1

 

 

AERMOD: 

Latest Features and Evaluation 

Results. 
2

 EPA-454/R-03-003 

 June 2003 

AERMOD: 

 Latest Features and Evaluation Results 

 U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Emissions Monitoring and Analysis Division 

Research Triangle Park, NC 27711 
3

DISCLAIMER 

This paper has been reviewed by the Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, and has been approved for publication. Mention of trade 

names or commercial products does not constitute endorsement or recommendation for use. 
4

Acknowledgments 

The Agency wishes to acknowledge AERMIC (the American Meteorological 

Society/Environmental Protection Agency Regulatory Model Improvement Committee), 

members of which have given a considerable amount of time, energy and dedication over the last 

10 years to develop the AERMOD air dispersion modeling system: 

R. J. Paine, ENSR Corporation 

A. Venkatram, College of Engineering, University of California at Riverside 

J. C. Weil, Cooperative Institute for Research in Environmental Sciences, 

University of Colorado 

R. B. Wilson, U.S. Environmental Protection Agency, Region X 

S.G. Perry1

, Atmospheric Sciences Modeling Division, 

Air Resources Laboratory, EPA/ NOAA 

R. F. Lee, Consultant, Meteorologist 

A. J. Cimorelli, U.S. Environmental Protection Agency, Region III 

W. D. Peters, US Environmental Protection Agency, OAQPS 

In addition, Mr. Roger Brode of MACTEC Federal Programs, Inc (formerly known as Pacific 

Environmental Services Inc) has provided considerable talent and support to the AERMOD 

project and has conducted the analyses on which this report is based. 

This report is based on an Air and Waste Management Association paper, No 69878, presented 

on June 26, 2003 in San Diego, CA at the Annual 2003 Meeting by Robert Paine, a member of 

the AERMIC, at the AB-3a session (Applications of Short-Range Dispersion Modeling – 

Innovative Approaches and Lessons Learned). 

This has been funded in part by the following U.S. EPA purchase orders: 1D-6635-NTSA, 1D6634-NTSA, 1D-6633-NTSA. Warren Peters was the Project Officer. 

 

1

On assignment to the Atmospheric Research and Exposure Assessment Laboratory, U.S. 

Environmental Protection Agency. 
5

ABSTRACT 

AERMOD is an advanced plume model that incorporates updated treatments of the boundary 

layer theory, understanding of turbulence and dispersion, and includes handling of terrain 

interactions. The model was formally proposed by EPA in April 2000 as a replacement for the 

ISCST3 model. Several model enhancements were made as a result of public comment, 

including the installation of the PRIME downwash algorithm. The latest version of the model, 

version 02222, has been placed on EPA’s web site for beta test purposes. This paper reviews the 

latest features and updated evaluation results for AERMOD version 02222. 
6

INTRODUCTION 

In 1991, the American Meteorological Society (AMS) and the United States Environmental 

Protection Agency (EPA) initiated a formal collaboration with the designated goal of introducing 

recent advances in boundary layer meteorology into regulatory dispersion models. A working 

group (AMS/EPA Regulatory Model Improvement Committee, AERMIC) of three AMS and 

five EPA scientists was formed for this collaborative effort. AERMIC members and participants 

in model code development and testing are listed as the authors of this paper. 

For many years now, we have known that an update to EPA’s basic regulatory models is needed 

(e.g., see Weil1

). Responding to this need, AERMIC was formed in 1991 to update EPA models 

with current state-of-the-art Planetary Boundary Layer (PBL) parameterizations. The early 

efforts of AERMIC are described by Weil2

. As we went through the design process and 

considered the nature of present regulatory models, AERMIC’s goal became more 

comprehensive. In addition to improving how regulatory models characterize the PBL, we 

decided that other areas such as terrain interactions and surface releases needed attention. This 

broadened scope resulted in the development of a complete replacement for EPA’s Industrial 

Source Complex Short-Term model version 3 (ISCST3)3

 by: 1) adopting ISCST3's input/output 

computer architecture; 2) updating, where practical, antiquated ISCST3 model algorithms with 

newly developed or current state-of-the-art modeling techniques; and 3) insuring that all 

processes presently modeled by ISCST3 will continue to be handled by the AERMIC Model 

(AERMOD). 

In developing AERMOD, we have strived to follow certain design criteria to yield a model with 

desirable regulatory attributes. We felt that the model should: 1) be robust in estimating 

regulatory design concentrations (i.e., provide reasonable estimates under a wide variety of 

conditions with minimal discontinuities); 2) be easily implemented (user friendly, reasonable 

input requirements and computer resources), as is the current ISCST3 model; 3) be based on 

state-of-the-art science that captures the essential physical processes while remaining 

fundamentally simple; and, 4) accommodate modifications with ease as the science evolves. 

We chose a phased approach in developing AERMOD. An initial version of the model subjected 

to a “developmental” model evaluation with five databases was released to the public prior to the 

Sixth EPA Modeling Conference in August 1995. After this release and receipt of public 

comments, AERMIC conducted additional (independent) evaluations and made some further 

improvements in response to public comments. A formal peer review4

 of AERMOD was 

conducted in 1998, and an independent model evaluation5,6 consisting of five additional 

databases was completed in 1998. 

The complete AERMOD modeling system consists of two pre-processors and the dispersion 

model itself. The AERMOD meteorological preprocessor (AERMET) is a stand-alone program 

which provides AERMOD with the information it needs to characterize the state of the surface 

and mixed layer, and the vertical structure of the PBL. The AERMOD mapping program 

(AERMAP) is a stand-alone terrain pre-processor, which is used to both characterize terrain and 

generate receptor grids for AERMOD. 
7

PROPOSED RULEMAKING 

On April 21, 2000, the EPA proposed7 to replace ISCST3 with AERMOD version 99351. EPA 

described this new model as an advanced dispersion technique that incorporates state-of-the-art 

boundary layer parameterization techniques, convective dispersion, plume rise formulations, and 

complex terrain/plume interactions. Relative to ISCST3, AERMOD as proposed contained new 

or improved algorithms for: 1) dispersion in both the convective and stable boundary layers; 2) 

plume rise and buoyancy; 3) plume penetration into elevated inversions; 4) treatment of elevated, 

near-surface, and surface level sources; 5) computation of vertical profiles of wind, turbulence, 

and temperature; and 6) the treatment of receptors on all types of terrain (from the surface up to 

and above the plume height). Table 1 provides a more extensive list of the comparison features 

between AERMOD and ISCST3. 

AERMOD as proposed did not incorporate newly developed building downwash algorithms that 

were developed independently and concurrently for the PRIME model (as installed in ISCPRIME8

), sponsored by EPRI. At the 7th USEPA Modeling Conference held in June 2000 and in 

the written comments provided afterward, many public comments focused upon the need to 

enhance AERMOD with the advancements in building downwash treatments that PRIME offers. 

IMPLEMENTATION OF PRIME IN AERMOD 

There were several issues involved with implementing9 the PRIME downwash algorithms into 

the AERMOD model. The PRIME algorithm as implemented in ISC-PRIME was designed to 

use vertical profiles of wind and temperature that are consistent with the ISCST3 profiles, 

whereas AERMOD generates vertical profiles of wind and temperature based on similarity 

scaling and can also incorporate a full profile of measurements. PRIME was implemented in 

AERMOD to use the AERMOD meteorological profiles. 

The ISC-PRIME model uses ambient turbulence intensities based on PG stability class to 

determine the distance at which the wake turbulence intensity has decayed to ambient levels, and 

also uses Pasquill-Gifford (PG)-based dispersion beyond the wake. The PRIME algorithm was 

implemented in AERMOD to use ambient turbulence intensities based on the AERMOD 

profiles. 

