of
[sigma][thetas], based on four 15-minuite values, to
minimize ``double counting'' of plume spread associated with
meander. The calculation of hourly [sigma][thetas]
discussed above is automatically applied within AERMET when sub-
hourly data are processed. There are other dispersion models that
employ P-G stability categories for the characterization of the
vertical and lateral dispersion. Methods for using site-specific
turbulence data for the characterization of P-G stability categories
are discussed in reference 108. When turbulence data are used in
this manner to determine the P-G stability category, the averaging
time for the turbulence measurements should be 15 minutes, with
hourly averaged values based on methodology in paragraph <IP_ADDRESS>(b).
    i. 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 10m. The insolation rate is typically assessed
using observations of cloud cover and ceiling height based on
criteria outlined by Turner.\72\ It is recommended that the P-G
stability category be estimated using the Turner method with site-
specific wind speed measured at or near 10m 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
108. 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.
    j. The SRDT method, described in section <IP_ADDRESS> of reference
108, is modified slightly from that published from earlier work
\112\ and has been evaluated with three site-specific
databases.\113\ The two methods of stability classification which
use wind fluctuation statistics, the [sigma]E and
[sigma]A methods, are also described in detail in section
6.4.4 of reference 108 (note applicable tables in section 6). For
additional information on the wind fluctuation methods, several
references are available.114 115 116 117

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 necessary to use prognostic
meteorological data in a regulatory modeling application.
    b. The EPA has developed a processor, the MMIF (Mesoscale Model
Interface Program) to process MM5 (Mesoscale Model 5) or WRF
(Weather Research and Forecasting) model data for input into various
models including AERMOD. MMIF can process data for input into 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).\118\ Specific guidance on processing MMIF for
AERMOD can be found in reference 104. 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 into AERMOD. If an alternative
method of processing data for input into 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 should be
devoted to the process of evaluating the prognostic meteorological
data. The modeling data should be compared to NWS observational data
in an effort to show that the data are accurately 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.\93\ 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.\93\
    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 judgement may be
needed to select the appropriate grid cell to use. In such cases,
the selected grid cell 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 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 which 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 low winds, especially those
introduced with AERMINUTE, the EPA implemented a wind speed
threshold in AERMET for use with ASOS derived winds.\96\ 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 typically the threshold of the instrument
used to collect the wind speed data. 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 and 100 meters. 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. Critical 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 appendix A, a post-
processor computer program, CALMPRO \119\ has been prepared, is
available on the EPA's SCRAM Web site (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, 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
adjustment should be made to the site-specific wind data. For NWS
ASOS data, especially data using the 1-minute ASOS winds, a wind
speed threshold option is allowed with a recommended speed of 0.5 m/
s. \94\ When using prognostic data processed by MMIF, a 0.5 m/s
threshold is also invoked by MMIF for input into AERMET.
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, including PSD, or other EPA
requirements to ensure consistency in their regulatory application.
This section recommends procedures specific to NSR, including PSD,
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
51 60 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 peer scientific 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).

    The Guideline, therefore, provides objective methods that allow
a determination to be made as to what air quality model or technique
is most appropriate for a particular application.
    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 Web site at
http://www.epa.gov/air/criteria.html. Under 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 periods recommended
for modeling (i.e., 5 years of NWS meteorological data, at least 1
year of site-specific, or at least 3 years of prognostic
meteorological data).
    e. This section discusses general recommendations on the
regulatory application of models for the purposes of NSR, including
PSD permitting, and particularly for estimating design
concentration(s), appropriately comparing these estimates to NAAQS
and PSD increment, and developing emissions limits. Lastly, this
section provides the criteria necessary for considering use of
analysis based on measured ambient data in lieu of modeling as the
sole basis for demonstrating compliance with NAAQS and PSD
increments.

9.2 Recommendations

9.2.1 Modeling Protocol

    a. Every effort should be made by the appropriate reviewing
authority (paragraph 3.0(b)) to meet with all parties involved in
either a SIP submission or revision or a PSD permit application
prior to the start of any work on such a project. During this
meeting, a protocol should be established between the preparing and
reviewing parties to define the procedures to be followed, the data
to be collected, the model to be used, and the analysis of the
source and concentration data to be performed. An example of the
content for such an effort is contained in the Air Quality Analysis
Checklist posted on the EPA's SCRAM Web site (section 2.3). This
checklist suggests the appropriate level of detail to assess the air
quality resulting from the proposed action. Special cases may
require additional data collection or analysis and this should be
determined and agreed upon at this pre-application meeting. The
protocol should be written and agreed upon by the parties concerned,
although it is not intended that this protocol be a binding, formal
legal document. Changes in such a protocol or deviations from the
protocol are often necessary as the data collection and analysis
progresses. However, the protocol establishes a common understanding
of how the demonstration required to meet regulatory requirements
will be made.

