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AERMOD Implementation Guide
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EPA-454/B-21-006
July 2021
AERMOD Implementation Guide
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, North Carolina
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Preface
This document provides information on the recommended use of AERMOD to address
specific issues and concerns related to the implementation of AERMOD for regulatory
applications. The following recommendations augment the use of experience and judgment in
the proper application of dispersion models. Advanced coordination with reviewing authorities,
including the development of modeling protocols, is recommended for regulatory applications of
AERMOD.
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Acknowledgments
The AERMOD Implementation Guide has been developed through the collaborative
efforts of EPA OAQPS, EPA Regional Office, State, and local agency dispersion modelers,
through the activities of the AERMOD Implementation Workgroup. The efforts of all
contributors are gratefully acknowledged.
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Contents
Section Page
1.0 What's new in this document? 1
2.0 Document background and purpose 1
2.1 Background (10/19/2007) 1
2.2 Purpose (10/19/2007) 2
3.0 Meteorological data and processing 3
3.1 Determining surface characteristics (01/09/2008) 3
3.1.1 Meteorological data representativeness considerations (01/09/2008) 3
3.1.2 Methods for determining surface characteristics (4/22/2021) 4
3.1.3 Use of AERSURFACE for determining surface characteristics (4/22/2021) 8
3.2 Selecting upper air sounding levels (10/19/2007) 9
3.3 Processing site-specific meteorological data for urban applications (04/22/2021) 10
3.4 Use of prognostic meteorological model data as inputs to AERMOD (04/17/2018)... 10
4.0 Terrain data and processing 12
4.1 Modeling sources with terrain-following plumes in sloping terrain (01/09/2008) 12
4.2 AERMAP DEM array and domain boundary (09/27/2005) 13
4.3 Terrain elevation data sources for AERMAP (04/22/2021) 13
4.4 Manually entering terrain elevations in AERMAP (03/19/2009) 17
4.5 Use of AERMAP to determine source elevations (03/19/2009) 17
5.0 Urban applications 19
5.1 Urban/rural determination (08/03/2015) 19
5.2 Selecting population data for AERMOD's urban mode (10/19/2007) 20
5.3 Optional urban roughness length - URBANOPT keyword (10/19/2007) 21
5.4 Meteorological data selections for urban applications (01/09/2008) 22
5.4.1 Urban applications using NWS meteorological data (01/09/2008) 22
5.4.2 Urban applications using site-specific meteorological data (01/09/2008) 22
6.0 Source characterization 23
6.1 Capped and horizontal stacks (12/20/2016) 23
6.2 Use of area source algorithm in AERMOD (09/27/2005) 23
7.0 Interim Guidance on Model Application 25
7.1 Guidance on NO2 Background Concentrations with PVMRM (7/26/2021) 25
7.2 Guidance on BUOYLINE Source Types (7/26/2021) 25
8.0 REFERENCES 27
Appendix A. EPA Model Clearinghouse memorandum dated July 9, 1993 30
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1.0 What's new in this document?
Revisions dated April 22, 2021:
• Section 3.1.2, Methods for determining surface characteristics - updated for
AERSURFACE version 20060
• Section 3.1.3, Use of AERSURFACE for determining surface characteristics - updated
for AERSURFACE version 20060
• Section 3.3, Processing site-specific meteorological data for urban applications - updated
for addition of turbulence options AERMOD version 21112
• Section 4.3, Terrain elevation data sources for AERMAP
• Section 7.1, Guidance on current implementation of BUOYLINE - deleted due to coding
bug fix implemented in AERMOD version 21112
• Section 7.2, Guidance on using site-specific data with AERMET - deleted due to coding
bug fix implemented in AERMET version 21112
Revisions dated July 26, 2021:
• Section 7.1 Guidance on N02 Background Concentrations with PVMRM - added
• Section 7.2 Guidance on BUOYLINE Source Types - added
2.0 Document background and purpose
2.1 Background (10/19/2007)
In April 2005, the AERMOD Implementation Workgroup (AIWG) was formed in
anticipation of AERMOD's promulgation as a replacement for the Industrial Source Complex
(ISCST3) model. AERMOD fully replaced ISCST3 as the regulatory model on December 9,
2006 (EPA, 2005a), after a one-year grandfather period. The primary purpose for forming the
AIWG was to develop a comprehensive approach for dealing with implementation issues for
which guidance is needed. A result of this initial AIWG was the publication of the first version
of the AERMOD Implementation Guide on September 27, 2005.
In 2007, a new AIWG was formed as a standing workgroup to provide support to EPA's
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Office of Air Quality Planning and Standards (OAQPS). This document represents the
combined efforts of AIWG and OAQPS in relation to the implementation of the AERMOD
regulatory model.
2.2 Purpose (10/19/2007)
This document provides information on the recommended use of AERMOD to address a
range of issues and types of applications. Topics are organized based on implementation issues,
with additional information as appropriate on whether they impact the modules of the AERMOD
modeling system (AERMOD, AERMET, and AERMAP) or related programs (AERSURFACE,
AERSCREEN, and BPIPPRM). The document contains a section which highlights changes
from the previous version, located in Section 1.0 of the document for use as a quick reference.
Each section is also identified with the date (mm/dd/yyyy) that it was added or last updated.
Only sections with substantive changes or new recommendations are identified with new
revision dates. Revision dates are not updated for sections with only minor edits to clarify the
wording, correct typographical errors, or update citations to reference the most recent version of
a document.
The recommendations contained within this document represent the current best use
practices as determined by EPA, through the implementation of AIWG. The document is not
intended as a replacement of, or even a supplement to the Guideline on Air Quality Models
(EPA, 2017). Rather, it is designed to provide consistent, technically sound recommendations to
address specific issues and concerns relevant to the regulatory application of AERMOD. As
always, advance coordination with the reviewing authorities on the application of AERMOD is
advisable. Modeling protocols should be developed, and agreed upon by all parties, in advance
of any modeling activity.
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3.0 Meteorological data and processing
3.1 Determining surface characteristics (01/09/2008)
When applying the AERMET meteorological processor (EPA, 2021a) to prepare the
meteorological data for the AERMOD model (EPA, 2021b), the user must determine appropriate
values for three surface characteristics: surface roughness length {z0j, albedo {r}, and Bowen
ratio {Bo}. The surface roughness length is related to the height of obstacles to the wind flow
and is, in principle, the height at which the mean horizontal wind speed is zero based on a
logarithmic profile. The surface roughness length influences the surface shear stress and is an
important factor in determining the magnitude of mechanical turbulence and the stability of the
boundary layer. The albedo is the fraction of total incident solar radiation reflected by the
surface back to space without absorption. The daytime Bowen ratio, an indicator of surface
moisture, is the ratio of sensible heat flux to latent heat flux and is used for determining planetary
boundary layer parameters for convective conditions driven by the surface sensible heat flux.
This section provides recommendations regarding several issues associated with determining
appropriate surface characteristics for AERMOD modeling applications.