The more significant issues were related to the use of a non-Gaussian probability distribution 

function (PDF) for the vertical dispersion in the convective boundary layer (CBL) in AERMOD, 

and AERMOD’s treatment of the direct, indirect and penetrated plumes in the CBL. The ISCPRIME model uses a Gaussian vertical distribution for both convective and stable conditions, 

consistent with the ISCST3 model. 
Table 1. Comparison of dispersion model features: AERMOD vs. ISCST3. 

Feature ISCST3 AERMOD (version 02222) Comments Types of sources modeled Point, area, and volume sources Same as ISCST3 Models are compPlume Rise Uses Briggs equations with 

stack-top wind speed and vertical temperature gradient 

In stable conditions, uses Briggs equations with winds and temperature gradient at stack top and halfway to final plume rise; in convective conditions, plume rise is superposed on the displacements by random convective velocities 

AERMOD is betconditions it factchanges above stconditions it accoand downdrafts 

Meteorological Data Input One level of data accepted An arbitrarily large number of data levels can be 

accommodated 

AERMOD can avarious stack and 

Profiling Meteorological Data 

Only wind speed is profiled AERMOD creates profiles of wind, temperature, and 

turbulence, using all available measurement levels 

AERMOD is muthis area 

Use of Meteorological Data in Plume Dispersion 

Stack-top variables for all downwind distances 

Variables measured throughout the plume depth (averaged from plume centerline to 2.15 sigma-z below centerline; changes with downwind distance) 

AERMOD treatmthat of ISCST3; adata throughout t

Plume Dispersion: General Treatment 

Gaussian treatment in horizontal and vertical 

Gaussian treatment in horizontal and in vertical for stable conditions; non-Gaussian probability density function in vertical for unstable conditions 

AERMOD’s unsdispersion is a mactual conditions 

Urban Treatment Urban option either on or off; 

no other specification available; all sources must be modeled either rural or urban 

Population is specified, so treatment can consider a variety of urban conditions; sources can individually be modeled rural or urban 

AERMOD provias a function of cselectively mode

Characterization of Modeling Domain Surface Characteristics 

Choice of rural or urban Selection by direction and month of roughness 

length, albedo, and Bowen ratio, providing user flexibility to vary surface characteristics 

AERMOD provimore options in tcharacteristics 

Boundary Layer Parameters Wind speed, mixing height, and 

stability class 

Friction velocity, Monin-Obukhov length, convective velocity scale, mechanical and convective mixing height, sensible heat flux 

AERMOD proviuse with up-to-da(PBL) parameter

Mixed Layer Height Holzworth scheme; uses 

interpolation based upon maximum afternoon mixing height 

Has convective and mechanical mixed layer height; convective height based upon hourly accumulation of sensible heat flux 

AERMOD’s formadvanced than thmechanical compinput data, providof the diurnal mi
Feature ISCST3 AERMOD (version 02222) Comments Terrain Depiction Elevation at each receptor point Controlling hill elevation and point elevation at each 

receptor, obtained from special terrain pre-processor (AERMAP) that uses digital elevation model (DEM) data 

AERMOD’s terrinformation for astreamline height data to obtain rec

Plume Dispersion: Plume Growth Rates 

Based upon 6 discrete stability classes only; dispersion curves (Pasquill-Gifford) are based upon surface release experiments (e.g., Prairie Grass) 

Uses profiles of vertical and horizontal turbulence (from measurements and/or PBL theory); variable with height; uses continuous growth functions rather than a discrete (stability-based) formulation 

Use of turbulencheight dependencstability class prosubstantial advantreatment 

Plume Interaction with Mixing Lid: convective conditions 

If plume centerline is above lid, a zero ground-level concentration is assumed 

Three plume components are considered: a “direct” plume that is advected to the ground in a downdraft, an “indirect” plume caught in an updraft that reaches the lid and eventually is brought to the ground, and a plume that penetrates the mixing lid and disperses more slowly in the stable layer aloft (and which can re-enter the mixed layer and disperse to the ground) 

The AERMOD tunderpredictions “all or nothing” tAERMOD’s use downdrafts in a papproach is a sigISCST3 

Plume Interaction with Mixing Lid: stable conditions 

The mixing lid is ignored (assumed to be infinitely high) 

A mechanically mixed layer near the ground is considered. Plume reflection from an elevated lid is considered. 

AERMOD’s use is an advancemenISCST3 approac

Building Downwash Combination of Huber-Snyder 

and Scire-Schulman algorithms; many discontinuities 

New PRIME downwash algorithm installed AERMOD benef

advances offered 
10

To address these issues, the AERMIC committee adopted an approach that defines two plume 

“states”, one corresponding to a plume that is influenced by building downwash, and the other 

corresponding to a plume that is not influenced by building downwash. AERMOD models the 

“wake state” plume using the PRIME algorithms with the adaptations described above, and 

models the “non-wake state” plume using the regular AERMOD algorithms for a source without 

building downwash. The contributions from the two plume states are combined using a 

weighting factor that is a function of the receptor location relative to the building wake. 

For a receptor located within the wake region, the AERMOD model uses the concentration 

calculated by the PRIME algorithm, and the model transitions to the AERMOD estimate 

(without downwash) beyond the wake region. The lateral and vertical extents of the wake region 

are defined internally by the PRIME algorithm. For purposes of transitioning to the AERMOD 

estimate, the longitudinal extent of the wake region is defined as the maximum of 15R and the 

distance where wake turbulence intensity decays to the ambient level, where R is the wake 

length scale and is a function of the building dimensions. Beyond the wake region, the total 

concentration is calculated as follows: 

TOTAL PRIME AERMOD χ = γ χ + (1 − γ ) χ (1)

The weighting function, γ, is equal to 1.0 within the wake region, and beyond the wake region is 

calculated as follows: 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ − − − − − − = 2

2

2

2

2

2

2

exp exp exp

( )

2

( )

2

( )

zg

zg

yg

yg

xg

xg x y z

σ

γ σ

σ

σ

σ

σ

 (2)

where: 

x = downwind distance of receptor from upwind edge of the building; 

y = lateral distance of receptor from building centerline; 

z = receptor height above stack base, including terrain and flagpole; 

σxg = maximum of 15R and the distance to transition from wake to ambient turbulence; 

σyg = lateral distance from building centerline to lateral edge of the wake at receptor location; 

and 

σzg = height of the wake at the receptor location. 

For applications involving terrain effects and building downwash, the AERMOD component is 

calculated with the standard terrain treatment, and the PRIME component is calculated with the 

minimum terrain weighting factor of 0.5, since the wake region is considered to be near neutral 

due to the building-enhanced turbulence. The use of the receptor height above stack base in the 

calculation of the vertical component of γ indicates that if the terrain is within the wake, then the 
11

PRIME component should dominate. However, if the plume is rising above the wake and terrain 

extends above the wake, then the AERMOD component should become imprtant. 

During the developmental evaluation of AERMOD with PRIME, preliminary results indicated a 

tendency for the model to overpredict during light wind convective conditions. The PRIME 

algorithm includes a test on the trajectory angle of the rising plume to determine if the plume 

will escape the effects of the building. If the trajectory of the plume falls below 45 degrees from 

horizontal before the plume rises above the top of the wake, then the plume is subjected to 

building downwash influences. The light wind convective conditions for the Bowline data were 

evaluated to determine a “best fit” for this critical trajectory angle based on the normalized mean 

square error, and a best fit was found for a critical angle of 20 degrees. Based on this result, 

PRIME was implemented in AERMOD using a critical angle of 20 degrees to determine if wake 

effects apply. 

OTHER MODIFICATIONS TO AERMOD 

Many commentors supported the implementation of AERMOD as an improved and advanced 

dispersion model. Some of the public comments (in addition to those advocating the installation 

of the PRIME advancements into AERMOD) led to additional improvements to AERMOD 

version 02222, released by EPA as a beta test version and described in an updated Model 

Formulation Document10. 

• An option to use representative measurements of delta-T and wind speed in lieu of cloud 

cover in AERMET for characterizing boundary layer parameters in stable conditions has 

been included. This option is new and has not been extensively tested by the user 

community. 