9.2.2 Design Concentration and Receptor Sites

    a. Under the PSD permitting program, an air quality analysis for
criteria pollutants is required to demonstrate that emissions from
the construction or operation of a proposed new source or
modification will not cause or contribute to a violation of the
NAAQS or PSD increments.
    i. For a NAAQS assessment, the design concentration is the
combination of the appropriate background concentration (section
8.3) with the estimated modeled impact of the source. The NAAQS
design concentration is then compared to the applicable NAAQS.
    ii. For a PSD increment assessment, the design concentration
includes impacts after the appropriate baseline date from all
increment consuming and increment expanding sources. The PSD
increment design concentration is then compared to the applicable
PSD increment.
    b. The specific form of the NAAQS for the pollutant(s) of
concern will also influence how the background and modeled data
should be combined for appropriate comparison with the respective
NAAQS in such a modeling demonstration. Given the potential for
revision of the form of the NAAQS and the complexities of combining
background and modeled data, specific details on this process can be
found in applicable modeling guidance available on the EPA's SCRAM
Web site (section 2.3). Modeled concentrations should not be rounded
before comparing the resulting design concentration to the NAAQS or
PSD increments. Ambient monitoring and dispersion modeling address
different issues and needs relative to each aspect of the overall
air quality assessment.
    c. The PSD increments for criteria pollutants are listed in 40
CFR 52.21(c) and 40 CFR 51.166(c). For short-term increments, these
maximum allowable increases in pollutant concentrations may be
exceeded once per year at each site, while the annual increment may
not be exceeded. The highest, second-highest increase in estimated
concentrations for the short-term averages as determined by a model
should be less than or equal to the permitted increment. The modeled
annual averages should not exceed the increment.
    d. Receptor sites for refined dispersion modeling should be
located within the modeling domain (section 8.1). In designing a
receptor network, the emphasis should be placed on receptor density
and location, not total number of receptors. Typically, the density
of receptor sites should be progressively more resolved near the new
or modifying source, areas of interest, and areas with the highest
concentrations with sufficient detail to determine where possible
violations of a NAAQS or PSD increment are most likely to occur. The
placement of receptor sites should be determined on a case-by-case
basis, taking into consideration the source characteristics,
topography, climatology, and monitor sites. Locations of particular
importance include: (1) The area of maximum impact of the point
source; (2) the area of maximum impact of nearby sources; and (3)
the area where all sources combine to cause maximum impact.
Depending on the complexities of the source and the environment to
which the source is located, a dense array of receptors may be
required in

some cases. In order to avoid unreasonably large computer runs due
to an excessively large array of receptors, it is often desirable to
model the area twice. The first model run would use a moderate
number of receptors more resolved nearby the new or modifying source
and over areas of interest. The second model run would modify the
receptor network from the first model run with a denser array of
receptors in areas showing potential for high concentrations and
possible violations, as indicated by the results of the first model
run. Accordingly, the EPA neither anticipates nor encourages that
numerous iterations of modeling runs be made to continually refine
the receptor network.

9.2.3 NAAQS and PSD Increments Compliance Demonstrations for New or
Modified Sources

    a. As described in this subsection, the recommended procedure
for conducting either a NAAQS or PSD increment assessment under PSD
permitting is a multi-stage approach that includes the following two
stages:
    i. The first stage is referred to as a single-source impact
analysis, since only the new or modifying source is considered in
the analysis. There are two possible levels of detail in conducting
a single-source impact analysis with the model user beginning with
use of a screening model and proceeding to use of a refined model as
necessary.
    ii. The second stage is referred to as a cumulative impact
analysis, since it takes into account all sources affecting the air
quality in an area. In addition to the project source impact, it
includes consideration of background, which includes contributions
from natural, nearby, and unknown sources.
    b. Each stage involves increasing complexity and details, as
required to fully demonstrate a new or modifying source will not
cause of contribution to a violation of any NAAQS or PSD increment.
As such, starting with a single-source impact analysis may alleviate
the need for a more time consuming and comprehensive cumulative
modeling analysis.
    c. The single-source impact analysis, or first stage of an air
quality analysis, begins by determining the potential of a proposed
new or modifying source to cause or contribute to a NAAQS or PSD
increment violation. In certain circumstances, a screening model or
technique may be used instead of the preferred model because it will
provide estimated worst-case ambient impacts from the proposed new
or modifying source. If these worst case ambient concentration
estimates indicate that there will not be a significant impact, then
the analysis is sufficient for the required demonstration under PSD.
If the ambient concentration estimates indicate that significant
impacts may occur, then the use of a refined model to estimate the
source's impact should be pursued. The refined modeling analysis
should use a model or technique consistent with the Guideline
(either a preferred model or technique or an alternative model or
technique) and follow the requirements and recommendations for model
inputs outlined in section 8. If the estimated ambient
concentrations indicate that there will not be a significant impact,
then the analysis is generally sufficient to demonstrate that the
source will not cause or contribute to an exceedance. However, if
the concentration estimates from the refined modeling analysis
indicate that significant impacts may occur, then a cumulative
impact analysis should be undertaken. The receptors that indicate
the location of significant impacts should be used to define the
modeling domain for use in the cumulative impact analysis (section
8.2.2).
    d. The cumulative impact analysis, or the second stage of an air
quality analysis, should be conducted with the same refined model or
technique to characterize the project source and then include the
appropriate background concentrations (section 8.3). The resulting
design concentrations are used to determine whether the source will
cause or contribute to a NAAQS or PSD increment violation. This
determination should be based on: (1) The appropriate design
concentration for each applicable NAAQS (and averaging period); and
(2) the significance of the source's contribution, in a temporal and
spatial sense, to any modeled violation, i.e., where and when the
predicted design concentration is greater than the NAAQS. For PSD
increment, the cumulative impact analysis should also consider the
amount of the air quality increment that has already been consumed
by other sources, or, conversely, whether increment has expanded
relative to the baseline concentration. Therefore, the applicant
should model the existing or permitted nearby increment-consuming
and increment-expanding sources, rather than using past modeling
analyses of those sources as part of background concentration. This
would permit the use of newly acquired data or improved modeling
techniques if such data and/or techniques have become available
since the last source was permitted.

