AERMOD IMPLEMENTATION GUIDE 

Last Revised: January 9, 2008 

AERMOD Implementation Workgroup 

U. S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, North Carolina 
ii 

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.
iii 

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. 
iv 

CONTENTS 

PREFACE.....................................................................................................................................................ii

ACKNOWLEDGMENTS ...........................................................................................................................iii

1.0 WHAT’S NEW IN THIS DOCUMENT..............................................................................................1

2.0 DOCUMENT BACKGROUND AND PURPOSE ..............................................................................2

2.1 BACKGROUND (10/19/07) .......................................................................................................2

2.2 PURPOSE (10/19/07)..................................................................................................................2

3.0 METEOROLOGICAL DATA AND PROCESSING ..........................................................................3

3.1 DETERMINING SURFACE CHARACTERISTICS (01/09/08) ...............................................3

3.1.1 Meteorological data representativeness considerations (01/09/08).................................................3

3.1.2 Methods for determining surface characteristics (01/09/08)...........................................................4

3.1.3 Use of AERSURFACE for determining surface characteristics (01/09/08) ...................................7

3.2 SELECTING UPPER AIR SOUNDING LEVELS (10/19/07)...................................................7

3.3 PROCESSING SITE-SPECIFIC METEOROLOGICAL DATA FOR URBAN 

APPLICATIONS (01/09/08) ......................................................................................................8

4.0 TERRAIN DATA AND PROCESSING..............................................................................................9

4.1 MODELING SOURCES WITH TERRAIN-FOLLOWING PLUMES IN SLOPING 

TERRAIN (01/09/08) .................................................................................................................9

4.2 AERMAP DEM ARRAY AND DOMAIN BOUNDARY (09/27/05) .......................................9

4.3 MANUALLY ENTERING TERRAIN ELEVATIONS IN AERMAP (09/27/05)...................10

5.0 URBAN APPLICATIONS.................................................................................................................11

5.1 URBAN/RURAL DETERMINATION (10/19/07)...................................................................11

5.2 SELECTING POPULATION DATA FOR AERMOD’S URBAN MODE (10/19/07) ...........12

5.3 OPTIONAL URBAN ROUGHNESS LENGTH – URBANOPT KEYWORD (10/19/07)......12

5.4 METEOROLOGICAL DATA SELECTIONS FOR URBAN APPLICATIONS (01/09/08)...13

5.4.1 Urban applications using NWS meteorological data (01/09/08)...................................................13

5.4.2 Urban applications using site-specific meteorological data (01/09/08) ........................................13

6.0 SOURCE CHARACTERIZATION...................................................................................................14

6.1 CAPPED AND HORIZONTAL STACKS (09/27/05) .............................................................14

6.2 USE OF AREA SOURCE ALGORITHM IN AERMOD (09/27/05).......................................14

7.0 REFERENCES...................................................................................................................................16
AERMOD Implementation Guide January 9, 2008

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1.0 WHAT’S NEW IN THIS DOCUMENT 

Revisions dated January 9, 2008:

The following sections have been affected by this revision: 

3.1 DETERMINING SURFACE CHARACTERISTICS 

This section includes an expanded discussion and updated recommendations regarding the 

determination of surface characteristics to be used in processing meteorological data for 

AERMOD. A discussion of the AERSURFACE tool has also been included. The previous 

Section 3.1.1 has been removed, and recommendations regarding meteorological data 

selections for urban applications in the previous Sections 3.1.2 to 3.1.4 have been moved to 

Sections 3.3 and 5.4. 

3.3 PROCESSING SITE-SPECIFIC METEOROLOGICAL DATA FOR URBAN 

APPLICATIONS 

This new section contains clarified recommendations regarding processing of site-specific 

meteorological data for urban applications, formerly contained in Section 3.1.4. 

4.1 MODELING SOURCES WITH TERRAIN-FOLLOWING PLUMES IN SLOPING 

TERRAIN 

This section has been revised to address the issue of modeling terrain-following plumes in 

up- and down-sloping terrain more generically, since the issue is not strictly limited to 

gently down-sloping terrain. 

5.4 METEOROLOGICAL DATA SELECTIONS FOR URBAN APPLICATIONS 

This new section contains clarified recommendations regarding meteorological data 

selections for urban applications, formerly contained in Section 3.1. 
AERMOD Implementation Guide January 9, 2008

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2.0 DOCUMENT BACKGROUND AND PURPOSE 

2.1 BACKGROUND (10/19/07) 

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 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 (10/19/07) 

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. This is located in Section 1 of the document for use as a quick 

reference. Each section is also identified with the date (mm/dd/yy) 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 or to correct typographical errors. 

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, 2005b). 