• AERMAP was modified to remove its dependence upon the terrain domain for determining 

controlling hill heights for each receptor. This change involved the concept that a terrain 

feature has an influence zone on the surrounding area up to about 10 hill relief heights. For 

each receptor, terrain features more distant than 10 hill relief heights are not considered in 

AERMAP. 

• AERMAP was modified to be able to convert receptor, source and elevation coordinates 

from North American Datum (NAD) of 1927 and other datums to NAD 1983 using the 

United States Geological Survey (USGS) sanctioned program, NADCON version 2.1. 

• AERMET was modified to read Forecast Systems Laboratory (FSL) upper air and Hourly 

United States Weather Observations (HUSWO) surface meteorological data formats. 

• A correction was made to AERMOD to avoid elevated concentrations for terrain below stack 

base from the virtual image source. This fix also addressed some of the public comments 

regarding anomalous AERMOD concentrations from a hypothetical stack located on the top 

of a hypothetical terrain feature. 

• In AERMOD, the urban mode was modified to allow the user to input the urban roughness 

length as an optional parameter. The latest version of AERMOD also includes an adjustment 
12

to the friction velocity and the Monin-Obukhov length for urban stable cases, by equating the 

"convective" sigma-w based on the urban "convective" velocity scale with the mechanical 

sigma-w based on the friction velocity evaluated at a height of 7 times the urban roughness 

length. 

• The reference urban mixing height for a reference population of 2,000,000 was changed from 

500 m to 400 m to better match literature references and observed data. 

• The minimum layer near the ground for calculating effective parameters was changed from 

the lowest 2 m to 5 m to avoid problems for high roughness length applications. 

• Enhancements were made to AERMOD based on the ISCST3 model version 00101. These 

included 

1) the use of globally allocatable arrays for data storage; 

2) expanded data structures to allow for output of concentration and deposition in a single 

model run (for use when deposition algorithms are added to AERMOD); 

3) EVENT (individual period) processing for short-term culpability analyses; 

4) post-1997 PM10 processing; 

5) TOXICS option enhancements such as optimizations for area sources, the Sampled 

Chronological input Model (SCIM) option, and Season and Hour-of-Day (SEASONHR) 

output file option; 

6) explicit treatment of multiple-year meteorological data files and ANNUAL averages; 

7) the SHRDOW and SHRDOW7 options for specifying emissions that vary by Season, 

HouR-of-Day, and Day-of-Week; and 

8) improved data structures for field length and filename lengths. 

• Other minor corrections and/or adjustments were made to the AERMOD code due to public 

comments or user/beta-test comments. They are documented in the comments sections of the 

model source codes. Some of the more notable changes involve the following features: 

3⁄4 The meander feature that has the most effect in very light winds is now applicable for 

both stable and unstable conditions, has been removed from the PRIME downwash 

component of the model, and combines "plume" and "pancake" components of 

concentrations rather than just blending the lateral dispersion term. 

3⁄4 There is a modification to the potential temperature gradient profile for extrapolating 

above the highest measurement height for cases with observed temperature profiles. 

3⁄4 A modification was made to the upper limit on the integration for the critical dividing 

streamline height. 
13

In addition to the full-scale version of AERMOD, an ad-hoc group within EPA and the states is 

working to develop a screening version of AERMOD, AERSCREEN. When completed, 

AERSCREEN will generate screening meteorological data appropriate for the site in question 

and execute the AERMOD model to provide a conservative estimate of concentration impacts. 

Another ad-hoc group, largely from the state of West Virginia, is working on an objective 

method to use digitized land use data to develop input values to AERMET for roughness length, 

albedo, and Bowen Ratio. This technique, referred to as AERSurface, will help to improve the 

consistency of the formulation of this input to AERMET. 

MODEL EVALUATION DESIGN 

The model evaluation was designed to provide diagnostic as well as descriptive information 

about the model performance. The procedures used were designed to address the following 

questions: 

• Does AERMOD provide good predictions for the “right” reasons (a model physics 

evaluation)? 

• How well does AERMOD predict the peak ground-level concentrations that are used to 

assess compliance with air quality regulations (an operational performance evaluation)? 

• Is AERMOD’s performance significantly better than that of other applied models, such as 

ISCST3, HPDM (Hanna and Paine11), RTDM (Paine and Egan12), and CTDMPLUS13,14? 

The ISCST3, RTDM, and CTDMPLUS models are currently approved by the EPA for general 

use in regulatory applications (“Appendix A” models). AERMOD is being proposed for use in 

place of these models, although CTDMPLUS would still be available for applications involving 

a well-defined hill or ridge. HPDM was developed by EPRI as a state-of-the-art model for use in 

simple terrain. Comparison of AERMOD performance to that of HPDM is useful as a 

benchmark. Other advanced models such as SCIPUFF15 and ADMS16 were considered when 

this model comparison effort was initiated. However, these models were not released to the 

public at that time and thus were not included. 

The average model error (or “residual”) examined over a broad range of input variables is used 

to evaluate the model physics. The residual is examined by plotting the ratio of the model 

prediction to observed values for data paired in time as a function of various model input 

variables (e.g., distance, wind speed, and mixing height). Residual plots can be examined for 

partial data sets such as for stable or unstable conditions. If a significant trend is observed in the 

predicted-to-observed ratio as a function of the abscissa variable, then the model physics 

associated with or responsible for this feature can be further examined. 

Operational performance of models for predicting compliance with air quality regulations, 

especially those involving a peak or near peak value at some unspecified time and location, can 

be assessed with quantile-quantile (Q-Q) plots (Chambers et al.17). Q-Q plots, are created by 

sorting by rank the predicted and the observed concentrations from a set of predictions initially 

paired in time and space. The sorted list of predicted concentrations are then plotted by rank 
14

against the observed concentrations also sorted by rank. These concentration pairs are no longer 

paired in time or location. However, the plot is useful for answering the question, “Over a 

period of time and over a variety of locations, does the distribution of the model predictions 

match those of observations?” Scatterplots, which use data paired in time (and / or space), 

provide a more strict test, answering the question: “At a given time and place, does the 

magnitude of the model prediction match the observation?” It is the experience of model 

developers (e.g., Weil, et al.18 and Liu and Moore19) that wind direction uncertainties can and do 

cause disappointing scatterplot results from what are otherwise well-performing dispersion 

models. Therefore, the Q-Q plot instead of the scatterplot is a more pragmatic procedure for 

demonstrating model performance of applied models. Venkatram20 makes a cogent argument for 

the use of Q-Q plots for evaluating regulatory models. 

In addition to the residual and Q-Q plots, the difference between AERMOD and ISCST3 was 

assessed with a robust test statistic (robust highest concentration, or RHC21) that represents a 

smoothed estimate of the highest concentrations, based on a tail exponential fit to the upper end 

of the concentration distribution. With this procedure, the effect of extreme values on model 

comparison is reduced. The RHC statistic is reported for each of the 10 databases. The robust 

highest concentration is given by: 

 (3) 

where n = Min (mo, m), mo is the number of values used to characterize the upper end of the 

concentration distribution, m is the number of values exceeding a specified threshold value, χ is 

the average of the n - 1 largest values, and χ{n} is the n

th largest value. In this evaluation, the 

value of mo was taken to be 26 except for databases with a limited sample size (for which mo was 

taken to be 11). 

Highlights of the evaluation results for the proposed regulatory version of AERMOD were first 

presented by Paine, et al.5. As with Paine, et al., the evaluation results here include selected 

residual plots and Q-Q plots to address the model performance issues noted above. In the 

AERMOD evaluation, many of the statistical tests and comparisons with observations were 

applied to analyze the performance of the model and various model algorithms. The observed 

peak concentration for a given arc of samplers was compared to the predicted arc maximum. The 

comparisons included time and downwind-distance pairings to challenge the model components. 