<IP_ADDRESS> Considerations in Developing Emissions Limits

    a. Emissions limits and resulting control requirements should be
established to provide for compliance with each applicable NAAQS
(and averaging period) and PSD increment. It is possible that
multiple emissions limits will be required for a source to
demonstrate compliance with several criteria pollutants (and
averaging periods) and PSD increments. Case-by-case determinations
must be made as to the appropriate form of the limits, i.e., whether
the emissions limits restrict the emission factor (e.g., limiting
lb/MMBTU), the emission rate (e.g., lb/hr), or both. The appropriate
reviewing authority (paragraph 3.0(b)) and appropriate EPA guidance
should be consulted to determine the appropriate emissions limits on
a case-by-case basis.

9.2.4 Use of Measured Data in Lieu of Model Estimates

    a. As described throughout the Guideline, modeling is the
preferred method for demonstrating compliance with the NAAQS and PSD
increments and for determining the most appropriate emissions limits
for new and existing sources. When a preferred model or adequately
justified and approved alternative model is available, model
results, including the appropriate background, are sufficient for
air quality demonstrations and establishing emissions limits, if
necessary. In instances when the modeling technique available is
only a screening technique, the addition of air quality monitoring
data to the analysis may lend credence to the model results.
However, air quality monitoring data alone will normally not be
acceptable as the sole basis for demonstrating compliance with the
NAAQS and PSD increments or for determining emissions limits.
    b. There may be rare circumstances where the performance of the
preferred air quality model will be shown to be less than reasonably
acceptable when compared with air quality monitoring data measured
in the vicinity of an existing source. Additionally, there may not
be an applicable preferred air quality model, screening technique,
or justifiable alternative model suitable for the situation. In
these unique instances, there may be the possibility of establishing
emissions limits and demonstrating compliance with the NAAQS and PSD
increments solely on the basis of analysis of observed air quality
data in lieu of an air quality modeling analysis. However, only in
the case of a modification to an existing source should air quality
monitoring data alone be a basis for determining adequate emissions
limits or for demonstration that the modification will not cause or
contribute to a violation of any NAAQS or PSD increment.
    c. The following items should be considered prior to the
acceptance of an analysis of measured air quality data as the sole
basis for an air quality demonstration or determining an emissions
limit:
    i. Does a monitoring network exist for the pollutants and
averaging times of concern in the vicinity of the existing source?
    ii. Has the monitoring network been designed to locate points of
maximum concentration?
    iii. Do the monitoring network and the data reduction and
storage procedures meet EPA monitoring and quality assurance
requirements?
    iv. Do the dataset and the analysis allow impact of the most
important individual sources to be identified if more than one
source or emission point is involved?
    v. Is at least one full year of valid ambient data available?
    vi. Can it be demonstrated through the comparison of monitored
data with model results that available air quality models and
techniques are not applicable?
    c. Comprehensive air quality monitoring in the area affected by
the existing source with proposed modifications will be necessary in
these cases. Additional meteorological monitoring may also be
necessary. The appropriate number of air quality and meteorological
monitors from a scientific and technical standpoint is a function of
the situation being considered. The source configuration, terrain
configuration, and meteorological variations all have an impact on
number and optimal placement of monitors. Decisions on the
monitoring network appropriate for this type of analysis can only be
made on a case-by-case basis.
    d. Sources should obtain approval from the appropriate reviewing
authority (paragraph