3.1.1 Meteorological data representativeness considerations (01/09/2008)
When using National Weather Service (NWS) data for AERMOD, data
representativeness can be thought of in terms of constructing realistic planetary boundary layer
(PBL) similarity profiles and adequately characterizing the dispersive capacity of the
atmosphere. As such, the determination of representativeness should include a comparison of
the surface characteristics {i.e., z„, B0 and r) between the NWS measurement site and the source
location, coupled with a determination of the importance of those differences relative to
predicted concentrations. Site-specific meteorological data are assumed by definition to be
representative of the application site; however, the determination of representativeness of site-
specific data for AERMOD applications should also include an assessment of surface
characteristics of the measurement and source locations and cannot be based solely on proximity.
The recommendations presented in this section for determining surface characteristics for
AERMET apply to both site-specific and non-site-specific {e.g., NWS) meteorological data.
The degree to which predicted pollutant concentrations are influenced by surface
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parameter differences between the application site and the meteorological measurement site
depends on the nature of the application (i.e., release height, plume buoyancy, terrain influences,
downwash considerations, design metric, etc.). For example, a difference in z„ for one
application may translate into an unacceptable difference in the design concentration, while for
another application the same difference in z0 may lead to an insignificant difference in design
concentration. If the reviewing agency is uncertain as to the representativeness of a
meteorological measurement site, a site-specific sensitivity analysis may be needed in order to
quantify, in terms of expected changes in the design concentration, the significance of the
differences in each of the surface characteristics.
If the proposed meteorological measurement site's surface characteristics are determined
to NOT be representative of the application site, it may be possible that another nearby
meteorological measurement site may be representative of both meteorological parameters and
surface characteristics. Failing that, it is likely that site-specific meteorological data will be
required.
3.1.2 Methods for determining surface characteristics (4/22/2021)
The availability of high-resolution digitized land cover databases provides an opportunity
to apply systematic procedures to determine surface characteristics based on an objective
analysis of the gridded land cover data across a domain. A proper analysis of such data must
take into consideration the relationship between surface characteristics and the meteorological
measurements on which the surface characteristics will be applied.
Based on model formulations and model sensitivities, the relationship between the
surface roughness upwind of the measurement site and the measured wind speeds is generally the
most important consideration. The effective surface roughness length should be based on an
upwind distance that captures the net influence of surface roughness elements on the measured
wind speeds needed to properly characterize the magnitude of mechanical turbulence in the
approach flow. A number of studies have examined the response of the atmosphere to abrupt
changes in the surface roughness, and provide some insight into the relationship between
measured winds and surface roughness [e.g., Blom and Warenta (1969), Businger (1986),
Hogstrom and Hogstrom (1978), Horst and Weil (1994), Irwin (1978), Rao, et al. (1974), and
Taylor (1969)]. Such changes in surface roughness result in the development of an internal
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boundary layer (IBL) which grows with distance downwind of the roughness change, and defines
the layer influenced by the transition in surface roughness. The size and structure of the IBL is
very complex, even for idealized cases of uniform roughness upwind and downwind of the
transition. The IBL is also affected by the magnitude and direction of the roughness change and
the stability of the upstream flow. The IBL generally grows more slowly for stable conditions
than for neutral or unstable approach flow and will also tend to grow more slowly for rough-to-
smooth transitions than for smooth-to-rough transitions. The relationship between surface
roughness and measured wind speeds is even more complex in real world applications given the
typically patchy nature of the heterogeneity of surface roughness elements.
The recommended upwind distance for surface roughness should take into account the
fact that surface roughness effects in AERMOD are more important for stable atmospheric
conditions than for neutral/unstable conditions, and that meteorological monitoring sites are
typically characterized by open (low roughness) exposures in order to accommodate
recommended siting criteria (EPA, 2000). For typical measurement programs, including NWS
stations, the reference wind measurements will be taken for an anemometer height of
approximately 10 meters above ground. An upwind distance based on the recommended siting
criterion of at least 10 times the height of nearby obstacles (EPA, 2000), which would
correspond to a distance of about 100m for typical obstacles such as trees and 2-3 story
buildings, is considered inadequate for this purpose. However, the previous recommendation to
use an upwind distance of 3 kilometers for surface roughness is considered too large because the
boundary layer up to typical measurement heights of 10m will generally respond to changes in
roughness length over much shorter distances. Including land cover information across an
upwind distance that is too large could misrepresent the amount of mechanical turbulence
present in the approach flow and bias model results, especially for low-level releases.
The recommended upwind distance for processing land cover data to determine the
effective surface roughness for input to AERMET is 1 kilometer relative to the location of the
meteorological tower. This recommended distance is considered a reasonable balance of the
complex factors cited in the discussion above. If land cover varies significantly by direction,
then surface roughness should be determined based on sector. However, the width of the sectors
should be no smaller than a 30-degree arc. Further information on the definition of sectors for
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surface roughness is provided in the AERMET User's Guide (EPA, 2021a). Exceptions to the
recommended default distance of 1 kilometer for surface roughness may be considered on a case-
by-case basis for applications involving site-specific wind speed measurements taken at heights
well above 10m, in situations with significant discontinuities in land cover just beyond the
recommended 1 kilometer upwind distance or for sites with significant terrain discontinuities
(e.g., the top of a mesa or a narrow, steep valley). Another factor that may need to be considered
in some cases for determining an effective surface roughness length is the potential contribution
of nearby terrain or other significant surface expression, not reflected in the land cover data, to
the generation of mechanical turbulence. Use of a non-default distance for surface roughness
estimation, or modification of surface roughness estimates to account for terrain/surface-
expression effects, should be documented and justified in a modeling protocol submitted to the
appropriate reviewing authority prior to conducting the modeling analysis.
The dependence of meteorological measurements and plume dispersion on Bowen ratio
and albedo is very different than the dependence on surface roughness. Effective values for
Bowen ratio and albedo are used to estimate the strength of convective turbulence during
unstable conditions by determining how much of the incoming radiation is converted to sensible
heat flux. These estimates of convective turbulence are not linked as directly with tower
measurements as the linkage between the measured wind speed and the estimation of mechanical
turbulence intensities driven by surface roughness elements. While local surface characteristics
immediately upwind of the measurement site are very important for surface roughness, effective
values of Bowen ratio and albedo determined over a larger domain are more appropriate.
The recommended approach for processing digitized land cover data to determine the
effective Bowen ratio and albedo for input to AERMET is to average the surface characteristics
across a representative domain without any direction or distance dependency. The recommended
default domain is a 10km by 10km region centered on the measurement site. Use of the
measurement location to define the domain is likely to be adequate for most applications.
However, a domain representative of the application site may be more appropriate for some
applications, particularly if most of sources are elevated releases. The use of an alternative
domain for Bowen ratio and albedo should be documented and justified in a modeling protocol
submitted to the appropriate reviewing authority prior to conducting the modeling analysis.