Residual plots (predicted/observed, paired in time and downwind distance) of concentration 

estimates were used to judge whether AERMOD was performing correctly and was yielding 

better results (than existing models). Generally, residuals were plotted as a function of distance, 

although residuals versus other parameters, such as friction velocity, Monin-Obukhov length, 

and mixing height, proved to be extremely valuable diagnostic tools. In addition, Q-Q plots were 

used to examine the ability of the model to reproduce the distribution of observed concentrations 

over a wide range of environmental conditions. Reproducing the measured distribution, 

particularly the high concentration end, is important in regulatory applications. 

RHC n n ( ) n= +−

⎛ −

⎝

⎜ ⎞

⎠ χ χχ { } { } ln ⎟ 3 1

2
15

For the intensive tracer data sets (Prairie Grass, Kincaid SF6, Indianapolis, and Tracy), 

concentration residuals of the form <Cp/Co> were plotted as a function of downwind distance 

(arc distance) for each of two stability regimes, convective and stable. Here, Co is the maximum 

observed concentration and Cp the maximum predicted concentration on an arc at a given time. 

The brackets, <Cp/Co>, indicate the mean of the ratio. These data were paired in time and 

downwind distance. For the tracer databases, observations and predictions corresponded to 

maximum concentrations on each arc of samplers, rather than at each individual sampler, to 

eliminate the effect of wind direction uncertainties on the evaluation results. The use of arc 

maxima was possible due to the dense coverage of samplers along each arc. For the other 

nondownwash data sets (Kincaid SO2, Lovett, Baldwin, Clifty Creek, Martins Creek, and 

Westvaco), where the sampler array was not sufficiently dense to arrange the data in arcs, 

residual plots by distance were not meaningful. In contrast to the tracer studies (where a Co, Cp

pair are available for each arc-distance of each time period), the long-term databases have only a 

single Co, Cp pair selected (for each time period) as the maximum observed and predicted 

concentrations, respectively in the entire receptor array. 

For the tracer databases, results for 1-hour averages are reported (with the Prairie Grass 10-

minute measurements used). For the long term SO2 data sets, 3-hour, 24-hour, and annual results 

are reported. All of the observed concentrations for the long-term databases are subject to an 

additional uncertainty related to the estimate of background concentration that is subtracted from 

the monitored observations. In addition, it should be realized that SO2 monitors typically have a 

detection limit on the order of 6 ppb (16 mg m-3), and baseline (zero) drifts of up to 10 ppb (26 

mg m-3). Concentrations below the detection limit are typically set to half of the limit (8 

mg m-3), even though they may actually be zero. Baseline drift is generally ignored. Therefore, 

the uncertainties due to the combined errors in SO2 measurements from the detection limit 

treatment, ignored baseline drifts, and background concentration estimates reflect on the 

reliability of the observed concentrations, particularly for annual averages. Peak short-term 

averages are not affected significantly because the uncertainty is typically a small percentage of 

the reported value. 

MODEL EVALUATION RESULTS FOR NON-DOWNWASH DATABASES 

The combined developmental and independent performance evaluation5,6 of AERMOD involved 

four short-term tracer studies and six conventional long-term SO2 monitoring databases in a 

variety of settings. The purpose of the evaluation studies was to be sure that AERMOD had 

been tested in a variety of types of environments for which it will be used. Compared with many 

widely used applied models, AERMOD has been subjected to a large degree of testing with these 

evaluation databases. 

The evaluation of AERMOD was accomplished in two phases. The first phase, the 

“developmental evaluation,” was performed concurrently with the development of the model. As 

each model feature was tested, a relevant portion of the developmental evaluation was repeated 

with five databases to identify any problems that might have been introduced with that feature. 

Because the model could have been inadvertently biased by particular characteristics of the 

developmental databases, a second phase, the “independent evaluation,” was conducted using 

five additional data sets. 
16

AERMOD is intended to handle a variety of pollutant source types, including surface and 

buoyant elevated sources, in a wide variety of settings such as rural and urban as well as flat and 

complex terrain. With this in mind, data were selected from five diverse field studies for the 

developmental evaluation. Due to space limitations, maps of the various sites are not provided 

in this report, but can be found on the Internet in Paine, et al.5

. A brief description of these data 

sets is provided below. 

Developmental Evaluation (No Downwash) 

The Prairie Grass study (Barad22; Haugen23) used a near-surface, non-buoyant tracer release in a 

flat rural area in Nebraska. This study involved a tracer of SO2 released at 0.46 m above the 

surface. Surface sampling arrays (arcs) were positioned from 50 m to 800 m downwind. 

Meteorological data included the 2-m level wind direction and speed, the root-mean-square wind 

direction fluctuation, and the temperature difference (∆T) between 2 m and 16 m. Other surface 

parameters, including friction velocity, Monin-Obukhov length, and lateral plume spread were 

estimated. Wind, turbulence, and temperature were obtained from a multi-leveled instrumented 

16-m meteorological tower. A total of 44 ten-minute sampling periods were used, including 

both convective and stable conditions. 

The Kincaid SF6 study (Liu and Moore19; Bowne et al.24) involved an elevated, highly-buoyant 

tracer release in a flat rural area of Illinois. Two intensive measurement periods each lasting six 

weeks were conducted during the spring and summer of 1980 and 1981. During these periods, 

approximately 200 monitors were placed on arcs ranging from about 500 m to 50 km downwind 

of the single 187-m stack and provided 1-hour averaged concentration samples for a total of 

1,801 arc-hours. Meteorological data included wind speed, direction, and temperature from a 

tower instrumented at 2, 10, 50, and 100 m levels, and nearby National Weather Service (NWS) 

data. Estimates of lateral plume spread were obtained from the sampling arcs. 

The Indianapolis study (Murray and Bowne25) consisted of an elevated, buoyant tracer (SF6) 

released in a flat-terrain urban to suburban area from a single 84-m stack. Data are available for 

approximately a four- to five-week period with 177 monitors providing 1-hour averaged samples 

along arcs from 250 m to 12 km downwind for a total of 1,297 arc-hours. Meteorological data 

included wind speed and direction, sigma-theta on a 94-meter tower; and wind speed, ∆T (2m - 

10m) and other supporting surface data at three other 10-m towers. Observed plume rise and 

estimates of plume sigma-y are also available from the database. 

The Kincaid SO2 study (Liu and Moore19; Bowne et al.24) was conducted at the same location as 

the Kincaid SF6 study. It involved a buoyant, continuous release of SO2 from a 187-m stack in 

rural flat terrain. The study included about six months of data between April 1980 and June 1981 

(a total of 4,614 hours of samples). There were 30 SO2 monitoring stations providing 1-hour 

averaged samples from about 2 km to 20 km downwind of the stack. The meteorological data 

were the same as in the Kincaid tracer study. 

The Lovett Power Plant study (Paumier et al.26) consisted of a buoyant, continuous release of 

SO2 from a 145-m tall stack located in a complex terrain, rural area in New York State. The data 

spanned one year from December 1987 through December 1988. Data were collected from 12 

monitoring sites (ten on elevated terrain and two near stack-base elevation) that were located 
17

about 2 to 3 km from the plant. The monitors provided hourly-averaged concentrations. The 

important terrain features rise approximately 250 m to 330 m above stack base at about 2 to 3 km 

downwind from the stack. Meteorological data include winds, turbulence, and ∆T from a tower 

instrumented at 10 m, 50 m, and 100 m. National Weather Service surface data were available 

from a station 45 km away. 

Independent Evaluation (No Downwash) 

The independent evaluation of AERMOD initially employed the first three databases described 

below. Results for two additional databases were added in response to peer review comments. 