3.0(b)) and the EPA Regional Office for the monitoring network prior
to the start of monitoring. A monitoring protocol agreed to by all
parties involved is necessary to assure that ambient data are
collected in a consistent and appropriate manner. The design of the
network, the number, type, and location of the monitors, the
sampling period, averaging time as well as the need for
meteorological monitoring or the use of mobile sampling or plume
tracking techniques, should all be specified in the protocol and
agreed upon prior to start-up of the network.
    e. Given the uniqueness and complexities of these rare
circumstances, the procedures can only be established on a case-by-
case basis for analyzing the source's emissions data and the
measured air quality monitoring data and for projecting with a
reasoned basis the air quality impact of a proposed modification to
an existing source in order to demonstrate that emissions from the
construction or operation of the modification will not cause or
contribute to a violation of the applicable NAAQS and PSD increment,
and to determine adequate emissions limits. The same attention
should be given to the detailed analyses of the air quality data as
would be applied to a comprehensive model performance evaluation. In
some cases, the monitoring data collected for use in the performance
evaluation of preferred air quality models, screening technique, or
existing alternative models may help inform the development of a
suitable new alternative model. Early coordination with the
appropriate reviewing authority (paragraph 3.0(b)) and the EPA
Regional Office is fundamental with respect to any potential use of
measured data in lieu of model estimates.

10.0 References

1. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 51; Sections 51.112, 51.117, 51.150, 51.160.
2. Environmental Protection Agency, 1990. New Source Review Workshop
Manual: Prevention of Significant Deterioration and Nonattainment
Area Permitting (Draft). Office of Air Quality Planning & Standards,
Research Triangle Park, NC. http://www.epa.gov/nsr.
3. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 51; Sections 51.166 and 52.21.
4. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 93; Sections 93.116, 93.123, and 93.150.
5. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 58 (Ambient Air Quality Surveillance).
6. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 50 (National Primary and Secondary Ambient Air
Quality Standards).
7. Baker, K.R., Kelly, J.T., 2014. Single source impacts estimated
with photochemical model source sensitivity and apportionment
approaches. Atmospheric Environment, 96: 266-274.
8. ENVIRON, 2012. Evaluation of Chemical Dispersion Models using
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119. Environmental Protection Agency, 1984. Calms Processor
(CALMPRO) User's Guide. Publication No. EPA-901/9-84-001. Office Of
Air Quality Planning & Standards, Region I, Boston, MA. (NTIS No. PB
84-229467)

Appendix A to Appendix W of Part 51--Summaries of Preferred Air Quality
Models

Table of Contents

A.0 Introduction and Availability
A.1 AERMOD (AMS/EPA Regulatory Model)
A.2 CTDMPLUS (Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations)
A.3 OCD (Offshore and Coastal Dispersion Model)

A.0 Introduction and Availability

    (1) This appendix summarizes key features of refined air quality
models preferred for specific regulatory applications. For each
model, information is provided on availability, approximate cost
(where applicable), regulatory use, data input, output format and
options, simulation of atmospheric physics, and accuracy. These
models may be used without a formal demonstration of applicability
provided they satisfy the recommendations for regulatory use; not
all options in the models are necessarily recommended for regulatory
use.
    (2) Many of these models have been subjected to a performance
evaluation using comparisons with observed air quality data. Where
possible, several of the models contained herein have been subjected
to evaluation exercises, including (1) statistical performance tests
recommended by the American Meteorological Society and (2) peer
scientific reviews. The models in this appendix have been selected
on the basis of the results of the model evaluations, experience
with previous use, familiarity of the model to various air quality
programs, and the costs and resource requirements for use.
    (3) Codes and documentation for all models listed in this
appendix are available from the EPA's Support Center for Regulatory
Air Models (SCRAM) Web site at http://www.epa.gov/ttn/scram. Codes
and documentation may also available from the National Technical
Information Service (NTIS), http://www.ntis.gov, and, when
available, is referenced with the appropriate NTIS accession number.

A.1 AERMOD (AMS/EPA Regulatory Model)

References

Environmental Protection Agency, 2004. AERMOD: Description of Model
Formulation. Publication No. EPA-454/R-03-004. Office of Air Quality
Planning & Standards, Research Triangle Park, NC; September 2004.
http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf.
Cimorelli, A., et al., 2005. AERMOD: A Dispersion Model for
Industrial Source Applications. Part I: General Model Formulation
and Boundary Layer Characterization. Journal of Applied Meteorology,
44(5): 682-693.
Perry, S. et al., 2005. AERMOD: A Dispersion Model for Industrial
Source Applications. Part II: Model Performance against 17 Field
Study Databases. Journal of Applied Meteorology, 44(5): 694-708.
Environmental Protection Agency, 2004. User's Guide for the AMS/EPA
Regulatory Model--AERMOD. Publication No. EPA-454/B-03-001. Office
of Air Quality Planning & Standards, Research Triangle Park, NC;
September 2004. http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#aermod.
Environmental Protection Agency, 2004. User's Guide for the AERMOD
Meteorological Preprocessor (AERMET). Publication No. EPA-454/B-03-
002. Office of Air Quality Planning & Standards, Research Triangle
Park, NC; November 2004. http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet.
User's Guide for the AERMOD Terrain Preprocessor (AERMAP).
Publication No. EPA-454/B-03-003. Office of Air Quality Planning &
Standards, Research Triangle Park, NC; October 2004.