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Beyond defining the appropriate domains to use for processing digitized land cover data,
additional considerations are needed regarding the computational methods for processing of the
data. Due to the fact that the width of a sector increases with distance from the measurement
site, the land cover farther from the site would receive a higher effective weight than land cover
closest to the site if a direct area-weighted averaging approach were used to calculate an
effective surface roughness. An inverse-distance weighting is recommended for determining
surface roughness from digitized land cover data to adjust for this factor, since the length of an
arc (across a sector) is proportional to the distance from the center. In addition, a geometric
mean is recommended for calculating the effective surface roughness due to the fact that the
AERMOD formulations are dependent on the ln(z0). Note that the arithmetic average of the
ln(z0) is mathematically equivalent to the geometric mean of z0. Since the Bowen ratio
represents the ratio between sensible heat flux and latent heat flux, the use of a geometric mean
is also recommended for calculating effective values of Bowen ratio. Geometric means are more
appropriate for calculating "average" values of ratios; for example, the "average" for Bowen
ratios of 0.5 and 2.0 should be 1.0, which is accomplished with the use of a geometric mean. A
simple arithmetic average is recommended for calculating effective values of albedo.
These recommendations for determining surface characteristics supersede previous
recommendations and should be followed unless case-by-case justification can be provided for
an alternative method. The recommendations described above are briefly summarized below:
1. The determination of the surface roughness length should be based on an inverse-
distance weighted geometric mean for a default upwind distance of 1 kilometer relative to
the measurement site. Surface roughness length may be varied by sector to account for
variations in land cover near the measurement site; however, the sector widths should be
no smaller than 30 degrees.
2. The determination of the Bowen ratio should be based on a simple unweighted
geometric mean (i.e., no direction or distance dependency) for a representative domain,
with a default domain defined by a 10km by 10km region centered on the measurement
site.
3. The determination of the albedo should be based on a simple unweighted arithmetic
mean (i.e., no direction or distance dependency) for the same representative domain as
defined for Bowen ratio, with a default domain defined by a 10km by 10km region
centered on the measurement site.
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An important aspect of determining surface characteristics from digitized land cover data
is the assignment of surface characteristic values for each of the parameters (surface roughness,
Bowen ratio and albedo) to the land cover categories contained in the dataset. Several references
are available to guide those assignments, including the AERSURFACE User's Guide (EPA,
(2020), the AERMET User's Guide (EPA, 2021a), Garrett (1992), Gifford (1968), Oke (1978),
Randerson (1984), and Stull (1988). Due to the somewhat subjective nature of this process, and
the fact that specific land cover categories may include a wide range of values for some surface
characteristics, the methods and assumptions used to assign surface characteristics based on land
cover categories should be thoroughly documented and justified.
3.1.3 Use of AERSURFACE for determining surface characteristics (4/22/2021)
EPA has developed a tool called AERSURFACE (EPA, 2020) that can be used in
determining reproducible surface characteristic values, including albedo, Bowen ratio, and
surface roughness length, for input to AERMET. The current version of AERSURFACE,
version 20060, supports the use of land cover data from the USGS National Land Cover
Database (NLCD) including years representative of 1992, 2001, 2006, 2011, and 2016. The
NLCD provides land cover data at a spatial resolution of 30 meters. The 1992 NLCD is based
on a 21-category classification scheme applied consistently over the continental U.S. Beginning
with the 2001 NLCD, coverage of the NLCD was expanded to cover Alaska, Hawaii, and Puerto
Rico, in addition to the continental U.S. The NLCD for all years post-1992 are based on 16
categories with an additional four categories that are specific only to Alaska, for a total of 20
distinct categories. Further, some post-1992 years of the NLCD include separate data files of
percent impervious and percent canopy data which can be input to AERSURFACE to
supplement the land cover data. EPA recommends supplementing land cover data with both
percent impervious and percent canopy data when both are available. In cases where only one is
available or neither are available, EPA recommends processing the land cover data with
supplementation,
AERSURFACE incorporates look-up tables of representative surface characteristic
values by land cover category and seasonal category. Further details regarding application of the
AERSURFACE tool are provided in the AERSURFACE User's Guide (EPA, 2020). The
AERSURFACE tool incorporates the recommended methods for determining surface
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characteristics from digitized land cover data described in Section 3.1.2. While the
AERSURFACE tool is not currently considered to be part of the AERMOD regulatory modeling
system, the recommended methodology described in Section 3.1.2 should be followed unless
case-by-case justification can be provided for an alternative method. The methods described in
the AERSURFACE User's Guide for supplementing land cover data with percent impervious
and percent canopy where both data products are available, should also be followed.
Beginning with AERSURFACE version 20060, wind sectors used for determining
roughness can be specified individually as an airport or non-airport sector based on whether or
not a sector consists of large impervious areas represented by runways with grassy areas in
between or the impervious areas are primarily buildings or other structures. Impervious areas in
sectors specified as airport sectors will be more heavily weighted toward lower roughness values
than those that are specified as non-airport sectors. Consult the AERSURFACE User's Guide
(EPA, 2020) for additional information on defining wind sectors for determining surface
roughness.
3.2 Selecting upper air sounding levels (10/19/2007)
The AERMET meteorological processor requires full upper air soundings (radiosonde
data) representing the vertical potential temperature profile near sunrise in order to calculate
convective mixing heights. For AERMOD applications within the U.S., the early morning
sounding, nominally collected at 12Z (or UTC/GMT), is typically used for this purpose. Upper
air soundings can be obtained from the Radiosonde Data of North America CDs for the period
1946 through 1997, which are available for purchase from the National Climatic Data Center
(NCDC). Upper air soundings for the period 1994 to the present are also available for free
download from the Radiosonde Database Access website (http://raob.fsl.noaa.gov/).
Both of these sources of upper air data offer the following three options for specifying
which levels of upper air data to extract:
1) all levels,
2) mandatory and significant levels, or
3) mandatory levels only.
Options 1 and 2 are both acceptable and should provide equivalent results when
processed through AERMET. The use of mandatory levels only, Option 3, will not provide an
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adequate characterization of the potential temperature profile, and is not acceptable for
AERMOD modeling applications.
3.3 Processing site-specific meteorological data for urban applications (04/22/2021)
The use of site-specific meteorological data obtained from an urban setting may require
some special processing if the measurement site is located within the influence of the urban heat
island and site-specific turbulence measurements are available (e.g., go and/or ow). As discussed
in Section 5.4, the urban algorithms in AERMOD are designed to enhance the turbulence levels
relative to the nearby rural setting during nighttime stable conditions to account for the urban
heat island effect. Since the site-specific turbulence measurements will reflect the enhanced
turbulence associated with the heat island, site-specific turbulence measurements should not be
used when applying AERMOD's urban option, in order to avoid double counting the effects of
enhanced turbulence due to the urban heat island. If site-specific measurements are available for
the site, the user can choose to not include them in AERMET or they can be processed in
AERMET and the new turbulence options, NOTURB or NOTURBST can be used to ignore the
turbulence measurements. The NOTURB option will reset non-missing turbulence to missing
when reading the profile file for all hours and the NOTURBST option will reset the non-missing
turbulence values to missing for stable hours only, when the enhanced turbulence due to the
urban heat island is applied in the model. See Section 3.5.9 of the AERMOD User's Guide
(EPA, 2021b) for more information about the options
As also discussed in Section 5.4 of this document, the AERMOD urban option
(URB ANOPT) should be selected for urban applications, regardless of whether the
meteorological measurement site is in an urban setting since the limited surface meteorological
measurements available from the meteorological measurement program (even with measured
turbulence) will not adequately account for the meteorological characteristics of the urban
boundary layer included in the AERMOD urban algorithms.