The Baldwin Power Plant (Hanna and Chang27) is located in a rural, flat terrain setting of 

southwestern Illinois and has three identical 184-m stacks aligned approximately north-south 

with a horizontal spacing of about 100 m. There were 10 SO2 monitors that surrounded the 

facility, ranging in distance from two to ten km. On-site meteorological data was available 

during the study period of 1 April 1982 through 31 March 1983 and consisted of hourlyaveraged wind speed, wind direction, and temperature measurements taken at 10 m and wind 

speed and wind direction at 100 m. 

The Clifty Creek Power Plant is located in rural southern Indiana along the Ohio River with 

emissions from three 208-m stacks during this study. The area immediately north of the facility 

is characterized by cliffs rising about 115 m above the river and intersected by creek valleys. Six 

nearby SO2 monitors (out to 16 km from the stacks) provided hourly averaged concentration 

data. Meteorological data from a nearby 60-m tower covered the two-year period from 1 

January 1975 through 31 December 1976, although only the data from 1975 were used in this 

evaluation. This database was also used in a major EPA-funded evaluation of rural air quality 

dispersion models in the early 1980s28. 

The Martins Creek Steam Electric Station is located in a rural area along the Delaware River on 

the Pennsylvania/New Jersey border, approximately 30 km northeast of Allentown, PA and 

95 km north of Philadelphia, PA. The area is characterized by complex terrain rising above the 

stacks. Sources included multiple tall stacks ranging from 122 to 183 m in height. The seven 

SO2 monitors29 were located on Scotts Mountain, which is about 2.5 - 8 km southeast of the 

Martins Creek facility. On-site meteorological data covered the period from 1 May 1992 

through 19 May 1993. Hourly temperature, wind speed, wind direction, and sigma-theta 

(standard deviation of the horizontal wind direction) at 10 m were recorded from an 

instrumented tower located in a flat area approximately 2.5 km west of the plant. In addition, 

hourly multi-level wind measurements were taken by a sodar located approximately three km 

southwest of the Martins Creek station. 

The Westvaco Corporation’s pulp and paper mill in rural Luke, Maryland is located in a complex 

terrain setting in the Potomac River valley (Strimaitis et al.30). A single 183-m buoyant source 

was modeled for this evaluation. There were 11 SO2 monitors surrounding the facility, with 

eight monitors well above stack top on the high terrain east and south of the mill at a distance of 

800 - 1500 m. Hourly meteorological data (wind, temperature, and turbulence) were collected 

between December 1980 and November 1991 at three instrumented towers: the 100-m Beryl 

tower in the river valley about 400 m southwest of the facility; the 30-m Luke Hill tower on a 
18

ridge 900 meters north-northwest of the facility; and the 100-m Met tower located 900 m eastsoutheast of the facility on a ridge across the river. 

The Tracy Power Plant (DiCristofaro et al.31) is located 27 km east of Reno, Nevada in the rural 

Truckee River valley completely surrounded by mountainous terrain. A field tracer study was 

conducted at the power plant in August 1984 with SF6 being released with the moderately 

buoyant plume from a 91-m stack. A total of 128 hours of data were collected over 14 

experimental periods. Stable atmospheric conditions were dominant for this study. On-site 

meteorological data (wind, temperature, and turbulence) for Tracy were collected from an 

instrumented 150-m tower located 1.2 km east of the power plant. The wind measurements from 

the tower were extended above 150 meters using a Doppler acoustic sounder and temperature 

measurements were extended with a tethersonde. 

Evaluation Results 

For completeness, EPA re-ran the evaluation of the 10 non-downwash databases. The earlier 

evaluation results indicated that AERMOD shows superior performance relative to ISCST3 over 

all of the databases tested. The newest evaluation results are very similar to the results reported 

by Pain et al.5. A summary of the robust highest concentration prediction results is provided in 

Table 2. More complete results5,6 that include Q-Q plots and some residual plots for the 

previous evaluation are available and would show very little change for the updated evaluation. 

Table 2. Summary of AERMOD evaluation results – nondownwash databases (previous results5

in parentheses).

Database Ratio of Modeled/Observed Robust Highest Concentrations* 

Prairie Grass (SO2) 

Flat, grassy field (Nebraska, USA) 

AERMOD: 

ISCST3: 

0.89 (0.87) (1-hr avg) 

1.50 (1-hr avg) 

Kincaid (SF6) 

Flat, rural (Illinois, USA) 

AERMOD: 

ISCST3: 

0.77 (0.76) (1-hr avg) 

0.68 (1-hr avg) 

Kincaid: (SO2) 

Flat, rural (Illinois, USA) 

AERMOD: 

ISCST3: 

AERMOD: 

ISCST3: 

AERMOD: 

ISCST3: 

0.98 (1.01) (3-hr avg) 

0.56 (3-hr avg) 

0.94 (0.97) (24-hr avg) 

0.45 (24-hr avg) 

0.30 (0.30) (annual peak) 

0.14 (annual peak) 

Baldwin (SO2): 

Flat, rural (Illinois, USA) 

AERMOD: 

ISCST3: 

AERMOD: 

ISCST3: 

AERMOD: 

ISCST3: 

1.24 (1.31) (3-hr avg) 

1.43 (3-hr avg) 

0.97 (1.02) (24-hr avg) 

1.14 (24-hr avg) 

0.97 (0.97) (annual peak) 

0.63 (annual peak) 

Indianapolis (SF6) 

Flat, urban (Indiana, USA) 

AERMOD: 

ISCST3: 

1.11 (1.20) (1-hr avg) 

1.30 (1-hr avg) 

Clifty Creek (SO2) 

Moderately hilly terrain, rural (Indiana, 

USA) 

AERMOD: 

ISCST3: 

1.05 (1.25) (3-hr avg) 

0.98 (3-hr avg) 
19

Database Ratio of Modeled/Observed Robust Highest Concentrations* 

AERMOD: 

ISCST3: 

AERMOD: 

ISCST3: 

0.67 (0.72) (24-hr avg) 

0.67 (24-hr avg) 

0.54 (0.54) (annual peak) 

0.31 (annual peak) 

Tracy (SF6): 

Hilly terrain, rural (Nevada, USA) 

AERMOD: 

ISCST3: 

CTDMPLUS: 

1.04 (1.07) (1-hr avg) 

2.81 (1-hr avg) 

0.77 (1-hr avg) 

Martins Creek (SO2): Hilly terrain, rural 

(Pennsylvania/New Jersey, USA) 

AERMOD: 

CTDMPLUS: 

ISCST3: 

RTDM: 

AERMOD: 

CTDMPLUS: 

ISCST3: 

RTDM: 

AERMOD: 

CTDMPLUS: 

ISCST3: 

RTDM: 

1.12 (1.06) (3-hr avg) 

4.80 (3-hr avg) 

7.25 (3-hr avg) 

3.33 (3-hr avg) 

1.78 (1.72) (24-hr avg) 

5.56 (24-hr avg) 

8.88 (24-hr avg) 

3.56 (24-hr avg) 

0.78 (0.74) (annual peak) 

2.19 (annual peak) 

3.37 (annual peak) 

1.32 (annual peak) 

Lovett (SO2) AERMOD: 

CTDMPLUS: 

ISCST3: 

AERMOD: 

CTDMPLUS: 

ISCST3: 

AERMOD: 

CTDMPLUS: 

ISCST3: 

1.03 (1.00) (3-hr avg) 

2.36 (3-hr avg) 

8.20 (3-hr avg) 

1.01 (1.00) (24-hr avg) 

2.02 (24-hr avg) 

9.11 (24-hr avg) 

0.85 (0.78) (annual peak) 

1.71 (annual peak) 

7.49 (annual peak) 

Westvaco (SO2): 

Hilly terrain, rural (Maryland/Virginia, 

USA) 

AERMOD: 

CTDMPLUS: 

ISCST3: 

AERMOD: 

CTDMPLUS: 

AERMOD: 

CTDMPLUS: 

1.06 (1.08) (3-hr avg) 

2.14 (3-hr avg) 

8.50 (3-hr avg, estimated*) 

1.07 (1.14) (24-hr avg) 

1.54 (24-hr avg) 

1.59 (1.64) (annual peak) 

0.93 (annual peak) 

*Notes: 

1. The Robust Highest Concentration (RHC) is a statistical estimator for the highest concentration. It is 

determined from a tail exponential fit to the high end of the frequency distribution of observed and 

predicted values. The number of points used for the fit is arbitrary, but usually ranges between 10 

and 25. 