http://www.epa.gov/ttn/scram/dispersion_related.htm#aermap.
Schulman, L. L., D.G. Strimaitis and J.S. Scire, 2000. Development
and evaluation of the PRIME plume rise and building downwash model.
Journal of the Air and Waste Management Association, 50: 378-390.
Schulman, L. L., and Joseph S. Scire, 1980. Buoyant Line and Point
Source (BLP) Dispersion Model User's Guide. Document P-7304B.
Environmental Research and Technology, Inc., Concord, MA. (NTIS No.
PB 81-164642).

Availability

    The model codes and associated documentation are available on
EPA's SCRAM Web site (paragraph A.0(3)).

Abstract

    AERMOD is a steady-state plume dispersion model for assessment
of pollutant concentrations from a variety of sources. AERMOD
simulates transport and dispersion from multiple point, area, or
volume sources based on an up-to-date characterization of the
atmospheric boundary layer. Sources may be located in rural or urban
areas, and receptors may be located in simple or complex terrain.
AERMOD accounts for building wake effects (i.e., plume downwash)
based on the PRIME building downwash algorithms. The model employs
hourly sequential preprocessed meteorological data to estimate
concentrations for averaging times from 1-hour to 1-year (also
multiple years). AERMOD can be used to estimate the concentrations
of nonreactive pollutants from highway traffic. AERMOD also handles
unique modeling problems associated with aluminum reduction plants,
and other industrial sources where plume rise and downwash effects
from stationary buoyant line sources are important. AERMOD is
designed to operate in concert with two pre-processor codes: AERMET
processes meteorological data for input to AERMOD, and AERMAP
processes terrain elevation data and generates receptor and hill
height information for input to AERMOD.

a. Recommendations for Regulatory Use

    (1) AERMOD is appropriate for the following applications:
     Point, volume, and area sources;
     Buoyant, elevated line sources (e.g., aluminum
reduction plants);
     Mobile (line) sources;
     Surface, near-surface, and elevated releases;
     Rural or urban areas;
     Simple and complex terrain;
     Transport distances over which steady- state
assumptions are appropriate, up to 50km;
     1-hour to annual averaging times; and
     Continuous toxic air emissions.
    (2) For regulatory applications of AERMOD, the regulatory
default option should be set, i.e., the parameter DFAULT should be
employed in the MODELOPT record in the Control Pathway. The DFAULT
option requires the use of terrain elevation data, stack-tip
downwash, sequential date checking, and does not permit the use of
the model in the SCREEN mode. In the regulatory default mode,
pollutant half-life or decay options are not employed, except in the
case of an urban source of sulfur dioxide where a four-hour half-
life is applied. Terrain elevation data from the U.S. Geological
Survey 7.5-Minute Digital Elevation Model (DEM) or equivalent
(approx. 30-meter resolution) should be used in all applications.
Starting in 2011, data from the National Elevation Dataset (NED,
http://ned.usgs.gov) can also be used in AERMOD, which includes a
range of resolutions, ranging from 1-m to 2 arc seconds and such
high resolution would always be preferred. In some cases, exceptions
of the terrain data requirement may be made in consultation with the
appropriate reviewing authority (paragraph 3.0(b)).