3.4 Use of prognostic meteorological model data as inputs to AERMOD (04/17/2018)
In recent years, interest has grown in the use of prognostic meteorological data, such as
the Weather Research and Forecasting (WRF) model to create inputs for dispersion modeling
with AERMOD. This is especially true in locations where it can be difficult to find an
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adequately representative NWS station or cost-prohibitive or infeasible to set up a site-specific
meteorological monitoring tower. As part of the recent update to the Guideline on Air Quality
Models (EPA,2017), the use of prognostic data is allowed for regulatory applications of
AERMOD where it is cost-prohibitive or not feasible to collect site-specific data and there is no
representative NWD or comparable station nearby. EPA developed the Mesoscale Model
Interface Program, or MMIF for processing prognostic meteorological data for AERMOD
(Environ, 2014). For more information see Section 8.4.5 of the Guideline and the MMIF
guidance document (EPA, 2018b) that offers recommendations on the use of prognostic data and
MMIF.
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4.0 Terrain data and processing
4.1 Modeling sources with terrain-following plumes in sloping terrain (01/09/2008)
Under the regulatory default mode (DFAULT option on the MODELOPT keyword), for
all situations in which there is a difference in elevation between the source and receptor,
AERMOD simulates the total concentration as the weighted sum of 2 plume states (Cimorelli, el
al., 2004): 1) a horizontal plume state (where the plume's elevation is assumed to be determined
by release height and plume rise effects only, and thereby allowing for impingement if terrain
rises to the elevation of the plume); and, 2) a terrain-responding plume state (where the plume is
assumed to be entirely terrain following).
For cases in which receptor elevations are lower than the base elevation of the source
{i.e., receptors that are down-slope of the source), AERMOD will predict concentrations that are
less than what would be estimated from an otherwise identical flat terrain situation. While this is
appropriate and realistic in most cases, for cases of down-sloping terrain where expert judgment
suggests that the plume is terrain-following {e.g., down-slope gravity/drainage flow), AERMOD
will tend to underestimate concentrations when terrain effects are taken into account. AERMOD
may also tend to underestimate concentrations relative to flat terrain results for cases involving
low-level, non-buoyant sources with up-sloping terrain since the horizontal plume component
will pass below the receptor elevation. Sears (2003) has examined these situations for low-level
area sources and has shown that as terrain slope increases the ratio of estimated concentrations
from AERMOD to ISC (which assumes flat terrain for area sources) decreases substantially.
To avoid underestimating concentrations in such situations, it may be reasonable in cases
of terrain-following plumes in sloping terrain to apply the non-DFAULT option to assume flat,
level terrain. This determination should be made on a case-by-case basis, relying on the
modeler's experience and knowledge of the surrounding terrain and other factors that affect the
air flow in the study area, characteristics of the plume (release height and buoyancy), and other
factors that may contribute to a terrain-following plume, especially under worst-case
meteorological conditions associated with the source. The decision to use the non-DFAULT
option for flat terrain, and details regarding how it will be applied within the overall modeling
analysis, should be documented and justified in a modeling protocol submitted to the appropriate
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reviewing authority prior to conducting the analysis.
4.2 AERMAP DEM array and domain boundary (09/27/2005)
Section 2.1.2 of the AERMAP User's Guide (EPA, 2018a) states that the DEM array and
domain boundary must include all terrain features that exceed a 10% elevation slope from any
given receptor. The 10% slope rule may lead to excessively large domains in areas with
considerable terrain features (e.g., fjords, successive mountain ranges, etc). In these situations,
the reviewing authority may make a case-by-case determination regarding the domain size
needed for AERMAP to determine the critical dividing streamline height for each receptor.
4.3 Terrain elevation data sources for AERMAP (04/22/2021)
AERMAP was revised (beginning with version 09040) to support processing of terrain
elevations from the National Elevation Dataset (NED) developed by the U.S. Geological Survey,
which has transitioned to the USGS 3D Elevation Program (3DEP) (Sugarbaker, et al., 2017).
For the purposes of this guide, the terms NED and 3DEP will be used interchangeably.
AERMAP still supports terrain elevations in the DEM format, and has also been
enhanced to process DEM files of mixed format (e.g., 7.5-minute and 1-degree DEM files) in the
same run. AERMAP currently does not support processing of elevation data in both the DEM
format and the GeoTIFF format for NED data in the same run.
The USGS DEM archives are now static and will not be updated in the future, while the
NED data are being actively supported and checked for quality. Therefore, NED represents a
more up-to-date and improved resource for terrain elevations for use with AERMAP. Due to a
number of problems that have been encountered with DEM data, AERMOD users are
encouraged to use NED/3DEP. Problems encountered with DEM data include incorrect geo-
referencing information for entire DEM files and elevations that reflect the tops of buildings and
trees in some cases. The use of NED data should avoid these issues and provides additional
advantages over the use of DEM data, including consistent horizontal resolution and reference
datum (generally NAD83 or WGS84). Some applications of AERMAP using DEM data may
involve inconsistent reference datums for adjacent DEM files, which can result in receptors
being located within gaps between files due to the datum shift. Gaps may also occur within
DEM files generated by various software tools to convert from one format to another when a
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NAD conversion is involved, e.g., converting 1-degree DEM data to the 7.5-minute DEM format
to fill areas not covered by available 7.5-minute data. The AERMAP User's Guide (EPA,
2018a) provides a more detailed discussion of issues associated with gaps between DEM files or
within DEM files, and describes how these cases are handled by AERMAP.
While NED is considered an improvement in the quality and consistency of elevation
data for use with AERMAP, there are some issues associated with the GeoTIFF format
supported by AERMAP that users should be aware of. The main issue of importance to
AERMAP users is that the NED GeoTIFF files currently available from the USGS may not
include the GeoKey specifying the units for the elevation data. The USGS documentation for
NED data (USGS, 2002) indicates that elevations are in units of meters and are provided in
floating point format. AERMAP will therefore assume units of meters if the elevation units
GeoKey is absent. However, non-standard (i.e., non-USGS) NED data in GeoTIFF format may
not be in units of meters. AERMAP provides an option for users to specify elevation units in
these cases. However, users must exercise caution in using such data unless the correct units can
be confirmed. The AERMAP User's Guide (EPA, 2018a) provides a more detailed discussion of
these and other potential issues associated with the GeoTIFF format supported for NED data.