2. The estimated 3-hour ISCST3 result for Westvaco is derived from the EPA Complex Terrain Model 

Development study (Strimaitis et al.30) in which several models, including CTDMPLUS and 

COMPLEX-I (now part of ISCST3), were evaluated. 
20

The overall model evaluation results for AERMOD version 02222 with nondownwashing 

databases can be summarized as follows, taking one composite (geometric mean) ratio of 

predicted to observed RHC value for short-term averages at each site, and also taking the annual 

average ratio at sites with year-long databases: 

 1.03 is the overall predicted-to-observed ratio for short-term averages (with a range among 

sites from 0.76 to 1.35). 

 0.73 is the overall predicted-to-observed ratio for annual averages (with a range among sites 

from 0.30 to 1.64). 

While the predicted-to-observed ratios did not vary substantially for AERMOD between simple 

and complex terrain sites, there was a large change in the average ratio for ISCST3 : 0.96 for 

simple terrain and 6.4 for complex terrain. 

MODEL EVALUATION RESULTS FOR DOWNWASH DATABASES 

A developmental evaluation of the AERMOD model with PRIME added was conducted on four 

developmental databases prior to its application to four independent databases. Paine32 describes 

these databases and others that were originally considered for the EPRI PRIME evaluation study. 

The developmental databases (described below) included one half of the days selected at random 

from a full year of data for the Bowline power plant database located on the Hudson River near 

Haverstraw, New York, the Millstone power plant located on the Connecticut coast, the Duane 

Arnold Energy Center (DAEC) located in eastern Iowa, and the Alaska North Slope field study 

near Prudhoe Bay, Alaska. The Bowline Point database was used in both the developmental and 

evaluation evaluations because it was the only full year database, and more complete 

development testing of both ISC-PRIME and AERMOD version 02222 required the use of half 

of this database. 

The main purpose of testing with the developmental databases was to assure that the AERMOD 

predictions were consistent with the ISC-PRIME predictions for stack-receptor combinations 

dominated by building downwash. However, as noted earlier, one correction was made to 

AERMOD to adjust the threshold trajectory angle of the rising plume that determines whether 

the plume will escape the effects of the building. As noted below, the evaluation results for the 

developmental databases included both underpredictions and overpredictions for both AERMOD 

and ISC-PRIME. However, no attempt was made in the developmental phase of testing to 

further adjust the downwash algorithm. 

Developmental Evaluation (Downwash) 

The Bowline Point site33, located in the Hudson River valley in New York State, is shown in 

Figure 1 (topographic map). The electric utility site included two 600-MW units, each with an 

86.9-m stack and a dominant roof tier with a height of 65.2 m high in a rural area. There were 

four monitoring sites as shown in Figure 1 that ranged from about 250 to 850 m from the stacks. 

Hourly emissions data was determined from load data, coal analyses, and site-specific 

relationships between loads and fuel consumption. Meteorological data was obtained from a 
21

100-m tower at the site. This site was also used as an independent evaluation database with the 

entire year included. 

Figure 1. Bowline Point Study Area (SO2 Releases) 

The Millstone nuclear power plant is located on the Connecticut coast, near Niantic. The model 

evaluation database34 features 36 hours of SF6 emissions from a 48-m reactor stack and 26 hours 

of Freon emissions from a 29-m turbine stack. Exit temperatures were close to ambient (about 

295K) with exit velocities of about 10 m/s for both the reactor stack (48.3 m) and the three 

turbine stacks (29.1 m). These stacks were associated with 45-m and 28-m building tiers, 

respectively. The monitoring data consisted of three arcs at 350, 800 and 1,500 m. 

Meteorological data were available from an on-site tower at the 10-m and 43-m levels. There 

was about an even split between stable and unstable hours, with mostly on-shore winds and 

fairly high wind speeds. There were only 3 stable hours with wind speed less than 4 m/s, and the 

majority were above about 7 m/s and several above 10 m/s. Figure 2 shows the layout of the 

study area. 
22

Figure 2. Millstone Study Area (SF6 and Freon Releases) 

The Duane Arnold Energy Center (DAEC) is located in rural Iowa, located about 16 km 

northwest of Cedar Rapids. It is located in a river valley with some bluffs on the east side. 

Terrain varies by about 30 m across the receptor network with the eastern half of the 

semicircular receptor arcs being flat and the western half elevated. The tracer study35 involved 

SF6 releases from two rooftops (46-m and 24-m levels) and the ground (1-m level). Building 

tiers for the rooftop releases were 43 and 24 m high, respectively. The 1-m and 24-m releases 

were non-buoyant, non-momentum, while the 46-m release was close to ambient, but had about a 

10 m/s exit velocity. The number of tracer release hours was 12, 16 and 11 from the release 

heights of 46 m, 24 m, and 1 m, respectively. There were two arcs of monitors at downwind 

distances of 300 and 1000 m (see Figure 3). Meteorological data consisted of winds at 10, 24, 

and 50 m. The meteorological conditions were mostly convective (30 out of 39 hours), with 

fairly light wind speeds. Only one hour had a wind speed above 4 m/s (4.6) , and almost half of 
23

the hours were less than 2 m/s. 

Figure 3. DAEC Study Area (SF6 Releases) 

The Alaska North Slope tracer study36,37 (see Figure 4) involved 44 hours of buoyant SF6

releases from a 39-m high turbine stack. Tracer sampler coverage ranged over seven arcs from 

50 to 3,000 m downwind. Meteorological data, including wind speed, wind direction, 

temperature, sigma-theta, and sigma-w, were available from an on-site tower at the 33-m level. 

Atmospheric stability and wind speed profiles were influenced by the smooth snow-covered 

tundra surface with negligible levels of solar radiation in the autumn months. All experiments 

(44 usable hours) were conducted during the abbreviated day light hours (0900 – 1600). Wind 

speeds taken at the 33-m level during the tests were less than 6 m/s during one and part of 

another test, between 6 and 15 m/s during four tests, and in excess of 15 m/s during three tests. 

Stability conditions were generally neutral or slightly stable. 
24

Figure 4. Depiction of Alaska North Slope Oil Gathering Center Turbine Stack, Meteorological 

Tower (X), and Camera Locations Used to Visualize Plume Rise43

Independent Evaluation (Downwash) 

Besides the full year of Bowline Point data (described above), the independent building 

downwash evaluation databases consist of the American Gas Association (AGA) tracer 

experiments, the Experimental Organic Cooled Reactor (EOCR) study, and the Lee Power Plant 

wind tunnel study. Previous model evaluation results for these databases have been reported by 

Paine and Lew38. 

The AGA experiments39 occurred during spring and summer 1980 at gas compressor stations in 

Texas and Kansas. At each test facility, one of the gas compressor stacks was retrofitted to 

accommodate SF6 tracer gas emissions. In addition, stack height extensions were provided for 
25

some of the experiments (with the normal stack height close to 10 m). The stack height to 

building height ratios for the tests ranged from 0.95 to 2.52. There were a total of 63 tracer 

releases over the course of the tests, and the tracer samplers were located between 50 and 200 m 

away from the release point (see Figure 5). An instrumented 10-m tower was operated at both 

experimental sites. The tracer releases were generally restricted to daytime hours. Stability 

classes range from neutral to extremely unstable, except for three hours that were slightly stable. 

Wind speeds range from 2 to 11 m/s over the 63 hours. 