b. Input Requirements

    (1) Source data: Required input includes source type, location,
emission rate, stack height, stack inside diameter, stack gas exit
velocity, stack gas temperature, area and volume source dimensions,
and source elevation. Building dimensions and variable emission
rates are optional. Buoyant line sources require coordinates of the
end points of the line, release height, emission rate, average line
source width, average building width, average spacing between
buildings, and average line source buoyancy parameter. For mobile
sources, traffic volume; emission factor, source height, and mixing
zone width are needed.
    (2) Meteorological data: The AERMET meteorological preprocessor
requires input of surface characteristics, including surface
roughness (zo), Bowen ratio, and albedo, as well as, hourly
observations of wind speed between 7zo and 100m (reference wind
speed measurement from which a vertical profile can be developed),
wind direction, cloud cover, and temperature between zo and 100m
(reference temperature measurement from which a vertical profile can
be developed). Meteorological data can be in the form of observed
data or prognostic modeled data as discussed in paragraph 8.4.1(d).
Surface characteristics may be varied by wind sector and by season
or month. When using observed meteorological data, a morning
sounding (in National Weather Service format) from a representative
upper air station is required. Latitude, longitude, and time zone of
the surface, site-specific (if applicable) and upper air
meteorological stations are required. The wind speed starting
threshold is also required in AERMET for applications involving
site-specific data). When using prognostic data, modeled profiles of
temperature and winds are input into AERMET. These can be hourly or
a time that represents a morning sounding. Additionally, measured
profiles of wind, temperature, vertical and lateral turbulence may
be required in certain applications (e.g., in complex terrain) to
adequately represent the meteorology affecting plume transport and
dispersion. Optionally, measurements of solar, or net radiation may
be input to AERMET. Two files are produced by the AERMET
meteorological preprocessor for input to the AERMOD dispersion
model. When using observed data, the surface file contains observed
and calculated surface variables, one record per hour. For
applications with multi-level site-specific meteorological data, the
profile contains the observations made at each level of the
meteorological tower (or remote sensor). When using prognostic data,
the surface file contains surface variables calculated by the
prognostic model and AERMET. The profile file contains the
observations made at each level of a meteorological tower (or remote
sensor), the one-level observations taken from other representative
data (e.g., National Weather Service surface observations), one
record per level per hour, or in the case of prognostic data, the
prognostic modeled values of temperature and winds at user-specified
levels.
    (i) Data used as input to AERMET should possess an adequate
degree of representativeness to insure that the wind, temperature
and turbulence profiles derived by AERMOD are both laterally and
vertically representative of the source area. The adequacy of input
data should be judged independently for each variable. The values
for surface roughness, Bowen ratio, and albedo should reflect the
surface characteristics in the vicinity of the meteorological tower
or representative grid cell when using prognostic data, and should
be adequately representative of the modeling domain. Finally, the
primary atmospheric input variables including wind speed and
direction, ambient temperature, cloud cover, and a morning upper air
sounding should also be adequately representative of the source
area, when using observed data.
    (ii) For recommendations regarding the length of meteorological
record needed to perform a regulatory analysis with AERMOD, see
section 8.4.2.
    (3) Receptor data: Receptor coordinates, elevations, height
above ground, and hill height scales are produced by the AERMAP
terrain preprocessor for input to AERMOD. Discrete receptors and/or
multiple receptor grids, Cartesian and/or polar, may be employed in
AERMOD. AERMAP requires input of DEM terrain data produced by the
U.S. Geological Survey (USGS), or other equivalent data. AERMAP can
be used optionally to estimate source elevations.

c. Output

    Printed output options include input information, high
concentration summary tables by receptor for user-specified
averaging periods, maximum concentration summary tables, and
concurrent values summarized by receptor for each day processed.
Optional output files can be generated for: A listing of occurrences
of exceedances of user-specified threshold value; a listing of
concurrent (raw) results at each receptor for each hour modeled,
suitable for post-processing; a listing of design values that can be
imported into graphics software for plotting contours; a listing of
results suitable for NAAQS analyses including NAAQS exceedances and
culpability analyses; an unformatted listing of raw results above a
threshold value with a special structure for use with the TOXX model
component of TOXST; a listing of concentrations by rank (e.g., for
use in quantile-quantile plots); and, a listing of concentrations,
including arc-maximum

normalized concentrations, suitable for model evaluation studies.

d. Type of Model

    AERMOD is a steady-state plume model, using Gaussian
distributions in the vertical and horizontal for stable conditions,
and in the horizontal for convective conditions. The vertical
concentration distribution for convective conditions results from an
assumed bi-Gaussian probability density function of the vertical
velocity.

e. Pollutant Types

    AERMOD is applicable to primary pollutants and continuous
releases of toxic and hazardous waste pollutants. Chemical
transformation is treated by simple exponential decay.

f. Source-Receptor Relationships

    AERMOD applies user-specified locations for sources and
receptors. Actual separation between each source-receptor pair is
used. Source and receptor elevations are user input or are
determined by AERMAP using USGS DEM terrain data. Receptors may be
located at user-specified heights above ground level.