The NED elevation data are currently available for the conterminous United States,
Hawaii, Puerto Rico, and the Virgin Islands at a horizontal resolution of 1 arc-second
(approximately 30 meters), and at a resolution of 2 arc-seconds for Alaska. Higher resolution
NED elevation data at l/3rd arc-second (about 10 meters) are available for most areas outside of
Alaska, and even l/9th arc-second data (about 3 meters) are available for some areas. These
higher resolution data may become more widely available in the future. The appropriate
horizontal resolution for the input terrain data and receptor network should be determined in
consultation with the reviewing authority based on the specific needs of the project. Higher
resolutions for both the terrain data and receptor network may be necessary in areas with
significant terrain relief than for areas with relatively flat terrain. While acceptable, using the
highest resolution elevation data available for determining receptor elevations and hill height
scales may not always be justified. Since spatial coverage of terrain data for some resolutions
may not be complete, it is also worth noting that use of a single resolution across the domain has
advantages, and AERMAP places some restrictions on the order of DEM or NED file inputs
when mixed resolution data are used.
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Regardless of the receptor and terrain data resolutions used in AERMAP, it is advisable
to check the accuracy of receptor elevations and hill height scales being input to AERMOD for
significant terrain features that are likely to be associated with peak concentrations based on
proximity and elevation in relation to the sources. Elevations for fenceline or other nearby
receptors located within areas that have been altered due to facility construction may require
special consideration since these changes in local topography may not be reflected in the USGS
terrain files. Use of receptor elevations derived from plant survey data may be an acceptable
alternative in these cases. The option available in AERMAP for the user to provide elevations
may be utilized to determine hill height scales for these special cases, rather than the default
option of determining elevations and hill height scales based on the input terrain data. However,
care should be exercised to ensure that the hill height scales determined by AERMAP are also
representative of the modified topography. If alternative data sources and/or methods are used to
estimate receptor elevations, users must recognize that receptor elevations input to AERMOD
should represent the best estimate of the actual terrain elevation at the receptor location. Use of a
"conservative" estimate of the maximum elevation in the vicinity of the receptor location, such
as the maximum within a "grid cell" centered on the receptor, is not appropriate for use in
AERMOD based on the formulation of the terrain algorithms in the model, and may not result in
a conservative estimate of concentrations.
Beginning with the version dated 09040, AERMAP can also process terrain elevations
derived from the Shuttle Radar Topography Mission (SRTM) data provided they are stored as
GeoTIFF files that are compatible with AERMSP. NED data represents the ground ("bare
earth") elevation, which is a more appropriate input for determining receptor elevations and hill
height scales for use in AERMOD. AERMOD users should therefore avoid the use of SRTM
data to determine elevations for use in AERMOD. However, SRTM data are available for most
of the globe, and may be the only practical alternative for applications beyond the U. S. While
AERMAP can process both NED and SRTM data in GeoTIFF format in the same run, the only
situation that might warrant such an approach would be applications along a border that extends
beyond the domain covered by the NED data. The SRTM elevation data are typically based on
the WGS84 horizontal datum, rather than the NAD83 datum used for most NED data. While
AERMAP treats the WGS84 and NAD83 datums as equivalent, AERMAP will issue a warning
message for any terrain file input as NED data that is not in the NAD83 datum to flag the
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possibility that non-NED data (or non-standard NED data) are being used.
Given the number of options available for elevation data inputs to AERMAP, and the
range of issues associated with elevation data, users are encouraged to clearly document the
source of elevation data used for AERMOD applications in the modeling protocol, including the
resolution and horizontal reference datum for the data and any pre-processing that might have
been done, such as converting from one format to another. Since the NED data are being
checked for quality and updated as needed, AERMAP users should also consider acquiring
updated terrain files on a periodic basis before use in regulatory modeling applications. If the
option to provide receptor elevations to AERMAP is utilized, rather than using the default option
of determining elevations based on the input terrain data, the sources and methods used to
determine the provided elevations should be clearly documented along with a justification for
use of that option.
As changes have occurred over time regarding availability of the elevation data and file
formats provided by the USGS, refer to the AERMAP User's Guide (EPA, 2018a) and the EPA
SCRAM website (https://www.epa.gov/scram) for current information on data coverage,
resolution, format, and access to elevation data. Additional information on NED/3DEP data
products and specifications can be found at the USGS National Map website at
https://www.usgs.gov/core-science-svstems/ngp/3dep.
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4.4 Manually entering terrain elevations in AERMAP (03/19/2009)
AERMAP currently does not have the capability of accepting hand-entered terrain data in
an "xyz" format. AERMAP only accepts terrain data from digitized elevation files in the DEM
or NED/GeoTIFF formats. Therefore, if no DEM or NED/GeoTIFF data are available for a
particular application, terrain elevations may need to be determined through other means. One
option may be to manually enter gridded terrain elevations in a form that mimics the DEM data
format. Instructions for how to accomplish this can be found on the SCRAM web site
https://www.epa.gov/scramin a document titled "On inputting XYZ data into AERMAP." As
noted in Section 4.3, if alternative sources and/or methods are used to estimate receptor
elevations, users must recognize that receptor elevations input to AERMOD should represent the
best estimate of the actual terrain elevation at the receptor location, and these alternative sources
and methods should be documented in the modeling protocol. As also noted in Section 4.3,
SRTM elevation data in GeoTIFF format is available for most of the globe, which may provide
another alternative source of elevation data for use in AERMAP. However, SRTM data
represents the heights of obstacles, such as buildings and trees, rather than ground elevations,
and should be used with caution and only as a last resort.
4.5 Use of AERMAP to determine source elevations (03/19/2009)
AERMAP includes the capability of estimating terrain elevations for sources based on the
same data and procedures used to estimate receptor elevations. However, the requirements for
determining source elevations are somewhat different than the requirements for determining
receptor elevations since a greater emphasis is placed on the accuracy of elevations at specific
locations in the case of sources. While the accuracy of specific receptor elevations is also
important, the main focus for receptors should be on how well the terrain features are defined by
the receptor network as a whole, which is based on both the accuracy of the terrain data and the
horizontal resolution of the receptor network. As noted in Section 4.3, it is advisable to check
the accuracy of receptor elevations and hill height scales for significant terrain features that are
likely to be associated with peak concentrations. These accuracy checks should also account for
the relative elevation differences between the source and receptor since that will determine the
elevation of the plume in relation to the terrain.
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Given the issues and uncertainties associated with estimating the elevation at a specific
location, and the potential sensitivity of AERMOD model results to differences in the relative
elevations of sources and nearby receptors, users are discouraged from relying solely on
AERMAP-derived source elevations in regulatory applications of AERMOD, especially for
emission sources within the facility being permitted. These concerns are particularly important
with newer facilities since regrading associated with construction of the facility may not be
reflected in the digitized terrain data. Source elevations based on a reliable plant survey are
generally considered to be the preferred option. If AERMAP-derived source elevations are used
for the permitted facility, then some effort should be made to verify the accuracy of the
elevations based on other reliable information, such as up-to-date topographic maps, taking into
account adjustments for the horizontal datum if necessary. Use of AERMAP-derived elevations
for other background sources included in the modeled inventory is generally of less concern than
their use for the permitted facility, depending on the complexity of the terrain and the distances
between sources within the modeled inventory. To facilitate proper review, the modeling
protocol should clearly document the data and method(s) used to determine source elevations for
input to AERMOD.