Figure 5. Plan View of the Locations of Tracer Samplers at Site 1, AGA Field Study 

(SF6 Releases) 

The EOCR study40 involved the simultaneous release of three tracer gases (SF6, F12, and Freon12B2) at three levels around the Experimental Organically Cooled Reactor (EOCR) test reactor 

building at the Idaho National Engineering Laboratory in Southeast Idaho. The terrain was flat 

with low-lying shrubs. The main building was 25 m high with an effective width of 25 m. The 

tracer releases typically occurred simultaneously, and were conducted during 22 separate time 

periods. Tracer sampler coverage was provided at eight concentric rings at distances of about 

50, 100, 200, 400, 800, 1200, and 1600 m from the release points (see Figure 6). The stability 

classes ranged from stable to unstable. The 10-m wind speeds for the cases selected ranged from 

3 to 8 m/s. 
26

Figure 6. Terrain Map Featuring the Entire EOCR Grid with the Source at the Grid Center 

(SF6 Releases). 

Arcs are at distances of about 40, 80, 200, 400, 800, 1200, and 1600 m. 

The Lee Power Plant wind tunnel study41 featured releases from steam boiler stacks with a 

common height of 64.8 m, affected by a building tier with a height of 42.6 m. The world’s 

largest fluid modeling study chamber at Monash University in Australia was used for these 

experiments (see plan view in Figure 7). Stable conditions were simulated by using an inverted 

model of the facility that was suspended from the ceiling of the tunnel. A stably stratified layer 

was developed along the tunnel by heating the inflowing air, and a buoyant plume was simulated 

by using a negatively buoyant gas mixture. A stable potential temperature lapse rate of 

0.035 deK/m was modeled with a stack-top real-world equivalent wind speed of 7 m/s with 

several wind directions being tested. In neutral conditions, stack-top speeds (at the 64.8-m level) 
27

ranged in real-world equivalents from 5 to 40 m/s. There were 78 combinations of wind 

direction, wind speed, and plume buoyancy tested for the neutral cases, and 14 combinations for 

the stable cases. The tracer sampler coverage included ground-level concentrations at six 

distances ranging from the cavity zone to beyond the wake (150-900 m). Since the actual Lee 

Power Plant area is rural, the models were run with a rural source characterization. The EPRI 

model evaluation38 considered both urban and rural source representations because of the 

enhanced turbulence levels present in the wind tunnel. Consistent with the EPRI model 

evaluation, the wind tunnel observations were adjusted from an assumed 5-minute duration to a 

full hour using a time-dependent 1/5 power law42. 

Figure 7. Plan View of the Lee Power Plant Model and Nearby Buildings Showing the Power 

Station Units and the Zero Reference Position Used in the Monash Wind Tunnel Tests 

Evaluation Results 

A summary of the robust highest concentration prediction results for the downwash databases is 

given in Table 3. Summaries of the results for each downwash database are provided below. 

Table 3. Summary of AERMOD evaluation Results – downwash databases.

Database Ratio of Modeled/Observed Robust Highest Concentrations* 

Bowline Point (buoyant, SO2) 

Hudson River Valley, New York 

AERMOD: 

ISC-PRIME: 

AERMOD: 

ISC-PRIME: 

1.14 (3-hr avg) 

1.23 (3-hr avg) 

1.43 (24-hr avg) 

1.42 (24-hr avg) 
28

Database Ratio of Modeled/Observed Robust Highest Concentrations* 

AERMOD: 

ISC-PRIME:

1.50 (annual avg) 

1.35 (annual avg) 

Alaska North Slope (buoyant, SF6) AERMOD: 

ISC-PRIME:

1.06 (1-hr avg) 

1.49 (1-hr avg) 

Duane Arnold Energy Center 

(nonbuoyant, SF6) (Iowa) 

AERMOD: 

ISC-PRIME: 

AERMOD: 

ISC-PRIME: 

AERMOD: 

ISC-PRIME:

0.69 (1-hr avg; 46-m release) 

0.76 (1-hr avg; 46-m release) 

0.25 (1-hr avg; 24-m release) 

0.29 (1-hr avg; 24-m release) 

0.51 (1-hr avg; 1-m release) 

0.38 (1-hr avg; 1-m release) 

Millstone Nuclear Power Plant 

(nonbuoyant, SF6) (Connecticut) 

AERMOD: 

ISC-PRIME: 

AERMOD: 

ISC-PRIME:

0.44 (1-hr avg; 46-m release) 

0.41 (1-hr avg; 46-m release) 

1.32 (1-hr avg; 29-m release) 

1.42 (1-hr avg; 29-m release) 

American Gas Association (buoyant, 

SF6) 

Texas and Kansas) 

AERMOD: 

ISC-PRIME:

0.92 (1-hr avg) 

0.76 (1-hr avg) 

Experimental Organic Cooling 

Reactor (nonbuoyant, SF6) (Idaho) 

AERMOD: 

ISC-PRIME:

1.72 (1-hr avg) 

1.69 (1-hr avg) 

Lee Power Plant (buoyant, wind 

tunnel) 

AERMOD: 

ISC-PRIME: 

AERMOD: 

ISC-PRIME:

0.51 (1-hr avg; neutral cases; rural) 

0.49 (1-hr avg; neutral cases; rural) 

2.50 (1-hr avg; stable cases; rural) 

2.11 (1-hr avg; stable cases; rural) 
29

AERMOD and ISC-PRIME had a similar evaluation outcome for the full-year Bowline Point 

database, featuring buoyant steam electric plant releases, with no significant differences in model 

performance. The 3-hour Q-Q plot is shown in Figure 8, and the 24-hour Q-Q plot is shown in 

Figure 9. For each averaging time, both models exhibit a modest overprediction tendency. 

Figure 8. Q-Q Plot for Bowline Point 3-Hour Averages (SO2) 

Figure 9. Q-Q Plot for Bowline Point 24-Hour Averages (SO2) 

Bowline 3-hr Q-Q Plot (χ/Q) - 87m Stack

1

10

100

1000

1 10 100 1000

Obs. Conc. (μg/m3

)

Pred. Conc. (μg/m3

)

AERMOD

ISC3P

Bowline 24-hr Q-Q Plot (χ/Q) - 87m Stack

1

10

100

1000

1 10 100 1000

Obs. Conc. (μg/m3

)

Pred. Conc. (μg/m3

)

AERMOD

ISC3P
30

Both models also had a similar evaluation outcome for two non-buoyant release heights (with 

two different tracer gases) at the Millstone Nuclear Power Plant. Figure 10 shows that both 

models overpredict by close to a factor of 2 for the 29-m Freon releases, but underpredict by 

about a factor of 2 for the 46-m SF6 releases (see Figure 11). 

Figure 10. Q-Q Plot for Millstone 1-Hour Averages for 29-m Releases (Freon) 

Figure 11. Q-Q Plot for Millstone 1-Hour Averages for 46-m Releases (SF6) 

Millstone Freon 1-hr Q-Q Plot (χ/Q) - 29m Stack

1

10

100

1 10 100

Obs. Conc. (μs/m3

)

Pred. Conc. (μs/m3

)

AERMOD

ISC3P

Millstone SF6 1-hr Q-Q Plot (χ/Q) - 48m Stack

1

10

100

1 10 100

Obs. Conc. (μs/m3

)

Pred. Conc. (μs/m3

)

AERMOD

ISC3P
31

The Duane Arnold Energy Center also featured similar evaluation outcomes for AERMOD and 

ISC-PRIME for the non-buoyant releases. Figure 12 shows a 1-m release Q-Q plot, in which 

both models generally underpredict, but AERMOD has less of an underprediction tendency. 

Both models underpredict for the 24-m releases (see Q-Q plot in Figure 13), with AERMOD 

showing a larger underprediction tendency except for the highest concentrations. The Q-Q plot 

in Figure 14 shows both models with peak concentrations that are nearly unbiased for the 46-m 

releases. 