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. 
AERMOD Implementation Guide January 9, 2008

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3.0 METEOROLOGICAL DATA AND PROCESSING 

3.1 DETERMINING SURFACE CHARACTERISTICS (01/09/08) 

When applying the AERMET meteorological processor (EPA, 2004a) to prepare the 

meteorological data for the AERMOD model (EPA, 2004b), the user must determine appropriate 

values for three surface characteristics: surface roughness length {zo}, 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 (01/09/08) 

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., zo, Bo 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. Sitespecific 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 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 zo for one application may 

translate into an unacceptable difference in the design concentration, while for another 

application the same difference in zo 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. 
AERMOD Implementation Guide January 9, 2008

4 

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 (01/09/08) 

Several sources of data may be utilized in determining appropriate surface characteristics for use 

in processing meteorological data for AERMOD. This may include printed topographic and land 

use, land cover (LULC) maps available from the U. S. Geological Survey (USGS), aerial photos 

from web-based services, site visits and/or site photographs, and digitized databases of land use 

and land cover data available from USGS. A sound understanding of the important physical 

processes represented in the AERMOD model algorithms and the sensitivity of those algorithms 

to surface characteristics is needed in order to properly interpret the available data and make an 

appropriate determination. The temporal representativeness of the source(s) of land cover data 

used relative to the meteorological data period to be processed should be considered as part of 

this assessment. 

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. While the following 

discussion offers specific recommendations regarding the methods for determining surface 

characteristics from digitized land cover data, the general principles on which these 

recommendations are based are also applicable to determining surface characteristics from other 

sources of non-digitized land use and land cover data. 

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), Högström and 

Högström (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 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 
AERMOD Implementation Guide January 9, 2008

5 

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 meteorological tower 

location. 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 surface 

roughness is provided in the AERMET user’s guide (EPA, 2004a). 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 
AERMOD Implementation Guide January 9, 2008

6 

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 the majority 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. 

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 further 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 in order 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(zo). Note that the arithmetic average of 

the ln(zo) is mathematically equivalent to the geometric mean of zo. 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 inversedistance 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 
AERMOD Implementation Guide January 9, 2008

7 

defined for Bowen ratio, with a default domain defined by a 10km by 10km region 

centered on the measurement site. 

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 Sections 4.7.7 and 5.4 of the AERMET user’s 

guide (EPA, 2004a), 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 (01/09/08) 

EPA has developed a tool called AERSURFACE (EPA, 2008) that can be used as an aid in 

determining realistic and reproducible surface characteristic values, including albedo, Bowen 

ratio, and surface roughness length, for input to AERMET, the meteorological processor for 

AERMOD. The current version of AERSURFACE supports the use of land cover data from the 

USGS National Land Cover Data 1992 archives (NLCD92). The NLCD92 archive provides 

land cover data at a spatial resolution of 30 meters based on a 21-category classification scheme 

applied consistently over the continental U.S. 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, 2008). 

The AERSURFACE tool incorporates the recommended methods for determining surface 

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, i.e. the use of AERSURFACE is not required for regulatory applications of AERMOD, 

the recommended methodology described in Section 3.1.2 should be followed unless case-bycase justification can be provided for an alternative method. 

3.2 SELECTING UPPER AIR SOUNDING LEVELS (10/19/07) 

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: 
AERMOD Implementation Guide January 9, 2008

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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 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 (01/09/08) 

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., σθ and/or σw). 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. 

As also discussed in Section 5.4, the AERMOD urban option (URBANOPT) should be selected 

for urban applications, regardless of whether the meteorological measurement site is located in 

an urban setting. This is due to the fact that 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. 
AERMOD Implementation Guide January 9, 2008

9 

4.0 TERRAIN DATA AND PROCESSING 

4.1 MODELING SOURCES WITH TERRAIN-FOLLOWING PLUMES IN SLOPING 

TERRAIN (01/09/08) 

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, et 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 

reviewing authority prior to conducting the analysis. 

4.2 AERMAP DEM ARRAY AND DOMAIN BOUNDARY (09/27/05) 

Section 2.1.2 of the AERMAP User’s Guide (EPA, 2004c) 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. 
AERMOD Implementation Guide January 9, 2008

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4.3 MANUALLY ENTERING TERRAIN ELEVATIONS IN AERMAP (09/27/05) 

AERMAP currently does not have the capability of accepting hand-entered terrain data (xyz 

data). AERMAP can accept terrain data from DEM files only. Therefore, if DEM data is not 

available, for a particular application, terrain elevations will need to be entered manually in a 

form that mimics the DEM data format. Instructions for how to accomplish this can be found on 

the SCRAM web site http://www.epa.gov/scram001/ in a document titled “On inputting XYZ 

data into AERMAP.” 
AERMOD Implementation Guide January 9, 2008

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5.0 URBAN APPLICATIONS 

5.1 URBAN/RURAL DETERMINATION (10/19/07) 

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, 2004b). To account for the dispersive nature of the “convectivelike” 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, et al., 2004). 