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5.0 Urban applications
5.1 Urban/rural determination (08/03/2015)
The URBANOPT keyword on the CO pathway in AERMOD, coupled with the
URBANSRC keyword on the SO pathway, should be used to identify sources to be modeled
using the urban algorithms in AERMOD (EPA, 2021b). To account for the dispersive nature of
the "convective-like" boundary layer that forms during nighttime conditions due to the urban
heat island effect, AERMOD enhances the turbulence for urban nighttime conditions over that
which is expected in the adjacent rural, stable boundary layer, and also defines an urban
boundary layer height to account for limited mixing that may occur under these conditions. The
magnitude of the urban heat island effect is driven by the urban-rural temperature difference that
develops at night. AERMOD currently uses the population input on the URBANOPT keyword
as a surrogate to define the magnitude of this differential heating effect. Details regarding the
adjustments in AERMOD for the urban boundary layer are provided in Section 5. 8 of the
AERMOD model formulation document (Cimorelli, el a/., 2004).
Section 7.2.2.1 of the Guideline on Air Quality Models (EPA, 2017) provides the basis
for determining the urban/rural status of a source. For most applications the Land Use Procedure
described in Section 7.2.3(c) is sufficient for determining the urban/rural status. However, there
may be sources located within an urban area, but located close enough to a body of water or to
other non-urban land use categories to result in a predominately rural land use classification
within 3 kilometers of the source following that procedure. Users are, therefore, cautioned
against applying the Land Use Procedure on a source-by-source basis but should also consider
the potential for urban heat island influences across the full modeling domain. Furthermore,
Section 7.2.3(f) of Appendix W recommends modeling all sources within an urban complex
using the urban option even if some sources may be defined as rural based on the procedures
outlined in Section 7.2.3. Such an approach is consistent with the fact that the urban heat island
is not a localized effect but is more regional in character.
Another aspect of the urban/rural determination that may require special consideration on
a case-by-case basis relates to tall stacks located within or adjacent to small to moderate size
urban areas. In such cases, the stack height, or effective plume height for very buoyant plumes,
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may extend above the urban boundary layer height. The urban boundary layer height, ziuc, can be
calculated from the population input on the URBANOPT keyword, I\ based on Equation 104 of
the AERMOD formulation document (Cimorelli, etal, 2004):
AP/Pj" (1)
where ziu0 is the reference height of 400 meters corresponding to the reference population, P0, of
2,000,000.
Prior to version 15181 of AERMOD, application of the urban option for these types of
sources may have artificially limited the plume height resulting in anomalously high
concentrations. Use of the urban option may not have been appropriate for such sources, since
the actual plume was likely to be transported over the urban boundary layer and not be affected
by urban enhanced dispersion. However, the potential for such anomalous results has been
mitigated beginning with version 15181 of AERMOD, which has incorporated a formulation bug
fix that modifies the treatment of plume rise for urban sources. Beginning with version 15181,
AERMOD emulates the plume rise approach used for penetrated plumes during convective
conditions if the initial plume height estimate is greater than or equal to the urban mixing height.
With the introduction of this formulation bug fix in version 15181 of AERMOD a more thorough
case-specific justification will be needed, in consultation with the appropriate reviewing
authority, to support excluding these elevated sources from application of the urban option.
5.2 Selecting population data for AERMOD's urban mode (10/19/2007)
For relatively isolated urban areas, the user may use published census data corresponding
to the Metropolitan Statistical Area (MSA) for that location. For urban areas adjacent to or near
other urban areas, or part of urban corridors, the user should attempt to identify that part of the
urban area that will contribute to the urban heat island plume affecting the source(s). If this
approach results in the identification of clearly defined MSAs, then census data may be used as
above to determine the appropriate population for input to AERMOD. Use of population based
on the Consolidated MSA (CMSA) for applications within urban corridors is not recommended,
since this may tend to overstate the urban heat island effect.
For situations where MSAs cannot be clearly identified, the user may determine the
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extent of the area, including the source(s) of interest, where the population density exceeds 750
people per square kilometer. The combined population within this identified area may then be
used for input to the AERMOD model. Users should avoid using a very fine spatial resolution of
population density for this purpose as this could result in significant gaps within the urban area
due to parks and other unpopulated areas, making it more difficult to define the extent of the
urban area. Population densities by census tract should provide adequate resolution in most
cases and may still be finer resolution than desired in some cases. Since census tracts vary in
size and shape, another acceptable approach would be to develop gridded estimates of population
data based on census block or block group data. In such cases, a grid resolution on the order of 6
kilometers is suggested. Plotting population density with multiple "contour" levels, such as 0-
500, 500-750, 750-1000, 1000-1500, etc., may also be beneficial in identifying which areas near
the edge of the urban complex to include even though the population density may fall below the
750 threshold. The user should also bear in mind that the urban algorithms in AERMOD are
dependent on population to the one-fourth power and are therefore not highly sensitive to
variations in population. Population estimates to two significant figures should be sufficiently
accurate for application of AERMOD.
5.3 Optional urban roughness length - URBANOPT keyword (10/19/2007)
The URBANOPT keyword on the CO pathway in AERMOD (EPA, 2021b) includes an
optional parameter to specify the urban surface roughness length. The urban surface roughness
parameter is used to define a reference height for purposes of adjusting dispersion for surface
and low-level releases to account for the enhanced turbulence associated with the nighttime
urban heat island. This optional urban roughness length is not used to adjust for differences in
roughness length between the meteorological measurement site, used in processing the
meteorological data, and the urban application site. Details regarding the adjustments in
AERMOD for the urban boundary layer, including the use of the urban roughness length
parameter, are provided in Section 5. 8 of the AERMOD model formulation document
(Cimorelli, etal., 2004).
The default value of 1 meter for urban surface roughness length, assumed if the
parameter is omitted, is considered appropriate for most applications. Any application of
AERMOD that utilizes a value other than 1 meter for the urban roughness length should be
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considered as a non-regulatory application, and would require appropriate documentation and
justification as an alternative model, subject to Section 3.2 of the Guideline on Air Quality
Models (EPA, 2017). The use of a value other than 1 meter for the urban surface roughness
length will be explicitly treated as a non-DFAULT option in the next update to the AERMOD
model.