Figure 12. Q-Qt for DAEC 1-Hour Averages for 1-m Releases (SF6) 

Figure 13. Q-Q Plot for DAEC 1-Hour Averages for 24-m Releases (SF6) 

DAEC SF6 1-hr Q-Q Plot (χ/Q) - 1m Stack

1

10

100

1000

1 10 100 1000

Obs. Conc. (μs/m3

)

Pred. Conc. (μs/m3

)

AERMOD

ISC3PT

DAEC SF6 1-hr Q-Q Plot (χ/Q) - 24m Stack

0.1

1

10

100

1000

1 10 100 1000

Obs. Conc. (μs/m3

)

Pred. Conc. (μs/m3

)

AERMOD

ISC3P
32

Figure 14. Q-Q Plot for DAEC 1-Hour Averages for 46-m Releases (SF6) 

The modeling results for the buoyant releases at the Alaska North Slope experimental site are 

shown as a Q-Q plot in Figure 15. The AERMOD predictions are nearly unbiased (but slightly 

conservative), while the ISC-PRIME results are more than a factor of 2 high except for the 

highest predictions. This result shows some of the biggest performance differences between 

AERMOD and ISC-PRIME among all seven downwash evaluation databases. 

Figure 15. Q-Q Plot for Alaska North Slope 1-Hour Averages for 39-m Releases (SF6) 

DAEC SF6 1-hr Q-Q Plot (χ/Q) - 46m Stack

0.1

1

10

100

1000

0.1 1 10 100 1000

Obs. Conc. (μs/m3

)

Pred. Conc. (μs/m3

)

AERMOD

ISC3PT

Alaska North Slope 1-hr Q-Q Plot (χ/Q) - 39m Stack

0.01

0.1

1

10

0.01 0.1 1 10

Obs. Conc. (μs/m3

)

Pred. Conc. (μs/m3

)

AERMOD

ISC3P
33

The Q-Q plot for the AGA experiments is shown in Figure 16. This plot shows that the 

AERMOD predictions parallel the 1:1 line over a larger concentration domain than the ISCPRIME predictions. For subsets of the modeling cases, AERMOD shows a significant 

improvement especially for the cases involving stack-to-building height ratios greater than 1.25. 

Residual plots of prediction concentrations as a function of stability indicate consistently less 

biased AERMOD results over all stability types. 

Figure 16. Q-Q Plot for AGA 1-Hour Averages (SF6) 

The Q-Q plot for the EOCR experiments is shown in Figure 17. Although the AERMOD 

predictions parallel the 1:1 line over a larger portion of the concentration domain, the two curves 

are nearly identical for the peak concentrations. 

AGA 1-hr Q-Q Plot (χ/Q)

1

10

100

1000

1 10 100 1000

Obs. Conc. (μs/m3

)

Pred. Conc. (μs/m3

)

AERMOD

ISC3P
34

Figure 17. Q-Q Plot for EOCR 1-Hour Averages (SF6) 

The combined stable and neutral case Q-Q plot for the Lee power plant wind tunnel experiments 

is shown in Figure 18. This figure shows underpredictions for the peak concentrations, in 

contrast to the EPRI evaluation results38 with urban source characterization. 

Figure 18. Q-Q Plot for Lee Wind Tunnel Data 

EOCR 1-hr Q-Q Plot (χ/Q)

0.1

1

10

100

1000

10000

0.1 1 10 100 1000 10000

Obs. Conc. (μs/m3

)

Pred. Conc. (μs/m3

)

AERMOD

ISC3P

Lee Wind Tunnel 1-hr Q-Q Plot (χ/Q)

0.01

0.1

1

10

100

0.01 0.1 1 10 100

Obs. Conc. (μs/m3

)

Pred. Conc. (μs/m3

)

AERMOD

ISC3P
35

The overall model evaluation results for AERMOD version 02222 and ISC-PRIME with 

downwashing databases can be summarized as follows, taking one composite (geometric mean) 

ratio of predicted to observed RHC value for short-term averages at each site: 

 0.97 is the overall predicted-to-observed ratio for short-term averages using AERMOD. 

 0.94 is the overall predicted-to-observed ratio for short-term averages using ISC-PRIME. 

CONCLUSIONS 

Several enhancements to AERMOD have been made as a result of the public comments received 

by EPA after the Notice of Proposed Rulemaking was issued on April 21, 2000. The most 

notable change to AERMOD was the incorporation of the PRIME downwash algorithm. Other 

model changes address complex terrain implementation issues, urban dispersion issues, 

NADCON coordinate conversions, use of site-specific delta-T and wind speed data instead of 

cloud cover, issues regarding terrain below stack base, ISCST3 updates, and various other 

responses to beta test comments. The resulting beta test version is referred to as version 02222. 

This AERMOD version has been re-run for the 10 non-downwash evaluation databases, with 

minor differences in the results from those previously reported. The overall short-term ratio of 

AERMOD version 02222 predicted/observed RHC concentrations is 1.03, averaged over the 

non-downwash databases, with improvements over the ISCST3 results, especially for complex 

terrain situations. 

Seven downwash databases, divided into developmental and independent evaluation phases, 

were used for the AERMOD evaluation. Comparisons with ISC-PRIME performance indicate 

similar results for most databases, with occasional notable improvements, such as for the Alaska 

North Slope database. The overall short-term ratio of AERMOD version 02222 predicted/ 

observed RHC concentrations is 0.97 averaged over the downwash databases. These results 

were comparable in performance to those of ISC-PRIME, as expected. 

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KEYWORDS 

AERMOD 

ISCST3 

Dispersion Model 

Model Evaluation 

PRIME 

Building Downwash 
40

EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION IS OBSOLETE

TECHNICAL REPORT DATA

(Please read Instructions on reverse before completing)

1. REPORT NO. 

 EPA-454/R-03-003 

2.

3. RECIPIENT'S ACCESSION NO.

5. REPORT DATE June 2003 4. TITLE AND SUBTITLE 

AERMOD: Latest Features and Evaluation Results 

6. PERFORMING ORGANIZATION CODE

7. AUTHOR(S) 8. PERFORMING ORGANIZATION REPORT NO.

10. PROGRAM ELEMENT NO.

9. PERFORMING ORGANIZATION NAME AND ADDRESS See below. 11. CONTRACT/GRANT NO. 

13. TYPE OF REPORT AND PERIOD COVERED

Final Technical Report 

12. SPONSORING AGENCY NAME AND ADDRESS 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Emissions Monitoring and Analysis Division 

Research Triangle Park, NC 27711 

14. SPONSORING AGENCY CODE

EPA/200/04 

15. SUPPLEMENTARY NOTES Air and Waste Management Association paper No 698878 presented in June 2003.

16. ABSTRACT 

 AERMOD is an advanced plume model that incorporates updated treatments of the boundary layer theory, 

understanding of turbulence and dispersion, and includes handling of terrain interactions. The model was 

formally proposed by EPA in April 2000 as a replacement for the ISCST3 model. Several model 

enhancements were made as a result of public comment, including the installation of the PRIME downwash 

algorithm. The latest version of the model, version 02222, has been placed on EPA’s web site for beta test 

purposes. This paper reviews the latest features and updated evaluation results for AERMOD version 02222. 

17. KEY WORDS AND DOCUMENT ANALYSIS

a. DESCRIPTORS b. IDENTIFIERS/OPEN ENDED TERMS c. COSATI Field/Group

Model Evaluation, AERMOD, ISCST3, PRIME, 

building downwash 

Air Dispersion Model 

19. SECURITY CLASS (Report Unclassified 21. NOOF PAGES 40 18. DISTRIBUTION STATEMENT Release Unlimited 

20. SECURITY CLASS (Page Unclassified 22. PRICE
____________________________________________________________________________________________________________

 United States Office of Air Quality Planning and Standards 

Environmental Protection Emissions Monitoring and Analysis Division Agency Research Triangle Park, NC 27711 ____________________________________________________________________________________________________________