Section 7.2.3 of the Guideline on Air Quality Models (EPA, 2005b) 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 caseby-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, may 

extend above the urban boundary layer height. Application of the urban option in AERMOD for 

these types of sources may artificially limit the plume height. Therefore, use of the urban option 

may not be appropriate for these sources, since the actual plume is likely to be transported over 

the urban boundary layer. A proper determination of whether these sources should be modeled 

separately without the urban option will depend on a comparison of the stack height or effective 

plume height with the urban boundary layer height. The urban boundary layer height, ziuc, can be 

calculated from the population input on the URBANOPT keyword, P, based on Equation 104 of 

the AERMOD formulation document (Cimorelli, et al., 2004): 

( )

1/ 4 P P0 z z iuc = iuo (1) 
AERMOD Implementation Guide January 9, 2008

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where ziu0 is the reference height of 400 meters corresponding to the reference population, P0, of 

2,000,000. Exclusion of these elevated sources from application of the urban option must be 

justified on a case-by-case basis in consultation with the appropriate reviewing authority.

5.2 SELECTING POPULATION DATA FOR AERMOD’S URBAN MODE (10/19/07) 

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 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 

(10/19/07) 

The URBANOPT keyword on the CO pathway in AERMOD (EPA, 2004b) 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, et al., 2004). 
AERMOD Implementation Guide January 9, 2008

13 

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 considered as a nonregulatory 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, 2005b). 

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 

(01/09/08) 

5.4.1 Urban applications using NWS meteorological data (01/09/08) 

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, et al., 2004). For urban applications using 

representative NWS meteorological data the AERMOD urban option (URBANOPT) should be 

selected (EPA, 2004b), regardless of whether the NWS site is located in a nearby rural or an 

urban setting. This is due to the fact that 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 (01/09/08) 

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., σθ or σw), some adjustments to the meteorological 

data input to AERMOD may be necessary, as discussed in Section 3.3. 
AERMOD Implementation Guide January 9, 2008

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6.0 SOURCE CHARACTERIZATION 

6.1 CAPPED AND HORIZONTAL STACKS (09/27/05) 

For capped and horizontal stacks that are NOT subject to building downwash influences a simple 

screening approach (Model Clearinghouse procedure for ISC) can be applied. This approach 

uses an effective stack diameter to maintain the flow rate, and hence the buoyancy, of the plume, 

while suppressing plume momentum by setting the exit velocity to 0.001 m/s. To appropriately 

account for stack-tip downwash, the user should first apply the non-default option of no stack-tip 

downwash (i.e., NOSTD keyword). Then, for capped stacks, the stack release height should be 

reduced by three actual stack diameters to account for the maximum stack-tip downwash 

adjustment while no adjustment to release height should be made for horizontal releases. 

Capped and horizontal stacks that are subject to building downwash should not be modeled using 

an effective stack diameter to simulate the restriction to vertical flow since the PRIME 

algorithms use the stack diameter to define the initial plume radius which, in turn, is used to 

solve conservation laws. The user should input the actual stack diameter and exit temperature 

but set the exit velocity to a nominally low value, such as 0.001 m/s. This approach will have 

the desired effect of restricting the vertical flow while avoiding the mass conservation problem 

inherent with effective diameter approach. The approach suggested here is expected to provide a 

conservative estimate of impacts. Also, since PRIME does not explicitly consider stack-tip 

downwash, no adjustments to stack height should be made. 

6.2 USE OF AREA SOURCE ALGORITHM IN AERMOD (09/27/05) 

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 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. 
AERMOD Implementation Guide January 9, 2008

15 

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. 
AERMOD Implementation Guide January 9, 2008

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7.0 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. 

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) (Available @ www.epa.gov/scram001/) 

EPA, 2004a: User's Guide for the AERMOD Meteorological Preprocessor (AERMET). 

EPA-454/B-03-002. U.S. Environmental Protection Agency, Research Triangle Park, NC. 

EPA, 2004b: User's Guide for the AMS/EPA Regulatory Model – AERMOD. EPA-454/B-03-

001. U.S. Environmental Protection Agency, Research Triangle Park, NC. 

EPA, 2004c: User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA-454/B-03-

003. U.S. Environmental Protection Agency, Research Triangle Park, NC. 

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, 2005b. Guideline on Air Quality Models. 40 CFR Part 51 Appendix W. 

EPA, 2008. AERSURFACE User’s Guide. EPA-454/B-08-001. U.S. Environmental Protection 

Agency, Research Triangle Park, NC. (Available @ www.epa.gov/scram001/) 

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. 

Högström, A., and U. Högström, 1978. A Practical Method for Determining Wind Frequency 

Distributions for the Lowest 200 m from Routine Data. J. Appl. Meteor., 17, 942-954. 
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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. 

Taylor, P.A., 1969. The Planetary Boundary Layer above a Change in Surface Roughness. J. 

Atmos. Sci., 26, 432-440.