5.4 Meteorological data selections for urban applications (01/09/2008)
5.4.1 Urban applications using NWS meteorological data (01/09/2008)
When modeling urban sources, the urban algorithms in AERMOD are designed to
enhance the turbulence levels relative to the nearby rural setting during nighttime stable
conditions to account for the urban heat island effect (Cimorelli, el al., 2004). For urban
applications using representative NWS meteorological data the AERMOD urban option
(URBANOPT) should be selected (EPA, 2021b), regardless of whether the NWS site is located
in a nearby rural or an urban setting. This is because the limited surface meteorological
measurements available from NWS stations will not account for the enhanced turbulence or
other meteorological characteristics of the urban boundary layer included in the AERMOD urban
algorithms. The determination of surface characteristics for processing NWS meteorological
data for urban applications should conform to the recommendations presented in Section 3.1.
5.4.2 Urban applications using site-specific meteorological data (01/09/2008)
In most cases, site-specific meteorological data used for urban applications should be
treated in a manner similar to NWS data described in Section 5.4.1, regardless of whether the
measurement site is located in a nearby rural or an urban setting. That is, the AERMOD urban
option should be selected and the surface characteristics should be determined based on the
recommendations in Section 3.1. This is due to the fact that the limited surface meteorological
measurements available from the meteorological measurement program will not adequately
account for the meteorological characteristics of the urban boundary layer included in the
AERMOD urban algorithms. However, if the measurement site is located in an urban setting
and site-specific turbulence measurements are available (e.g., go or ow), some adjustments to the
meteorological data input to AERMOD may be necessary, as discussed in Section 3.3.
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6.0 Source characterization
6.1 Capped and horizontal stacks (12/20/2016)
Beginning with version 16216, AERMOD includes regulatory options for modeling
capped and horizontal stacks using the POINTCAP and POINTHOR source types, respectively.
For capped and horizontal sources that are not subject to building downwash, the options are
consistent with the approach that was approved by the Model Clearinghouse in July 1993 (see
Appendix A). This approach uses an effective stack diameter to maintain the flow rate to
maintain plume buoyancy, while suppressing plume momentum by setting the exit velocity to
0.001 m/s. For capped and horizontal sources that are subject to building downwash, the options
have been adapted to account for the PRIME algorithm. Since the PRIME component in
AERMOD incorporates a numerical plume rise algorithm that simulates the full trajectory of the
plume, AERMOD sets the initial plume trajectory angle as horizontal for the POINTHOR
option. For the POINTCAP option, AERMOD assigns the initial diameter of the plume to be 2
times the actual stack diameter to account for initial spread of the plume associated with the cap.
AERMOD also assigns the initial horizontal velocity of the plume for the POINTCAP option to
be the initial exit velocity specified by the user divided by 4.
6.2 Use of area source algorithm in AERMOD (09/27/2005)
Because of issues related to excessive run times and technical issues with model
formulation, the approach that AERMOD uses to address plume meander has not been
implemented for area sources. As a result, concentration predictions for area sources may be
overestimated under very light wind conditions (i.e., u « 1.0 m/s). In general, this is not
expected to be a problem for meteorological data collected using standard wind instruments
since instrument thresholds are generally too high. However, the problem could arise with
meteorological data derived from very low threshold instruments, such as sonic anemometers.
While not currently accepted for regulatory applications of AERMOD, this problem has also
arisen when data from a gridded meteorological model was used to drive AERMOD.
Meteorological grid models can at times produce extremely light winds. During such conditions
time-averaged plumes tend to spread primarily as a result of low frequency eddy translation
rather than eddy diffusion. AERMOD treats this meander effect by estimating the concentration
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from two limiting states: 1) a coherent plume state that considers lateral diffusive turbulence only
(the mean wind direction is well defined) and 2) a random plume state (mean wind direction is
poorly defined) that allows the plume to spread uniformly, about the source, in the x-y plane.
The final concentration predicted by AERMOD is a weighted sum of these two bounding
concentrations. Interpolation between the coherent and random plume concentrations is
accomplished by assuming that the total horizontal "energy" is distributed between the wind's
mean and turbulent components.
In order to avoid overestimates for area sources during light wind conditions, it is
recommended that, where possible, a volume source approximation be used to model area
sources. This approach can be applied with confidence for situations in which the receptors are
displaced from the source. However, for applications where receptors are located either directly
adjacent to, or inside the area source, AERMOD's area source algorithm will need to be used.
For these circumstances, caution should be exercised if excessive concentrations are predicted
during extremely light wind conditions. On a case-by-case basis, the reviewing authority should
decide whether such predictions are unrealistic. One possible remedy would be to treat such
hourly predictions as missing data.
It is EPA's intention to correct this problem. A version of AERMOD that includes
meander for area sources will be developed as soon as practicable.
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7.0 Interim Guidance on Model Application
This section contains information on known model implementation issues (e.g., coding
bugs or issues that limit or prevent how the model can be run for particular applications) and
how to address these issues to successfully apply the model. This section may be updated
periodically with new information as implementation issues are identified and sections removed
when issues are addressed by updates to the model.
7.1 Guidance on NO2 Background Concentrations with PVMRM (7/26/2021)
A coding bug was discovered in AERMOD version 21112 when providing background
concentrations of NO2 when using the Tier 3 Plume Volume Molar Ratio Method (PVMRM) for
NO2 conversion. In this case, the background concentrations of NO2 provided via the AERMOD
input control file are added to the internal modeled concentrations twice, resulting in final
modeled concentrations that are too high. When using the PVMRM option to model NO2
concentrations, users should input background concentrations of NO2 that are one-half of the
amount of the actual background concentration. This bug does not affect any of the other NO2
conversion options (i.e., ARM2, OLM, GRSM, or TTRM). Actual NO2 background
concentrations should be entered when using any of these other NO2 conversion options.
7.2 Guidance on BUOYLINE Source Types (7/26/2021)
A coding bug was discovered in AERMOD version 21112 related to error handling
which in some circumstances AERMOD will complete processing without an error but
concentrations for BUOYLINE source types will not be generated. This can occur when one or
more BUOYLINE sources are defined and the required BLPINPUT keyword is omitted, as well
as the BLPGROUP keyword. When all BUOYLINE source types defined in the input control
file are considered part of a single BLPGROUP, the BLPGROUP keyword is not required, and
the BLPGrpID is an optional parameter for the BLPINPUT keyword. However, the BLPINPUT
keyword is required. If the BLPINPUT keyword is also omitted, AERMOD will appear to
complete successfully, but concentrations will not be generated for the BUOYLINE source types
since the BUOYLINE source characteristics have not been specified via the BLPINPUT
keyword and parameters.
When modeling BUOYLINE source types, EPA recommends that the user include the
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BLPGrpID with the BLPINPUT keyword even when all BUOYLINE source types are modeled
as a single BLPGROUP, and assign each BUOYLINE source type to a BLPGROUP via the
BLPGrpID parameter with the BLPGROUP keyword.
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8.0 REFERENCES
Blom, J. and L. Wartena, 1969. The Influence of Changes in Surface Roughness on the
Development of the Turbulent Boundary Layer in the Lower Layers of the Atmosphere. J.
Atmos. Sci., 26, 255-265.
Businger, J. A., 1986. Evaluation of the Accuracy with Which Dry Deposition Can Be Measured
with Current Micrometeorological Techniques. J. Climate Appl. Meteor.,25, 1100-1124.