g. Plume Behavior

    (1) In the convective boundary layer (CBL), the transport and
dispersion of a plume is characterized as the superposition of three
modeled plumes: The direct plume (from the stack), the indirect
plume, and the penetrated plume, where the indirect plume accounts
for the lofting of a buoyant plume near the top of the boundary
layer, and the penetrated plume accounts for the portion of a plume
that, due to its buoyancy, penetrates above the mixed layer, but can
disperse downward and re-enter the mixed layer. In the CBL, plume
rise is superposed on the displacements by random convective
velocities (Weil et al., 1997).
    (2) In the stable boundary layer, plume rise is estimated using
an iterative approach to account for height-dependent lapse rates,
similar to that in the CTDMPLUS model (see A.2 in this appendix).
    (3) Stack-tip downwash and buoyancy induced dispersion effects
are modeled. Building wake effects are simulated for stacks subject
to building downwash using the methods contained in the PRIME
downwash algorithms (Schulman, et al., 2000). For plume rise
affected by the presence of a building, the PRIME downwash algorithm
uses a numerical solution of the mass, energy and momentum
conservation laws (Zhang and Ghoniem, 1993). Streamline deflection
and the position of the stack relative to the building affect plume
trajectory and dispersion. Enhanced dispersion is based on the
approach of Weil (1996). Plume mass captured by the cavity is well-
mixed within the cavity. The captured plume mass is re-emitted to
the far wake as a volume source.
    (4) For elevated terrain, AERMOD incorporates the concept of the
critical dividing streamline height, in which flow below this height
remains horizontal, and flow above this height tends to rise up and
over terrain (Snyder et al., 1985). Plume concentration estimates
are the weighted sum of these two limiting plume states. However,
consistent with the steady-state assumption of uniform horizontal
wind direction over the modeling domain, straight-line plume
trajectories are assumed, with adjustment in the plume/receptor
geometry used to account for the terrain effects.

h. Horizontal Winds

    Vertical profiles of wind are calculated for each hour based on
measurements and surface-layer similarity (scaling) relationships.
At a given height above ground, for a given hour, winds are assumed
constant over the modeling domain. The effect of the vertical
variation in horizontal wind speed on dispersion is accounted for
through simple averaging over the plume depth.

i. Vertical Wind Speed

    In convective conditions, the effects of random vertical updraft
and downdraft velocities are simulated with a bi-Gaussian
probability density function. In both convective and stable
conditions, the mean vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

    Gaussian horizontal dispersion coefficients are estimated as
continuous functions of the parameterized (or measured) ambient
lateral turbulence and also account for buoyancy- induced and
building wake-induced turbulence. Vertical profiles of lateral
turbulence are developed from measurements and similarity (scaling)
relationships. Effective turbulence values are determined from the
portion of the vertical profile of lateral turbulence between the
plume height and the receptor height. The effective lateral
turbulence is then used to estimate horizontal dispersion.

k. Vertical Dispersion

    In the stable boundary layer, Gaussian vertical dispersion
coefficients are estimated as continuous functions of parameterized
vertical turbulence. In the convective boundary layer, vertical
dispersion is characterized by a bi-Gaussian probability density
function, and is also estimated as a continuous function of
parameterized vertical turbulence. Vertical turbulence profiles are
developed from measurements and similarity (scaling) relationships.
These turbulence profiles account for both convective and mechanical
turbulence. Effective turbulence values are determined from the
portion of the vertical profile of vertical turbulence between the
plume height and the receptor height. The effective vertical
turbulence is then used to estimate vertical dispersion.

l. Chemical Transformation

    Chemical transformations are generally not treated by AERMOD.
However, AERMOD does contain an option to treat chemical
transformation using simple exponential decay, although this option
is typically not used in regulatory applications, except for sources
of sulfur dioxide in urban areas. Either a decay coefficient or a
half-life is input by the user. Note also that the Plume Volume
Molar Ratio Method and the Ozone Limiting Method (section <IP_ADDRESS>)
and for point-source NO2 analyses are available.

m. Physical Removal

    AERMOD can be used to treat dry and wet deposition for both
gases and particles.

n. Evaluation Studies

American Petroleum Institute, 1998. Evaluation of State of the
Science of Air Quality Dispersion Model, Scientific Evaluation,
prepared by Woodward-Clyde Consultants, Lexington, Massachusetts,
for American Petroleum Institute, Washington, DC, 20005-4070.
Brode, R.W., 2002. Implementation and Evaluation of PRIME in AERMOD.
Preprints of the 12th Joint Conference on Applications of Air
Pollution Meteorology, May 20-24, 2002; American Meteorological
Society, Boston, MA.
Brode, R.W., 2004. Implementation and Evaluation of Bulk Richardson
Number Scheme in AERMOD. 13th Joint Conference on Applications of
Air Pollution Meteorology, August 23-26, 2004; American
Meteorological Society, Boston, MA.
Environmental Protection Agency, 2003. AERMOD: Latest Features and
Evaluation Results. Publication No. EPA-454/R-03-003. Office of Air
Quality Planning & Standards, Research Triangle Park, NC. http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mep.pdf.
Heist, D., et al, 2013. Estimating near-road pollutant dispersion: A
model inter-comparison. Transportation Research Part D: Transport
and Environment, 25: pp 93-105.

A.2 CTDMPLUS (Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations)

References

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T.
Mills, D.G. Strimaitis, R.J. Yamartino and E.M. Insley, 1989. User's
Guide to the Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations (CTDMPLUS). Volume 1: Model Descriptions and
User Instructions. EPA Publication No. EPA-600/8-89-041.
Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#ctdmplus. (NTIS No. PB
89-181424)
Perry, S.G., 1992. CTDMPLUS: A Dispersion Model for Sources near
Complex Topography. Part I: Technical Formulations. Journal of
Applied Meteorology, 31(7): 633-645.