Cimorelli, A. J., S. G. Perry, A. Venkatram, J. C. Weil, R. J. Paine, R. B. Wilson, R. F. Lee, W.
D. Peters, R. W. Brode, and J. O. Paumier, 2004. AERMOD: Description of Model
Formulation, EPA-454/R-03-004. U.S. Environmental Protection Agency, Research
Triangle Park, NC.1
EPA, 2000. Meteorological Monitoring Guidance for Regulatory Modeling Applications.
Publication No. EPA-454/R-99-005. Office of Air Quality Planning & Standards,
Research Triangle Park, NC. (PB 2001-103606) 1
EPA, 2005a. Revision to the Guideline on Air Quality Models: Adoption of a Preferred General
Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule.
40 Federal Register, Volume 70, Page 68218
EPA, 2020. User's Guide for AERSURFACE Tool. EPA-454/B-20-008. U.S. Environmental
Protection Agency, Research Triangle Park, NC.1
EPA, 2017. Guideline on Air Quality Models. 40 CFR Part 51 Appendix W.1
EPA, 2018a: User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA-454/B-18-
004. U.S. Environmental Protection Agency, Research Triangle Park, NC.1
EPA, 2018b: Guidance on the Use of the Mesoscale Model Interface Program (MMIF) for
AERMOD Applications. EPA-454/B-18-005. U.S. Environmental Protection Agency,
Research Triangle Park, NC.1
EPA, 2021a: User's Guide for the AERMOD Meteorological Preprocessor (AERMET). EPA-
454/B-21-004. U.S. Environmental Protection Agency, Research Triangle Park, NC.1
1 (Available at https://www.epa.gov/scram/)
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EPA, 2021b: User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA-454/B-21-
001. U.S. Environmental Protection Agency, Research Triangle Park, NC.1
Garratt, J. R., 1992: The Atmospheric Boundary Layer. Cambridge University Press, New York,
New York, 334pp.
Gifford, F.A., 1968: "An Outline of Theories of Diffusion in the Lower Layers of the
Atmosphere," in Meteorology and Atomic Energy, ed., D.H. Slade. Division of Technical
Information, U.S. Atomic Energy Commission, Springfield, VA, 445pp.
Hogstrom, A., and U. Hogstrom, 1978. A Practical Method for Determining Wind Frequency
Distributions for the Lowest 200 m from Routine Data. J. Appl. Meteor., 17, 942-954.
Horst, T.W., and J.C. Weil, 1994. How Far is Far Enough?: The Fetch Requirements for
Micrometeorological Measurement of Surface Fluxes. J. Atmos. and Oceanic Tech., 11,
1018-1025.
Irwin, J.S., 1978. Proposed Criteria for Selection of Urban Versus Rural Dispersion Coefficients.
(Draft Staff Report), Meteorology and Assessment Division, U.S. Environmental
Protection Agency, Research Triangle Park, NC. (Docket No. A-80-46, II-B-8).
Oke, T. R., 1978: Boundary Layer Climates. John Wiley and Sons, New York, New York,
372pp.
Randerson, D., 1984, "Atmospheric Boundary Layer," in Atmospheric Science and Power
Production, ed., D. Randerson. Technical Information Center, Office of Science and
Technical Information, U.S. Department of Energy, Springfield, VA, 850pp.
Rao, K.S., J.C. Wyngaard, and O.R. Cote, 1974. The Structure of the Two-Dimensional Internal
Boundary Layer over a Sudden Change in Surface Roughness. J. Atmos. Sci., 31, 738-746.
Sears, C., 2003. Letter to Docket No. A-99-05 Availability of Additional Documents Relevant to
Anticipated Revisions to Guideline on Air Quality Models Addressing a Preferred General
Purpose (flat and complex terrain) Dispersion Model and Other Revisions (Federal Register
/ Vol. 68, No. 173 / Monday, September 8, 2003).
Stull, R. B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic
Publishers, The Netherlands, 666pp.
Sugarbaker, L.J., Eldridge, D.F., Jason, A.L., Lukas, Vicki, Saghy, D.L., Stoker, J.M., and
Thunen, D.R., 2017: Status of the 3D Elevation Program, 2015: U.S. Geological Survey
Open-File Report 2016-1196, 13 p., http://dx.doi.org/10.3133/ofr20161196.
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Taylor, P.A., 1969. The Planetary Boundary Layer above a Change in Surface Roughness. J.
Atmos. Sci., 26, 432-440.
U.S. Geological Survey, 2002: The National Map - Elevation, Fact Sheet 106-02 (November
2002). http://egsc.usgs.gov/isb/pubs/factsheets/fsl0602.html U.S. Department of the
Interior, Reston, Virginia 20192.
U.S. Geological Survey, 2009: Shuttle Radar Topography Mission DTED,
http://edc.usgs.gov/products/elevation/srtmdted.html U.S. Department of the Interior,
Reston, Virginia 20192.
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Appendix A. EPA Model Clearinghouse memorandum dated July 9,1993
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July 9, 1993
MEMORANDUM
SUBJECT: Proposal for Calculating Plume Rise for Stacks with Horizontal
Releases or Rain Caps for Cookson Pigment, Newark, New Jersey
FROM: Joseph A. Tikvart, Chief
Source Receptor Analysis Branch, TSD (MD-14)
TO: Ken Eng, Chief
Air Compliance Branch, Region II
In response to your request, the Model Clearinghouse has reviewed your
proposal for treating horizontal and capped stacks at Cookson Pigment so
that the model (SCREEN or ISC2) will properly treat plume rise from the
Cookson Pigment stacks. We concur in principle with the approach, with
some relatively minor changes.
First, the analysis provided by New Jersey Department of Environmental
Protection is technically correct. We suggest, however, that the exit
velocity for horizontal and capped stacks be set to a lower figure than 0.1
m/s. A 0.1 m/s exit velocity may still result in significant momentum
plume rise being calculated, even though these kinds of sources should have
zero momentum rise. We therefore suggest setting the stack exit velocity
to a lower value, such as .001.
For horizontal stacks that are not capped, we suggest turning stack
tip downwash off, whether there are buildings or not. Stack tip downwash
calculations are inappropriate for horizontal stacks.
For vertical stacks that are capped, turn stack tip downwash off and
reduce the stack height by three times the actual stack diameter. The cap
will probably force stack tip downwash most of the time. The maximum
amount of the stack tip downwash (as calculated in ISC2) is three times the
stack diameter. Reducing the stack height by this amount, while turning
off the stack tip downwash option, causes the maximum stack tip downwash
effect. The resulting concentrations may err slightly on the high side.
For stacks with small diameters, such as those at Cookson Pigment, the
error should be quite small. Note, however, that this approach may not be
valid for large diameter stacks (say, several meters).
cc: A. Colecchia
D. Wilson
31
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/B-21-006
Environmental Protection Air Quality Assessment Division July 2021
Agency Research Triangle Park, NC
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