Availability

    The model codes and associated documentation are available on
the EPA's SCRAM Web site (paragraph A.0(3)).

Abstract

    CTDMPLUS is a refined point source Gaussian air quality model
for use in all stability conditions for complex terrain
applications. The model contains, in its entirety, the technology of
CTDM for stable and neutral conditions. However, CTDMPLUS can also
simulate daytime, unstable conditions, and has a number of
additional capabilities for improved user friendliness. Its use of
meteorological data

and terrain information is different from other EPA models;
considerable detail for both types of input data is required and is
supplied by preprocessors specifically designed for CTDMPLUS.
CTDMPLUS requires the parameterization of individual hill shapes
using the terrain preprocessor and the association of each model
receptor with a particular hill.

a. Recommendation for Regulatory Use

    CTDMPLUS is appropriate for the following applications:
     Elevated point sources;
     Terrain elevations above stack top;
     Rural or urban areas;
     Transport distances less than 50 kilometers; and
     1-hour to annual averaging times when used with a post-
processor program such as CHAVG.

b. Input Requirements

    (1) Source data: For each source, user supplies source location,
height, stack diameter, stack exit velocity, stack exit temperature,
and emission rate; if variable emissions are appropriate, the user
supplies hourly values for emission rate, stack exit velocity, and
stack exit temperature.
    (2) Meteorological data: For applications of CTDMPLUS, multiple
level (typically three or more) measurements of wind speed and
direction, temperature and turbulence (wind fluctuation statistics)
are required to create the basic meteorological data file
(``PROFILE''). Such measurements should be obtained up to the
representative plume height(s) of interest (i.e., the plume
height(s) under those conditions important to the determination of
the design concentration). The representative plume height(s) of
interest should be determined using an appropriate complex terrain
screening procedure (e.g., CTSCREEN) and should be documented in the
monitoring/modeling protocol. The necessary meteorological
measurements should be obtained from an appropriately sited
meteorological tower augmented by SODAR and/or RASS if the
representative plume height(s) of interest is above the levels
represented by the tower measurements. Meteorological preprocessors
then create a SURFACE data file (hourly values of mixed layer
heights, surface friction velocity, Monin-Obukhov length and surface
roughness length) and a RAWINsonde data file (upper air measurements
of pressure, temperature, wind direction, and wind speed).
    (3) Receptor data: Receptor names (up to 400) and coordinates,
and hill number (each receptor must have a hill number assigned).
    (4) Terrain data: User inputs digitized contour information to
the terrain preprocessor which creates the TERRAIN data file (for up
to 25 hills).

c. Output

    (1) When CTDMPLUS is run, it produces a concentration file, in
either binary or text format (user's choice), and a list file
containing a verification of model inputs, i.e.,
     Input meteorological data from ``SURFACE'' and
``PROFILE'',
     Stack data for each source,
     Terrain information,
     Receptor information, and
     Source-receptor location (line printer map).
    (2) In addition, if the case-study option is selected, the
listing includes:
     Meteorological variables at plume height,
     Geometrical relationships between the source and the
hill, and
     Plume characteristics at each receptor, i.e.,

--Distance in along-flow and cross flow direction
--Effective plume-receptor height difference
--Effective [sigma]y & [sigma]z values, both flat terrain and hill
induced (the difference shows the effect of the hill)
--Concentration components due to WRAP, LIFT and FLAT

    (3) If the user selects the TOPN option, a summary table of the
top four concentrations at each receptor is given. If the ISOR
option is selected, a source contribution table for every hour will
be printed.
    (4) A separate output file of predicted (1-hour only)
concentrations (``CONC'') is written if the user chooses this
option. Three forms of output are possible:
    (i) A binary file of concentrations, one value for each receptor
in the hourly sequence as run;
    (ii) A text file of concentrations, one value for each receptor
in the hourly sequence as run; or
    (iii) A text file as described above, but with a listing of
receptor information (names, positions, hill number) at the
beginning of the file.
    (5) Hourly information provided to these files besides the
concentrations themselves includes the year, month, day, and hour
information as well as the receptor number with the highest
concentration.

d. Type of Model

    CTDMPLUS is a refined steady-state, point source plume model for
use in all stability conditions for complex terrain applications.

e. Pollutant Types

    CTDMPLUS may be used to model non- reactive, primary pollutants.

f. Source-Receptor Relationship

    Up to 40 point sources, 400 receptors and 25 hills may be used.
Receptors and sources are allowed at any location. Hill slopes are
assumed not to exceed 15[deg], so that the linearized equation of
motion for Boussinesq flow are applicable. Receptors upwind of the
impingement point, or those associated with any of the hills in the
modeling domain, require separate treatment.

g. Plume Behavior